# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 1 Introduction
### Physiological Biomarkers in Complex Systems Theoretical Framework  
The use of RR time series as physiological biomarkers for the early detection of cardiac fatigue is supported by several studies that highlight the potential of HRV analysis and related metrics. RR time series and HRV analysis are promising tools for the early detection of cardiac fatigue. The integration of nonlinear analysis techniques and advanced ML models can enhance their predictive accuracy and provide valuable insights for both clinical and athletic applications. The frequent monitoring and analysis of RR intervals can help in the timely intervention and management of cardiac health. Cardiac health, especially when monitoring older people, necessitates careful evaluation.  
Since cardiac fatigue has been shown to affect the complexity of physiological signals, the fatigue-induced loss of complexity in RR time series may represent a key aspect of nonlinear analysis [26].  
It has been suggested that the loss of complexity is a general characteristic of pathologic dynamics, since illness and aging seem to impair people's ability to adapt [27]. HRV analysis reflects the autonomic nervous system's influence on the heart, which can be indicative of cardiac stress and fatigue [28].  
Despite their widespread use, standard linear techniques like time-domain and frequency-domain analysis may oversimplify the complex dynamics of HR regulation [29]. By taking into consideration the patterns seen in cardiovascular dynamics, nonlinear approaches provide a more comprehensive strategy.  
To address this purpose, the nonlinear Recurrence Quantification Analysis (RQA) method was used to properly analyze RR time series. RQA is a statistical, graphical and analytical tool successfully used in various domains, including medicine, seismic geology and finance, to explore nonlinear dynamical systems [30; 31]. Specifically, RQA is an accurate method for analyzing the stochastic and chaotic dynamics of physiological data (such as RR time series) [29] and describing physiological changes [6; 31; 32]. In terms of complex systems theory, _fatigue_ was hypothesized as an order parameter [7].  
Another approach is that proposed by using detrended fluctuation analysis via an index called the Cardiac Stress Index, derived from the RR time series, which has been shown to correlate with perceived exertion during exercise [33]. This index can quantify cardiac stress and potentially detect early signs of cardiac fatigue [30].  
HRV can be evaluated using time-based measures or frequency-domain measures [29]. The basis of frequency-domain analysis is the mathematical transformation of signals from the time domain to the frequency domain (represented in cycles per beat with different amplitudes and frequencies) using Fast Fourier Transforms. Power spectral density analysis provides fundamental knowledge of how power (i.e., variance) is distributed as a function of frequency [34]. Entropy has been used as a measure of the complexity of physiological systems, and other nonlinear techniques provide more details regarding HRV based on the entropy measure [35]. Specifically, as in [36; 37], entropy approaches can be effective in the diagnosis of heart diseases. The main ECG and HRV features are compared in Table 1.