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<p>Detecting cardiac arrhythmia is now a very important field in computational biomedical engineering. This is because abnormal heart rhythms impact more than 59 million people throughout the world and traditional diagnostic methods have their own problems. Electrocardiography (ECG) and photoplethysmography (PPG) are still the main ways to monitor the heart in a clinical setting or on a wearable device. However, rapid advancements in signal processing, machine learning (ML), deep learning (DL), and the new field of quantum machine learning (QML) have made arrhythmia diagnosis a computationally accelerated, algorithm-driven process. This study gives a comprehensive overview of the latest developments in AI-based arrhythmia detection, with a focus on how these methods improve early diagnosis, reliability, and understanding in cardiac monitoring systems. The work rigorously analyzes publicly accessible ECG and PPG datasets, feature extraction techniques, classification methods, and performance evaluation criteria utilized in the literature. The emphasis is on the amalgamation of Quantum Machine Learning (QML) with deep neural architectures, where quantum computation enhances feature representation, dimensionality reduction, and classification accuracy via quantum parallelism and entanglement. The survey includes studies published in well-known databases like IEEE, Springer, ScienceDirect, PubMed, Frontiers, and MDPI between 2014 and 2025. The studies used keywords like “cardiac arrhythmia detection,” “atrial fibrillation,” “irregular heartbeat diagnosis,” “machine learning,” “deep learning,” “quantum machine learning,” “quantum hybrid neural networks,” and “hyperparameter optimization techniques.” This study brings together the latest findings, points out areas where more research is needed, and suggests where AI-driven cardiac diagnostics could go in the future. It ends by suggesting a mixed framework that combines ML, DL, and QML methods to make arrhythmia prediction and classification more accurate, scalable, and reliable.</p>

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A Comprehensive Review of AI-Driven Arrhythmia Detection: From Classical Machine Learning to Quantum Machine Learning With ECG and PPG Signals

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Shankar Sachin Nimmaganti,
  • Vishnu Vardhana Reddy Karna

摘要

Detecting cardiac arrhythmia is now a very important field in computational biomedical engineering. This is because abnormal heart rhythms impact more than 59 million people throughout the world and traditional diagnostic methods have their own problems. Electrocardiography (ECG) and photoplethysmography (PPG) are still the main ways to monitor the heart in a clinical setting or on a wearable device. However, rapid advancements in signal processing, machine learning (ML), deep learning (DL), and the new field of quantum machine learning (QML) have made arrhythmia diagnosis a computationally accelerated, algorithm-driven process. This study gives a comprehensive overview of the latest developments in AI-based arrhythmia detection, with a focus on how these methods improve early diagnosis, reliability, and understanding in cardiac monitoring systems. The work rigorously analyzes publicly accessible ECG and PPG datasets, feature extraction techniques, classification methods, and performance evaluation criteria utilized in the literature. The emphasis is on the amalgamation of Quantum Machine Learning (QML) with deep neural architectures, where quantum computation enhances feature representation, dimensionality reduction, and classification accuracy via quantum parallelism and entanglement. The survey includes studies published in well-known databases like IEEE, Springer, ScienceDirect, PubMed, Frontiers, and MDPI between 2014 and 2025. The studies used keywords like “cardiac arrhythmia detection,” “atrial fibrillation,” “irregular heartbeat diagnosis,” “machine learning,” “deep learning,” “quantum machine learning,” “quantum hybrid neural networks,” and “hyperparameter optimization techniques.” This study brings together the latest findings, points out areas where more research is needed, and suggests where AI-driven cardiac diagnostics could go in the future. It ends by suggesting a mixed framework that combines ML, DL, and QML methods to make arrhythmia prediction and classification more accurate, scalable, and reliable.