DOI: 10.14704/nq.2022.20.8.NQ44535
CLASSIFICATION OF STROKE SYMPTOMS IN PATIENTS USING SELF ORGANIZING MAPS
ANUREKHA R, KAVITHA M S, KARTHIK S
Abstract
In this paper we use self-organizing maps (SOM) to classify the stroke symptoms from EMG signals
for hand movement classification. The process involves pre-processing, feature extraction and
classification of EMG signals from the input signals. The model is split into train and testing datasets
with 80:20 ratio of 10-fold cross validation approach. The simulation is conducted in python to test the efficacy of the proposed model. The results show that the proposed method achieves higher
grade of classification accuracy than the existing methods.
Keywords
Self-Organizing Maps, EMG signals, Stroke, Biomedical signals
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