Abstract
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.
Highlights
Stroke more often leaves disability than death
EEG signals of post-stroke patients can be recognized by observing wave density or rhythm, amplitude and differences in the magnitude of channel pair, and the presence of Alpha, Beta, Delta, and Theta waves
The blue line is the original signal, and the orange line is the result of a signal that has performed wavelet extraction
Summary
Stroke more often leaves disability than death. Stroke is the third-largest cause of disability in the world [1]. Some studies used EEG signals to identify ischemic stroke patients [6], investigate that stroke patients are able to use BCI [7], and extracted significant variable [8]. Several previous studies used Wavelet extraction to determine the significant variables of EEG signals for post-stroke patients [8]. Previous studies used Wavelet extraction of EEG signals to find significant variables of stroke [8] and the classification of emotions [15]. An EEG signal can be viewed as an image, so its processing uses two-dimensional CNN, like previous studies for the detection of epileptic attacks [17]. Some researchers used to identify EEG signals between stroke patients and healthy people [18], ischemic detection [19]. This study proposed the CNN method for classifying EEG signals against post-stroke patients into both classes.
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