Key result
An artificial neural network classification method using the RMS signal achieved sensitivity, specificity, and accuracy indexes of 97.68%, 99.08%, and 98.72% for differentiating five types of heartbeats.
Why the study?
The study was conducted to present a method for classifying cardiac arrhythmias based on RMS signal analysis and heartbeat cyclostationarity.
Does using the RMS signal improve the classification of cardiac arrhythmias compared to multi-lead ECG signals?
Does using the RMS signal improve the classification of cardiac arrhythmias compared to multi-lead ECG signals?
An artificial neural network-based classification method using RMS signals from ECGs accurately differentiates between five types of cardiac beats.
May support RMS-ANN for multi-class heartbeat classification; leaves open prospective validation before clinical adoption.
The goal of this paper is to present a method of classification for cardiac arrhythmias. This method is based on the analysis of the (RMS) signal extracted from the electrocardiogram (ECG) leads and the heart beat cyclostationarity nature. The coefficients of spectral correlation fuction are useful in order to differentiate among the five types of beats: Normal (N), Premature Ventricular Contraction (PVC), Premature Atrial Contraction (APC), Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). Some simple statistical measures for spectral correlation coefficients are extracted while Principal Component Analysis (PCA) is used to reduce its features. Finally, the classification using Artificial Neural Networks (ANNs) is implemented. Two experiments were performed, one that used a multi-lead ECG signal, and the other that used the RMS signal as input. These experiments showed that when using the RMS signal better results are obtained. This last method led to some specific results: sensitivity, specificity and accuracy indexes of 97.68%, 99.08% and 98.72%, respectively.
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Gómez et al. (2021) studied Cardiac arrhythmias. RMS signal-based classification using Artificial Neural Networks vs. Multi-lead ECG signal-based classification was evaluated on Classification accuracy. An artificial neural network classification method using the RMS signal achieved sensitivity, specificity, and accuracy indexes of 97.68%, 99.08%, and 98.72% for differentiating five types of heartbeats.
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