PVC Recognition for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Network

Since common electrocardiography (ECG) diagnostics approaches are time-consuming and arrhythmia-type sensitive, deep-learning methods are state-of-the-art for their detection accuracy. However, premature ventricular contractions' (PVC) localization via common deep-learning approaches requires large training set, therefore Multiple Instance Learning (MIL) framework was applied, where model is trained from whole-signal annotations. Proposed MIL framework is based on 1D Convolutional Neural Network (CNN), with global max-pooling in the last layer. The detection of PVCs' positions was done by the peak detector with specified parameters-threshold, minimal distance and peak prominence. Our method was tested on database containing 1590 ECGs, including 672 signals with PVCs. Dice coefficient reaches 0.947. This simple deep-learning method for the localization of PVC achieves a promising performance while being trainable from the whole-signal annotations instead of positional labels.

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“…The accuracy is comparable with the results of another recent studies with CNN classification (e.g. 0.979 [13], 0.979 [14]).…”

Section: Resultssupporting

confidence: 87%

Deep-Learning Premature Contraction Localization in 12-lead ECG From Whole Signal Annotations

Novotná¹,

Vičar²,

Ronzhina³

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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“…The MFSWT can transform one-dimensional time-series signals into two-dimensional time-frequency images as the input of the CNN. The test results of this method achieved a high accuracy of 97.89% [22].…”

Section: And Wijayantomentioning

confidence: 89%

“…The studies [22,23,26] performed wavelet transform on the ECG to obtain 2-D time-frequency images. Moreover, the research [24,25] used the features extracted by a trained autoencoder to recognize PVC.…”

Section: And Wijayantomentioning

confidence: 99%

Searching for Premature Ventricular Contraction from Electrocardiogram by Using One-Dimensional Convolutional Neural Network

Wang

Chen

et al. 2020

Electronics

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Premature ventricular contraction (PVC) is a common cardiac arrhythmia that can occur in ordinary healthy people and various heart disease patients. Clinically, cardiologists usually use a long-term electrocardiogram (ECG) as a medium to detect PVC. However, it is time-consuming and labor-intensive for cardiologists to analyze the long-term ECG accurately. To this end, this paper suggests a simple but effective approach to search for PVC from the long-term ECG. The recommended method first extracts each heartbeat from the long-term ECG by applying a fixed time window. Subsequently, the model based on the one-dimensional convolutional neural network (CNN) tags these heartbeats without any preprocessing, such as denoise. Unlike previous PVC detection methods that use hand-crafted features, the proposed plan rationally and automatically extracts features and identify PVC with supervised learning. The proposed PVC detection algorithm acquires 99.64% accuracy, 96.97% sensitivity, and 99.84% specificity for the MIT-BIH arrhythmia database. Besides, when the number of samples in the training set is 3.3 times that of the test set, the proposed method does not misjudge any heartbeat from the test set. The simulation results show that it is reliable to use one-dimensional CNN for PVC recognition. More importantly, the overall system does not rely on complex and cumbersome preprocessing.

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“…Finally, these extracted features were classified using eight different classifiers. Zhao et al [ 22 ] suggested an approach by combining the convolutional neural network (CNN) and modified frequency slice wavelet transform (MFSWT). Firstly, in each recording, the first 10s ECG waveforms were transformed into time-frequency images employing MFSWT (frequency range of 0–50 Hz).…”

Section: Introductionmentioning

confidence: 99%

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach

Sarshar

Mirzaei

2022

Journal of Healthcare Engineering

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Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstration of the cyclical contraction and relaxation of human heart muscles. This signal is a primary and noninvasive tool employed to recognize the actual life threat related to the heart. Abnormal ECG heartbeat and arrhythmia are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is one of the most common arrhythmias which begins from the lower chamber of the heart and can cause cardiac arrest, palpitation, and other symptoms affecting all activities of a patient. Nowadays, computer-assisted techniques reduce doctors' burden to assess heart arrhythmia and heart disease automatically. In this study, we propose a PVC recognition based on a deep learning approach using the MIT-BIH arrhythmia database. Firstly, 10 heartbeat and statistical features including three morphological features (RS amplitude, QR amplitude, and QRS width) and seven statistical features are computed for each signal. The extraction process of these features is conducted for 20 s of ECG data that create a feature vector. Next, these features are fed into a convolutional neural network (CNN) to find unique patterns and classify them more effectively. The obtained results prove that our pipeline improves the diagnosis performance more effectively.

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PVC Recognition for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Network

Cited by 10 publications

References 9 publications

Deep-Learning Premature Contraction Localization in 12-lead ECG From Whole Signal Annotations

Deep-Learning Premature Contraction Localization in 12-lead ECG From Whole Signal Annotations

Searching for Premature Ventricular Contraction from Electrocardiogram by Using One-Dimensional Convolutional Neural Network

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach

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