Key result
Machine learning enhances ECG and PPG accuracy for automated arrhythmia detection and monitoring.
Why the study?
Given the need to develop vital sign medical sensors for real-life and clinical research needs, the review examined computer-based machine learning techniques.
Does the application of machine learning algorithms to medical sensors improve the accuracy and reliability of heart health monitoring compared to traditional approaches?
Systematic Review
Does the application of machine learning algorithms to medical sensors improve the accuracy and reliability of heart health monitoring compared to traditional approaches?
Integrating artificial intelligence and machine learning with wearable ECG and PPG sensors holds significant promise for automating and improving real-time cardiovascular disease detection.
The rapid advances in science and technology in the field of artificial neural networks have led to noticeable interest in the application of this technology in medicine. Given the need to develop medical sensors that monitor vital signs to meet both people's needs in real life and in clinical research, the use of computer-based techniques should be considered. This paper describes the latest progress in heart rate sensors empowered by machine learning methods. The paper is based on a review of the literature and patents from recent years, and is reported according to the PRISMA 2020 statement. The most important challenges and prospects in this field are presented. Key applications of machine learning are discussed in medical sensors used for medical diagnostics in the area of data collection, processing, and interpretation of results. Although current solutions are not yet able to operate independently, especially in the diagnostic context, it is likely that medical sensors will be further developed using advanced artificial intelligence methods.
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Staszak et al. (2023) conducted a systematic review in Cardiovascular disease monitoring. Machine learning algorithms applied to medical sensors vs. Traditional signal analysis methods was evaluated on Accuracy and reliability of medical sensors supported by machine learning. Machine learning applied to electrocardiography and photoplethysmography sensors enhances the accuracy and automation of heart health monitoring and arrhythmia detection.
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