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Home > Archives > Volume 20, No 8 (2022) > Article

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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