首页|Data on Machine Learning Reported by Junhua Mei and Colleagues (Sleep-phasic hea rt rate variability predicts stress severity: Building a machine learning-based stress prediction model)
Data on Machine Learning Reported by Junhua Mei and Colleagues (Sleep-phasic hea rt rate variability predicts stress severity: Building a machine learning-based stress prediction model)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Wuhan, Peopl e's Republic of China, by NewsRx correspondents, research stated, "We propose a novel approach for predicting stress severity by measuring sleep phasic heart ra te variability (HRV) using a smart device. This device can potentially be applie d for stress self-screening in large populations." Financial support for this research came from National Key Research and Developm ent Program of China. Our news editors obtained a quote from the research, "Using a Holter electrocard iogram (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 o f cyclic alternating pattern (CAP; unstable sleep), non-cyclic alternating patte rn (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based o n cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indic es during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart-d evice PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants alo ng with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or RE M sleep than in NCAP. Using the smart device data only, the optimal machine lear ning-based stress prediction model exhibited accuracy of 80.3 %, se nsitivity 87.2 %, and 63.9 % for specificity."
WuhanPeople's Republic of ChinaAsiaCardio DeviceCyborgsEmerging TechnologiesHealth and MedicineHeart RateHemodynamicsMachine LearningMedical DevicesRapid Eye Movement
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