Studying Heart Rhythms With a Wearable Sensor to Detect and Predict Seizures in People With Epilepsy
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorAcibadem University
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial 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.
Trial design
Design model
Case-control
Time perspective
Prospective
Treatments tested in this trial
Textile-based wearable electrocardiography
DeviceA 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.
Treatment groups
40 Participants
are divided into 2 treatment groupsGroup A: Drug-resistant temporal lobe epilepsy1 intervention
Group B: Healthy volunteers1 intervention
Trial outcomes
Primary outcomes
1
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.
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).2
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.
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).3
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.
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).4
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.
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).Secondary outcomes
1
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.
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.2
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.
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.3
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.
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.4
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.
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.Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Acibadem University
Lead sponsor
Istanbul Technical University
Collaborator
Source ClinicalTrials.gov
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