Key result
A stacked ensemble model using raw ECG signals predicted stroke with 99.7% accuracy, demonstrating high efficiency for automatic stroke diagnosis.
Why the study?
Stroke is a major cause of death and disability requiring early diagnosis, motivating the development of an efficient classification model using ECG features.
Does a stacked ensemble CNN model using raw ECG signals accurately predict stroke?
Does a stacked ensemble CNN model using raw ECG signals accurately predict stroke?
A stacked ensemble CNN model using raw ECG signals demonstrated exceptionally high accuracy (99.7%) for automatic stroke prediction.
High-accuracy ECG stroke prediction model; hypothesis-generating and requires prospective validation before clinical use.
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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Kunwar et al. (2022) studied Stroke (n=71). Stacked ensemble model (CNNs) using raw ECG signals was evaluated on Stroke prediction accuracy. A stacked ensemble model using raw ECG signals predicted stroke with 99.7% accuracy, demonstrating high efficiency for automatic stroke diagnosis.
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