Papers by Keyword: Heart Rate Variability (HRV)

Abstract: Determination of Heart Rate Variability (HRV) derived from the Pulse Rate Variability (PRV) of the SpO₂ signals measurement can be used to monitor cardiac activity. One disadvantage of the use of SpO₂ probe is due to existence unavoidable movement artifacts. These artifacts tend to reduce the accuracy of PRV determination. In order to quantify the influence of moving artifacts on the measured SpO₂ signals, the Short-time Fourier Transform (STFT) method is used and this has not been done in previous studies. This method is regarded to be suitable since the artifacts only occurs momentarily, i.e. as the finger moves. Three modes of finger movements were simulated, in addition to the still finger as a control, i.e. in direction of up-down, left-right, and rotating one. Contributing spectra from each of these movements will be recognized, and suitable filtering schemes are then being applied to suppress the influence of these moving artifacts. Parallelly measurements using three-leads ECG were also done to determine the HRV for each of the finger movements condition. Results show that by implementing filtering scheme to each mode of finger movements may reduce the error rate in HRV determination from SpO₂ measurements, i.e. from 6 - 25 % (without filtering) to be only 0 - 1.56 %. Meanwhile measurements both HRV and PRV under still finger show only 0-3.33 % difference for each of data groups.

Abstract: 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.

Abstract: 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 (L_max) 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.

Abstract: 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 α₁ of 90-dBC noise was significantly higher than the α₁ of 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.

Abstract: The ECG data storage system based on 32-bit stm32 microprocessor, parsing complicated communication protocol of BMD101 electrocardiograph signal acquisition module. It is implemented that ECG data real-time storage of BMD101 of high sampling rate with SPI serial bus to carry out the serial correspondence between SD card and chip. This paper uses the physionet medical packet to analysis the difference between HRV of two groups of ECG data which reflects the reliability of the ECG data storage system. The analysis results show that the storage system with almost no loss of ECG data, stable performance, can realize ECG data storage for a long time.

Abstract: Most studies considering spectral features of HRV during sleep divided total frequency band into low frequency (LF, 0.04~0.15Hz) and high frequency (HF, 0.15~0.4 Hz) roughly, and were limited to a few measures like the power in LF and HF, or the ratio of them. To make full use of HRV, more comprehensive spectral features were evaluated in this paper. LF was further divided into true LF (0.04~0.1Hz) and medium frequency (0.1~0.15Hz). Spectrum power, mean frequency and spectral entropy of different spectral bands, fractal dimension and peak in HF (20 measures in total) were calculated for wake, REM, light sleep and deep sleep. The significance between sleep stages of each feature was evaluated. The random forest method was adopted for sleep staging and features importance rank. The results suggested that almost all the new proposed features showed significant differences during different sleep stages. They can improve sleep stages classification performance notably. Our study provided new features for sleep stages classification based on ECG.