Research Potential of a Heart Rate Variability Diagnostic System for the Study of Stress and Health Risk in Peacekeeping Operations

2004 ◽  
Author(s):  
R. Nikolova ◽  
◽  
L. Alexiev ◽  
M. Vukov
2012 ◽  
Vol 36 (2) ◽  
pp. 747-756 ◽  
Author(s):  
Julian F. Thayer ◽  
Fredrik Åhs ◽  
Mats Fredrikson ◽  
John J. Sollers ◽  
Tor D. Wager

2017 ◽  
Vol 17 (2) ◽  
pp. 428 ◽  
Author(s):  
Margaretha Viljoen ◽  
Nicolaas Claassen

Author(s):  
Avinash H Rajanna ◽  
Jayanthi C R ◽  
Swetha R ◽  
Subramani ◽  
Deepak Padmanabhan ◽  
...  

Background: The Outbreak of SARS-CoV-2 has caused a major pandemic posing a threat to the millions of lives all over the world. The evidence shows that there is a relation between the autonomic nervous system and coronaviruses and likewise, levels of inflammatory markers - C-reactive protein (CRP) and autonomic dysfunction. Autonomic dysfunction is elicited using heart rate variability which in turn quantified using autonomous regulatory index (ARI). Hence this study was conducted to determine if ARI measured using patented NEUROCOR Precision HRV® Solution instrument could be used as a non-invasive measure of autonomic dysfunction among COVID-19 subjects. Materials & Methods: An exploratory study was conducted among randomly selected 22 COVID-19 male patients aged more than 18 years, admitted to COVID ward, Victoria Hospital, Bengaluru for 5 days, using ANS Recorder, a non-invasive heart rate variability recorder heart rhythm data were collected, one test per day continuously for 5 days and a patented NEUROCOR Precision HRV® Solution, an ANS Analysis Software instrument was used to record, analyze and interpret the heart rate variability in terms of ARI and CRP levels were measured. Data was analyzed using SPSS version 18.0. A P value of < 0.05 was considered statistically significant. Results: The occurrence of autonomic dysfunction in COVID-19 patients using the Patented NEUROCOR Precision HRV® Solution was found to be among 50.0%. The median scores of average ARI indices were significantly lesser among those with higher health risk (28.39) compared to those with lower health risk (65.95) (P<0.05). The Median ARI index showed a weak negative correlation (r = -0.13, P>0.05) with CRP (P>0.05). ARI index showed a significantly excellent predictive ability in detecting the higher health risk with the areas under the curves (AUC) being 0.93 with an optimal cut-off of 40.85 with maximum sensitivity and specificity of 100.0% and 93.0%. Conclusion: Autonomous Regulatory Index (ARI) index with significantly excellent predictive ability in detecting the higher health risk can be used as a non-invasive measure of autonomic dysfunction among COVID-19 subjects.


2020 ◽  
Vol 32 (02) ◽  
pp. 2050009
Author(s):  
Kirti Tripath ◽  
Harsh Sohal ◽  
Shruti Jain

This article proposes a computer-aided diagnostic system for feature-based selection classification (CAD-FSC) to detect arrhythmia, atrial fibrillation and normal sinus rhythm. The CAD-FSC methodology encompasses of ECG signal processing phases: ECG pre-processing, R-peak detection, feature extraction, feature selection and ECG classification. Digital filters are used to pre-process the ECG signal and the R-peak is detected by using the Pan-Tompkin’s algorithm. The heart rate variability (HRV) features are extracted in time and frequency domains. Among them, the prominent features are selected with analysis of variance (ANOVA) using Statistical Package for the Social Sciences (SPSS) tool. Cubic support vector machine (C-SVM), coarse Gaussian support vector machine (CG-SVM), cubic k-nearest neighbor (C-kNN) and weighted k-nearest neighbor (W-kNN) classifiers are utilized to validate the CAD-FSC system for three-stage classification. The C-SVM outperforms all other classifiers by giving higher overall accuracy of 98.4% after feature selection of time domain and frequency domain.


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