Electrocardiogram Heartbeat Classification Using Machine Learning and Ensemble Convolutional Neural Network-Bidirectional Long Short-Term Memory Technique

Automated classification of cardiac rhythms from electrocardiogram (ECG) signals is significant for diagnosis of cardiovascular dysfunctioning. A biggest challenge in automated ECG classification is to address the task's specific characteristics, such as time dependencies between observations and a strong class imbalance. To address these issues, this article proposes machine learning ensemble techniques (random forest, support vector machine, Xgboost, Adaboost, and stacked ensemble classifier) and an ensemble of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) architecture for classification of cardiac arrhythmias in ECG signals. The proposed model has been trained and tested on the MIT-BIH arrhythmias database, which contains a total of 109 443 ECG beats with 90 589 normal beats, 8039 supraventricular beats (SB), 7236 ventricular beats (VB), 2776 fusion beats, and 803 unknown beats, respectively. Here, in this article, we incorporate a synthetic minority oversampling technique for balancing dataset along with time-series feature extraction library for extracting features and an ensemble of CNN-LSTM to predict fast features of upcoming ECG signals to enhance the performance of heartbeat classification as compared to the state-of-the-art techniques. The achieved average accuracy (Acc), sensitivity (SE), precision (PE), and F1-score using stacked ensemble machine learning algorithm for detection arrhythmias heartbeat classes are 98.67%, 96%, 97.80%, and 96.80%, respectively. With the ensemble CNN-BiLSTM method, an average accuracy of 99.88% has been achieved. The SE and PE values for SB and VB beats are 84.40%, 99.79%, 96.40%, and 98.54%, respectively. Experimental results demonstrate that an ensemble CNN-BiLSTM technique outperforms as compared to the existing classification techniques in terms of sensitivity, accuracy, and precision.