Tilt table test (TTT) is a standard examination for patients with suspected autonomic nervous system (ANS) dysfunction or uncertain causes of syncope. Currently, the analytical method based on blood pressure (BP) or heart rate (HR) changes during the TTT is linear but normal physiological modulations of BP and HR are thought to be predominately nonlinear. Therefore, this study consists of two parts: the first part is analyzing the HR during TTT which is compared to three methods to distinguish normal controls and subjects with ANS dysfunction. The first method is power spectrum density (PSD), while the second method is detrended fluctuation analysis (DFA), and the third method is multiscale entropy (MSE) to calculate the complexity of system. The second part of the study is to analyze BP and cerebral blood flow velocity (CBFV) changes during TTT. Two measures were used to compare the results, namely correlation coefficient analysis (nMxa) and MSE. The first part of this study has concluded that the ratio of the low frequency power to total power of PSD, and MSE methods are better than DFA to distinguish the difference between normal controls and patients groups. While in the second part, the nMxa of the three stages moving average window is better than the nMxa with all three stages together. Furthermore the analysis of BP data using MSE is better than CBFV data.

Previous studies have indicated that the chronic effects of exposure to low-frequency noise causes annoyance. However, during the past two decades, most studies have employed questionnaires to characterize the effects of noise on psychosomatic responses. This study investigated cardiovascular activity changes in exposure to low-frequency noise for various noise intensities by using recurrence plot analysis of heart rate variability (HRV) estimation. The authors hypothesized that distinct noise intensities affect cardiovascular activity, which would be reflected in the HRV and recurrence quantification analysis (RQA) parameters. The test intensities of noises were no noise, 70-dBC, 80-dBC, and 90-dBC. Each noise level was sustained for 5 min, and the electrocardiogram (ECG) was recorded simultaneously. The cardiovascular responses were evaluated using RQA of the beat-to-beat (RR) intervals obtained from ECG signals. The results showed that the mean RR interval variability and mean blood pressure did not substantially change relative to the noise levels. However, the length of the longest diagonal line (Lmax) of the RQA of the background noise (no noise) condition was significantly lower than the 70-dBC, 80-dBC, and 90-dBC noise levels. The laminarity showed significant changes in the noise levels of various intensities. In conclusion, the RQA-based measures appear to be an effective tool for exposure to low-frequency noise, even in short-term HRV time series.

During the past two decades, most studies have employed questionnaires to characterize the effects of noise on behavior and health. Developments in physiological techniques have provided a noninvasive method for recording cardiovascular autonomic activity by using an electrocardiogram (ECG). We investigated cardiovascular activity changes in exposure to exposure to low-frequency noise for various noise intensities by using detrended fluctuation analysis (DFA) of heart rate variability (HRV). We hypothesized that distinct noise intensities would affect cardiovascular activity, which would be reflected in the HRV and DFA parameters. A total of 17 healthy volunteers participated in this study. The test intensities of noises were no noise, 70-dBC, 80-dBC, and 90-dBC. Each noise was sustained for 5 minutes and the ECG was recorded simultaneously. The cardiovascular responses were evaluated using DFA of the beat-to-beat (RR) intervals obtained from ECG signals. The results showed that the mean RR intervals variability and mean blood pressure did not substantially change relative to the noises. However, the short-term scaling exponent (α1) of the DFA of the background noise (no noise) condition was lower than the 70-dBC, 80-dBC and 90-dBC noises (P< 0.05, repeated measures analysis of variance). The α1of 90-dBC noise was significantly higher than the α1of BN condition according to a Mann–Whitney U test (P< 0.01). We concluded that exposure to low-frequency noise significantly affects the temporal correlations of HRV, but it does not influence RR intervals variability.

During the past two decades, most researchers employed a questionnaire to characterize the effect of noise on psychosomatic responses. Developments in physiological techniques offer a non-invasive method for recording brain activity with electroencephalography (EEG). This method for assessing the impact of noise on attention is growing in popularity. The aim of this study was to investigate brain activity changes in response to noise exposure during attention-demanding tasks by using EEG power and phase coherence estimation. We hypothesized that brain rhythms could be affected by environmental stimuli and would be reflected in the EEG power and phase coherence. Nineteen healthy right-handed university students (mean age = 21.5 ± 2.0 years) participated in this study. The experiment comprised recording EEG data for participants in the following steps: rest with eyes closed (< 50 dBA), rest with eyes open, listening in a noisy environment (85 dBA), performance on an attention-demanding task in a quiet environment (< 50 dBA), and performance on an attention-demanding task in a noisy environment (85 dBA). Significant differences were observed between stages, and the participants performed more effectively in the quiet environment, where they showed higher rates of correct responses (p <.05). From the assessment of the EEG power and phase coherence estimation, the study demonstrated the following: (1) Alpha-2 (10-13 Hz) power and phase coherence decreased when participants shifted from closed eyes to open eyes, while theta power increased. (2) In contrast, during the noise exposure phase, whether during an attention-demanding task or not, beta (13-30 Hz) phase coherence decreased in the brain, but theta phase coherence was not affected compared to the results in the quiet environment. We suggest that the high frequency of neural synchronization is relevant for cognitive performance, and that participants at risk for selective attention are affected by noise exposure.

Ecological studies have shown that the chronic effects of exposure to environmental noise cause annoyance. However, in the past, most studies have used questionnaires to evaluate the effects of noise pollution on psychosomatic responses. This study investigated cardiovascular activity changes in exposure to low-frequency noise at various noise intensities. The authors hypothesized that distinct noise intensities affect cardiovascular activity, which would be reflected in the spectral analysis parameters. The evaluation intensities of low frequency noises (from 20 to 200 Hz) were background noise (BN), 70-dBC, 80-dBC, and 90-dBC. The electrocardiographic (ECG) data was recorded for 5 minutes under various noise levels. The cardiovascular responses were evaluated using spectral analysis of the beat-to-beat (RR) intervals obtained from ECG signals. The results showed that the average blood pressure and mean RR interval variability did not substantially change relative to the noise levels. However, the low-frequency (LF) power and the ratio of LF power to high-frequency power (LF/HF) from ECG under the BN condition were significantly lower than the 80-dBC, and 90-dBC noise levels. In addition, the normalized LF of the background noise condition was significantly lower than the low-frequency of the noise levels at various intensities. In conclusion, the frequency-domain-based measures appear to be a powerful tool for exposure to low-frequency noise, even in short-term heart rate variability time series.