# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Diabetes Detection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author, Year** & **Feature Extraction Methods Used** & \begin{tabular}{c} **Feature Selection/** \\ **Reduction Methods** \\ **Employed** \\ \end{tabular} & **Classifiers Used** & **Inference** \\ \hline [PERSON] et al. (2018) [(100)] & - & - & Deep learning (CNN and CNN LSTM) & Diabetic and normal HRV features could be distinguished with 93.6\% and 95.1\% accuracy using CNN and CNN-LSTM networks, respectively. \\ \hline [PERSON] et al. (2020) [(97)] & \begin{tabular}{c} Poincare plot, recurrence \\ plot, entropy, DFA, and \\ correlation dimension \\ \end{tabular} & - & ANN and SVM & An ANN design (13:7:1) with a learning rate of 0.01 yielded a classification accuracy of 86.3\%. At the same time, SVM had a slightly higher accuracy of 09.5\%. in distinguishing diabetics and control patients. Non-linear HRV attributes exhibit changes as a result of diabetes and can thus be employed in the construction of a prognostic system for detecting diabetes. \\ \hline [PERSON] et al. (2021) [(101)] & \begin{tabular}{c} Cardiac deceleration rate \\ index (CDR) \\ \end{tabular} & - & SVM & This study evaluated the seriousness of T2 DM in women between 60 and 70 years of age. The SVM classifier was validated using 10-fold cross-validation. An accuracy of 97.5\% was achieved. \\ \hline [PERSON] et al. (2019) [(102)] & \begin{tabular}{c} Time and frequency \\ domain methods \\ \end{tabular} & - & Decision tree & The study proposes two mathematical models to determine the blood glucose level of an individual. The first model considers the age of the individual and the HRV features, while the second model also considers the individual HRV values. The second model was found to be 10.1\% more sensitive to critically high glycemic levels than the first. \\ \[PERSON] et al. (2020) [(103)] & \begin{tabular}{c} Adaptive neuro-fuzzy \\ methods \\ \end{tabular} & - & KNN & Biomedical sensors, an ARM processor, and an FPGA were employed to identify, test, analyze, and report normal of abnormal situations. The KNN classifier used in this paper attained maximum accuracy of 99%, which is greater than that achieved with other ML algorithms such as ANN, SVM, softmax, random forest, and PCA. \\ \[PERSON] et al. (2020) [(104)] & \begin{tabular}{c} Time-domain methods \\ (features: SDNN, RMSD) \\ \end{tabular} & - & \begin{tabular}{c} DL architecture with \\ three hidden layers \\ \end{tabular} & - & \begin{tabular}{c} The authors used DL to predict glucose levels with the help of HRV features. A dataset of 159 sensors was used. The resulting architecture has three hidden layers. These layers are made up of 32 neurons, 25 neurons, and 61 neurons, respectively. The outlier removal methods, IQR and Z-Score, result in a higher F1 score value and accuracy, respectively. \\ \[PERSON] et al. (2019) [(105)] & \begin{tabular}{c} Time and frequency \\ domain methods \\ \end{tabular} & \(T\)-test & - & This study aimed to develop a functional hypoglycemia alert device for TDM patients. It was found that the LF/HR ratio increased and RMSD increased in the case of hypoglycemic events, although some instances showed opposing effects. \\ \hline [PERSON] et al. (2020) [(106)] & \
\begin{tabular}{c} Time and frequency \\ domain methods \\ \end{tabular} & - &
\begin{tabular}{c} Gradient boosting \\ decision tree \\ \end{tabular} & This study proposes a model that detects hypoglycemic based on data from smartwatch sensors. The authors used SHAP (Shapley additive explanations) values to mitigate false positive values. SHAP assigns an attribution value to all the instances and classes. The model considers the sensor data and the historical patterns and alerts the user if need be. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of published papers in the last 5 years with novel approaches for diabetes detection based on HRV analysis.