穿戴式随行监护系统心电信号质量评价研究

穿戴式生理参数监测技术是一种新型的生理监护技术,代表未来监护技术的发展方向,但该类技术应用于临床尚有许多问题亟待解决。本文针对自主研发的穿戴式随行监护系统(SensEcho-5B)的心电信号质量评价问题开展了探索性研究。首先基于模板匹配法开发出一种心电信号质量评价算法,用于心电信号的自动、定量评价,在100名受试者(15名健康人和85名心血管疾病患者)随机抽取的100 h心电信号数据集上进行了算法性能测试。在此基础上使用SensEcho-5B与心电Holter同步采集了30名受试者(7名健康人和23名心血管疾病患者)的24 h心电数据,使用心电信号质量评价算法对两个系统同步记录的心电信号质量进行评价。算法性能测试结果:敏感度为100%,特异度为99.51%,准确率为99.99%。30名受试者的对照试验结果:SensEcho-5B所检测到的心电信号,信号质量较差时间的中位数(Q1,Q3)为8.93(0.84,32.53)min,Holter所检测到的心电信号,信号质量较差时间的中位数(Q1,Q3)为14.75(4.39,35.98)min(秩和检验P=0.133)。研究结果表明,本文提出的心电信号质量评价算法能够对穿戴式随行监护系统的心电信号质量进行有效评价;随行监护系统SensEcho-5B与对照Holter相比,心电信号质量相当。后续研究将进一步在真实临床环境中采集大样本量的随行监护生理数据,并对心电信号质量进行分析和评价,从而使监护系统的性能得到持续优化。     

穿戴式心电信号质量的三分类评估方法

研究一种穿戴式心电信号质量的三分类评估方法。心电信号质量三分类源于临床诊断需求,具体分为如下3类:一是临床有用,信号质量好;二是临床有用,信号质量差;三是临床无用。该方法首先提取心电信号时域、频域、非线性域中共计12个特征,然后构建特征矩阵,通过融合径向基核函数的支持向量机(SVM)分类器,实现穿戴式心电信号质量的三分类。实际结果表明,所提出的方法在375例经临床专家标注的独立测试集上信号质量三分类F测度结果分别为0.909、0.827和0.973,整体分类准确度为92.3%,相比于基于CNN的模型和传统SVM模型,准确度分别升高2.2%和6.4%。研究证明,新的信号质量三分类模型在穿戴式动态心电信号质量分类中有一定的应用价值。     

Smart Vest: wearable multi-parameter remote physiological monitoring system

The wearable physiological monitoring system is a washable shirt, which uses an array of sensors connected to a central processing unit with firmware for continuously monitoring physiological signals. The data collected can be correlated to produce an overall picture of the wearer's health. In this paper, we discuss the wearable physiological monitoring system called ‘Smart Vest’. The Smart Vest consists of a comfortable to wear vest with sensors integrated for monitoring physiological parameters, wearable data acquisition and processing hardware and remote monitoring station. The wearable data acquisition system is designed using microcontroller and interfaced with wireless communication and global positioning system (GPS) modules. The physiological signals monitored are electrocardiogram (ECG), photoplethysmogram (PPG), body temperature, blood pressure, galvanic skin response (GSR) and heart rate. The acquired physiological signals are sampled at 250 samples/s, digitized at 12-bit resolution and transmitted wireless to a remote physiological monitoring station along with the geo-location of the wearer. The paper describes a prototype Smart Vest system used for remote monitoring of physiological parameters and the clinical validation of the data are also presented.     

Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

Abstract

The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)—left arm up down, right arm up down, waist twisting and walking—have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time–frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects.     

Quantification of error between the heartbeat intervals measured form photoplethysmogram and electrocardiogram by synchronisation

Abstract

Currently, heartbeat intervals required for the analysis of heart rate variability (HRV) are derived from electrocardiogram (ECG). Many investigators have explored the possibility of using photoplethysmography (PPG), for the analysis of HRV. However, all these studies are based on statistical approach and have used the correlation coefficient for the comparison of HRV data obtained using ECG and PPG, which is inappropriate as the causal relationship between the R-peaks in ECG and the systolic peaks in PPG is well known in physiology. In this study, the heart beat intervals measured from PPG, are compared, beat by beat, with the corresponding beat intervals of same cardiac cycle obtained from the synchronously recorded ECG and the differences between them are taken as errors. These errors are verified to exactly match with the variations in the pulse transit times (PTTs), beat by beat. The error in the measurement of heartbeat intervals using PPG is quantified by obtaining the root mean square of the errors associated with each beat interval for a subject. The rms error, which is found to vary between 0.17 and 1.81% across the study group of 42 subjects, can be treated as insignificant, considering the nonstationary character of physiological signals. The errors are compared and interpreted with the variations in PTT. In view of these findings, PPG can be considered as a low cost, safe and more convenient alternative to ECG, as a wearable sensor outside hospital environment, for the analysis of HRV, without compromising on accuracy.     

Objective detection of chronic stress using physiological parameters

The aim of this study was to design a system to diagnose chronic stress, based on blunted reactivity of the autonomic nervous system (ANS) to cognitive load (CL). The system concurrently measures CL-induced variations in pupil diameter (PD), heart rate (HR), pulse wave amplitude (PWA), galvanic skin response (GSR), and breathing rate (BR). Measurements were recorded from 58 volunteers whose stress level was identified using the State-Trait Anxiety Inventory. Number-multiplication questions were used as CLs. HR, PWA, GSR, and PD were significantly (p?<?0.05) changed during CL. CL-induced changes in PWA (16.87?±?21.39), GSR (??13.71?±?7.86), and PD (11.56?±?9.85) for non-stressed subjects (n?=?36) were significantly different (p?<?0.05) from those in PWA (2.92?±?12.89), GSR (??6.87?±?9.54), and PD (4.51?±?10.94) for stressed subjects (n?=?22). ROC analysis for PWA, GSR, and PD illustrated their usefulness to identify stressed subjects. By inputting all features to different classification algorithms, up to 91.7% of sensitivity and 89.7% of accuracy to identify stressed subjects were achieved using 10-fold cross-validation. This study was the first to document blunted CL-induced changes in PWA, GSR, and PD in stressed subjects, compared to those in non-stressed subjects. Preliminary results demonstrated the ability of our system to objectively detect chronic stress with good accuracy, suggesting the potential for monitoring stress to prevent dangerous stress-related diseases.

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Graphical abstract Chronic stress degrads the autonomic nervous system reaction to cognitive loads. Measurement of reduced changes in physiological signals during asking math questions was useful to identify people with high STAI score (stressed subjects)

     

用于睡眠呼吸暂停检测的心电特征稳定性分析

基于心电信号(electrocardiogram,ECG)的睡眠呼吸暂停检测具有十分重要的意义,很多研究致力于提高检测的准确率却忽视了特征的稳定性。本研究对用于睡眠呼吸暂停检测的心电特征进行稳定性分析,并建立呼吸暂停事件检测模型。基于集成稳定特征选择策略,将最小冗余最大相关(minimal-redundancy-maximal-relevance,mRMR)特征选择方法与稳健排序聚合(robust rank aggregation,RRA)方法结合,对45个心电特征进行稳定性分析。使用10折交叉验证及支持向量机(SVM)进行特征验证及检测模型建立。最终使用14个特征建立分类模型,在独立测试集上实现Acc=90.03%,Se=86.71%,Sp=91.73%,所选特征在稳定性及检测准确率方面有明显提高。     

Development of a secure body area network for a wearable physiological monitoring system using a PSoC processor

Wearable physiological monitoring systems have gained popularity in the recent years due to their ability to continuously monitor physiological signals, thereby making them suitable for home-healthcare applications. The electrocardiogram (ECG), phonocardiogram (PCG) and photoplethysmogram (PPG) signals have been studied and it has been observed that there is a correlation between the three signals. This paper proposes the development of a secure body area network (BAN), for a wearable physiological monitoring system. The BAN is composed of three nodes, for ECG, PPG and PCG signals. The peak-peak distances of these signals are calculated first, in the coordinator of BAN. The coordinator is designed in such a manner that signals from it are transmitted to a monitoring station, only if the difference between the peak-peak distances of both ECG-PPG signals and ECG-PCG signals fall below a threshold. The entire operation of the coordinator is implemented using a real-time processor, Cypress(?) Programmable System on Chip (PSoC).     

一种可穿戴式心电图动态监护系统

介绍一种基于柔性电极可穿戴心电监护系统的软硬件设计方案,主要解决现阶段心电监护系统中存在的高成本、高耗能、心电图信号连续性差等问题。同时在电极设计上采用银纤维布料的方法,消除一次性AgCl/Ag湿电极在可穿戴心电系统中无法长期佩戴的弊端。经志愿者测试验证分析,系统有着较高的稳定性和实用性,对可穿戴心电监护体系的发展有实际应用推广价值。     

A wearable mobihealth care system supporting real-time diagnosis and alarm

This paper describes a wearable mobihealth care system aiming at providing long-term continuous monitoring of vital signs for high-risk cardiovascular patients. We use a portable patient unit (PPU) and a wearable shirt (WS) to monitor electrocardiogram (ECG), respiration (acquired with respiratory inductive plethysmography, RIP), and activity. Owing to integrating fabric sensors and electrodes endowed with electro-physical properties into the WS, long-term continuous monitoring can be realized without making patients feel uncomfortable and restricting their mobility. The PPU analyzes physiological signals in real time and determines whether the patient is in danger or needs external help. The PPU will alert the patient and an emergency call will be automatically established with a medical service center (MSC) when life-threatening arrhythmias or falls are detected. With advanced gpsOne technology, the patient can be located and rescued immediately whether he/she is indoors or outdoors in case of emergency.     

Assessing the potential of electrodermal activity as an alternative access pathway

An embedded multiple-case study was conducted with six able-bodied participants to evaluate the potential of electrodermal activity (EDA) as an alternative access pathway to electronic aids to daily living. Electrodermal signals were recorded while participants alternated between rest and three different mental or breathing exercises. In a subsequent experimental session, the exercise exerting the greatest influence on EDA was used to volitionally generate an ‘active’ state. Two classification algorithms, namely, a probabilistic classifier and a handcrafted rule base were developed and tailored to each individual's physiological patterns to discriminate between participant states. Through cross-validation, participant state was correctly identified to an accuracy exceeding 80% using either classification algorithm. This result demonstrates that consciously controlled EDA could conceivably serve as a binary switch, and encourages further research towards EDA-based alternative access for people who are locked-in.     

K-nearest neighbour-based algorithm for P- and T-waves detection and delineation

The aim of an automated Electrocardiogram (ECG) delineation system is the reliable detection of the characteristic waveforms and determination of peaks and limits of individual QRS-complex, P- and T-waves. In this paper, a classical statistical pattern recognition algorithm characterized with high accuracy and stability, i.e., K-Nearest Neighbour (KNN) has been proposed for locating the fiducial points along with their waveform boundaries in ECG signals. First, the QRS-complex along with its onset and offset points of each beat is detected from the ECG signal. After that P- and T-wave, relative to each QRS-complex along with their onset and offset points, are then identified using this algorithm. The feature extraction is done using the gradient of the ECG signals. The performance of the proposed algorithm has been evaluated on two standard manually annotated databases, (i) CSE and (ii) QT, and also on ECG data acquired using BIOPAC®MP100 system in laboratory settings. The results in terms of accuracy, i.e., 92.8% for CSE database obtained, clearly indicate a high degree of agreement with the manual annotations made by the referees of CSE dataset-3. Further, the delineation results of the CSE and QT database are compared with the accepted tolerances as recommended by the CSE working party. The results for ECG records acquired using the BIOPAC®MP100 system, in terms of QRS duration, heart rate, QT-interval, P-wave duration and PR-interval using KNN algorithm have also been computed.     

基于非参数相关系数的心肌病自动诊断

针对肥厚型心肌病和扩张型心肌病患者的心电图导联间的相关性,提出心肌病自动诊断的一种方法。该研究从12导联ECG信号中分割出来的单个心跳片段进行识别,以健康人群为对照识别出DCM和HCM的片段。从片段中提取264个非参数相关系数特征并通过变量筛选得到12个特征,输入到支持向量机中进行建模,采用10折交叉验证评价模型。模型的总准确率为99.88%±0.08%。模型使用的特征少,运行速度快,准确率高,有助于临床心肌病的自动化诊断,节约医疗资源。     

Quantitative assessment of the use of continuous wavelet transform in the analysis of the fundamental frequency disturbance of the synthetic voice

The aim of this work is to investigate quantitatively the capability of the Continuous Wavelet Transform (CWT) as a tool to estimate (calculate) Jitter and Shimmer, assessing the error between these indices calculated in each Wavelet decomposition and the ones for the original signal, for several dilatation levels. Two synthetic vowels /a/ were generated with the fundamental frequencies of 120 Hz for male and 220 Hz for female, by an autoregressive 22 coefficient all-pole model, and Jitter and Shimmer were introduced to the signal using five different percentage variations. The signals were decomposed by CWT in eight levels of dilatation (1, 2, 4, 8, 16, 32, 64 and 128), using the Mexican Hat, Meyer and Morlet real bases. Jitter and Shimmer were calculated for the original signals and all eight levels of decompositions and then the errors between the indices in the decompositions and the original signals were calculated. It can be concluded that CWT can be used as a tool for pre-processing the signal to measure Shimmer preferentially, and Jitter, instead of using the original signal to do that. The Mexican Hat base provided the lowest errors for Shimmer analysis, where the best dilatation level was 8 (error below 0.1%). In addition, the errors associated with Shimmer index, in general, are lower than the ones associated with Jitter index.     

A new approach for identification of heartbeats in multimodal physiological signals

In this paper, a technique is proposed for detection of heartbeats in multimodal data. Recording of multiple physiological signals from the same subject is common practice nowadays. Multiple physiological signals are generally available but are processed separately without taking into consideration information from other relevant signals. The heartbeats are generally detected from R peaks in electrocardiogram (ECG) signal, however, if ECG is noisy, other signals reflecting the cardiac activity may be used for identifying heartbeats. This paper describes a new method for detection of heartbeats using ECG and arterial blood pressure (ABP) signals. The physiological data are segmented into various fragments and signal quality is determined using the judgment of noise level. If the ECG data fragment is noisy, heartbeats are computed from the ABP fragment. The evaluation was performed on training data set of computing in cardiology challenge 2014. The proposed methodology has resulted in better detection accuracy as compared to the unimodal methods.     

A field (researcher's) guide to cortisol: tracking HPA axis functioning in everyday life

Abstract

Researchers have been incorporating ambulatory cortisol sampling into studies of everyday life for over a decade. Such work provides an important supplement to acute laboratory stress paradigms and provides a novel perspective on the interrelationships between stress, psychological resources, and health. However, the results of many field studies have been inconclusive and more studies have been undertaken than published. We describe some of the challenges facing naturalistic cortisol researchers, including lack of power, methodological and analytical problems, and patterns of confusing or conflictual results. We then summarize key findings of published naturalistic cortisol studies to date, grouped by type of cortisol outcome (morning awakening response, diurnal slope, area under the curve, and associations between momentary experiences and cortisol). We propose research questions relevant to everyday stress researchers and suggest next steps for researchers who are interested in incorporating naturalistic cortisol sampling into future studies.     

Smart wearable systems: Current status and future challenges

ObjectiveExtensive efforts have been made in both academia and industry in the research and development of smart wearable systems (SWS) for health monitoring (HM). Primarily influenced by skyrocketing healthcare costs and supported by recent technological advances in micro- and nanotechnologies, miniaturisation of sensors, and smart fabrics, the continuous advances in SWS will progressively change the landscape of healthcare by allowing individual management and continuous monitoring of a patient's health status. Consisting of various components and devices, ranging from sensors and actuators to multimedia devices, these systems support complex healthcare applications and enable low-cost wearable, non-invasive alternatives for continuous 24-h monitoring of health, activity, mobility, and mental status, both indoors and outdoors. Our objective has been to examine the current research in wearable to serve as references for researchers and provide perspectives for future research.MethodsHerein, we review the current research and development of and the challenges facing SWS for HM, focusing on multi-parameter physiological sensor systems and activity and mobility measurement system designs that reliably measure mobility or vital signs and integrate real-time decision support processing for disease prevention, symptom detection, and diagnosis. For this literature review, we have chosen specific selection criteria to include papers in which wearable systems or devices are covered.ResultsWe describe the state of the art in SWS and provide a survey of recent implementations of wearable health-care systems. We describe current issues, challenges, and prospects of SWS.ConclusionWe conclude by identifying the future challenges facing SWS for HM.     

Diagnostic accuracy of rapid nucleic acid tests for group A streptococcal pharyngitis: systematic review and meta-analysis

BackgroundAcute pharyngitis is one of the most common conditions in outpatient settings and an important source of inappropriate antibiotic prescribing. Rapid antigen detection tests (RADTs) offer diagnosis of group A streptococcus at the point of care but have limited sensitivity. Rapid nucleic acid tests (RNATs) are now available; a systematic review of their accuracy is lacking.ObjectivesTo evaluate the accuracy of RNATs in patients with pharyngitis; to explore test-level and study-level factors that could explain variability in accuracy; and to compare the accuracy of RNATs with that of RADTs.Data sourcesMEDLINE, Embase, Web of Science (1990–2020).Study eligibility criteriaCross-sectional studies and randomized trials.ParticipantsPatients with pharyngitis.Index test/s and reference standardsRNAT commercial kits compared with throat culture.MethodsWe assessed risk of bias and applicability using QUADAS-2. We performed meta-analysis of sensitivity and specificity using the bivariate random-effects model. Variability was explored by subgroup analyses and meta-regression.ResultsWe included 38 studies (46 test evaluations; 17 411 test results). RNATs were most often performed in a laboratory. The overall methodological quality of primary studies was uncertain because of incomplete reporting. RNATs had a summary sensitivity of 97.5% (95% CI 96.2%–98.3%) and a summary specificity of 95.1% (95% CI 93.6%–96.3%). There was low variability in estimates across studies. Variability in sensitivity and specificity was partially explained by test type (p < 0.05 for both). Sensitivity analyses limited to studies with low risk of bias showed robust accuracy estimates. RNATs were more sensitive than RADTs (13 studies; 96.8% versus 82.3%, p 0.004); there was no difference in specificity (p 0.92).ConclusionsThe high diagnostic accuracy of RNATs may allow their use as stand-alone tests to diagnose group A streptococcus pharyngitis. Based on direct comparisons, RNATs have greater sensitivity than RADTs and equal specificity. Further studies should evaluate RNATs in point-of-care settings.     

融合CNN和BiLSTM的心律失常心拍分类模型

为更加准确地从动态心电中提取异常心拍,设计一种融合卷积神经网络(CNN)和多层双边长短时记忆网络(BiLSTM)的心律失常心拍分类模型。心电信号首先被分割成0.75 s和4 s两种不同尺度大小的心拍信号,然后利用11层CNN网络和3层BiLSTM网络分别对小/大尺度心拍信号进行特征提取与合并,并使用3层全连接网络对合并特征进行降维,最后利用softmax函数实现分类。针对MIT心律失常数据库异常心拍类型分布不均衡的问题,采用添加随机运动噪声和基线漂移噪声的样本扩展方法,降低模型的过拟合。采用基于患者的5折交叉检验进行模型验证。MIT心律失常数据库116 000个心拍的分类结果表明:所建立的模型针对4类心拍(正常、房性早搏、室性早搏、未分类)的识别准确率为90.42%,比单独使用CNN(76.45%)和BiLSTM(83.28%)的模型分别提高13.97%和7.14%。所提出的融合CNN和BiLSTM的心律失常心拍分类模型,相比单一基于CNN模型或者BiLSTM模型的机器学习算法,有更好的异常心拍分类准确率。