A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG

• Published in: PloS one Vol. 12; no. 3; p. e0173138
• Main Authors: Chen, Duo, Wan, Suiren, Xiang, Jing, Bao, Forrest Sheng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.03.2017; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Algorithms; Analysis; Biology and Life Sciences; Child; Child, Preschool; Computational efficiency; Computer and Information Sciences; Computer applications; Data processing; Datasets; Decomposition; Discrete Wavelet Transform; EEG; Electroencephalography; Electroencephalography - methods; Engineering and Technology; Epilepsy; Female; Frequencies; Humans; Infant; Male; Medicine and Health Sciences; Physical Sciences; Research and Analysis Methods; Seizing; Seizures; Seizures (Medicine); Seizures - diagnosis; Signal analysis; Signal processing; Signal Processing, Computer-Assisted; Skewness; Wavelet Analysis; Wavelet transforms; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0173138

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection. To address this, we developed a method to decompose EEG data into 7 commonly used wavelet families, to the maximum theoretical level of each mother wavelet. Wavelets and decomposition levels providing the highest accuracy in each wavelet family were then searched in an exhaustive selection of frequency bands, which showed optimal accuracy and low computational cost. The selection of frequency bands and features removed approximately 40% of redundancies. The developed algorithm achieved promising performance on two well-tested EEG datasets (accuracy >90% for both datasets). The experimental results of the developed method have demonstrated that the settings of DWT affect its performance on seizure detection substantially. Compared with existing seizure detection methods based on wavelet, the new approach is more accurate and transferable among datasets.