# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 3 Results
### SVM and Exercise-Induced Fatigue (Physical Fatigue)
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**n** & **Healthy Status** & **Sample Size (F/M)** & **Reference Measure/ Questionnaire** & **Method/Classifier Algorithms** & **Model Performance** & **Authors (First)** & **Publication Year** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{SCI} & 99 (45 SCI) & HRV from ECG, eye blink duration/IFS & Multivariate analysis and Pearson correl. & \multirow{4}{*}{NA} & [PERSON] [2] & 2016 \\ \cline{4-7}  & & & & & & & \\ \cline{4-7}  & & & & & & & \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{Healthy} & \multirow{4}{*}{8 (3 F/5 M)} & HRV from chest belt, Polar H3 & Entropy-based nonlinear features, ML & \multirow{4}{*}{NA} & \multirow{4}{*}{[PERSON] [37]*} & \multirow{4}{*}{2020} \\  & & & & & & & \\ \cline{4-7}  & & & & & & & \\ \cline{4-7}  & & & & & & & \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{Hypertension} & 139 (49 F/90 M) & HRV from ECG hotter and NN & Meta-learning and NN & \multirow{4}{*}{Meta-learning and NN} & Sensitivity, (71.4\%) and specificity & \multirow{4}{*}{[PERSON] [50]*} & \multirow{4}{*}{2015} \\  & & & & & & & \\ \cline{4-7}  & & & & & & & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{Healthy} & 80 (38 F/42 M) & HRV from ECG/RPE & DT, SVM, KNN and LightGBM & LiothGBM: (86\%), Flscore (0.801) & \multirow{4}{*}{[PERSON] [51]*} & \multirow{4}{*}{2022} \\ \cline{4-7}  & & & & & & & \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{Healthy drivers} & 12 M & HRV from ECG & PSD of HRV, NN & Accuracy (90\%) & [PERSON] [52]* & 2011 \\ \cline{4-7}  & & & & & & & \\ \hline \multirow{4}{*}{6} & \multirow{4}{*}{Healthy} & \multirow{4}{*}{8} & HR from fiber optic sensor, RespR, GMD form fiber optic sensors/RPE & \multirow{4}{*}{NN, MobileNetV3} & Mental task accuracy (94\%); physical fatigue (95.8\%) & [PERSON] [53]* & 2024 \\ \hline \multirow{4}{*}{7} & \multirow{4}{*}{Healthy} & \multirow{4}{*}{27} & HRV from PPG, 14 parameters (HR, RespR...), PhF, RF, VAS (13 F/14 M) & Unsupervised ML, MF, VAS (multi-sensor - multi-sensor & Weighted precision - 0.70 \(\pm\) 0.03, recall - recall - 0.73 \(\pm\) 0.03 & [PERSON] [55] & 2020 \\ \cline{4-7}  & & & & & & \\ \hline \multirow{4}{*}{8} & \multirow{4}{*}{Healthy} & \multirow{4}{*}{32 (9 F/23 M)} & HRV from 3-lead ECG, EEG, EMG and EDA/GSR (sensor shift) & \multirow{4}{*}{LSTM, Random Forest} & Cognitive fatigue: LSTM model, accuracy (84.1\%), recall (0.90). & [PERSON] [56]* & 2022 \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & HRV from 3-lead ECG, EEG, EMG and EDA/GSR (sensor shift) & \multirow{4}{*}{Forest} & \multirow{4}{*}{Pursical fatigue:} & \multirow{4}{*}{2022} \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \hline \multirow{4}{*}{9} & \multirow{4}{*}{Healthy} & \multirow{4}{*}{14 M} & HRV from chest belt, Polar RS 800 & \multirow{4}{*}{Binary SVM} & Accuracy (82\%) & [PERSON] [57]* & 2020 \\ \cline{4-7}  & & & & & & & \\ \cline{4-7}  & & & & & & & \\ \hline \multirow{4}{*}{10} & \multirow{4}{*}{Healthy} & \multirow{4}{*}{16 M} & HRV from PPG SVM & Accuracy (82.9\%) & [PERSON] [59]* & 2024 \\ \cline{4-7}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The 10 eligible papers after the filtering process. NA implies that the data were not available.