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Electroencephalography signal classification for automatic interpretation of electroencephalogram based on Artificial Intelligence

The visual analysis of the electroencephalogram (EEG) is an expensive and time-consuming task. It can extract only 5% of the information held in the signal. Computer-assisted diagnosis could offer a way to obtain fast and reliable results and significantly reduce inter-and intra-assessor variability. In this document, we will present a tool for automatic analysis of EEG based on artificial neural networks. The proposed method consists in using signal processing and artificial intelligence algorithms to improve the interpretation of the EEG. For this purpose, we have two databases from the Nihon Kohden and Cadwell systems whose files are encrypted. The first step was to develop an application to decrypt and read the files. Thanks to this, the files could be decrypted in a standard format and the signals could be read. After that, we applied our method of automatic interpretation of the EEG. First, we preprocessed the signals using an Notch filter (50 Hz) and a bandpass filter (1–30Hz). Then, we extracted the features in the time-frequency domain based on three elements: the wavelet transform, its means, and its standard deviations. These features represent what we have used as inputs to our neural networks for classification. Our algorithm efficiently interpreted EEG signals with a correct classification rate of 97.9%, a sensitivity of 96.9%, and a specificity of 98.9%. These results have been deployed in an application that allows not only to visualize automatically the signals and the power spectral densities but also to extract the characteristics while displaying the wavelet transform related to the EEG signals of each chain.