Multi-class Arrhythmia Detection based on Neural Network with Multi-stage Features Fusion

Wang, Ruxin; Yao, Qihang; Fan, Xiaomao; Li, Ye

doi:10.1109/smc.2019.8913905

Cited by 24 publications

(11 citation statements)

References 22 publications

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“…Wang et al [ 138 ] proposed a CNN-based method for multi-class arrhythmia detection with multiple-stage features fusion. The significant contributions of this study were as follows: connection operations to fuse different levels of features extracted by the neural network at various stages for target task processing were skipped.…”

Section: Resultsmentioning

confidence: 99%

“…It was demonstrated that the proposed method was insensitive to noise, and filtering could be applied before the method. N/D Wang et al [ 138 ] The results of proposal method achieved an average F1-score of 81.3% in classification of 8 types of arrhythmias and sinus rhythm. N/D Wu et al [ 139 ] The random forest prediction model that the authors implemented reached higher sensitivity and accuracy values for the detection of left ventricular hypertrophy than other methods previously proposed.…”

Section: Discussionmentioning

confidence: 99%

“… 2-Lead ECG kNN Patient-independent Congestive Heart Failure Wang et al [ 137 ] 2019 Shanghai (China) Journal article MIT–BIH arrhythmia [ 48 ] database (48 ECG records) An improved CNN was proposed to automatically classify the heartbeat of arrhythmia. 2-Lead ECG CNN, ML Patient-independent Arrhythmia Wang et al [ 138 ] 2019 Shenzhen (China) Conference Proceedings ICBEB [ 24 ] dataset (6,877 subjects) An end-to-end deep learning method for multiclass arrhythmia detection with multiple stage features fusion was proposed. 12-Lead ECG CNN Patient-specific Arrhythmia Wu et al [ 139 ] 2019 Shandong (China) Journal article Cleveland Heat Disease [ 140 ] database and Statlog Heart Disease [ 140 ] database A method that uses K-Nearest Neighbor to impute missing values of ECG data and Z-score to standardize ECG data for the requirement of the random forest was proposed.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Denysyuk¹,

Pinto²,

Silva³

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Denysyuk¹,

Pinto²,

Silva³

et al. 2023

Heliyon

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“…Features obtained from two scale specific networks are fused using a spatial attention module. CNN and attention module based multi-level feature fusion framework is proposed in [16] for multiclass arrhythmia detection. Heart-beat classification is performed by extracting features from various layers of CNN.…”

Section: Related Workmentioning

confidence: 99%

ECG Heart-Beat Classification Using Multimodal Image Fusion

Ahmad

Tabassum

Guan

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart-beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF) and then fuse these images to create a single imaging modality. We use AlexNet for feature extraction and classification and thus employ end-to-end deep learning. We perform experiments on PhysioNet's MIT-BIH dataset for five different arrhythmias in accordance with the AAMI EC57 standard and on PTB diagnostics dataset for myocardial infarction (MI) classification. We achieved an state-of-an-art results in terms of prediction accuracy, precision and recall.

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“…Multi-stage feature fusion framework based on CNN and attention module was proposed in [50] for multiclass arrhythmia detection. Classification is performed by extracting features from different layers of CNN.…”

Section: Fusion Based Approachesmentioning

confidence: 99%

ECG Heartbeat Classification Using Multimodal Fusion

et al. 2021

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Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manually extracted features or large and complex deep learning networks which merely utilize the 1D ECG signal directly. Since intelligent multimodal fusion can perform at the state-of-the-art level with an efficient deep network, therefore, in this paper, we propose two computationally efficient multimodal fusion frameworks for ECG heart beat classification called Multimodal Image Fusion (MIF) and Multimodal Feature Fusion (MFF). At the input of these frameworks, we convert the raw ECG data into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF). In MIF, we first perform image fusion by combining three imaging modalities to create a single image modality which serves as input to the Convolutional Neural Network (CNN). In MFF, we extracted features from penultimate layer of CNNs and fused them to get unique and interdependent information necessary for better performance of classifier. These informational features are finally used to train a Support Vector Machine (SVM) classifier for ECG heart-beat classification. We demonstrate the superiority of the proposed fusion models by performing experiments on PhysioNet's MIT-BIH dataset for five distinct conditions of arrhythmias which are consistent with the AAMI EC57 protocols and on PTB diagnostics dataset for Myocardial Infarction (MI) classification. We achieved classification accuracy of 99.7% and 99.2% on arrhythmia and MI classification, respectively. Source code at https://github.com/zaamad/ECG-Heartbeat-Classification-Using-Multimodal-Fusion INDEX TERMS Convolutional neural network, deep learning, ECG, image fusion, multimodal fusion.

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Multi-class Arrhythmia Detection based on Neural Network with Multi-stage Features Fusion

Cited by 24 publications

References 22 publications

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

ECG Heart-Beat Classification Using Multimodal Image Fusion

ECG Heartbeat Classification Using Multimodal Fusion

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