# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 2 Materials and Methods  
We sourced scientific articles and reviews using the PubMed, Scopus and Google Scholar search engines. As in our previous review on NHS practices in Europe, we selected a search time-span window of 20 years. The literature search, conducted in January 2025, followed the PRISMA 2020 guidelines (the PRISMA website was visited in January 2025) and utilized the following keywords (mesh terms): \"ECG\", \"cardiovascular fatigue\", \"cardiac pathologies\", \"HRV\" or \"heart rate\", \"wearable devices\", \"AI\" or \"Machine Learning\". Only research and review articles were deemed appropriate. The standard English language filter was not used. Table 2 displays the results of the most notable queries. Figure 2 reports the flow diagram of the literature search. The PRISMA checklist is available in the Supplementary Materials. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**2005–2025** & \multicolumn{3}{c}{**“Cardiovascular Fatigue”**} & \multicolumn{3}{c}{**“Cardiovascular Fatigue” and**} & \multicolumn{3}{c}{**“Cardiac Pathologies”**} \\  & \multicolumn{3}{c}{**and “Heart Rate”**} & \multicolumn{3}{c}{**“AI”**} & \multicolumn{3}{c}{**and “Heart Rate”**} \\ \hline  & **Article** & **Review** & **2024–2025** & **Article** & **Review** & **2024–2025** & **Article** & **Review** & **2024–2025** \\ \hline PubMed & 190 (27) & 59 & 9 (3) & 17 (10) & 40 (2) & 2 & 54 & 12 & 1 \\ Scopus & 4 & 1 & 0 & 31 & 17 & 2 & 159 & 39 & 25 \\ Google Scholar & 12 * & 0 & & 67 * & 10 * & 1320 * & 188 * \\ \hline TOTAL * & 194 & 9 & & 48 & 4 & 213 & 26 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the three search engine queries (623, 455 articles and 168 reviews). The star symbols “** indicate that, for the Google Scholar database, the term “Scientific” does not distinguish between articles and reviews. The shaded cells refer to the total number of manuscripts in 2024–25, per database. (* indicates that Google Scholar database results were not included in the sum). Figure 1: Block schemes. (**a**) From ECG to classification for clinical interpretation in a theoretical framework; (**b**) the multi-stage pipeline with the main sources of variability and noise: limitations in the whole HRV data processing method can be found in these steps (subject, sensor, analysis, method, etc. ); (**c**) the timeline of possible healthcare applicative procedures. The sections in green focus on the main research question. To create a comprehensive overview of the state of the art for this methodology, a manual search was also conducted to find analysis methods and connect them to pertinent research publications. Two reviewers ([PERSON] and [PERSON]) extracted information about the studies' features, equipment description and diagnostic usefulness metrics. A preliminary literature search on \"cardiovascular and fatigue and (ambient or assisted or living)\" returned only 17 papers, revealing a lack of information on these topics (filters applied: clinical trial, meta-analysis, randomized controlled trial, humans, age: 65+ years). Age-related specific research was carried out only on PubMed; of the 565 papers on \"cardiovascular and fatigue\" (filters applied: clinical trial, meta-analysis, randomized controlled trial, humans), 11% (62/565) focused on children and adolescents (age < 18 years) and 74% (417/565) on adults (age > 18 years). Among these, 34% (190/565) involved older adults (age > 65 years). This first query outcome showed a linear increase in the number of publications mentioning \"cardiovascular fatigue\" (in the title or abstract) over the past 20 years. However, this trend was not observed in studies focusing on children and adolescents, as shown in Figure 3. The decrease in the number of publications in 2022-2023 could be influenced by the COVID-19 pandemic.