Intelligent Systems with Applications (Feb 2023)

A stacked ensemble model for automatic stroke prediction using only raw electrocardiogram

  • Prashant Kunwar,
  • Prakash Choudhary

Journal volume & issue
Vol. 17
p. 200165

Abstract

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Stroke is one of the main causes of death and disability in elderly people worldwide. Early diagnosis of this disease is desirable. An electrocardiogram (ECG) signal is a powerful tool in the diagnosis of stroke. Many recent studies are using ECG signals in the diagnosis of stroke. This study is done with the aim to develop an efficient classification model which can be used in the diagnosis of stroke disease using ECG features. This study uses ECG data collected from 71 different subjects of which 35 were stroke patients and 36 were normal patients. This study proposes a stacked ensemble model which is built by stacking three different convolutional neural networks (CNN) models. The raw ECG signals are used as input to the model for training and testing. The result shows that the proposed model is capable of predicting stroke with an accuracy of 99.7%. F1-score, precision and recall are 99.69%, 99.67% and 99.71% respectively. Hence, this study reports that with the proposed model, ECG can be used as an aid in the diagnosis of stroke disease with high efficiency.

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