Physical activity is vital for promoting health and rehabilitation, and ensuring cardiovascular safety during such activities is paramount. Electrocardiography (ECG) and its longitudinal monitoring remain crucial for the early detection of cardiac diseases. Recent advancements in nonlinear RR analysis and machine learning offer promising approaches to identifying subtle precursors of cardiac pathologies in monitoring systems using simple heart rate (HR) wearable sensors. Therefore, using HR sensors in human activity recognition (HAR) is recommendable. After defining fatigue in a cardiological context, and focusing on an AI-based methods suite for HAR, the main research question of this scoping review is as follows: “Can RR time series be successfully used as physiological biomarkers for the early detection of cardiac fatigue?” The reported data on assessment of fatigue are focused on the last two decades. The aim of this scoping review was to collect, present and discuss the existing literature on the effectiveness of AI-based methods for processing RR time series as a predictive biomarker for cardiac fatigue compared to commonly used questionnaires for this outcome in adult populations. Methods: Queries were conducted in the PubMed, Scopus and Google Scholar databases for the time period 2005–2025. Only research articles and review papers were considered suitable candidates. Results: Data from 10 papers were considered, related to the information researched. Conclusions: Information on HRV-based objective measures is quite scarce and there is an urgent need to adopt a multidisciplinary approach and to improve advanced AI-based nonlinear analyses to differentiate cardiac physiological status from cardiac pathological status.
The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
Zimatore, Giovanna
;Alessandria, Marco;Campanella, Matteo;
2025-01-01
Abstract
Physical activity is vital for promoting health and rehabilitation, and ensuring cardiovascular safety during such activities is paramount. Electrocardiography (ECG) and its longitudinal monitoring remain crucial for the early detection of cardiac diseases. Recent advancements in nonlinear RR analysis and machine learning offer promising approaches to identifying subtle precursors of cardiac pathologies in monitoring systems using simple heart rate (HR) wearable sensors. Therefore, using HR sensors in human activity recognition (HAR) is recommendable. After defining fatigue in a cardiological context, and focusing on an AI-based methods suite for HAR, the main research question of this scoping review is as follows: “Can RR time series be successfully used as physiological biomarkers for the early detection of cardiac fatigue?” The reported data on assessment of fatigue are focused on the last two decades. The aim of this scoping review was to collect, present and discuss the existing literature on the effectiveness of AI-based methods for processing RR time series as a predictive biomarker for cardiac fatigue compared to commonly used questionnaires for this outcome in adult populations. Methods: Queries were conducted in the PubMed, Scopus and Google Scholar databases for the time period 2005–2025. Only research articles and review papers were considered suitable candidates. Results: Data from 10 papers were considered, related to the information researched. Conclusions: Information on HRV-based objective measures is quite scarce and there is an urgent need to adopt a multidisciplinary approach and to improve advanced AI-based nonlinear analyses to differentiate cardiac physiological status from cardiac pathological status.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
