Heart rate variability for medical decision support systems: A review

Area of Science:

• Biomedical Engineering
• Physiology
• Data Science

Background:

• Heart Rate Variability (HRV) reflects complex physiological processes, offering potential for health monitoring and disease detection.
• Modern sensors facilitate HRV data acquisition, but signal non-stationarity, non-linearity, and noise-like characteristics pose analytical challenges.
• Aging and comorbidities further complicate the interpretation of HRV signals, limiting clinical applications.

Purpose of the Study:

• To review scientific literature addressing the challenges in Heart Rate Variability (HRV) analysis.
• To explore the application of advanced signal processing and Artificial Intelligence (AI) in overcoming HRV analysis obstacles.
• To highlight opportunities for improved disease detection and health assessment using HRV.

Main Methods:

• Review of scientific studies utilizing advanced signal processing techniques for HRV analysis.
• Investigation of Artificial Intelligence (AI) and machine learning approaches applied to HRV data.
• Analysis of methods designed to handle non-stationary and non-linear characteristics of HRV signals.

Main Results:

• Advanced signal processing and AI methods show promise in analyzing complex HRV signals.
• These advanced techniques can help differentiate between various physiological influences on HRV.
• Improved methods facilitate more accurate interpretation of HRV, even in the presence of aging and comorbidities.

Conclusions:

• Advanced signal processing and AI are crucial for unlocking the full potential of HRV analysis.
• Overcoming analytical challenges enhances the utility of HRV for disease detection and health monitoring.
• Future research should focus on refining these methods for broader clinical implementation.