Reliable Detection of Focal Onset Impaired Awareness Seizures in Patients with Epilepsy Using Wearable ECG: Development and Validation Study

• Wearable ECG enables reliable detection of FIAS using deep learning methods • Automatic data quality assessment (ADQA) enhances detection reliability and robustness • The algorithm showed 66.6% sensitivity in the 38 patients who met the quality criteria Underreporting of seizures, particularly focal onset impaired awareness seizures (FIAS), compromises the effectiveness of patient care and condition management in patients with epilepsy. Traditional reliance on patient self-reporting can lead to inaccuracies, hindering effective treatment. Wearable-based seizure detection algorithms offer a promising solution, however, developing an efficient method for detecting FIAS remains a challenge. Additionally, as data quality can vary in wearable settings, the absence of continuous data quality assessment poses a concern for the reliability of such algorithms. The objective of our study is to develop and evaluate the performance and feasibility of FIAS detection algorithm with automatic data quality assessment (ADQA) using a wearable electrocardiography (ECG) device. We will also conduct an exploratory analysis of inter-individual variability in autonomic seizure signatures to identify potential future candidates, or “responders” to this system. Performance will be evaluated using sensitivity, false alarm rate per 24 hours (FAR/24), positive predictive value, and F1-Score. A multicenter study was conducted across three epilepsy centers and recruited patients of all ages who were admitted to video-EEG monitoring for a minimum of 24 hours consecutively. Data was collected using a wearable ECG device. The algorithm involved R-peak detection to identify heartbeats, extraction of knowledge domain heart rate variability features, ADQA, heart rate (HR) filter to address class imbalance, and a deep learning model for the final detection step. The algorithm was validated in a leave-one-patient-out (LOPO) approach using expert-labeled ictal events from video-EEG monitoring as ground truth. A total of 236 patients were recruited, of whom 49 patients experienced at least one FIAS, resulting in 3278 hours of ECG data and 260 seizures. Two patients with 33 seizures were excluded due to a technical error in the recording files, leaving 47 patients for analysis. After data quality screening, 161 seizures from 38 patients met the quality criteria. In this group, the median sensitivity was 66.6% (95% CI:33.3%-100%) with a median FAR/24 of 5.2 (95% CI:3.5-8.2). An exploratory responder analysis identified 20 patients with a detection sensitivity of ≥66.6%, for whom the median sensitivity was 100% (95% CI: 92%–100%) and the median FAR/24 was 4.3 (95% CI: 3–7). Finally, removing ADQA from the test data reduced the algorithm’s reliability, while removing it from training and test data reduced sensitivity, robustness, and reliability. The proposed algorithm demonstrated reasonable performance in patients whose wearable ECG data met the ADQA quality criteria (n = 38), with the highest detection performance observed in an exploratory responder subgroup (n = 20). These findings highlight the potential of ECG-based wearable systems for improving FIAS monitoring and underscore the importance of data quality in ensuring reliable algorithm performance. German Clinical Trials Register: DRKS00026939