Studying Heart Rhythms With a Wearable Sensor to Detect and Predict Seizures in People With Epilepsy

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.