# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Diabetes Detection  
Diabetes has emerged as one of the most common diseases worldwide. When blood sugar levels are not under control, the extra blood sugar builds up in the blood vessels and impacts how blood flows to different organs in the body. An inappropriate amount of secretion of insulin causes diabetes [94]. Primarily, two forms of diabetes exist. Type 1 diabetes: If a person has type 1 diabetes, they need insulin injections or other treatments to receive enough insulin into the body. This is because the body loses the ability to make the right amount of insulin, which can cause high blood sugar levels and other health issues. Type 2 diabetes: An individual with this form of diabetes does not need insulin and is not resistant to it. In this case, the body makes enough insulin, but it does not use that insulin for energy conversion. Since there is not enough energy, the body makes even more insulin, which causes the blood sugar to rise to abnormal levels [95]. Diabetes has been proven to be the leading cause of heart disease, renal malfunction, strokes, limb paralysis, and vision loss in individuals all over the globe. The majority of diabetes patients have other co-morbidities, such as obesity or hypertension. Diabetes has been linked to cholesterol, blocked arteries, myocardial infarction, and other serious consequences [96]. Recent studies reveal a strong link between HRV and glucose levels, indicating that diabetes causes gradual autonomic dysfunction and reduced heart rate variability [97]. A reduction in HRV features can indicate the potential of a volunteer being diabetic. Several studies attempt to develop viable solutions to the problem of diabetes detection based on an HRV analysis. [PERSON] et al. (2019) proposed the automatic identification of diabetic patients by employing deep learning models on spectrogram images of RR intervals. The deep learning (DL) approach included pre-trained CNN models like DenseNet, AlexNet, ResNet, and VGGNet [98]. The spectrogram images were constructed utilizing the short-time Fourier transform (STFT) on the RR intervals. Among all the DL models, the DenseNet model presented an accuracy of 97.62% and a sensitivity of 100% for diagnosing diabetic patients. In another study, [PERSON] et al. (2021) analyzed the effect of type 2 diabetes mellitus (T2 DM) on HRV features to determine the resultant autonomic dysfunction in diabetic patients [99]. The time-domain attributes (mean RRI and SDNN), frequency-domain features (total power, LF, and HF), and non-linear features (SD1 and SD2) were lower in diabetic patients than those of the control group in the resting state. When an orthostatic challenge was conducted as a stimulus to study the ANS reactivity, a blunted response of the ANS reactivity was exhibited by the T2 DM patients. The authors then implemented the ML algorithms to detect autonomic dysfunction in T2 DM patients. The Classification and Regression Tree (CART) model was able to identify the extent of autonomic dysfunction in individuals with TD2M with the highest accuracy of 84.04%. Thus, the authors recommended the use of HRV features in amalgamation with the CART model for the detection of autonomic dysfunction in TD2M patients. Table 1 lists some other papers issued in the last 5 years discussing the application of an HRV analysis in diabetes detection.