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Adaptive nocturnal seizure detection using heart rate and low-complexity novelty detection
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| Abstract |
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| 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. (C) 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved. |
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English
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Seizure: European journal of epilepsy. - London | |
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London : 2018
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1059-1311
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59(2018), p. 48-53
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000437818500010
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29747021
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