ECG Beat Arrhythmia Classification by using 1-D CNN in case of Class Imbalance

Sarvan, Cagla; Özkurt, Nalan

doi:10.1109/tiptekno.2019.8895014

Cited by 15 publications

(12 citation statements)

References 6 publications

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“…As both lower and higher-level features are considered, the classification performance is improved significantly. Although [5,15] followed the same approach used in this work in terms of kernel size, the overall classification performance of the proposed model improved compared to [5,15] due to the use of the novel weighted mechanism.…”

Section: Discussionmentioning

confidence: 99%

A novel weighted approach for automated cardiac arrhythmia beat classification using convolutional neural networks

2022

IJATEE

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Arrhythmia is a cardiac disorder in which the normal blood pumping activity of the heart becomes irregular. This heart malfunction can result in serious heart disease and even death. Therefore, detection and proper treatment of arrhythmia are essential. The abnormal heart behaviour can be recorded using an electrocardiogram (ECG). A one-dimensional convolutional neural network (1D-CNN) with a novel weighted approach was proposed to detect and classify arrhythmia types from ECG signals. The proposed classifier was trained and evaluated using the Massachusetts institute of technology-Beth Israel hospital (MITBIH) arrhythmia database to classify five arrhythmia beat categories (N, S, V, F, and Q), as recommended by the Association for Advancement of Medical Instrumentation (AAMI). The proposed model obtained an overall sensitivity of 94.35%, precision of 94.02%, specificity of 99.5%, and accuracy of 99.65%. The experimental results demonstrate that the proposed CNN model can achieve cutting-edge performance and can be used for arrhythmia diagnosis in real-time.

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Section: Discussionmentioning

confidence: 99%

A novel weighted approach for automated cardiac arrhythmia beat classification using convolutional neural networks

2022

IJATEE

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“…This method takes as input raw ECG signals from 30,000 unique patients and can classify 12 different arrhythmias with a sensitivity and productivity superior to that of cardiologists. Likewise, Sarvan and Nalan [109] used raw ECG signals and fed them into a 9-leyer deep CNN. Although their method obtained high accuracy and specificity, the sensitivity was just 26.85%.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

Cretu

Tindale

Abbod

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Cardiac arrhythmia is a condition caused by an impaired electrical conduction of the heart, resulting in irregular rhythms that can increase the risk of stroke or even lead to sudden cardiac deaths. Luckily, bio-signals such as electrocardiogram (ECG) and arterial blood pressure (ABP) can be utilized to assess the health of the patient. This work presents a comprehensive review on the recent machine learning (ML) and deep learning methods applied for arrhythmia classification using both, ECG and ABP signals, including preliminary steps such as pre-processing, feature extraction and feature optimization.

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“…Therefore, the width and height of the calibration curve as the identification standard are known. Therefore, based on the method of mapping the time and amplitude of ECG signals to the coordinates in the picture, it is of practical significance to recognize and diagnose the parameters of each refined waveform of ECG signals [34,35]. Therefore, establishing the position relation between coordinates and realizing the mapping between the real signal value and the image coordinates are the necessary steps for the algorithm model to realize the diagnosis of heart disease.…”

Section: Analysis and Diagnosismentioning

confidence: 99%

A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

Song

2020

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The proper evaluation of heart health requires professional medical experience. Therefore, in clinical diagnosis practice, the development direction is to reduce the high dependence of the diagnosis process on medical experience and to more effectively improve the diagnosis efficiency and accuracy. Deep learning has made remarkable achievements in intelligent image analysis technology involved in the medical process. From the aspect of cardiac diagnosis, image analysis can extract more profound and abundant information than sequential electrocardiogram (ECG) signals. Therefore, a new region recognition and diagnosis method model of a two-dimensional ECG (2D-ECG) signal based on an image format is proposed. This method can identify and diagnose each refined waveform in the cardiac conduction cycle reflected in the image format ECG signal, so as to realize the rapid and accurate positioning and visualization of the target recognition area and finally get the analysis results of specific diseases. The test results show that compared with the results obtained by a one-dimensional sequential ECG signal, the proposed model has higher average diagnostic accuracy (98.94%) and can assist doctors in disease diagnosis with better visualization effect.

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ECG Beat Arrhythmia Classification by using 1-D CNN in case of Class Imbalance

Cited by 15 publications

References 6 publications

A novel weighted approach for automated cardiac arrhythmia beat classification using convolutional neural networks

A novel weighted approach for automated cardiac arrhythmia beat classification using convolutional neural networks

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

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