# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 1 Introduction
### AI and ML  
The use of artificial intelligence (AI) in healthcare is expanding. In recent years, various tools and advanced statistical algorithms have been effectively used to support medical professionals. AI-based devices must undergo proper training to assess large amounts of data and make fast, accurate decisions [38]. Specifically, an ML model is trained by exposing it to examples and adjusting its internal parameters accordingly. Once training is complete, the model is given new data and applies what it has learned to analyze and interpret it. The more accurate the training, the better the model's performance.  
ML can be divided into supervised ML and unsupervised ML, depending on whether the algorithm is trained with labeled or unlabeled data. Examples of supervised learning include Logistic Regression [39], Support Vector Machines (SVMs) [40], Decision Trees (DTs) [41] and Neural Networks (NNs) [42; 43], which are used for tasks such as image and pattern classification. In contrast, unsupervised learning techniques, such as clustering and dimensionality reduction, are used to identify patterns in data without predefined labels [44]. A third category of ML is Reinforcement Learning, in which a computer program interacts with a dynamic environment to accomplish a specific task by learning through rewards and penalties [45].  
The early detection of cardiac fatigue using HR sensors requires a multi-stage pipeline that includes sensor technology, signal acquisition, preprocessing, feature extraction and clinical interpretation.  
The block scheme shown in Figure 1 illustrates the procedure used to classify RR time series measured by ECG or by any different wearable device type. The dimensionality reduction step, which follows the parameter extraction procedure, implies the reduction in the number of extracted parameters in the set prior to the ECG data classification attempted in the final stage of the process. The key preprocessing steps include, for example, the following: (i) bandpass filtering (e.g., 0.5-40 Hz for ECG) to remove baseline wander and powerline interference; (ii) motion artifact reduction: adaptive filtering or signal decomposition (e.g., empirical mode decomposition [46]); (iii) peak detection: accurate R-peak detection (by ECG) or pulse wave peak detection (by PPG) using methods like the Pan-Tompkins algorithm or derivative-based algorithms [47].  
ECG is by far the most popular cardiac monitoring technique in healthcare, and it has advantages over PPG in terms of stability and reliability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Feature** & **Physiological Insight** & **Fatigue-Related Change** \\ \hline ECG & QTc Interval & Ventricular repolarization & Prolongation \\  & QT Variability Index & Electrical instability & Increase \\  & T-Wave Alternans & Repolarization & Increase \\  & ST Segment & Myocardial strain & Mild depression/elevation \\  & Heart Rate Recovery & Vagal reactivation post-stress & Slower recovery (\(\leq\)12 bpm@1 min) \\ HRV & RMSSD/SDNN (HRV) & Parasympathetic activity & Decrease \\  & LF/HF Ratio (HRV) & Sympathetic–vagal balance & Increase \\  & Entropy & Complexity, autonomic balance & Decrease \\  & DET (RQA) & Signal complexity, predictability & Increase \\ \hline \hline \end{tabular} bpm, beats per minute.  
\end{table}
Table 1: ECG and HRV features related to cardiac fatigue.