Volume no :
6 |Issue no :
1Article Type :
Scholarly ArticleAuthor :
K. Moniga, A.M. Aarthi, S. Amalarani, J. Farhath NaseemPublished Date :
May, 2025Publisher :
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CARDIAC ARRYTHMIA DETECTION USING AI/ML
CARDIAC ARRYTHMIA DETECTION USING AI/ML Historically, the detection of arrhythmia relied on human clinicians interpreting
electrocardiograms (ECGs) themselves. Though effective, this practice is labor-intensive,
susceptible to inter-observer variation, and available only where there are adequate resources.
As large-scale medical datasets and computation power are now available, artificial intelligence
(AI) and machine learning (ML) methods have been proposed to assist clinical diagnosis within
the domain of cardiology.
AI/ML techniques have proven to have great potential for automation of ECG analysis, learning
subtle patterns, and high accuracy of arrhythmia classification. AI and ML methods are scalable,
enable real time processing, and are capable of revealing subtle signals that may not be
noticeable to human clinicians. Recent advances in deep learning have also made it possible to
consider end-to-end models for analyzing raw ECG signals without handcrafted feature
extraction and heavyweight preprocessing.
This research delves deeper into the use of AI and ML techniques for the detection and
classification of cardiac arrhythmia from ECG signals. We seek to analyze the performances of
multiple algorithms, discuss the challenges, and introduce an effective model that can help
clinicians make accurate diagnoses of arrhythmia in real time. The primary aim is to advance
the developing area of AI-based cardiology and enhance the quality of care for patients using
technology-driven innovation.
CARDIAC ARRYTHMIA DETECTION USING AI/ML
In spite of its benefits, it encompasses the requirement for large, high-quality and well-
balanced datasets, explainability of black-box AI models, and incorporating AI tools within
clinical workflows. Moreover, the heterogeneity of the ECG signals resulting from the variability
among patients, noise, and comorbid conditions makes the generalizability of ML models to the
real-world difficult.CARDIAC ARRYTHMIA DETECTION USING AI/MLHistorically, the detection of arrhythmia relied on human clinicians interpreting
electrocardiograms (ECGs) themselves. Though effective, this practice is labor-intensive,
susceptible to inter-observer variation, and available only where there are adequate resources.
As large-scale medical datasets and computation power are now available, artificial intelligence
(AI) and machine learning (ML) methods have been proposed to assist clinical diagnosis within
the domain of cardiology.
This research delves deeper into the use of AI and ML techniques for the detection and
classification of cardiac arrhythmia from ECG signals. We seek to analyze the performances of
multiple algorithms, discuss the challenges, and introduce an effective model that can help
clinicians make accurate diagnoses of arrhythmia in real time. The primary aim is to advance
the developing area of AI-based cardiology and enhance the quality of care for patients using
technology-driven innovation.
In spite of its benefits, it encompasses the requirement for large, high-quality and well-
balanced datasets, explainability of black-box AI models, and incorporating AI tools within
clinical workflows. Moreover, the heterogeneity of the ECG signals resulting from the variability
among patients, noise, and comorbid conditions makes the generalizability of ML models to the
real-world difficult.
Proposed Methods
The raw signals are noisy and contain artifacts from movement, activity from muscles, and
power-line artifacts. To improve the quality of the signals, the following methods of
preprocessing are employed:
DWT signal is a signal which is converted using the Discrete Wavelet Transform (DWT), an
extremely useful mathematical tool for the analysis of non-stationary signals, including ECGs, by
breaking them up into parts composed of different frequencies.
