TY - GEN
T1 - Automatic Detection of Seizures Using Extreme Learning Machine with a Single Feature
AU - Qin, Yingmei
AU - Han, Chunxiao
AU - Lu, Meili
AU - Wang, Ruofan
AU - Yang, Li
AU - Che, Yanqiu
N1 - Publisher Copyright:
© 2018 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2018/10/5
Y1 - 2018/10/5
N2 - Automatic seizure detection is of great importance in clinical practice of epilepsy. This paper presents a classification system based on discrete wavelet transform (DWT) and the extreme learning machine (ELM) for epileptic seizure detection by distinguishing ictal and interictal electroencephalogram (EEG) signals. The original EEG signal is first decomposed by Daubechies order 4 wavelet into several sub-bands. Then, standard deviation, log of amplitude, and quartiles are calculated for the original and decomposed signals to construct feature vectors. Different combination of these features are fed into ELM and support vector machine (SVM). After comparing different combination strategies, we find that, using ELM, even with a single feature (standard deviation) from a single sub-band signal (4-8Hz), one can obtain a satisfactory classification result, which remarkably reduce the computational complexity and make the detection system more practical.
AB - Automatic seizure detection is of great importance in clinical practice of epilepsy. This paper presents a classification system based on discrete wavelet transform (DWT) and the extreme learning machine (ELM) for epileptic seizure detection by distinguishing ictal and interictal electroencephalogram (EEG) signals. The original EEG signal is first decomposed by Daubechies order 4 wavelet into several sub-bands. Then, standard deviation, log of amplitude, and quartiles are calculated for the original and decomposed signals to construct feature vectors. Different combination of these features are fed into ELM and support vector machine (SVM). After comparing different combination strategies, we find that, using ELM, even with a single feature (standard deviation) from a single sub-band signal (4-8Hz), one can obtain a satisfactory classification result, which remarkably reduce the computational complexity and make the detection system more practical.
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U2 - 10.23919/ChiCC.2018.8483638
DO - 10.23919/ChiCC.2018.8483638
M3 - Conference contribution
AN - SCOPUS:85056080558
T3 - Chinese Control Conference, CCC
SP - 4430
EP - 4433
BT - Proceedings of the 37th Chinese Control Conference, CCC 2018
A2 - Chen, Xin
A2 - Zhao, Qianchuan
PB - IEEE Computer Society
T2 - 37th Chinese Control Conference, CCC 2018
Y2 - 25 July 2018 through 27 July 2018
ER -