Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Clinical Neurology, Neurosciences, Neurosciences & Neurology, Seizure detection, ECG, Heart rate, Personalization, EPILEPTIC-SEIZURES, ACCELEROMETRY, ONSET, Algorithms, Brain, Electrocardiography, Electroencephalography, False Positive Reactions, Heart, Heart Rate, Humans, Monitoring, Physiologic, Pattern Recognition, Automated, Photoperiod, Precision Medicine, Retrospective Studies, Seizures, Sensitivity and Specificity, STADIUS-18-58, 1103 Clinical Sciences, 1109 Neurosciences, 1701 Psychology, Neurology & Neurosurgery, 3202 Clinical sciences, 3209 Neurosciences, 5202 Biological psychology

Abstract:

PURPOSE: Automated seizure detection at home is mostly done using either patient-independent algorithms or manually personalized algorithms. Patient-independent algorithms, however, lead to too many false alarms, whereas the manually personalized algorithms typically require manual input from an experienced clinician for each patient, which is a costly and unscalable procedure and it can only be applied when the patient had a sufficient amount of seizures. We therefore propose a nocturnal heart rate based seizure detection algorithm that automatically adapts to the patient without requiring seizure labels. METHODS: The proposed method initially starts with a patient-independent algorithm. After a very short initialization period, the algorithm already adapts to the patients' characteristics by using a low-complex novelty detection classifier. The algorithm is evaluated on 28 pediatric patients with 107 convulsive and clinical subtle seizures during 695 h of nocturnal multicenter data in a retrospective study that mimics a real-time analysis. RESULTS: By using the adaptive seizure detection algorithm, the overall performance was 77.6% sensitivity with on average 2.56 false alarms per night. This is 57% less false alarms than a patient-independent algorithm with a similar sensitivity. Patients with tonic-clonic seizures showed a 96% sensitivity with on average 1.84 false alarms per night. CONCLUSION: The proposed method shows a strongly improved detection performance over patient-independent performance, without requiring manual adaptation by a clinician. Due to the low-complexity of the algorithm, it can be easily implemented on wearables as part of a (multimodal) seizure alarm system.