Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.

We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24-h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non-cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart-device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning-based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication.

Thermal stability of levopimaric acid and its oxidation products.

Biofuels are renewable alternatives to fossil fuels. Levopimaric acid‒base biofuels have attracted increasing attention. However, their stability remains a critical issue in practice. Thus, there is a strong impetus to evaluate the thermal stability of levopimaric acid. Through thermogravimetry (TG) and a custom-designed mini closed pressure vessel test (MCPVT) operating under isothermal and stepped temperature conditions, we investigated thermal oxidation characteristics of levopimaric acid under oxygen atmosphere. Thin-layer chromatography (TLC) and iodimetry were used to measure the hydrogen peroxides generated by levopimaric acid oxidation. A high pressure differential scanning calorimeter (HPDSC) was used to assess hydroperoxide thermal decomposition characteristics. Gas chromatography-mass spectrometry (GC-MS) was used to characterize the oxidation products. The thermal decomposition kinetics of levopimaric acid were thus elucidated, and a high peroxide value was detected in the levopimaric acid. The decomposition heat (QDSC) and exothermic onset temperature (Tonset) of hydroperoxides were 338.75 J g-1 and 375.37 K, respectively. Finally, levopimaric acid underwent a second-stage oxidation process at its melt point (423.15 K), resulting in complex oxidation products. Thermal oxidation of levopimaric acid could yield potential thermal hazards, indicating that antioxidants must be added during levopimaric acid application to protect against such hazardous effects.

Correlation of Circadian Rhythms of Heart Rate Variability Indices with Stress, Mood, and Sleep Status in Female Medical Workers with Night Shifts.

Purpose: Heart rate variability (HRV) indices have been used as stress indicators. Rare studies investigated the associations of circadian rhythms of the HRV indices with the stress, mood, and sleep conditions in populations under stress. Methods: In total 257 female participants (203 shift workers and 54 non-shift workers) were included. All the participants completed a structured questionnaire to assess the stress, mood, and sleep conditions and performed 24-hour Holter electrocardiogram monitoring on the day away from shifts. Using epochs of 1-min or 5-min beat-to-beat intervals, the HRV indices (SDNN, RMSSD, LF, HF, LF/HF, and LFnu, SD1, SD2, SD1/SD2) were plotted as a function of time and fitted into cosine periodic curves, respectively. Three mathematical parameters based on the cosine periodic curves were extracted, MESOR (M, overall averages of the cosine curve), amplitude (A, amplitude of the peak of the cosine curve), and acrophase (θ, latency to the peak) to quantify the circadian rhythms of the HRV indices. Multivariable linear regression models were used to reveal the associations of these parameters with the clinical assessments of stress, mood, or sleep conditions, as well as with the 24-h averages of the HRV indices. Results: The parameters M and A of SDNN, RMSSD, LF, and HF, and θ of LF/HF and LFnu significantly differ between shift and non-shift workers. The parameter θ of LF/HF positively correlates with the severity of stress and anxiety. The parameter A of LF/HF and LFnu also positively correlates with daytime sleepiness and sleep fragmentation. In addition, the parameters M and A instead of θ of SDNN, RMSSD, LF, LF/HF, and LFnu significantly correlate with the 24-h averages of HRV indices. Conclusion: The circadian rhythms of the HRV indices over 24 hours can, to some extent, predict the severity of stress, emotion and sleep conditions in female populations under stress.