Pulse-based photoplethysmography quality assessment improves wearable seizure detection performance

Wearable photoplethysmography (PPG) is increasingly used for seizure detection due to its ability to unobtrusively estimate heart rate (HR). However, the vulnerability of PPG to body movements often leads to unreliable HR estimates, compromising accurate seizure detection. Although this limitation is well recognized, methods for rejecting artifactual HR measures-and their impact on seizure detection performance-have not yet been studied.

We sourced data from the PROMISE trial, a home-based study in children with refractory epilepsy that evaluated the performance of the NightWatch (NW) in detecting nocturnal motor seizures. We applied a novel pulse-based quality assessment (PQA) method that evaluates the quality of each individual PPG pulse and excludes compromised ones prior to HR estimation. We compared the performance of PQA with two alternatives: HR-based quality assessment (HRQA), rejecting extreme outliers in consecutive inter-beat intervals, and no quality assessment (noQA). We assessed the impact of PQA, HRQA, and noQA on seizure detection performance by applying HR increase thresholds from 30% to 80% relative to the baseline. A permutation test with bootstrapping was applied for all statistical comparisons.

We analyzed 741 NW alarms, comprising 135 true seizures and 606 non-seizure events in 28 children (46% female; age: 9.1 ± 3.3 years). Averaged across all evaluated detection thresholds, both PQA and HRQA significantly improved seizure detection performance compared to noQA (p < 0.005). PQA led to mean increases of 0.248 in sensitivity, 0.025 in positive predictive value (PPV), and 0.082 in F1-score. HRQA showed mean increases of 0.081 in sensitivity, 0.040 in PPV, and 0.056 in F1-score. At clinically relevant sensitivity levels (0.8, 0.85, and 0.9), PQA showed a relative improvement over HRQA, with F1-scores increasing by 4%-15% and PPV by 5%-19%.

Implementing PPG quality assessment-particularly pulse-based-improves the performance of wearable HR-based seizure detection.

This study aimed to improve seizure detection in wearable devices by introducing a new method for assessing the quality of the heart rate (HR) data derived from a light sensor called photoplethysmography (PPG). By applying our method to data from children with epilepsy, we showed that discarding low-quality PPG data before HR estimation leads to better seizure detection, allowing more seizures to be correctly identified. This improvement is important because it enhances seizure detection performance through a software-only upgrade.