What does this research mean for the field?

Q: What does this research mean for the field?

An ensemble Convolutional Neural Network and Bi-directional Long Short-Term Memory (CNN-BiLSTM) architecture, combined with SMOTE for class balancing, achieves 99.88% accuracy in classifying cardiac arrhythmias from ECG signals, outperforming existing state-of-the-art techniques. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

October 16, 2023

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

Structured PICO

Population

MIT-BIH arrhythmias database containing 109,443 ECG beats (90,589 normal, 8,039 supraventricular, 7,236 ventricular, 2,776 fusion, 803 unknown)

Intervention

Ensemble of convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) architecture with SMOTE and TSFEL

Comparator

Existing classification techniques and stacked ensemble machine learning algorithms (Random Forest, SVM, Xgboost, Adaboost)

Outcome

Classification accuracy, sensitivity, precision, and F1 score for detecting arrhythmias heartbeat classessurrogate

An ensemble CNN-BiLSTM architecture achieved 99.88% accuracy in classifying cardiac arrhythmias from ECG signals, outperforming traditional machine learning techniques.

Abstract

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 work 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 bi-directional 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 total 109443 ECG beats with 90589 normal beats (NB), 8039 supraventricular beats (SB), 7236 ventricular beats (VB), 2776 fusion beats (FB) and 803 unknown beats (QB) respectively. Here, in this study, we incorporate synthetic minority oversampling technique (SMOTE) for balancing dataset along with time-series feature extraction library (TSFEL) for extracting feature and an ensemble of CNN-LSTM to predict fast features of upcoming ECG signals to enhance the performance of heartbeat classification as compare 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 ensemble CNN-BiLSTM method, the 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 compare to existing classification techniques in terms sensitivity, accuracy and precision.

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