Sungmin You1, Baek Hwan Cho2, Soonhyun Yook1, Joo Young Kim1, Young-Min Shon3, Dae-Won Seo4, In Young Kim5. 1. Department of Biomedical Engineering, Hanyang University, Seoul, South Korea. 2. Medical AI Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea. 3. Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea. 4. Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea. Electronic address: daewon3.seo@samsung.com. 5. Department of Biomedical Engineering, Hanyang University, Seoul, South Korea. Electronic address: iykim@hanyang.ac.kr.
Abstract
BACKGROUND AND OBJECTIVE: Epilepsy is a neurological disorder of the brain, which involves recurrent seizures. An encephalogram (EEG) is a gold standard method in the detection and analysis of epileptic seizures. However, the standard EEG recording system is too obstructive to be used in daily life. Behind-the-ear EEG is an alternative approach to record EEG conveniently. Previous researchers applied machine learning to automatically detect seizures with EEG, but the epileptic EEG waveform contains subtle changes that are difficult to be identified. Furthermore, the extremely small proportion of ictal events in the long-term monitoring may cause the imbalance problem and, consequently, poor prediction performance in supervised learning approaches. In this study, we present an automatic seizure detection algorithm with a generative adversarial network (GAN) trained by unsupervised learning and evaluated it with behind-the-ear EEG. METHODS: We recorded behind-the-ear EEGs from 12 patients who have various types of epilepsy. Data were reviewed separately by two epileptologists, who determined the onsets and ends of seizures. First, we conducted unsupervised learning with the normal records for the GAN to learn the representation of normal states. Second, we performed automatic seizure detection with the trained GAN as an anomaly detector. Last, we combined the Gram matrix with other anomaly losses to improve detection performance. RESULTS: The proposed approach achieved detection performance with an area under the receiver operating curve of 0.939 and sensitivity of 96.3% with a false alarm rate of 0.14 per hour in the test dataset. In addition, we confirmed distinguishability with the distribution of the anomaly scores in terms of EEG frequency bands. CONCLUSIONS: It is expected that the proposed anomaly detection via GAN with the behind-the-ear EEG can be effectively used for long-term seizure monitoring in daily life.
BACKGROUND AND OBJECTIVE:Epilepsy is a neurological disorder of the brain, which involves recurrent seizures. An encephalogram (EEG) is a gold standard method in the detection and analysis of epileptic seizures. However, the standard EEG recording system is too obstructive to be used in daily life. Behind-the-ear EEG is an alternative approach to record EEG conveniently. Previous researchers applied machine learning to automatically detect seizures with EEG, but the epileptic EEG waveform contains subtle changes that are difficult to be identified. Furthermore, the extremely small proportion of ictal events in the long-term monitoring may cause the imbalance problem and, consequently, poor prediction performance in supervised learning approaches. In this study, we present an automatic seizure detection algorithm with a generative adversarial network (GAN) trained by unsupervised learning and evaluated it with behind-the-ear EEG. METHODS: We recorded behind-the-ear EEGs from 12 patients who have various types of epilepsy. Data were reviewed separately by two epileptologists, who determined the onsets and ends of seizures. First, we conducted unsupervised learning with the normal records for the GAN to learn the representation of normal states. Second, we performed automatic seizure detection with the trained GAN as an anomaly detector. Last, we combined the Gram matrix with other anomaly losses to improve detection performance. RESULTS: The proposed approach achieved detection performance with an area under the receiver operating curve of 0.939 and sensitivity of 96.3% with a false alarm rate of 0.14 per hour in the test dataset. In addition, we confirmed distinguishability with the distribution of the anomaly scores in terms of EEG frequency bands. CONCLUSIONS: It is expected that the proposed anomaly detection via GAN with the behind-the-ear EEG can be effectively used for long-term seizure monitoring in daily life.