Identification of post-stroke EEG signal using wavelet and convolutional neural networks

Post-stroke patients need ongoing rehabilitation to restore dysfunction caused
by an attack so that a monitoring device is required. EEG signals reflect
electrical activity in the brain, which also informs the condition of post-stroke
patient recovery. However, the EEG signal processing model needs to
provide information on the post-stroke state. The development of deep
learning allows it to be applied to the identification of post-stroke patients.
This study proposed a method for identifying post-stroke patients using
convolutional neural networks (CNN). Wavelet is used for EEG signal
information extraction as a feature of machine learning, which reflects
the condition of post-stroke patients. This feature is Delta, Alpha, Beta,
Theta, and Mu waves. Moreover, the five waves, amplitude features are also
added according to the characteristics of the post-stroke EEG signal.
The results showed that the feature configuration is essential as distinguish.
The accuracy of the testing data was 90% with amplitude and Beta features
compared to 70% without amplitude or Beta. The experimental results also
showed that adaptive moment estimation (Adam) optimization model was
more stable compared to Stochastic gradient descent (SGD). But SGD can
provide higher accuracy than the Adam model.