Key result
A proposed model using Wavelet Transform and support vector machine classification successfully extracted heart electrophysiology patterns to automatically detect cardiac arrhythmias.
Why the study?
Cardiac arrhythmias can develop into more severe cardiovascular diseases, making early diagnosis essential to prevent progression to conditions that respond poorly to advanced-stage treatments.
An automated machine learning model using Wavelet Transform and support vector machines can detect cardiac arrhythmias from ambulatory ECG signals to assist in early diagnosis.
May aid early arrhythmia detection on ambulatory ECG; hypothesis-generating and requires prospective validation before clinical use.
According to the WHO (World Health Organization), the beginning of specific cardiovascular illnesses is the leading cause of death worldwide. Cardiac arrhythmias, in particular, can develop into cardiovascular diseases like heart disease, so it's essential to figure out how to make an early diagnosis to stop the arrhythmia from developing into a condition that, in more advanced stages, wouldn't respond well to treatments. It was possible to extract typical heart electrophysiology patterns like the QRS complex, the P.R. segment, the Q.R. segment, the R.S. segment, and the S.T. segment by characterizing the signals picked up by external ambulatory monitors and using the T.W. (Wavelet Transform) for this type of signal analysis. Digital filters were used in the filtering process, and the signal was then described, facilitating easier differentiation through a support vector machine-based classification method established by comparing the outcomes from the various methodologies. The research showed that it is possible to create an automatic tool for detecting cardiac issues as a decision support tool for sending patients for examination by a specialist doctor using the proposed model.
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Ahmed et al. (2023) studied Cardiac arrhythmias. Wavelet Transform and support vector machine-based classification was evaluated on Detection of cardiac issues. A proposed model using Wavelet Transform and support vector machine classification successfully extracted heart electrophysiology patterns to automatically detect cardiac arrhythmias.
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