Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural network

Panda, Rohan; Jain, Sahil; Tripathy, Rajesh Kumar; Acharya, U. Rajendra

doi:10.1016/j.compbiomed.2020.103939

Cited by 68 publications

(58 citation statements)

References 59 publications

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“…The MMDCNN contains four convolutions, two max-pooling, and four dense layers for both MI detection and localization stages. The mathematical expression to compute the tth feature map for first convolution layer is given as follows [29,36]:…”

Section: Multi-channel Multi-scale Deep Convolutional Neural Networkmentioning

confidence: 99%

“…The parameters I and M are total number of modes and channels, respectively. Similarly, the mathematical expression for the evaluation of feature maps in other convolution layers are evaluated as follows [29,36]:…”

Section: Multi-channel Multi-scale Deep Convolutional Neural Networkmentioning

confidence: 99%

“…t is denoted as the tth feature map for lth convolution layer. Moreover, the mathematical expression to evaluate the pooling layer feature map is given as follows [29,36]:…”

Section: Multi-channel Multi-scale Deep Convolutional Neural Networkmentioning

confidence: 99%

“…The VCG is a multichannel signal, and, therefore, the MVMD can be used to decompose the signal into modes. Moreover, deep learning-based methods have been used in the multi-scale or modal domain of ECG signals to detect cardiac ailments [29]. For VCG signal, the deep learning method has not been explored in the multi-scale domain to detect and localization of MI.…”

Section: Introductionmentioning

confidence: 99%

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Multichannel Multiscale Two-Stage Convolutional Neural Network for the Detection and Localization of Myocardial Infarction Using Vectorcardiogram Signal

et al. 2021

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Myocardial infarction (MI) occurs due to the decrease in the blood flow into one part of the heart, and it further causes damage to the heart muscle. The 12-channel electrocardiogram (ECG) has been widely used to detect and localize MI pathology in clinical studies. The vectorcardiogram (VCG) is a 3-channel recording system used to measure the heart’s electrical activity in sagittal, transverse, and frontal planes. The VCG signals have advantages over the 12-channel ECG to localize posterior MI pathology. Detection and localization of MI using VCG signals are vital in clinical practice. This paper proposes a multi-channel multi-scale two-stage deep-learning-based approach to detect and localize MI using VCG signals. In the first stage, the multivariate variational mode decomposition (MVMD) decomposes the three-channel-based VCG signal beat into five components along each channel. The multi-channel multi-scale VCG tensor is formulated using the modes of each channel of VCG data, and it is used as the input to the deep convolutional neural network (CNN) to classify MI and normal sinus rhythm (NSR) classes. In the second stage, the multi-class deep CNN is used for the categorization of anterior MI (AMI), anterior-lateral MI (ALMI), anterior-septal MI (ASMI), inferior MI (IMI), inferior-lateral MI (ILMI), inferior-posterior-lateral (IPLMI) classes using MI detected multi-channel multi-scale VCG instances from the first stage. The proposed approach is developed using the VCG data obtained from a public database. The results reveal that the approach has obtained the accuracy, sensitivity, and specificity values of 99.58%, 99.18%, and 99.87%, respectively, for MI detection. Moreover, for MI localization, we have obtained the overall accuracy value of 99.86% in the second stage for our proposed network. The proposed approach has demonstrated superior classification performance compared to the existing VCG signal-based MI detection and localization techniques.

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Section: Multi-channel Multi-scale Deep Convolutional Neural Networkmentioning

confidence: 99%

Section: Multi-channel Multi-scale Deep Convolutional Neural Networkmentioning

confidence: 99%

“…t is denoted as the tth feature map for lth convolution layer. Moreover, the mathematical expression to evaluate the pooling layer feature map is given as follows [29,36]:…”

Section: Multi-channel Multi-scale Deep Convolutional Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multichannel Multiscale Two-Stage Convolutional Neural Network for the Detection and Localization of Myocardial Infarction Using Vectorcardiogram Signal

et al. 2021

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“…In recent years, convolutional neural network (CNN)-based approaches have been used to detect ventricular arrhythmias by using ECG signals [21]. Panda et al [22] decomposed ECG signals by using fixed frequency range empirical wavelet transform (FFREWT) into various modes and input them into a novel deep CNN to detect shockable ventricular cardiac arrhythmias. The proposed approach achieved an accuracy of 99.036%, 99.800%, and 81.250% for the classification of shockable versus nonshockable, VF versus non-VF, and VT versus VF, respectively.…”

Section: Related Workmentioning

confidence: 99%

Predicting Ventricular Fibrillation Through Deep Learning

Tseng

2020

IEEE Access

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Sudden Cardiac Arrest Detection Using Deep Learning and Principal Component Analysis

Pham,

Nguyen,

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural network

Cited by 68 publications

References 59 publications

Multichannel Multiscale Two-Stage Convolutional Neural Network for the Detection and Localization of Myocardial Infarction Using Vectorcardiogram Signal

Multichannel Multiscale Two-Stage Convolutional Neural Network for the Detection and Localization of Myocardial Infarction Using Vectorcardiogram Signal

Predicting Ventricular Fibrillation Through Deep Learning

Sudden Cardiac Arrest Detection Using Deep Learning and Principal Component Analysis

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