Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features
Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features
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DOI：
10.1109/tbme.2015.2512276
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发表时间：
2016-06-01
影响因子：
4.6
通讯作者：
Matsushima, Eisuke
中科院分区：
文献类型：
--
作者：
Fujiwara, Koichi;Miyajima, Miho;Matsushima, Eisuke
目的：本研究提出了一种新的癫痫发作预测方法，通过整合心率变异性（HRV）分析和异常监测技术。研究方法：由于癫痫发作前期过度的神经元活动影响自主神经系统，自主神经功能影响HRV，因此假设可以通过监测HRV来预测癫痫发作。在所提出的方法中，通过使用多变量统计过程控制，这是一个众所周知的异常监测方法，监测八个HRV特征用于预测癫痫发作。结果：我们将所提出的方法应用于从14例患者收集的临床数据。在收集的数据中，8例患者共有11次觉醒发作前发作，发作间期总长度约为57 h。应用结果表明，11次觉醒发作中有10次可在发作前预测发作，其敏感性为91%，假阳性率为0.7次/h。结论：本研究提出了一种新的基于心率变异性的癫痫发作预测方法，为实现基于心率变异性的癫痫发作预测系统提供了可能。意义：所提出的方法可以在日常生活中使用，因为可以通过使用可穿戴传感器来容易地测量心率。
Objective: The present study proposes a new epileptic seizure prediction method through integrating heart rate variability (HRV) analysis and an anomaly monitoring technique. Methods: Because excessive neuronal activities in the preictal period of epilepsy affect the autonomic nervous systems and autonomic nervous function affects HRV, it is assumed that a seizure can be predicted through monitoring HRV. In the proposed method, eight HRV features are monitored for predicting seizures by using multivariate statistical process control, which is a well-known anomaly monitoring method. Results: We applied the proposed method to the clinical data collected from 14 patients. In the collected data, 8 patients had a total of 11 awakening preictal episodes and the total length of interictal episodes was about 57 h. The application results of the proposed method demonstrated that seizures in ten out of eleven awakening preictal episodes could be predicted prior to the seizure onset, that is, its sensitivity was 91%, and its false positive rate was about 0.7 times per hour. Conclusion: This study proposed a new HRV-based epileptic seizure prediction method, and the possibility of realizing an HRV-based epileptic seizure prediction system was shown. Significance: The proposed method can be used in daily life, because the heart rate can be measured easily by using a wearable sensor.