A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and bidirectional long short-term memory

Abstract

Background: Analysis of electrocardiogram (ECG) provides a straightforward and non-invasive approach for cardiologists to diagnose and classify the nature and severity of variant cardiac diseases including cardiac arrhythmia. However, the interpretation and analysis of ECG are highly working-load demanding, and the subjective may lead to false diagnoses and heartbeats classification. In recent years, many deep learning works showed an excellent role in accurate heartbeats classification. However, the imbalance of heartbeat classes is universal in most of the available ECG databases since abnormal heartbeats are always relatively rare in real life scenarios. In addition, many existing approaches achieved prominent results by removing noise and extracting features in data preprocessing, which relies heavily on powerful computers. It is a pressing need to develop efficient and automatic light weighted algorithms for accurate heartbeats classification that can be used in portable ECG sensors.Objective: This study aims at developing a robust and efficient deep learning method, which can be embedded into wearable or portable ECG monitors for classifying heartbeats.Methods: We proposed a novel and light weighted deep learning architecture with weight-based loss based on a convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) that can automatically identify five types of ECG heartbeats according to the AAMI EC57 standard. It was also true that the raw ECG signals were simply segmented without noise removal and other feature extraction processing. Moreover, to tackle the challenge of classification bias due to imbalanced ECG datasets for different types of arrhythmias, we introduced a weight-based loss function to reduce the influence of over-weighted categories in the ECG dataset. For avoiding the influence of the division of validation dataset, k-fold method was adopted to improve the reliability of the model.Results: The proposed algorithm is trained and tested on MIT-BIH Arrhythmia Database, and achieves an average of 99.33% accuracy, 93.67% sensitivity, 99.18% specificity, 89.85% positive prediction, and 91.65% F1 score.

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals

“… 32 Imbalanced datasets can result in biased and poor performance of the model for minority classes because the model's training is heavily inclined toward the majority class through the backpropagation of the loss function. 10 To prevent the loss function from being disproportionately influenced by the larger sample category in the imbalanced dataset, we implemented the SMOTE–Tomek algorithm. First, SMOTE, which is an oversampling method, increases minority class samples so that the minority class samples are expanded.…”

“…Correctly detecting the type of arrhythmia is crucial for physicians before administering treatment because it not only helps save a patient's life but also alleviates sequelae, thereby reducing the burden and cost of healthcare. 10 The current standard method for detecting arrhythmia types is visual identification, which can lead to physicians’ subjective biases. Due to the large morphological variances, it is not simple to manually detect ECGs.…”

“… 15 , 16 According to a study, arrhythmia classification by deep learning techniques can achieve higher efficiency and accuracy than expert manual detection. 10 , 17 Among deep learning techniques, especially convolutional neural networks (CNN), recurrent neural networks (RNN), which primarily contain long short-term memory (LSTM), and gated recurrent unit (GRU) networks have been particularly successful in ECG signal processing and were significantly improved. 18 …”

“…24 Most researchers combined CNN with BiLSTM and achieved satisfactory results in classifying arrhythmias from ECG signals. 10 The GRU network is another type of recurrent neural network, which is similar to an LSTM network but has fewer parameters and converges faster than LSTM. GRU adaptively remembers and forgets its state based on the input signal to the unit.…”

“…Furthermore, the high noise and complexity of ECG place a substantial strain on computational resources; therefore, more robust models with fewer parameters are greatly desired. 10 …”