Searching for methods for quantitative assessment of individual resistance to emotional stress in the norm and in pathology based on the analysis of heart rate variability. Factor model

2017 ◽  
Vol 12 (13) ◽  
pp. 53-63
Author(s):  
O.Yu. Mayorov ◽  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Emotional Stress ◽  
Quantitative Assessment ◽  
Factor Model ◽  
Individual Resistance

scholarly journals Quantitative assessment of autonomic nervous system responses induced by graded training workloads in healthy elderly, through indices obtained from heart rate variability analysis

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
R Fenici ◽  
M Picerni ◽  
D Brisinda
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Quantitative Assessment ◽  
Training Session ◽  
Time Varying ◽  
Autonomic Nervous ◽  
Healthy Elderly ◽  
Hrv Analysis

Abstract Background Quantitative assessment of individual body adaptability to physical training performed with the purposes of health maintenance is particularly necessary in the elderly age, to avoid the risk of overstrain induced by inappropriate exercises workload and physical stress. For that purpose, heart rate monitors and heart rate variability (HRV) analysis are nowadays commercially available. However, their reliability to guide individualized fitness training in elderly people needs to be tested, knowing that users might not have medical education. Objective To preliminary quantify autonomic nervous system (ANS) responses to graded physical effort and recovery in healthy elderly basing on the parasympathetic nervous system (PNSi), the sympathetic nervous system (SNSi) and the stress (STRi) indices, derived by short-term and time-varying HRV analysis. Methods ECG of a 75 healthy male subject was monitored, from April to November 2020, during three times/week training sessions with a professional bike–ergometer. Each session consisted of 10 minutes baseline rest, 5 minutes warm-up, 30 minutes work and 10 minutes recovery. According to age, the training workload was graded from low (65–75 watt/min), to moderate (75–85 watt/min), semi-intensive (85–95 watt/min) and intensive (95–110 watt/min). For this pilot study, ECG data of only 40 training sessions (10 sessions for each workload to evaluate reproducibility) were analyzed with Kubios Premium software (version 3.4.1), in the time (TD) and frequency (FD) domains, with nonlinear (NL) methods and with time-varying (TV) algorithms. Short-time HRV was calculated from 2-minutes intervals. The PNSi, SNSi and STRi induced by each workload were averaged and compared. Results Average values of PNSi, SNSi and STRi were significantly different (p<0.05) among training sessions carried out with different workloads (Table 1A) and among measurements obtained at rest, at every 5 minutes step of each 30 minutes training session, and at 1 and 5 minutes of recovery (Table 1B). Interestingly, the correlation between SNSi and STRi was strictly linear (R= 0,98), whereas that between PNSi and STRi was better fitted by a cubic function (R=0,82 with cubic vs 0.68 with linear function), when evaluated either as a function of the sessions' workloads (Figure 1A), or of four time-intervals of each training session (Figure 1B). PNSi and SNSi were inversely correlated, with cross-point at about 15 minutes of training and 75 watt/min workload. Conclusions The calculation of PNSi, SNSi and STRi from HRV analysis is an efficient method for quick and simplified quantitative assessment of dynamic ASN adaptation to effort-induced stress from HRV analysis. If confirmed, the method may be useful for safer and even remote monitoring of training/rehabilitation in elderly. However, more detailed evaluation of spectral and NL parameters may be necessary to interpret more complex patterns of abnormal cases. FUNDunding Acknowledgement Type of funding sources: None. Table 1 Figure 1

Emotional Stress and Heart Rate Variability Measures Associated With Cardiovascular Risk in Relocated Katrina Survivors

2012 ◽  
Vol 74 (2) ◽  
pp. 160-168 ◽  
Author(s):  
Phebe Tucker ◽  
Betty Pfefferbaum ◽  
Haekyung Jeon-Slaughter ◽  
Qaiser Khan ◽  
Theresa Garton
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Cardiovascular Risk ◽  
Emotional Stress

Relationship between heart rate variability and the severity of psychotic symptoms in schizophrenia

2011 ◽  
Vol 23 (4) ◽  
pp. 161-166 ◽  
Author(s):  
Jong-Hoon Kim ◽  
Jun-Hyung Ann ◽  
Jinyoung Lee
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Patient Group ◽  
Factor Model ◽  
Spectral Power ◽  
Five Factor Model ◽  
Approximate Entropy ◽  
Psychotic Symptoms ◽  
Symptom Dimensions ◽  
The Relationship

Kim J-H, Ann J-H, Lee J. Relationship between heart rate variability and the severity of psychotic symptoms in schizophrenia.Objective: The relationship between autonomic neurocardiac function and schizophrenia remains elusive. This study investigated the relationship between the heart rate variability (HRV) parameters and the severity of psychotic symptoms in schizophrenic patients.Methods: Twenty-one patients receiving risperidone monotherapy and 21 matched normal control subjects were evaluated for HRV analysis. The severity of schizophrenic symptoms was assessed using the Positive and Negative Syndrome Scale (PANSS) and a five-factor model of the PANSS was used.Results: The value of the ratio of low-to-high frequency spectral power was significantly higher in the patient group. The patient group also showed a significantly lower value of approximate entropy. After controlling the dosage of risperidone, the PANSS total score had significant negative correlations with the standard deviation of all RR intervals (SDNN) and the square root of the mean squared differences of successive normal sinus intervals (RMSSD). With respect to the PANSS factors, the score of the PANSS cognitive/disorganisation factor had significant negative correlations with SDNN and RMSSD.Conclusion: These results provide some evidence that the severity of psychotic symptoms, especially cognitive/disorganisation symptom dimensions, may be associated with reduced HRV, suggesting a potential involvement of neuroautonomic dysfunction in the pathophysiology of specific symptoms of schizophrenia.

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