Epilepsy, Temporal LobeEpHR-DPNCT07835594
Studying Heart Rhythms With a Wearable Sensor to Detect and Predict Seizures in People With Epilepsy
An observational study of Epilepsy, Temporal Lobe and Drug Resistant Epilepsy, run by Acibadem University at 1 site in Turkey (Türkiye).
Trial summary
The goal of this observational study is to understand heart rhythm changes in adults with epilepsy. It will also explore whether computer programs can use heart recordings to detect or predict seizures. The study will include 20 adults with temporal lobe epilepsy and 20 healthy volunteers. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Epilepsy, Temporal Lobe, Drug Resistant Epilepsy
- What is being tested?
- Textile-based wearable electrocardiography
- Type of treatment
- Medical device
- How many people
- 40 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 15 Oct 2026to 15 Oct 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The planned study population comprises 20 adults with drug-resistant temporal lobe epilepsy and 20 healthy adult volunteers, all aged 18 years or older. Epilepsy participants will be recruited among people admitted for clinically indicated video-EEG monitoring at Sancaktepe Şehit Prof. Dr. İlhan Varank Training and Research Hospital in Istanbul, Türkiye. Healthy volunteers will be recruited through the researchers' university and professional networks and assessed against the eligibility criteria. They will be selected to achieve age and sex distributions as similar as feasible to those of the epilepsy group.
Who may be able to join
- Epilepsy group:
- Age 18 years or older.
- Diagnosis of temporal lobe epilepsy and admission to the epilepsy monitoring unit for video-electroencephalography (video-EEG) monitoring.
- Ongoing seizures despite treatment with at least two appropriately selected and tolerated antiseizure medicines at adequate doses and for adequate durations.
- History or clinical suspicion of focal to bilateral tonic-clonic seizures.
- Provision of written informed consent.
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- Healthy volunteer group:
- Age 18 years or older.
- No known epilepsy or history of seizures.
- Provision of written informed consent.
Who cannot join
- Epilepsy group:
- A neurodevelopmental disorder that prevents cooperation or following instructions.
- A physical condition that prevents placement of the chest band.
- Inability to securely attach the electrocardiography (ECG) or EEG sensors for reliable recording.
- Absence of an epilepsy diagnosis or identification of psychogenic nonepileptic seizures.
- Pregnancy or breastfeeding.
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- An implanted cardiac or neurostimulation device, such as a pacemaker, vagus nerve stimulator (VNS), or deep brain stimulation (DBS) device.
- Allergy, skin sensitivity, or a skin lesion that prevents use of the electrodes or chest band.
- A concomitant disease or medication use that may substantially affect heart rate or heart rate variability (HRV).
- Having been diagnosed with major depressive disorder.
- Healthy volunteer group:
- A history of epilepsy or seizures.
- Cardiovascular disease, a condition that may cause autonomic neuropathy, or medication use that may substantially affect heart rate or HRV.
- Allergy, skin sensitivity, or a skin lesion that prevents use of the electrodes or chest band.
- Having been diagnosed with major depressive disorder.
- Pregnancy or breastfeeding.
- Analysis-specific provisions:
- The absence of a recorded target seizure during monitoring will not by itself exclude an epilepsy participant from the study; these recordings may contribute to heart rate and HRV analyses. Recordings from participants with one or two target seizures may also contribute to patient-independent machine learning models. Patient-specific machine learning models require at least three recorded target seizures from the same participant.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site
Sancaktepe Şehit Prof. Dr. İlhan Varank Training and Research Hospital
Istanbul, 34785
Özdem Ertürk Çetin, MD, +90 506 912 47 06, ozdemerturk@yahoo.com
Özdem Ertürk Çetin, MD
About this study
Official title: Heart Rhythm Analysis, Seizure Detection and Prediction Using a Textile-Based Wearable Sensor in Patients With Epilepsy
The goal of this observational study is to understand heart rhythm changes in adults with epilepsy. It will also explore whether computer programs can use heart recordings to detect or predict seizures.
The study will include 20 adults with temporal lobe epilepsy and 20 healthy volunteers. Participants with epilepsy will have ongoing seizures despite treatment with at least two suitable medicines. All participants will be 18 years or older.
Read more
In temporal lobe epilepsy, seizures start in a specific region of the brain. These seizures may spread to both sides of the brain, causing muscle stiffening and rhythmic jerking. Researchers will focus on these spreading seizures when developing seizure detection and prediction methods.
The main questions are:
- How do heart rate and heartbeat timing differ between people with and without epilepsy?
- How do these measures change before, during, and after seizures?
- Can computer programs learn patterns in heart recordings to detect seizures or predict them before they start?
Researchers will compare heart rate and heart rate variability between the two groups. Heart rate variability describes changes in the time between one heartbeat and the next. They will compare recordings during rest, wakefulness, and sleep.
Participants will:
- Wear a fabric chest band that records the heart's electrical activity.
- Have brain activity recorded through sensors on the scalp, alongside video recording.
- Rest quietly while awake for one hour during the recording period.
Participants with epilepsy will have recordings during their usual hospital monitoring. Healthy volunteers will have 24 hours of recording at the same hospital.
Researchers will use the brain recordings and video to identify when seizures happen. They will develop and test computer programs that learn patterns in the heart recordings.
Read the detailed description
This prospective, single-center observational study will characterize heart rate and heart rate variability (HRV) in adults with drug-resistant temporal lobe epilepsy and explore their use in detecting and predicting focal to bilateral tonic-clonic seizures. The study combines comparisons between epilepsy participants and healthy volunteers with analyses of physiological changes surrounding seizures and development of computational models.
The planned sample comprises 20 adults with epilepsy and 20 healthy volunteers. Recruitment will use non-probability sampling. Epilepsy participants will be recruited from patients admitted for clinically indicated inpatient video-electroencephalography (video-EEG) monitoring. Healthy volunteers will be recruited through the researchers' university and professional networks, with age and sex distributions as similar as feasible to those of the epilepsy group.
Recordings will take place at Sancaktepe Şehit Prof. Dr. İlhan Varank Training and Research Hospital. A textile-based wearable chest band will record electrocardiography (ECG) simultaneously with video-EEG. Epilepsy participants will be recorded during their routine inpatient monitoring, while healthy volunteers will undergo 24 hours of recording at the same hospital. Similar equipment and recording conditions will be used in both groups to reduce methodological differences related to the recording environment, physical burden and comfort.
Both groups will complete a standardized one-hour awake resting recording on the first morning, between 09:00 and 12:00, following a 15-minute adaptation period. The timing of the last meal and caffeine and nicotine use will be recorded. Neurologists will review video-EEG recordings from both epilepsy participants and healthy volunteers to identify sleep and wakefulness. In epilepsy participants, they will evaluate interictal epileptiform discharges, seizure onset and offset, seizure classification and postictal EEG changes. In healthy volunteers, they will verify the absence of electrographic seizure activity during the recording. Analyses by sleep stage will be limited to periods that can be reliably classified.
Heart rate and HRV will be compared between groups during standardized rest, sleep and wakefulness. Within epilepsy participants, analyses will examine interictal, preictal, ictal and postictal changes while accounting for repeated observations from the same participant. Medication type, dose and timing relative to the recordings will also be considered in the analysis and interpretation of heart rate and HRV findings. Effect sizes and confidence intervals will accompany the comparisons.
Textile ECG will be the primary data source for seizure detection and prediction models. Synchronized EEG and video will provide reference annotations and may also contribute complementary information through transfer learning and multimodal modelling. Conventional machine learning and deep learning approaches will be compared, including transfer learning, adaptation using limited examples from a new patient (few-shot learning), evaluation in previously unseen patients without adaptation (zero-shot evaluation) and anomaly detection. Where appropriate, models using ECG, EEG and video separately and in combination will be compared to assess the contribution of each modality. Suitable external datasets may support model pretraining.
Model performance will be evaluated offline using separate test recordings. Patient-independent evaluations will separate participants between development and testing, while patient-specific evaluations will preserve the chronological separation of training or adaptation data from later test recordings. Related segments from the same seizure will not be split between training and testing. Recordings will be assigned to training, validation and test sets before being divided into analysis windows. Model development and tuning will exclude test recordings. Results will be summarized across cross-validation folds or repeated runs.
The sample size reflects the exploratory nature of the study and experience from related research. Statistical power to detect moderate effects may be limited. The study is intended to characterize physiological patterns, estimate effect sizes and obtain preliminary model performance estimates to inform subsequent studies. Routine clinical care will remain unchanged, and study recordings and model outputs will not guide medical decisions.
- Conditions
- Epilepsy, Temporal Lobe, Drug Resistant Epilepsy
- Being tested
- Textile-based wearable electrocardiography
- Keywords
- Heart Rate Variability, Wearable Electrocardiography, Textile Sensors, Seizure Detection, Seizure Prediction, Machine Learning
- Medical subject headings
- Epilepsy, Temporal Lobe, Drug Resistant Epilepsy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Textile-based wearable electrocardiographyDevice
A textile-based wearable chest band will record electrocardiography (ECG) simultaneously with video-EEG. Epilepsy participants will wear the device during their clinically indicated inpatient monitoring, without changes to routine monitoring duration or clinical care. Healthy volunteers will undergo 24 hours of recording at the same hospital. Both groups will complete a standardized one-hour awake resting recording. ECG data will be used for heart rate and heart rate variability analyses and development and offline evaluation of seizure detection and prediction models. Study recordings and model outputs will not guide participants' medical care.
| Study group | Role | What they receive |
|---|---|---|
| Drug-resistant temporal lobe epilepsy | 20 adults aged 18 years or older with drug-resistant temporal lobe epilepsy undergoing clinically indicated inpatient video-electroencephalography (video-EEG) monitoring. Participants have ongoing seizures despite adequate trials of at least two appropriately selected and tolerated antiseizure medicines, and a history or clinical suspicion of focal to bilateral tonic-clonic seizures. Textile-based wearable electrocardiography (ECG) will be recorded simultaneously with routine video-EEG. Recording includes a one-hour awake resting period. Routine monitoring duration and clinical care will not be changed for the study. Receives: Device: Textile-based wearable electrocardiography | |
| Healthy volunteers | 20 healthy adults aged 18 years or older without known epilepsy or a history of seizures. Volunteers will be selected to achieve age and sex distributions as similar as feasible to the epilepsy group. Participants will undergo 24 hours of simultaneous video-electroencephalography (video-EEG) and textile-based wearable electrocardiography (ECG) at the same hospital as the epilepsy group, using a similar recording setup. Recording includes a one-hour awake resting period. Heart rate and heart rate variability will be compared with the epilepsy group. Receives: Device: Textile-based wearable electrocardiography |
What is being measured
The primary outcome is the main question the study is designed to answer.
Mean heart rate by recording state
Mean heart rate will be calculated in beats per minute for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Measured: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Heart rate variability: SDNN by recording state
The standard deviation of normal-to-normal heartbeat intervals (SDNN) will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Measured: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Heart rate variability: RMSSD by recording state
The root mean square of successive differences between normal-to-normal heartbeat intervals (RMSSD) will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Measured: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Heart rate variability: pNN50 by recording state
pNN50 is the percentage of successive normal-to-normal heartbeat interval pairs that differ by more than 50 milliseconds. It will be calculated for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Measured: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Heart rate variability: low-frequency power by recording state
Low-frequency (LF) power of normal-to-normal heartbeat interval variability will be calculated in milliseconds squared for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Measured: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Heart rate variability: high-frequency power by recording state
High-frequency (HF) power of normal-to-normal heartbeat interval variability will be calculated in milliseconds squared for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Measured: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Heart rate variability: LF/HF ratio by recording state
The ratio of low-frequency power to high-frequency power (LF/HF) of normal-to-normal heartbeat interval variability will be calculated for each analyzed recording period. The ratio is dimensionless. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Measured: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Heart rate variability: SD1 by recording state
Poincare plot SD1 is the standard deviation of normal-to-normal heartbeat interval pairs perpendicular to the line of identity. It will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Measured: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Heart rate variability: SD2 by recording state
Poincare plot SD2 is the standard deviation of normal-to-normal heartbeat interval pairs along the line of identity. It will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Measured: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Heart rate variability: sample entropy by recording state
Sample entropy, a dimensionless measure of the irregularity of normal-to-normal heartbeat interval sequences, will be calculated for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Measured: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Event-level sensitivity for focal to bilateral tonic-clonic seizure detection
The percentage of eligible focal to bilateral tonic-clonic seizures confirmed by video-EEG that are correctly detected by each model: correctly detected seizures divided by all eligible reference seizures in the test recordings, multiplied by 100. Reference events will be annotated independently of ECG changes and model outputs. Alarm-to-event matching rules will be specified before outcome analysis. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
False alarms per hour for seizure detection
The number of detection alarms not matched to a reference seizure, divided by the total evaluable monitoring time in hours. Reference seizures will be established by independent video-EEG annotation. Alarm-to-event matching and alarm-merging rules will be specified before outcome analysis and applied consistently to held-out recordings. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Event-level sensitivity for focal to bilateral tonic-clonic seizure prediction
The percentage of eligible lead focal to bilateral tonic-clonic seizures correctly preceded by a model warning within the prespecified prediction timing rules. Correctly predicted lead seizures will be divided by all eligible lead seizures in the test recordings and multiplied by 100. Independent targets must be preceded by at least two seizure-free hours. The prediction horizon, seizure occurrence period and alarm-to-event matching rules will be specified before outcome analysis. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
False alarms per hour for seizure prediction
The number of prediction alarms not followed by an eligible reference seizure within the prespecified seizure occurrence period, divided by the total evaluable monitoring time in hours. Lead seizure eligibility, prediction horizon, seizure occurrence period and alarm-matching rules will be specified before outcome analysis. Video-EEG annotations will provide the reference events. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Secondary outcomes (12)
Window-level classification sensitivity
The percentage of reference-positive analysis windows classified as positive: true-positive windows divided by all reference-positive windows, multiplied by 100. Reference-positive windows represent seizure activity for detection and the prespecified preictal target for prediction. Calculated separately for seizure detection and seizure prediction classification tasks and for each model approach, using prespecified reference window labels derived from video-EEG. Evaluation will use held-out recordings, preserving participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Values will be summarized across validation folds or repeated runs, with standard deviations or appropriate confidence intervals.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level classification specificity
The percentage of reference-negative analysis windows classified as negative: true-negative windows divided by all reference-negative windows, multiplied by 100. Calculated separately for seizure detection and seizure prediction classification tasks and for each model approach, using prespecified reference window labels derived from video-EEG. Evaluation will use held-out recordings, preserving participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Values will be summarized across validation folds or repeated runs, with standard deviations or appropriate confidence intervals.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level positive predictive value
The percentage of positive model classifications that are reference-positive: true-positive windows divided by all predicted-positive windows, multiplied by 100. This measure is also called precision. Calculated separately for seizure detection and seizure prediction classification tasks and for each model approach, using prespecified reference window labels derived from video-EEG. Evaluation will use held-out recordings, preserving participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Values will be summarized across validation folds or repeated runs, with standard deviations or appropriate confidence intervals.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level negative predictive value
The percentage of negative model classifications that are reference-negative: true-negative windows divided by all predicted-negative windows, multiplied by 100. Calculated separately for seizure detection and seizure prediction classification tasks and for each model approach, using prespecified reference window labels derived from video-EEG. Evaluation will use held-out recordings, preserving participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Values will be summarized across validation folds or repeated runs, with standard deviations or appropriate confidence intervals.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level F1 score
The F1 score is the harmonic mean of window-level precision and sensitivity. It is dimensionless, ranges from 0 to 1, and is higher when classification performance is better. Calculated separately for seizure detection and seizure prediction classification tasks and for each model approach, using prespecified reference window labels derived from video-EEG. Evaluation will use held-out recordings, preserving participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Values will be summarized across validation folds or repeated runs, with standard deviations or appropriate confidence intervals.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level balanced accuracy
Balanced accuracy is the arithmetic mean of window-level sensitivity and specificity, expressed as a percentage. It gives equal weight to performance on positive and negative reference classes. Calculated separately for seizure detection and seizure prediction classification tasks and for each model approach, using prespecified reference window labels derived from video-EEG. Evaluation will use held-out recordings, preserving participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Values will be summarized across validation folds or repeated runs, with standard deviations or appropriate confidence intervals.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level receiver operating characteristic area under the curve
The area under the receiver operating characteristic curve (ROC-AUC) will summarize discrimination across classification thresholds using window-level prediction scores. It is dimensionless and ranges from 0 to 1. Calculated separately for seizure detection and seizure prediction classification tasks and for each model approach, using prespecified reference window labels derived from video-EEG. Evaluation will use held-out recordings, preserving participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Values will be summarized across validation folds or repeated runs, with standard deviations or appropriate confidence intervals.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level precision-recall area under the curve
The area under the precision-recall curve (AUPRC) will summarize the relationship between precision and sensitivity across classification thresholds using window-level prediction scores. It is dimensionless and ranges from 0 to 1. Calculated separately for seizure detection and seizure prediction classification tasks and for each model approach, using prespecified reference window labels derived from video-EEG. Evaluation will use held-out recordings, preserving participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Values will be summarized across validation folds or repeated runs, with standard deviations or appropriate confidence intervals.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level classification accuracy
The percentage of all analysis windows classified correctly: the sum of true-positive and true-negative windows divided by all evaluated windows, multiplied by 100. Accuracy will be interpreted alongside the other metrics because seizures are much less frequent than non-seizure periods. Calculated separately for seizure detection and seizure prediction classification tasks and for each model approach, using prespecified reference window labels derived from video-EEG. Evaluation will use held-out recordings, preserving participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Values will be summarized across validation folds or repeated runs, with standard deviations or appropriate confidence intervals.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Mean seizure detection latency
For correctly detected focal to bilateral tonic-clonic seizures, the interval from the clinical seizure onset annotated using video-EEG to the corresponding model alarm will be calculated in seconds. Mean latency will be reported for each detection model, with variability or confidence intervals. Alarm matching will follow prespecified rules. Timing will be assessed offline from time-stamped recordings and algorithm outputs; it does not represent prospective deployment of a clinical alarm. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Mean seizure prediction lead time
For correctly predicted eligible lead focal to bilateral tonic-clonic seizures, the interval from the corresponding model warning to reference seizure onset will be calculated in minutes. Mean lead time will be reported for each prediction model, with variability or confidence intervals. Reference onset, lead seizure eligibility, prediction horizon, seizure occurrence period and the rule for selecting the matched warning will be specified before outcome analysis. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Percentage of evaluable recording time spent in seizure warning
The total duration of the model-defined seizure warning state divided by the total evaluable recording duration, multiplied by 100. Overlapping warning intervals will contribute only once to total warning time. The duration and timing rules defining the warning state will be specified before outcome analysis and applied consistently. This measure will be reported separately for each prediction model alongside event-level sensitivity and false alarms per hour. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
Measured: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Sep 2026
- Study start15 Oct 2026 (estimated)
- Primary completion15 Oct 2028 (estimated)
- Study completion15 Oct 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (17)Papers the sponsor has linked to this study.
- Other trials for Epilepsy, Temporal LobeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Acibadem University
- Sponsor type
- University, hospital or other
- Collaborators
- Istanbul Technical University
- Responsible party
- Sponsor
- Investigators
- Filiz Onat, MD, PhD, Principal investigator, Acibadem UniversityMustafa Aykut Kural, MD, PhD, Principal investigator, Acibadem UniversityBerk Can Kantarcı, MD, Principal investigator, Acibadem University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07835594
- Sponsor's protocol ID
- 2026-13/585
- Acronym
- EpHR-DP
- Status last verified
- Sep 2026
- First submitted
- 16 Sep 2026
- Last updated
- 23 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Beniczky S, Wiebe S, Jeppesen J, Tatum WO, Brazdil M, Wang Y, Herman ST, Ryvlin P. Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology. Clin Neurophysiol. 2021 May;132(5):1173-1184. doi: 10.1016/j.clinph.2020.12.009. Epub 2021 Mar 5.
PubMed 33678577Jeppesen J, Christensen J, Ahrenfeldt Petersen O, Fenger S, Armand Larsen S, Wustenhagen S, Wagner SR, Johansen P, Beniczky S. Seizure detection using wearable electrocardiogram connected to a smartphone: a phase 3 clinical validation study. EBioMedicine. 2025 Oct;120:105952. doi: 10.1016/j.ebiom.2025.105952. Epub 2025 Sep 29.
PubMed 41027311Quigley KS, Gianaros PJ, Norman GJ, Jennings JR, Berntson GG, de Geus EJC. Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement. Psychophysiology. 2024 Sep;61(9):e14604. doi: 10.1111/psyp.14604. Epub 2024 Jun 14.
PubMed 38873876Lotufo PA, Valiengo L, Bensenor IM, Brunoni AR. A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs. Epilepsia. 2012 Feb;53(2):272-82. doi: 10.1111/j.1528-1167.2011.03361.x. Epub 2012 Jan 5.
PubMed 22221253Carter JR, Jenkins NDM, Bigalke JA, Robinson AT, Keller-Ross ML, Greaney JL, Fonkoue IT, Fadel PJ, Macefield VG, Charkoudian N, Levine BD, Joyner MJ. Guidelines for rigor and reproducibility of heart rate variability within human cardiovascular research. Am J Physiol Heart Circ Physiol. 2026 Sep 1;331(3):H918-H943. doi: 10.1152/ajpheart.00041.2026. Epub 2026 Jul 24.
PubMed 42495990Beniczky S, Ryvlin P. Standards for testing and clinical validation of seizure detection devices. Epilepsia. 2018 Jun;59 Suppl 1:9-13. doi: 10.1111/epi.14049.
PubMed 29873827Tatum WO, Rubboli G, Kaplan PW, Mirsatari SM, Radhakrishnan K, Gloss D, Caboclo LO, Drislane FW, Koutroumanidis M, Schomer DL, Kasteleijn-Nolst Trenite D, Cook M, Beniczky S. Clinical utility of EEG in diagnosing and monitoring epilepsy in adults. Clin Neurophysiol. 2018 May;129(5):1056-1082. doi: 10.1016/j.clinph.2018.01.019. Epub 2018 Feb 1.
PubMed 29483017Tatum WO, Mani J, Jin K, Halford JJ, Gloss D, Fahoum F, Maillard L, Mothersill I, Beniczky S. Minimum standards for inpatient long-term video-EEG monitoring: A clinical practice guideline of the international league against epilepsy and international federation of clinical neurophysiology. Clin Neurophysiol. 2022 Feb;134:111-128. doi: 10.1016/j.clinph.2021.07.016. Epub 2021 Dec 13.
PubMed 34955428Tang J, El Atrache R, Yu S, Asif U, Jackson M, Roy S, Mirmomeni M, Cantley S, Sheehan T, Schubach S, Ufongene C, Vieluf S, Meisel C, Harrer S, Loddenkemper T. Seizure detection using wearable sensors and machine learning: Setting a benchmark. Epilepsia. 2021 Aug;62(8):1807-1819. doi: 10.1111/epi.16967. Epub 2021 Jul 15.
PubMed 34268728Seth EA, Watterson J, Xie J, Arulsamy A, Md Yusof HH, Ngadimon IW, Khoo CS, Kadirvelu A, Shaikh MF. Feasibility of cardiac-based seizure detection and prediction: A systematic review of non-invasive wearable sensor-based studies. Epilepsia Open. 2024 Feb;9(1):41-59. doi: 10.1002/epi4.12854. Epub 2023 Nov 28.
PubMed 37881157Kalilani L, Sun X, Pelgrims B, Noack-Rink M, Villanueva V. The epidemiology of drug-resistant epilepsy: A systematic review and meta-analysis. Epilepsia. 2018 Dec;59(12):2179-2193. doi: 10.1111/epi.14596. Epub 2018 Nov 13.
PubMed 30426482Jiang RY, Varughese RT, Kothare SV. Sudden Unexpected Death in Epilepsy: A Narrative Review of Mechanism, Risks, and Prevention. J Clin Med. 2025 May 10;14(10):3329. doi: 10.3390/jcm14103329.
PubMed 40429323Gharbi O, Lamrani Y, St-Jean J, Jahani A, Toffa DH, Tran TPY, Robert M, Nguyen DK, Bou Assi E. Detection of focal to bilateral tonic-clonic seizures using a connected shirt. Epilepsia. 2024 Aug;65(8):2280-2294. doi: 10.1111/epi.18021. Epub 2024 May 23.
PubMed 38780375GBD 2021 Nervous System Disorders Collaborators. Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol. 2024 Apr;23(4):344-381. doi: 10.1016/S1474-4422(24)00038-3. Epub 2024 Mar 14.
PubMed 38493795Begley C, Wagner RG, Abraham A, Beghi E, Newton C, Kwon CS, Labiner D, Winkler AS. The global cost of epilepsy: A systematic review and extrapolation. Epilepsia. 2022 Apr;63(4):892-903. doi: 10.1111/epi.17165. Epub 2022 Feb 23.
PubMed 35195894
Study at a glance
From the registry record, last updated by the sponsor on 23 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Heart Rhythm Analysis, Seizure Detection and Prediction Using a Textile-Based Wearable Sensor in Patients With Epilepsy
- ClinicalTrials.gov ID
- NCT07835594
- Acronym / sponsor ID
- EpHR-DP / 2026-13/585
- Conditions
- Epilepsy, Temporal Lobe, Drug Resistant Epilepsy
- Keywords
- Heart Rate Variability, Wearable Electrocardiography, Textile Sensors, Seizure Detection, Seizure Prediction, Machine Learning
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- Acibadem UniversityUniversity, hospital or other
- Collaborators
- Istanbul Technical University
- Study start
- 15 Oct 2026 (estimated)
- Primary completion
- 15 Oct 2028 (estimated)
- Study completion
- 15 Oct 2028 (estimated)
- First posted
- 23 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of this observational study is to understand heart rhythm changes in adults with epilepsy. It will also explore whether computer programs can use heart recordings to detect or predict seizures.
The study will include 20 adults with temporal lobe epilepsy and 20 healthy volunteers. Participants with epilepsy will have ongoing seizures despite treatment with at least two suitable medicines. All participants will be 18 years or older.
In temporal lobe epilepsy, seizures start in a specific region of the brain. These seizures may spread to both sides of the brain, causing muscle stiffening and rhythmic jerking. Researchers will focus on these spreading seizures when developing seizure detection and prediction methods.
The main questions are:
- How do heart rate and heartbeat timing differ between people with and without epilepsy?
- How do these measures change before, during, and after seizures?
- Can computer programs learn patterns in heart recordings to detect seizures or predict them before they start?
Researchers will compare heart rate and heart rate variability between the two groups. Heart rate variability describes changes in the time between one heartbeat and the next. They will compare recordings during rest, wakefulness, and sleep.
Participants will:
- Wear a fabric chest band that records the heart's electrical activity.
- Have brain activity recorded through sensors on the scalp, alongside video recording.
- Rest quietly while awake for one hour during the recording period.
Participants with epilepsy will have recordings during their usual hospital monitoring. Healthy volunteers will have 24 hours of recording at the same hospital.
Researchers will use the brain recordings and video to identify when seizures happen. They will develop and test computer programs that learn patterns in the heart recordings.
Detailed description
This prospective, single-center observational study will characterize heart rate and heart rate variability (HRV) in adults with drug-resistant temporal lobe epilepsy and explore their use in detecting and predicting focal to bilateral tonic-clonic seizures. The study combines comparisons between epilepsy participants and healthy volunteers with analyses of physiological changes surrounding seizures and development of computational models.
The planned sample comprises 20 adults with epilepsy and 20 healthy volunteers. Recruitment will use non-probability sampling. Epilepsy participants will be recruited from patients admitted for clinically indicated inpatient video-electroencephalography (video-EEG) monitoring. Healthy volunteers will be recruited through the researchers' university and professional networks, with age and sex distributions as similar as feasible to those of the epilepsy group.
Recordings will take place at Sancaktepe Şehit Prof. Dr. İlhan Varank Training and Research Hospital. A textile-based wearable chest band will record electrocardiography (ECG) simultaneously with video-EEG. Epilepsy participants will be recorded during their routine inpatient monitoring, while healthy volunteers will undergo 24 hours of recording at the same hospital. Similar equipment and recording conditions will be used in both groups to reduce methodological differences related to the recording environment, physical burden and comfort.
Both groups will complete a standardized one-hour awake resting recording on the first morning, between 09:00 and 12:00, following a 15-minute adaptation period. The timing of the last meal and caffeine and nicotine use will be recorded. Neurologists will review video-EEG recordings from both epilepsy participants and healthy volunteers to identify sleep and wakefulness. In epilepsy participants, they will evaluate interictal epileptiform discharges, seizure onset and offset, seizure classification and postictal EEG changes. In healthy volunteers, they will verify the absence of electrographic seizure activity during the recording. Analyses by sleep stage will be limited to periods that can be reliably classified.
Heart rate and HRV will be compared between groups during standardized rest, sleep and wakefulness. Within epilepsy participants, analyses will examine interictal, preictal, ictal and postictal changes while accounting for repeated observations from the same participant. Medication type, dose and timing relative to the recordings will also be considered in the analysis and interpretation of heart rate and HRV findings. Effect sizes and confidence intervals will accompany the comparisons.
Textile ECG will be the primary data source for seizure detection and prediction models. Synchronized EEG and video will provide reference annotations and may also contribute complementary information through transfer learning and multimodal modelling. Conventional machine learning and deep learning approaches will be compared, including transfer learning, adaptation using limited examples from a new patient (few-shot learning), evaluation in previously unseen patients without adaptation (zero-shot evaluation) and anomaly detection. Where appropriate, models using ECG, EEG and video separately and in combination will be compared to assess the contribution of each modality. Suitable external datasets may support model pretraining.
Model performance will be evaluated offline using separate test recordings. Patient-independent evaluations will separate participants between development and testing, while patient-specific evaluations will preserve the chronological separation of training or adaptation data from later test recordings. Related segments from the same seizure will not be split between training and testing. Recordings will be assigned to training, validation and test sets before being divided into analysis windows. Model development and tuning will exclude test recordings. Results will be summarized across cross-validation folds or repeated runs.
The sample size reflects the exploratory nature of the study and experience from related research. Statistical power to detect moderate effects may be limited. The study is intended to characterize physiological patterns, estimate effect sizes and obtain preliminary model performance estimates to inform subsequent studies. Routine clinical care will remain unchanged, and study recordings and model outputs will not guide medical decisions.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Drug-resistant temporal lobe epilepsy | – | Device: Textile-based wearable electrocardiography20 adults aged 18 years or older with drug-resistant temporal lobe epilepsy undergoing clinically indicated inpatient video-electroencephalography (video-EEG) monitoring. Participants have ongoing seizures despite adequate trials of at least two appropriately selected and tolerated antiseizure medicines, and a history or clinical suspicion of focal to bilateral tonic-clonic seizures. Textile-based wearable electrocardiography (ECG) will be recorded simultaneously with routine video-EEG. Recording includes a one-hour awake resting period. Routine monitoring duration and clinical care will not be changed for the study. |
| Healthy volunteers | – | Device: Textile-based wearable electrocardiography20 healthy adults aged 18 years or older without known epilepsy or a history of seizures. Volunteers will be selected to achieve age and sex distributions as similar as feasible to the epilepsy group. Participants will undergo 24 hours of simultaneous video-electroencephalography (video-EEG) and textile-based wearable electrocardiography (ECG) at the same hospital as the epilepsy group, using a similar recording setup. Recording includes a one-hour awake resting period. Heart rate and heart rate variability will be compared with the epilepsy group. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The planned study population comprises 20 adults with drug-resistant temporal lobe epilepsy and 20 healthy adult volunteers, all aged 18 years or older. Epilepsy participants will be recruited among people admitted for clinically indicated video-EEG monitoring at Sancaktepe Şehit Prof. Dr. İlhan Varank Training and Research Hospital in Istanbul, Türkiye. Healthy volunteers will be recruited through the researchers' university and professional networks and assessed against the eligibility criteria. They will be selected to achieve age and sex distributions as similar as feasible to those of the epilepsy group.
Key inclusion criteria (10 listed)
- Age 18 years or older.
- Diagnosis of temporal lobe epilepsy and admission to the epilepsy monitoring unit for video-electroencephalography (video-EEG) monitoring.
- Ongoing seizures despite treatment with at least two appropriately selected and tolerated antiseizure medicines at adequate doses and for adequate durations.
- History or clinical suspicion of focal to bilateral tonic-clonic seizures.
- Provision of written informed consent.
Key exclusion criteria (18 listed)
- A neurodevelopmental disorder that prevents cooperation or following instructions.
- A physical condition that prevents placement of the chest band.
- Inability to securely attach the electrocardiography (ECG) or EEG sensors for reliable recording.
- Absence of an epilepsy diagnosis or identification of psychogenic nonepileptic seizures.
- Pregnancy or breastfeeding.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Textile-based wearable electrocardiographyA textile-based wearable chest band will record electrocardiography (ECG) simultaneously with video-EEG. Epilepsy participants will wear the device during their clinically indicated inpatient monitoring, without changes to routine monitoring duration or clinical care. Healthy volunteers will undergo 24 hours of recording at the same hospital. Both groups will complete a standardized one-hour awake resting recording. ECG data will be used for heart rate and heart rate variability analyses and development and offline evaluation of seizure detection and prediction models. Study recordings and model outputs will not guide participants' medical care. | Device | Drug-resistant temporal lobe epilepsy, Healthy volunteers |
Primary outcome
Mean heart rate by recording state
Time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Mean heart rate will be calculated in beats per minute for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: SDNN by recording state
Time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
The standard deviation of normal-to-normal heartbeat intervals (SDNN) will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: RMSSD by recording state
Time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
The root mean square of successive differences between normal-to-normal heartbeat intervals (RMSSD) will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: pNN50 by recording state
Time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
pNN50 is the percentage of successive normal-to-normal heartbeat interval pairs that differ by more than 50 milliseconds. It will be calculated for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: low-frequency power by recording state
Time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Low-frequency (LF) power of normal-to-normal heartbeat interval variability will be calculated in milliseconds squared for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: high-frequency power by recording state
Time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
High-frequency (HF) power of normal-to-normal heartbeat interval variability will be calculated in milliseconds squared for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: LF/HF ratio by recording state
Time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
The ratio of low-frequency power to high-frequency power (LF/HF) of normal-to-normal heartbeat interval variability will be calculated for each analyzed recording period. The ratio is dimensionless. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: SD1 by recording state
Time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Poincare plot SD1 is the standard deviation of normal-to-normal heartbeat interval pairs perpendicular to the line of identity. It will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: SD2 by recording state
Time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Poincare plot SD2 is the standard deviation of normal-to-normal heartbeat interval pairs along the line of identity. It will be calculated in milliseconds for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Heart rate variability: sample entropy by recording state
Time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).
Sample entropy, a dimensionless measure of the irregularity of normal-to-normal heartbeat interval sequences, will be calculated for each analyzed recording period. Textile-based wearable electrocardiography (ECG) recordings will be used. Values from the standardized one-hour awake resting recording will be compared between the epilepsy and healthy volunteer groups. Sleep and wakefulness will also be compared between groups, with sleep stage analyses limited to reliably classified periods. Within epilepsy participants, interictal, preictal, ictal and postictal values will be compared using video-EEG reference annotations and accounting for repeated observations. Equal-length, technically usable segments will be analyzed under prespecified rules. Effect sizes and confidence intervals will be reported.
Event-level sensitivity for focal to bilateral tonic-clonic seizure detection
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
The percentage of eligible focal to bilateral tonic-clonic seizures confirmed by video-EEG that are correctly detected by each model: correctly detected seizures divided by all eligible reference seizures in the test recordings, multiplied by 100. Reference events will be annotated independently of ECG changes and model outputs. Alarm-to-event matching rules will be specified before outcome analysis. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
False alarms per hour for seizure detection
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
The number of detection alarms not matched to a reference seizure, divided by the total evaluable monitoring time in hours. Reference seizures will be established by independent video-EEG annotation. Alarm-to-event matching and alarm-merging rules will be specified before outcome analysis and applied consistently to held-out recordings. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
Event-level sensitivity for focal to bilateral tonic-clonic seizure prediction
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
The percentage of eligible lead focal to bilateral tonic-clonic seizures correctly preceded by a model warning within the prespecified prediction timing rules. Correctly predicted lead seizures will be divided by all eligible lead seizures in the test recordings and multiplied by 100. Independent targets must be preceded by at least two seizure-free hours. The prediction horizon, seizure occurrence period and alarm-to-event matching rules will be specified before outcome analysis. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
False alarms per hour for seizure prediction
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
The number of prediction alarms not followed by an eligible reference seizure within the prespecified seizure occurrence period, divided by the total evaluable monitoring time in hours. Lead seizure eligibility, prediction horizon, seizure occurrence period and alarm-matching rules will be specified before outcome analysis. Video-EEG annotations will provide the reference events. Performance will be evaluated by internal validation on held-out recordings, with participant-level separation for patient-independent models and separation by seizure event and time for patient-specific models. Results will be reported separately by model approach, with variability across validation folds or repeated runs.
12 secondary outcomes
Window-level classification sensitivity
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level classification specificity
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level positive predictive value
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level negative predictive value
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level F1 score
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level balanced accuracy
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level receiver operating characteristic area under the curve
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level precision-recall area under the curve
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Window-level classification accuracy
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Mean seizure detection latency
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Mean seizure prediction lead time
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Percentage of evaluable recording time spent in seizure warning
Time frame: During clinically indicated video-EEG monitoring, ordinarily 48-72 hours per epilepsy participant; performance will be assessed offline using held-out portions of these recordings.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Sep 2026.
- Background: PubMed 33678577Beniczky S, Wiebe S, Jeppesen J, Tatum WO, Brazdil M, Wang Y, Herman ST, Ryvlin P. Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology. Clin Neurophysiol. 2021 May;132(5):1173-1184. doi: 10.1016/j.clinph.2020.12.009. Epub 2021 Mar 5.
- Background: PubMed 41027311Jeppesen J, Christensen J, Ahrenfeldt Petersen O, Fenger S, Armand Larsen S, Wustenhagen S, Wagner SR, Johansen P, Beniczky S. Seizure detection using wearable electrocardiogram connected to a smartphone: a phase 3 clinical validation study. EBioMedicine. 2025 Oct;120:105952. doi: 10.1016/j.ebiom.2025.105952. Epub 2025 Sep 29.
- Background: PubMed 38873876Quigley KS, Gianaros PJ, Norman GJ, Jennings JR, Berntson GG, de Geus EJC. Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement. Psychophysiology. 2024 Sep;61(9):e14604. doi: 10.1111/psyp.14604. Epub 2024 Jun 14.
- Background: PubMed 22221253Lotufo PA, Valiengo L, Bensenor IM, Brunoni AR. A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs. Epilepsia. 2012 Feb;53(2):272-82. doi: 10.1111/j.1528-1167.2011.03361.x. Epub 2012 Jan 5.
- Background: PubMed 42495990Carter JR, Jenkins NDM, Bigalke JA, Robinson AT, Keller-Ross ML, Greaney JL, Fonkoue IT, Fadel PJ, Macefield VG, Charkoudian N, Levine BD, Joyner MJ. Guidelines for rigor and reproducibility of heart rate variability within human cardiovascular research. Am J Physiol Heart Circ Physiol. 2026 Sep 1;331(3):H918-H943. doi: 10.1152/ajpheart.00041.2026. Epub 2026 Jul 24.
- Background: PubMed 29873827Beniczky S, Ryvlin P. Standards for testing and clinical validation of seizure detection devices. Epilepsia. 2018 Jun;59 Suppl 1:9-13. doi: 10.1111/epi.14049.
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- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Filiz Onat, MD, PhD principal investigator
Acibadem University
Mustafa Aykut Kural, MD, PhD principal investigator
Acibadem University
Berk Can Kantarcı, MD principal investigator
Acibadem University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Epilepsy, Temporal Lobe and Drug Resistant Epilepsy, run by Acibadem University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 10 inclusion and 18 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 23 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Filiz Onat, MD, PhD (+90 532 371 21 31, filiz.onat@acibadem.edu.tr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mean heart rate by recording state (time frame: During one monitoring admission: approximately 48-72 hours for epilepsy participants and 24 hours for healthy volunteers, including the one-hour awake resting recording on the first morning (09:00-12:00).). The study lists 14 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07835594, which also lists 17 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Sep 2026.