Epileptic Seizure Detection Based on Attitude Angle Signal of Wearable Device

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 10
• Main Authors: Wang, Jiabing, Hu, Dinghan, Lai, Xiaoping, Jiang, Tao, Jiang, Tiejia, Gao, Feng, Vidal, Pierre-Paul, Cao, Jiuwen
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Angular velocity; angular velocity signal; attitude angle signal; Attitudes; Convulsions & seizures; Epilepsy; Hospitals; long short-term memory (LSTM); multimodal signal analysis; Neurology; Pitch (inclination); Rolling motion; seizure detection; Seizures; Three axis; Wearable computers; wearable device; Wearable technology
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2025.3529058

Wearable wristband device-based epilepsy detection has the merits of noninvasiveness, portability, low costs, and good environmental adaptability. However, attention has been paid to exploring the attitude angle signals collected by wearable devices for epilepsy detection. In this article, a systematic analysis of whether the wearable device-based attitude angle signals, particularly the PITCH and ROLL angles, can be applied to epilepsy seizure detection, is studied. The relationship among attitude angle signals, acceleration, and angular velocity signals at the feature level is analyzed, and the detection effectiveness of combining different attitude angle features for classifier training and testing is presented and discussed. The long-term recorded data were collected by wearable devices from 28 epileptic patients, of which 11 were from the Fourth Affiliated Hospital of Anhui Medical University and 17 from the Department of Neurology, Children's Hospital, Zhejiang University School of Medicine. Each recording includes the measurement of three-axis acceleration (ACC), three-axis gyroscope (GYR), ROLL, PITCH, surface electromyography (SEMG), and electrodermal activity (EDA), with at least one seizure recorded for each subject. Experimental results show that ROLL and PITCH angles can be utilized for epilepsy detection, with better performance than using ACC and GYR. Moreover, the attitude angle feature training by a long short-term memory (LSTM) network can achieve the highest accuracy and efficiency.