ECG Analysis via Machine Learning Techniques: News and Perspectives

Vocaturo, Eugenio; Zumpano, Ester

doi:10.1109/bibm52615.2021.9669776

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…The standard 12-lead ECG, along with other reduced-lead (5- or 3-electrode) configurations, can accurately measure signals and help diagnose complex heart conditions (Drew et al, 1999 ; Petrenas et al, 2015 ; Francis, 2016 ; Zègre-Hemsey et al, 2016 ). Various machine learning approaches have been applied for predicting cardiovascular diseases (Krittanawong et al, 2017 ; Altan et al, 2018a , b , 2021 ; Shameer et al, 2018 ; Vocaturo and Zumpano, 2021 ). One of the most well-known and popular methods used to classify ECG data is a Support Vector Machine (SVM) (Zadeh et al, 2010 ; Li et al, 2015 ; Dinakarrao et al, 2019 ) with various kernels, feature extraction methods, and categories of arrhythmia.…”

Section: Related Workmentioning

confidence: 99%

Real-time arrhythmia detection using convolutional neural networks

Vu,

Petty,

Yakut

et al. 2023

Front. Big Data

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Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow. In this paper, we present our work on supporting real-time arrhythmic detection using convolutional neural networks, which take images of electrocardiogram (ECG) segments as input, and classify the arrhythmia conditions. To support real-time processing, we have carried out extensive experiments and evaluated the computational cost of each step of the classification workflow. Our results show that it is feasible to achieve real-time arrhythmic detection using convolutional neural networks. To further demonstrate the generalizability of this approach, we used the trained model with processed data collected by a customized wearable sensor from a lab setting, and the results shown that our approach is highly accurate and efficient. This research provides the potentials to enable in-home real-time heart monitoring based on 2D image data, which opens up opportunities for integrating both machine learning and traditional diagnostic approaches.

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