Objective Electrocardiogram(ECG)signal quality degrades when the level of activity is high and motion artifacts are severe.Poor quality signals may result in false alarms,poor patient monitoring,imprecise measurement,and misleading diagnosis.The quantitative assessment of ECG signal quality forms the basis of automatic ECG noise reduction and heart disease diagnosis.Methods The ECG signal quality index(SQI)was obtained by statistically analyzing the heartbeat similarity and the slope character,respectively,namely rSQI and kSQI.Results Using MIT-BIH Noise Stress Test Database to test,both rSQI and kSQI decreased when the Signal Noise Ratio(SNR)decreased,which revealed the ECG signal quality.Based on the quasiperiodic property,the waveform similarity,as a beat-to-beat index,is obtained by cross correlation between two ECG cycles with high precision but heavy computation.Slope-based method dispenses with QRS detection and is very simple and real-time,but its sensitivity is lower than similarity-based method and it only get statistical data.Conclusion Both morphology similarity and slope character algorithms could provide objective estimation of ECG quality.Slope-based method is an attractive measure due to its simplicity and mathematical convenience,while similarity-based method is more accurate and robust for ECG quality assessment.

Objective To explore the role in sleep staging from blood volume pulse(BVP)and triaxial acceleration(ACC)of body movement obtained by wristband.Methods The BVP and ACC obtained by Empatica E4 wristband were used from all 100 cases of sleep disorder subjects in the DREAMT public database.Two frequency domain characteristics(eS,LF/HF)and one time domain characteristic(vA)of the BVP baseline and the activity counts(CS)of the ACC were used for sleep-awakening classification based on random forest.Results The results of sleep-awakening classification of all 100 cases of sleep disorder subjects were obtained by leaving-one-out strategy.The accuracy is 79.8%and the Kappa coefficient is 0.56 by 4 features from BVP and ACC;the accuracy is 70.4%and the Kappa coefficient is 0.36 by 3 features of BVP;the accuracy is 75.1%and the Kappa coefficient is 0.47 based on activity counts.Conclusion The BVP and ACC obtained by the wristband can be used for the rough estimation of sleep and awakening for sleep disorder subjects,among which the importance of ACC is higher than that of BVP.

Humans ; Parkinson Disease/diagnosis* ; Machine Learning

【Objective】 To explore the impact of the COVID-19 epidemic on platelets supply management and its countermeasures. 【Methods】 Relevant data on platelets collection, supply, daily inventory and adjustment were collected through the blood information management system of our blood center after the outbreak of the epidemic in Xi′an (from December 9, 2021 to February 2, 2022), and then compared with the data during the corresponding period of last year (from December 9, 2020 to February 2, 2021). 【Results】 In this study, in the first half month of the epidemic, the collection and supply of platelets were slightly higher than the same period of the previous year. After the first-level response to the epidemic was initiated, the collection and issuing of platelets decreased significantly compared with the same period of the previous year. Half a month after the home isolation had been gradually lifted, the collection and issuing of platelets increased significantly as compared with the home isolation period. Otherwise, the collection and issuing of platelets had been increased as compared with the same period of the previous year. 【Conclusion】 After the outbreak of the epidemic, the timely adjustment of platelet collection strategy as well as the launch of emergency plan for platelets collection and supply during the first-level response to the epidemic effectively ensured the supply of clinical platelets in Xi′an during the period of home isolation.