Wavelet Decomposition and Feedforward Neural Network for Classification of Acute Ischemic Stroke based on Electroencephalography

Abstract

Stroke is one of the leading causes of death in Indonesia. From 2013 to 2018, the prevalence of stroke increased from 7% to 10.9%. There are two types of strokes, namely Hemorrhagic and Acutte Ischemic Stroke (AIS) with the majority of it being AIS. Early detection and diagnosis are essential in stroke as it is a life-threatening disease, and the stroke treatment is based on its type. Currently, the gold imaging standards in stroke diagnosis are Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI). However, the mentioned services for stroke diagnosis are primarily available in hospitals classified as "class A" (general hospitals with extensive facilities and medical services). Compared to CT scans and MRI, electroencephalography (EEG) is a cost-friendly, non-invasive device studied for various brain-related diseases. This study aimed to determine the optimal epoch length to classify four stroke classes (healthy, minor, moderate, and severe) during resting condition for a machine learning-based AIS computer-aided diagnostics system. 32-channel EEG, CT scan, and NIHSS Scores were the obtained data. The features were delta-theta to alpha-beta ratio (DTABR), delta to alpha ratio (DAR), relative power ratio (RPR), and asymmetry, which were extracted using wavelet decomposition technique. The epoch length was varied by 1s, 2s, 10s, 30s, 60s, and 120s. The severity of stroke were classified using a feedforward neural network. The best performance was obtained at the 60-second epoch length with 89% accuracy using 15 hidden layers. This EEG-based diagnostic system would be expected to be implemented in "class C" hospitals, where only essential medical services and facilities are available, usually in rural areas.