Search

Patients with depression have lower heart rate variability (HRV) compared with controls. However, studies have indicated HRV difference between male and female controls. The gender effect might be interactive with the depression effect on the HRV, resulting in a low accuracy of recognising the patients with depression from the controls. Our study explores the effect of gender-related depression on HRV. Four ANS tests including resting, deep breathing, Valsalva, and orthostatic test are employed as stimuli. HRV were collected from 182 subjects comprising 91 depressive patients (33 females/58 males) and 91 controls (33 females/58 males) in the four tests. Time and frequency domains and nonlinear parameters are employed to quantify HRV. Two-way ANOVA is applied to evaluate the effect of gender-related depression. Most HRV parameters of the patients significantly differ from those of the controls, but some parameters indicate different depression effect between the males and females in the deep breathing and Valsalva test. Some HRV parameters illustrate significant difference between the male and female controls. Therefore, the effect of depression on HRV of each gender should be investigated., (Copyright © 2018. Published by Elsevier B.V.)

Background: Doctors mainly use scale tests and subjective judgment in the clinical diagnosis of depression. Researches have demonstrated that depression is associated with the dysfunction of the autonomic nervous system (ANS), where its modulation can be evaluated by heart rate variability (HRV). Depression patients have lower HRV than healthy subjects. Therefore, HRV may be used to distinguish depression patients from healthy people., Methods: HRV signals were collected from 76 female subjects composed of 38 depression patients and 38 healthy people. Time domain, frequency domain, and non-linear features were extracted from the HRV signals of these subjects, who were subjected to the Ewing test as an ANS stimulus. Then, these multiple features were input into Bayesian networks, served as a classifier, to distinguish depression patients from healthy people. Hence, accuracy, sensitivity, and specificity were calculated to evaluate the performance of the classifier., Results: Recognition results indicate 86.4% accuracy, 89.5% sensitivity, and 84.2% specificity. The individuals subjected to the Ewing test showed better recognition results than those at individual test states (resting state, deep breathing state, Valsalva state, and standing state) of the Ewing test. The root mean square of successive differences (RMSSD) of the HRV exhibits a significant relevance with recognition., Conclusion: Bayesian networks can be applied to the recognition of depression patients from healthy people and the recognition results demonstrate the significant association between depression and HRV. The Ewing test is a good ANS stimulus for acquiring the difference of HRV between depression patients and healthy people to recognize depression. The RMSSD of the HRV is important in recognition and may be a significant index in distinguishing depression patients from healthy people., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Major depression disorder (MDD) patients express dysfunction autonomic nervous system (ANS) and reduced heart rate variability (HRV). However, previous researches mainly focused on examining resting state without considering a series of stimulation tests of ANS between MDD patients and healthy people. For this purpose, 40 healthy people and 40 MDD patients participated and finished 10 min clinical autonomic test—Ewing test. Parameters of HRV such as time domain, frequency domain, and nonlinear dynamical parameters were calculated. Most of HRV parameters during Ewing test of MDD patients are lower than healthy people, and inversely, the ratio of low frequency to high frequency is higher when standing up. In addition, heart rate of healthy people in deep breathing and Valsalva test states are higher than that in resting state, nonlinear dynamical parameter (RCMSE1) of healthy people in standing up state is higher than that in Valsalva test state. The experimental results suggest that parasympathetic nervous system of MDD patients is influenced by mental state and become dysfunction under long-term depression, it cannot exert normal physiological functions when it is activated. Sympathetic activity increases on MDD patients, and this enhancement is more obvious which expresses higher complexity of HRV time series. [ABSTRACT FROM AUTHOR]

Hypnosis can act on the autonomic nervous system which can be presented by heart rate variability (HRV). So HRV implies many information related to hypnosis. Because HRV signal is time-variant and non-stationary, the traditional methods, such as Fourier transform and AR spectral estimation, are unable to analyze it. Hence, wavelet time-frequency analysis is applied here to not only offer superior time and frequency resolution, but also detect sudden amplitude and frequency jumps. The electrocardiograms of subjects were recorded under hypnosis, from which the corresponding HRV signals are obtained. The instant parameters including HRV, very low frequency (VLF), low frequency (LF), high frequency (HF), and the ratio of LF to HF (LF/HF) components of each HRV signal are then computed. Furthermore, mean, coefficient of variation, skewness, and kurtosis of independent frequency components in four hypnotic states (resting state, inducing state, imagining state and awaking state) were also obtained from the instant parameters to describe variation during hypnosis. The experiment results show that the parameters of HRV can reflect some physiological features of hypnosis, e.g., the LF/HF was more concentrative and steady in imagining state. [ABSTRACT FROM AUTHOR]