Background: Recently, attempts have been made to use the pulse rate variability (PRV) as a surrogate for heart rate variability (HRV). PRV, however, may be caused by the fluctuations of left ventricular pre-ejection period and pulse transit time besides HRV. We examined whether PRV differs not only from HRV but also depending on the measurement site.Results: In five healthy subjects, pulse waves were measured simultaneously on both wrists and both forearms together with single-lead electrocardiogram (ECG) in the supine and sitting positions. Although average pulse interval showed no significant difference from average R-R interval in either positions, PRV showed greater power for the low-frequency (LF) and high-frequency (HF) components and lower LF/HF than HRV. The deviations of PRV from HRV in the supine and sitting positions were 13.2% and 7.9% for LF power, 24.5% and 18.3% for HF power, and − 15.0% and − 30.2% for LF/HF, respectively. While the average pulse interval showed 0.8% and 0.5% inter-site variations among the four sites in the supine and sitting positions, respectively, the inter-site variations in PRV were 4.0% and 3.6% for LF power, 3.8% and 4.7% for HF power, and 18.0% and 17.5% for LF/HF, respectively.Conclusions: These suggest that PRV shows not only systemic differences from HRV but also considerable inter-site variations.

Exposures to melanopsin-stimulating (melanopic) component-rich blue light enhance arousal level. We examined their effects in office workers. Eight healthy university office workers were exposed to blue and orange lights for 30 min during lunch break on different days. We compared the effects of light color on autonomic arousal level assessed by heart rate variability (HRV) and behavioral alertness by psychomotor vigilance tests (PVT). Heart rate was higher and high-frequency (HF, 0.150.45 Hz) power of HRV was lower during exposure to the blue light than to orange light. No significant difference with light color was observed, however, in any HRV indices during PVT or in PVT performance after light exposure. Exposure to blue light during lunch break, compared with that to orange light, enhances autonomic arousal during exposure, but has no sustained effect on autonomic arousal or behavioral alertness after exposure.

Although heart rate variability and actigraphic data have been used for sleep-wake or sleep stage classifications, there are few studies on the combined use of them. Recent wearable sensors, however, equip both pulse wave and actigraphic sensors. This paper presents results on the performance of sleep stage classification by a combination of heart rate and actigraphic signals. We studied 40,643 epochs (length 3 min) of polysomnographic data in 289 subjects. A combined model, consisting of autonomic functional indices from heart rate variability and body movement indices derived from actigraphic data, discriminated non-rapid-eye-movement (REM) sleep from waking/REM sleep with 76.9% sensitivity, 74.5% specificity, 75.8% accuracy, and a Cohen’s kappa of 0.514. The combination was also useful for discriminating between REM sleep and waking at 77.2% sensitivity, 72.3% specificity, 74.5% accuracy, and a kappa of 0.491.

There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy.

A variety of heart rate variability (HRV) indices have been reported to estimate sleep stages, but the associations are modest and lacking solid physiological basis. Non-REM (NREM) sleep is associated with increased regularity of respiratory frequency, which results in the concentration of high frequency (HF) HRV power into a narrow frequency range. Using this physiological feature, we developed a new HRV sleep index named Hsi to quantify the degree of HF power concentration. We analyzed 11,636 consecutive 5-min segments of electrocardiographic (ECG) signal of polysomnographic data in 141 subjects and calculated Hsi and conventional HRV indices for each segment. Hsi was greater during NREM (mean [SD], 75.1 [8.3]%) than wake (61.0 [10.3]%) and REM (62.0 [8.4]%) stages. Receiver-operating characteristic curve analysis revealed that Hsi discriminated NREM from wake and REM segments with an area under the curve of 0.86, which was greater than those of heart rate (0.642), peak HF power (0.75), low-to-high frequency ratio (0.77), and scaling exponent α (0.77). With a cutoff >70%, Hsi detected NREM segments with 77% sensitivity, 80% specificity, and a Cohen’s kappa coefficient of 0.57. Hsi may provide an accurate NREM sleep maker for ECG and pulse wave signals obtained from wearable sensors.

In this study, we examined whether or not the strenuous exercise in the evening change nocturnal heart rate variability (HRV) and recovers. Subjects were 8 healthy men belonging to the mature futsal team (age: 35±3 years) and Futsal was held from 5:00 p.m. to 6:30 p.m. on Sunday. Pulse waves during sleeping were measured at home on exercise day and on control day. The mean pulse interval (MPI), standard deviation pulse interval (SDPI), low frequency component (LF, 0.04-0.15 Hz), high frequency component (HF, 0.15-0.45 Hz) and the ratio of LF to HF (LF/HF) were calculated from pulse interval time series every 30 min. As a result, MPI, SDPI, LF, HF of exercise day were significantly lower than control day (P<0.0001). LF/HF of exercise day was significantly higher than control day (P<0.05). Regarding change of HRV for every 30 min, MPI (P<0.05), SDPI (P=0.0003), LF (P=0.0038), HF (P<0.05) were observed significant changes. MPI and HF before wake-up did not reach the level of control day. It is thought that strenuous exercise in the evening promotes sympathetic nervous activity during night sleep and suggesting that pulse rate and HRV have not recovered by the wake-up time.