Human heart rate fluctuates in a complex and non-stationary manner, due to continuous influences from autonomic nervous system and other factors (harmones,temp,etc) on the sinoatrial node (S.A) (Pacemaker of the heart). ANS dysfunction is known to be associated with various cardiovascular and lifestyle diseases. The importance of traditional ancient Indian practice like Yoga has increased significantly due to the observed beneficial effects of it in various lifestyle diseases. Preliminary studies have shown that yoga may have its beneficial effect by influencing autonomic nervous system. Heart rate variability (HRV) is a most promising predictive and prognostic marker of autonomic (ANS) activity. HRV is analyzed by time and frequency domain parameters (Fast Fourier Transform). Being linear parameters these are not able to extract full information regarding the non linear behavior of heart rate fluctuations.In this paper, we propose to analyze HRV by using linear as well as non-linear methods during different yogaasanas. These mathematical models will be useful to understand the underlying physiological mechanisms during different yogasanas.

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BACKGROUND: According to the World Health Organization, one in ten adults will have Type 2 Diabetes Mellitus (T2DM) in the next few years. Autonomic dysfunction is one of the significant complications of T2DM. Autonomic dysfunction is usually assessed by standard Ewing’s test and resting Heart Rate Variability (HRV) indices. OBJECTIVE: Resting HRV has limited use in screening due to its large intra and inter-individual variations. Therefore, a combined approach of resting and orthostatic challenge HRV measurement with a machine learning technique was used in the present study. METHODS: A total of 213 subjects of both genders between 20 to 70 years of age participated in this study from March 2018 to December 2019 at Smt. Kashibai Navale Medical College and General Hospital (SKNMCGH) in Pune, India. The volunteers were categorized according to their glycemic status as control (n= 51 Euglycemic) and T2DM (n= 162). The short-term ECG signal in the resting and after an orthostatic challenge was recorded. The HRV indices were extracted from the ECG signal as per HRV-Taskforce guidelines. RESULTS: We observed a significant difference in time, frequency, and non-linear resting HRV indices between the control and T2DM groups. A blunted autonomic response to an orthostatic challenge quantified by percentage difference was observed in T2DM compared to the control group. HRV patterns during rest and the orthostatic challenge were extracted by various machine learning algorithms. The classification and regression tree (CART) model has shown better performance among all the machine learning algorithms. It has shown an accuracy of 84.04%, the sensitivity of 89.51%, a specificity of 66.67%, with an Area Under Receiver Operating Characteristic Curve (AUC) of 0.78 compared to resting HRV alone with 75.12% accuracy, 86.42% sensitivity, 39.22% specificity, with an AUC of 0.63 for differentiating autonomic dysfunction in non-diabetic control and T2DM. CONCLUSION: It was possible to develop a Classification and Regression Tree (CART) model to detect autonomic dysfunction. The technique of percentage difference between resting and orthostatic challenge HRV indicates the blunted autonomic response. The developed CART model can differentiate the autonomic dysfunction using both resting and orthostatic challenge HRV data compared to only resting HRV data in T2DM. Thus, monitoring HRV parameters using the CART model during rest and after orthostatic challenge may be a better alternative to detect autonomic dysfunction in T2DM as against only resting HRV.

Background and Objectives: Though 24hours BP monitoring is a useful tool that provide insights in masked and white coat hypertension as well as can be used as a marker of allostatic load (by monitoring circadian variations) and applicable in chronotherapy, is still sparingly used clinically. Hence, the objective of present study is to explore the pattern of circadian variation of blood pressure in normotensive and hypertensive Indians to pave the way for future chronobiological research and predictive medicine. Materials and Methods:In this cross-sectional study, total 125 volunteers, referred from the medicine OPD/IPD, enrolled. All the volunteers underwent 24 hour ambulatory blood pressure monitoring. BP recording of 100 participants (37 female and 63 male, mean age = 43.15±14.47 years) was used for final analysis. On the basis of guidelines provided for diagnosis of hypertension by European Society of Heart, individuals were grouped as i) normotensives (n=37) and ii) hypertensives (n=63). For descriptive analysis, different blood pressure parameters from three segments viz. overall, awake period and asleep period were used. Between groups comparison was done using Mann-Whitney and ANNOVA test. P-value < 0.05 had been considered as significant. Observation and Results: A significance difference in circadian systolic, diastolic and mean arterial BP was observed between groups. Although a higher percentage of non-dippers (65%) was observed in hypertensives but 38% of normotensives have also shown non-dipping status that indicates altered circadian rhythm or allostatic load. Conclusion:We observed altered circadian blood pressure pattern in both groups though higher percentage of non-dipping status in hypertensive patients. In normotensives it could subserve as an early marker of disease process, in hypertensives, it may be useful for application of Chronopharmacology.