Performance Improvement of Residual Skip Convolutional Neural Network for Myocardial Disease Classification

Gopika, P.; Sowmya, V.; Gopalakrishnan, E. A.

doi:10.1007/978-981-13-8461-5_25

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Each lead is in line with an independent channel or feature branch, and the feature map of each lead will be concatenated and integrated into fully connected layers for detection and localization. (125). In Strodthoff 's approach, the two variants of CNN including fully convolutional architectures and ResNet-inspired architectures with skip-connections were adopted to distinguish AMI and IMI, and both architectures showed similar performance when applied to ECG data with multiple leads.…”

Section: Cnnmentioning

confidence: 99%

Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review

Xiong

Lee

Chan

2022

Front. Cardiovasc. Med.

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Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.

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Section: Cnnmentioning

confidence: 99%

Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review

Xiong

Lee

Chan

2022

Front. Cardiovasc. Med.

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show abstract

“…Transferred knowledge from the classification of arrhythmia is used to classify ECG signals with and without myocardial infraction with an accuracy of 95.9%. Gopika et al [31] further showed an improved accuracy from 95.9 to 99% using the features proposed by Kachuee et al [30].…”

Section: Introductionmentioning

confidence: 99%