Browsing by Author "Chen, Yin-Lin"

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An energy efficient real-time seizure detection method in rats with spontaneous temporal lobe epilepsy
(2013)
Wang, Yu-Lin
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Liang, Sheng-Fu
;
Shaw, Fu-Zen
;
Huang, Yu-Shin
;
Chen, Yin-Lin
The presence of an on-line seizure detection system could drive an antiepileptic stimulator in real time to suppress seizure generation and to enhance the patients' safety and quality of life. In this paper, the continuous long-term EEGs of three Wistar rats with spontaneous temporal lobe seizure were analyzed. We proposed the development of an energy efficient real-time seizure detection method that employs a hierarchical architecture. The first stage was used to fast detect the seizure-like EEG segment, and a classifier was utilized in the second stage for final confirmation. Only when a suspected seizure segment is found, the second stage is activated. With 2-staged architecture, it saved about 99.4% computation energy in the experiment. Therefore, it is useful to improve the longevity of the closed-loop seizure control system. Three classifiers, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM), were applied for comparison. From the experimental results, three classifiers yielded the comparable performances. However, considering of the trade-off between detection performances and power consumption, LDA which yielded the 100% detection rate, 0.22 FP/hr, and 1.69 s detection latency is suggested for a portable closed-loop seizure controller.
Epileptic Pattern Recognition and Discovery of the Local Field Potential in Amygdala Kindling Process
(2016-03)
Wang, Yu-Lin
;
Chen, Yin-Lin
;
Su, Alvin Wen-Yu
;
Shaw, Fu-Zen
;
Liang, Sheng-Fu
Epileptogenesis, which occurs in an epileptic brain, is an important focus for epilepsy. The spectral analysis has been popularly applied to study the electrophysiological activities. However, the resolution is dominated by the window function of the algorithm used and the sample size. In this report, a temporal waveform analysis method is proposed to investigate the relationship of electrophysiological discharges and motor outcomes with a kindling process. Wistar rats were subjected to electrical amygdala kindling to induce temporal lobe epilepsy. During the kindling process, different morphologies of afterdischarges (ADs) were found and a recognition method, using template matching techniques combined with morphological comparators, was developed to automatically detect the epileptic patterns. The recognition results were compared to manually labeled results, and 79%-91% sensitivity was found. In addition, the initial ADs (the first 10 s) of different seizure stages were specifically utilized for recognition, and an average of 85% sensitivity was achieved. Our study provides an alternative viewpoint away from frequency analysis and time-frequency analysis to investigate epileptogenesis in an epileptic brain. The recognition method can be utilized as a preliminary inspection tool to identify remarkable changes in a patient\'s electrophysiological activities for clinical use. Moreover, we demonstrate the feasibility of predicting behavioral seizure stages from the early epileptiform discharges.
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.