A combined CNN and biLSTM network achieved 95.90% sensitivity, 95.90% accuracy, and 96.34% specificity in classifying 5 ECG beat classes.
Does a combined CNN and biLSTM network accurately classify ECG heart signals into 5 beat classes?
A combined CNN and biLSTM network demonstrates high accuracy and sensitivity in classifying ECG beat patterns, showing potential for automated cardiac health evaluation.
Computer-aided detection and diagnosis in ECG signals for heart diseases are gaining increasing attention. However, developing and selecting the highly performing diagnostic model suitable for clinical implications is still challenging. In this paper, we proposed a combined network of convolutional neural network (CNN) and Recurrent Neural Network (RNN), designed for the classification of ECG heart signals for diagnostic purpose. The proposed network consists of 2 convolutional layers with 5 5 kernels and ReLU activations, followed by 4 residual blocks, 2 bidirectional long short-term memory (biLSTM) layers, as well as 2 fully connected layers. Each residual block involved the structure of a Squeeze-and-Excitation Network (SENet) with lightweight property to recalibrate the feature map of the network. The last dense layer has 5 outputs, equivalent to the classes considered: non-ectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. To train and evaluate the combined CNN and RNN, we transferred the knowledge acquired on beat classification tasks in 2017 PhysioNet/CinC Challenge to that in PhysionNet’s MIT-BIH dataset. The developed network achieved a recognition sensitivity of 95. 90%, accuracy = 95. 90% and specificity = 96. 34% with classification time of single sample = 6. 23 s in detecting 5 ECG classes. A comparative analysis proved the high performance of the proposed combined CNN and RNN against previous methods, demonstrating the potential of our proposed network in the analysis of beat patterns. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate cardiac health with the highest precision.
Xu et al. (Wed,) conducted a other in ECG beat classification. Combined CNN and biLSTM network vs. Previous methods was evaluated on Recognition sensitivity, accuracy, and specificity in detecting 5 ECG classes. A combined CNN and biLSTM network achieved 95.90% sensitivity, 95.90% accuracy, and 96.34% specificity in classifying 5 ECG beat classes.
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