Statistical analysis of ST segments in ECG signals for detection of ischaemic episodes

Kumar, Amit; Singh, Mandeep

doi:10.1177/0142331216667811

Cited by 4 publications

(1 citation statement)

References 54 publications

(90 reference statements)

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“…A rule-based method to classify ST morphology into normal and abnormal was introduced in Reference [38] and achieved 90.1% accuracy and 98.9% sensitivity when evaluated with ESCDB. Another method has been introduced by Reference [45] to detect ischemia based on statistical features of the ST-segment deviation and performed classification of normal and abnormal beats with 97.71% sensitivity and 96.89% ppv on the ESCDB database.…”

Section: Traditional Approachesmentioning

confidence: 99%

Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

et al. 2021

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Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.

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Section: Traditional Approachesmentioning

confidence: 99%