Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features

• Published in: IEEE transactions on biomedical engineering Vol. 63; no. 6; pp. 1321 - 1332
• Main Authors: Fujiwara, Koichi, Miyajima, Miho, Yamakawa, Toshitaka, Abe, Erika, Suzuki, Yoko, Sawada, Yuriko, Kano, Manabu, Maehara, Taketoshi, Ohta, Katsuya, Sasai-Sakuma, Taeko, Sasano, Tetsuo, Matsuura, Masato, Matsushima, Eisuke
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-06-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Anomalies; Autonomic nervous system; Databases, Factual; Electrocardiography - classification; Epilepsy; Epilepsy - diagnosis; Epilepsy - physiopathology; Feature extraction; Female; Frequency-domain analysis; Heart rate; Heart Rate - physiology; Heart rate variability; Heart rate variability analysis; Humans; Male; Middle Aged; Monitoring; Multivariate Analysis; Multivariate statistical process control; Patients; Prediction methods; Process control; Rail to rail inputs; Seizing; Seizure prediction; Sensors; Signal Processing, Computer-Assisted; Statistical process control; Young Adult; seizure prediction; multivariate statistical process control (MSPC); Epilepsy; heart rate variability analysis
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2015.2512276

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.