Browsing by Author "Wu, Ssu-Yen"

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Detection of Spontaneous Temporal Lobe Epilepsy in Rats by Means of 1-axis Accelerometor Signal
(2013-01-01)
Wang, Yu-Lin
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Liang, Sheng-Fu
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Shaw, Fu-Zen
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Huang, Yu-Hsin
;
Wu, Ssu-Yen
Epilepsy is the most common psychological disorders in humans. Patients suffering from epilepsy usually experience behavioral symptoms, such as involuntary movement and rage reaction. Conventional monitoring with electroencephalogram and video is unpleasant for patients and not feasible for long-term monitoring. In this work, a seizure detection system with an accelerometer set on the subject\'s head is proposed to monitor the behavioral activities and to detect the seizure events in subjects. The Wistar rats with temporal lobe epilepsy (TLE) were used in our experiment. To reduce the computational energy, only one axis signal was utilized for seizure detection. A three-state finite state machine (FSM) was applied to determine the seizure activities, and the temporal features of y-axis ACC signal were calculated for state transition. The results show that our proposed on-line detection method can achieve 100% accuracy with extremely low false alarm rate of 0.035. It has the advantage of easy measurement and good performance for real-world applicability.
Use of accelerometers to detect motor states in a seizure of rats with temporal lobe epilepsy
(2012)
Wang, Yu-Lin
;
Liang, Sheng-Fu
;
Shaw, Fu-Zen
;
Su, Alvin W. Y.
;
Chen, Yin-Lin
Epilepsy is the most common psychological disorders in humans. Patients suffering from epilepsy usually experience behavioral symptoms, such as involuntary movement and rage reaction. Conventional monitoring with EEG and video is unpleasant for patients. Use of the accelerometer and electroencephalogram to access the state of vigilance has been discussed recently. In addition to simply detecting the occurrence of a seizure event or not, we proposed a concept of motor detection method which detects the subtle change of motor states in a kindling-induced seizure by using the accelerometer's signals in this study. The Wistar rats with temporal lobe epilepsy were used in our experiments, and the accelerometers were set on the rat's head. The features of 3-axis accelerometer signals were extracted, and the support vector machine (SVM) was applied to determine the motor states. The results show that our proposed on-line detection method can detect the changes of motor responses in a kindling-induced seizure with 78% accuracy in average. It has the advantage of easy measurement for real-world applicability.