A novel P-QRS-T wave localization method in ECG signals based on hybrid neural networks

https://doi.org/10.1016/j.compbiomed.2022.106110Get rights and content

Highlights

  • Without complex feature extraction or feature selection stages, the proposed algorithm can be embedded in portable devices.

  • The proposed method can be used as the initial step of ECG heartbeat classification and combined with other classifiers.

  • This wave localization algorithm can also be applied to the 12-lead ECG, including abnormal waveforms.

  • The method based on ResNet and LSTM is robust against noise and has achieved excellent performance in four ECG databases.

Abstract

As the number of people suffering from cardiovascular diseases increases every year, it becomes essential to have an accurate automatic electrocardiogram (ECG) diagnosis system. Researchers have adopted different methods, such as deep learning, to investigate arrhythmias classification. However, the importance of ECG waveform features is generally ignored when deep learning approaches are applied to classification tasks. P-wave, QRS-wave, and T-wave, containing plenty of physiological information, are three critical waves in the ECG heartbeat. The accurate localization of these critical ECG wave components is a prerequisite for ECG classification and diagnosis. In this study, a novel P-QRS-T wave localization method based on hybrid neural networks is proposed. The raw ECG signal is preprocessed sequentially by filtering, heartbeat extraction, and data standardization. The hybrid neural network is constructed by combining the residual neural network (ResNet) and the Long Short-Term Memory (LSTM). It predicts the relative positions of the P-peak, QRS-peak, and T-peak for each heartbeat. The proposed algorithm was validated on four ECG databases with input noise of different signal-to-noise ratio (SNR) levels. The results show that the proposed method can accurately predict the positions of the three key waves. The proposed P-QRS-T localization approach can improve the efficiency of ECG delineation. Integrated with cardiac disease classification methods, it can contribute to the development of advanced automatic ECG diagnosis systems.

Introduction

According to the World Health Organization, cardiovascular diseases (CVDs) are the leading cause of death in the world [1]. Nowadays, the stressful social life and irregular lifestyle result in a trend of younger patients with CVDs. Electrocardiogram (ECG) signals are widely used in clinical studies for the detection of heart diseases. During each cardiac cycle, the excitation generated by the sinus node is transmitted sequentially to the atrium and ventricle. A complete heartbeat is mainly composed of three waveforms: P-wave, QRS-wave, and T-wave.

The analysis of the P-QRS-T waves is essential because the shape and period of the P-QRS-T waves can be substantially altered by some pathologies in the automated ECG signal diagnosis systems. In addition, accurate localization of P-QRS-T waves is usually the initial step for cardiac disease classification through machine learning or deep learning [[2], [3], [4]]. Therefore, it is of great significance to investigate the algorithm that can precisely locate P-QRS-T waves of the ECG.

Various waveform detection algorithms have been proposed in previous work. In the last four decades, the localization and detection of QRS complexes have been extensively studied. Pan-Tompkins [5] proposed the well-known derivative-based method, which achieved 99.3% accuracy in detecting QRS waves. There are also some other ECG waveform detection approaches, such as wavelet transform [[6], [7], [8]], digital filters [9], and adaptive threshold [10]. Due to low amplitude, low SNR, and multiple variations in morphology, the detection of P-wave and T-wave is much more challenging compared to QRS-wave detection. Martinez et al. [11] developed a wavelet transform-based ECG signal delineation system and validated it on several manually annotated datasets. In Ref. [12], P-wave and T-wave delineation based on a Bayesian approach was investigated. Karimipour et al. [13] proposed a real-time detection algorithm based on the correlation analysis of the relationship between their template and signal. Marsanova et al. [14] adopted phasor transform combined with a classification algorithm to detect P-wave during ventricular extrasystoles.

Machine learning and deep learning approaches are widely applied in various signal processing and data analysis fields. Several methods for wave detection are established on the basis of the hidden Markov model (HMM) [15], K-Nearest Neighborhood (KNN) [16], and Convolutional Neural Network (CNN) [17]. Wang [18] employed an end-to-end one-dimensional residual neural network to detect QRS waves. In Ref. [19], Elgendi et al. proposed an approach for fast detection of T-wave using dynamic thresholds based on moving averages models. Their approach was validated on two famous databases, the QT database (QTDB) [20,21] and MIT-BIH Arrhythmia databases (MITDB) [22]. To predict the positions of ECG key waves, a P-QRS-T localization method using ECGNet was introduced in Ref. [23]. However, this algorithm was only validated by the automatically annotated waves in the QTDB.

In this study, we propose an ECG waveform localization method based on a hybrid network combining residual neural network (ResNet) and Long Short-Term Memory (LSTM), which can recognize the location of P-QRS-T wave peaks. Firstly, a new heartbeat division method is proposed, then the ECG signal is filtered by a denoising algorithm. After the heartbeat is processed by the hybrid neural network, the position of the P-QRS-T wave peak can be obtained. This method was validated in four ECG databases and achieved excellent performance.

Compared with other wave detection methods, the proposed algorithm has the following four advantages:

  • (i)

    The proposed method does not require complex feature extraction or feature selection. Benefitting from its low computational complexity, the model can be embedded in portable terminals.

  • (ii)

    In the field of ECG automatic diagnosis, deep learning frameworks generally take fixed-length heartbeat as input. Therefore, the proposed wave localization algorithm can be used as the initial step of ECG heartbeats classification and can be directly combined with other advanced classifiers.

  • (iii)

    The positions of the P-peak, QRS-peak, and T-peak are presented for each heartbeat. Based on the three peaks, the start and end points of the critical ECG wave components can be detected easily. It enriches the acquired waveform information, which further improves the accuracy and efficiency of the automatic ECG heartbeats classification system.

  • (iv)

    The proposed method has achieved excellent performance in four ECG databases with input noise of different magnitudes. The proposed hybrid neural network architecture based on ResNet and LSTM is robust against noise and promising for practical application.

Section snippets

Methodology

The workflow of the proposed P-QRS-T wave localization method is illustrated in Fig. 1. Initially, the ECG signal is preprocessed by a three-step procedure. Next, ECG heartbeats with annotations of P-peaks, QRS-peaks, and T-peaks are segmented for training and validation. Then, the proposed hybrid neural network model is adopted to obtain the detailed features of these three peaks. Finally, the relative positions of the P-QRS-T wave peaks are presented.

Experiment and results

The data used in this paper are shown in Table 1. All ECG heartbeats were obtained by the continuous division scheme mentioned before. For the data in QTDB, both the heartbeat annotated by the automatic algorithm and those annotated manually by experts are used to validate the accuracy of the algorithm. The proposed method is trained and validated on four databases with a ratio of 4:1 splitting training and validation data. For the data in QTDB and MITDB, the size of the heartbeat are 250*1 and

Discussion

In this study, a novel P-QRS-T wave localization method based on hybrid neural networks is proposed. The proposed algorithm has been verified by four databases and has achieved excellent performance. As presented in Table 8, we obtain an RMSE result of 4.28. The ECGNet mentioned in Ref. [23] reached an RMSE value of 5.57. In Ref. [13], RMSE is also calculated to represent the average error between the true and predicted positions of the waves. With the verification of the QTDB, as shown in

Conclusion

In order to accurately locate the ECG critical waveforms, a novel P-QRS-T wave localization method in ECG Signals based on hybrid neural networks is proposed. Firstly, ECG signals are preprocessed by a three-step procedure. For the preprocessing of ECG data, we propose a new heartbeat division scheme for the filtered ECG signal. The data and labels utilized for training and validation are standardized and normalized. Then, we collect the database that can be employed for the localization of

Funding

This work was supported by the National Key R&D Program of China (No. 2018YFB1307005) and the Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX0102).

Declaration of competing interest

The authors declared that they have no conflicts of interest to this work.

Acknowledgements

None.

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