Key result
A CNN-LSTM classifier using Poincare and RR interval features achieved an overall accuracy of 96.17% and an F1 score of 0.9589 for classifying ECG signals into normal, AF, and other rhythms.
Why the study?
Detection of AF remains a major concern because it is a main cause of stroke.
Does a CNN-LSTM classifier using Poincare and RR interval features accurately detect atrial fibrillation in ECG signals?
Does a CNN-LSTM classifier using Poincare and RR interval features accurately detect atrial fibrillation in ECG signals?
A CNN-LSTM classifier combining Poincare geometry and RR interval features can accurately detect atrial fibrillation from ECG signals, potentially aiding physicians as an assistive diagnostic tool.
Proposed ECG classifier for AF detection is hypothesis-generating; leaves open clinical performance pending prospective validation.
Detection of atrial fibrillation (AF) remains one of the major concerns in the field of medical research. AF is one of the main cause for stroke. AF is characterized by irregular heartbeats and absence of P waves in electrocardiogram (ECG) signal. In this article, we propose a method, combining Poincare plot derived and RR interval‐based features to classify given ECG signal into normal, AF and other rhythms. Classification and regression tree, K‐nearest neighbor, support vector machine, artificial neural network, ResNet18, convolutional neural network (CNN)‐long short term memory (LSTM) are implemented for classification of ECG signal. The class specific accuracies for the three rhythms are computed. Physionet challenge 2017 database is used for evaluation and testing of the developed algorithm. The database has 5154 normal, 771 AF, 2557 other rhythm and 46 noisy signals. Three Poincare derived features viz: SD1, SD2, and ratio of SD1 to SD2, three RR interval features viz: Mean stepping increment of RR interval, approximate entropy and sample entropy are computed and are given to classifiers. During fivefold cross‐validation, CNN‐LSTM classifier showed best result with class specific accuracy for normal of 96.65%, AF of 97.55%, other rhythms of 94.87%, overall accuracy of 96.17% and F 1 score of 0.9589. The developed technique can bring change in conventional practice in AF diagnosis and can aid the physician as an assisted tool.
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S.K et al. (2023) studied Atrial fibrillation (n=8,528). CNN-LSTM classifier using Poincare plot derived and RR interval-based features vs. Other machine learning classifiers was evaluated on Overall accuracy and F1 score for rhythm classification. A CNN-LSTM classifier using Poincare and RR interval features achieved an overall accuracy of 96.17% and an F1 score of 0.9589 for classifying ECG signals into normal, AF, and other rhythms.
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