# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 4 Discussion  
When we started this study, we were surprised to find only six scientific articles when searching for \"cardiac fatigue\" on PubMed database and to receive the following alert: Quoted phrase not found in phrase index: \"cardiovascular fatigue\". All the six papers regarded \"exercise-induced cardiac fatigue\", and the review found by [PERSON] et al. [5] was about echocardiographic literature. By adding terms such as HRV, \"heart rate\", \"AI\" and \"machine learning\", the number of papers increased tenfold, and the relation between nonlinear HRV features and ML models could be investigated.  
A lot of research has been performed on HRV to find early indicators of cardiovascular diseases [3, 24, 59] but there is a lack of information regarding cardiac fatigue.  
Since it is relatively inexpensive and non-invasive to collect, the automatic prediction of pathological events using cardiac activity data has been the subject of extensive research, particularly in the past five years, using ML techniques. The first two papers selected reinforce the importance of HR monitoring and the relationship between the cardiocirculatory system and fatigue; the others highlight the importance of considering additional physiological indices and integrated processing with AI.  
Thus, to better understand the connection between self-reported non-pathological fatigue and multimodal sensor data, some researchers have employed supervised and unsupervised ML techniques. They first imputed missing time-series data from a multisensory wearable device using a recurrent NN-based algorithm.  
The research on wearable sensors shows that they have rapidly improved in elasticity and weight; novel electrodes were proposed to effectively collect ECG signals, as in the case of the portable semi-dry electrodes proposed by [PERSON] et al. [60], to both solve the issues of conventional dry electrodes' high impedance and wet electrodes' difficult conductive liquid replenishment. To increase the reliability of physical fatigue measurement, future research should involve a broader range of participants, including individuals with disorders such as hyperhyroidism, diabetes, hypertension and heart disease, as well as subjects of varying ages and levels of physical fitness. Additionally, further deep learning [61] and ML models, along with attributes derived from other physiological inputs, will be considered. In fact, especially in the cases of unhealthy individuals, the heartbeat alone is not sufficient to assess cardiac fatigue.  
The answer to the research question regarding the use of HR time series as physiological biomarkers for the early detection of cardiac fatigue is positive, especially when a multidisciplinary approach is applied. Integrating cardiology, sports science, time-series nonlinear analysis and AI is essential for developing personalized, digitally enhanced cardiac monitoring aimed at the subjective assessment of cardiac fatigue. In the future, these objectives can be achieved by leveraging the potential of tele-exercise and real-time remote monitoring.