Neural network for photoplethysmographic respiratory rate monitoring

The reflection mode photoplethysmographic (PPG) signal was studied with the aim of determining respiratory rate. The PPG signal includes respiratory synchronous components, seen as frequency modulation of the heart rate (respiratory sinus arrhythmia), amplitude modulation of the cardiac pulse and respiratory-induced intensity variations (RIIVs) in the PPG baseline. PPG signals were recorded from the foreheads of 15 healthy subjects. From these signals, the systolic wavefrm diastolic waveform, respiratory sinus arrhythmia, pulse amplitude and RIIVs were extracted. Using basic algorithms, the rates of false positive and false negative detection of breaths were calculated separately for each of the five components. Furthermore, a neural network was assessed in a combined pattern recognition approach. The error rates (sum of false positive and false negative breath detections) for the basic algorithms ranged from 9.7% (pulse amplitude) to 14.5% (systolic waveform). The corresponding values for the neural network analysis were 9.5–9.6%. These results suggest the use of a combined PPG system for simultaneous monitoring of respiratory rate and arterial oxygen saturation (pulse oximetry).     

Novel Algorithm to Suppress Random Pulse Interference in Spikes

Spikes detection and sorting play an important role in study of neural information coding. Spikes were generally obtained by threshold detection after filtered in traditional detection, which failed to suppress the random pulse interference(RPI), In this paper, a novel algorithm was provided to suppress RPI using integrated feature. The raw neural signals from the primary visual cortex in rats were detected with microelectrode array. After the feature differences between spikes and RPls were compared, the features which include waveform and non-waveform features were extracted respectively, and then the integrated feature was established based on Fisher's discrimi nant ratio to separate between spikes and RPls. The test results of simulation and experiment show that the separability capability of the integrated feature is nearly two times greater than the individual feature, the average correct recognition rate of spikes and RPls is over 93%, and the detection rate of spike is effectively improved.     

ECG signal analysis for the assessment of sleep-disordered breathing and sleep pattern

The diagnosis of sleep-disordered breathing (SDB) usually relies on the analysis of complex polysomnographic measurements performed in specialized sleep centers. Automatic signal analysis is a promising approach to reduce the diagnostic effort. This paper addresses SDB and sleep assessment solely based on the analysis of a single-channel ECG recorded overnight by a set of signal analysis modules. The methodology of QRS detection, SDB analysis, calculation of ECG-derived respiration curves, and estimation of a sleep pattern is described in detail. SDB analysis detects specific cyclical variations of the heart rate by correlation analysis of a signal pattern and the heart rate curve. It was tested with 35 SDB-annotated ECGs from the Apnea-ECG Database, and achieved a diagnostic accuracy of 80.5%. To estimate sleep pattern, spectral parameters of the heart rate are used as stage classifiers. The reliability of the algorithm was tested with 18 ECGs extracted from visually scored polysomnographies of the SIESTA database; 57.7% of all 30 s epochs were correctly assigned by the algorithm. Although promising, these results underline the need for further testing in larger patient groups with different underlying diseases.     

Pulse wave transit time for monitoring respiration rate

In this study, we investigate the beat-to-beat respiratory fluctuations in pulse wave transit time (PTT) and its subcomponents, the cardiac pre-ejection period (PEP) and the vessel transit time (VTT) in ten healthy subjects. The three transit times were found to fluctuate in pace with respiration. When applying a simple breath detecting algorithm, 88% of the breaths seen in a respiration air-flow reference could be detected correctly in PTT. Corresponding numbers for PEP and VTT were 76 and 81%, respectively. The performance during hypo- and hypertension was investigated by invoking blood pressure changes. In these situations, the error rates in breath detection were significantly higher. PTT can be derived from signals already present in most standard monitoring set-ups. The transit time technology thus has prospects to become an interesting alternative for respiration rate monitoring.     

Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings

Sleep apnoea is a common disorder that is usually diagnosed through expensive studies conducted in sleep laboratories. Sleep apnoea is accompanied by a characteristic cyclic variation in heart rate or other changes in the waveform of the electrocardiogram (ECG). If sleep apnoea could be diagnosed using only the ECG, it could be possible to diagnose sleep apnoea automatically and inexpensively from ECG recordings acquired in the patient's home. This study had two parts. The first was to assess the ability of an overnight ECG recording to distinguish between patients with and without apnoea. The second was to assess whether the ECG could detect apnoea during each minute of the recording. An expert, who used additional physiological signals, assessed each of the recordings for apnoea. Research groups were invited to access data via the world-wide web and submit algorithm results to an international challenge linked to a conference. A training set of 35 recordings was made available for algorithm development, and results from a test set of 35 different recordings were made available for independent scoring. Thirteen algorithms were compared. The best algorithms made use of frequency-domain features to estimate changes in heart rate and the effect of respiration on the ECG waveform. Four of these algorithms achieved perfect scores of 100% in the first part of the study, and two achieved an accuracy of over 90% in the second part of the study.     

基于超声回波信号的指端脉搏波提取

脉搏波可作为检测人体心血管系统生理病理状态的重要依据。为了验证用超声波测量脉搏波的可能、解决脉搏波的测量部位受限的问题,本研究提出一种从超声回波信号中提取脉搏波的方法。设计一种跟随式超声传感器,用数据采集系统采集指端超声回波信号,经过滤波、选点及小波去噪等处理后得到较为纯净的脉搏波信号;同时采集心电信号以及光电容积脉搏波信号作为参考信号。结果表明,可以从提取的指端脉搏波中准确地获取心率;与同步测得的光电容积脉搏波数据相关系数大部分在0.8以上;波形中的重搏前波、重搏波等细节部分也能明显地表现出来。本研究提出的方法实现了从指端超声回波信号中获取完整可靠的脉搏波信号,为日后获取不同部位的脉搏信号提供了基础。     

Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders

Sleep apnea is one of the most common sleep disorders. Here, patients suffer from multiple breathing pauses longer than 10 s during the night which are referred to as apneas. The standard method for the diagnosis of sleep apnea is the attended cardiorespiratory polysomnography (PSG). However, this method is expensive and the extensive recording equipment can have a significant impact on sleep quality falsifying the results. To overcome these problems, a comfortable and novel system for sleep monitoring based on the recording of tracheal sounds and movement data is developed. For apnea detection, a unique signal processing method utilizing both signals is introduced. Additionally, an algorithm for extracting the heart rate from body sounds is developed. For validation, ten subjects underwent a full-night PSG testing, using the developed sleep monitor in concurrence. Considering polysomnography as gold standard the developed instrumentation reached a sensitivity of 92.8% and a specificity of 99.7% for apnea detection. Heart rate measured with the proposed method was strongly correlated with heart rate derived from conventional ECG (r ² = 0.8164). No significant signal losses are reported during the study. In conclusion, we demonstrate a novel approach to reliably and noninvasively detect both apneas and heart rate during sleep.     

Pulse onset detection using neighbor pulse-based signal enhancement

Detecting onsets of cardiovascular pulse wave signals is an important prerequisite for successfully conducting various analysis tasks involving the concept of pulse wave velocity. However, pulse onsets are frequently influenced by inherent noise and artifacts in signals continuously acquired in a clinical environment. The present work proposed and validated a neighbor pulse-based signal enhancement algorithm for reducing error in the detected pulse onset locations from noise-contaminated pulsatile signals. Pulse onset was proposed to be detected using the first principal component extracted from three adjacent pulses. This algorithm was evaluated using test signals constructed by mixing arterial blood pressure, cerebral blood flow velocity and intracranial pressure pulses recorded from neurosurgical patients with white noise of various levels. The results showed that the proposed pulse enhancement algorithm improved (p<0.05) pulse onset detection according to all three different onset definitions and for all three types of pulsatile signals as compared to results without using the pulse enhancement. These results suggested that the proposed algorithm could help achieve robustness in pulse onset detection and facilitate pulse wave analysis using clinical recordings.     

基于聚偏氟乙烯薄膜的穿戴式呼吸检测系统

【摘 要】 针对人体呼吸信号的特点设计一种基于聚偏氟乙烯（PVDF）压电薄膜材料的可穿戴式呼吸检测系统。人体呼吸时PVDF薄膜受力产生的感应电荷较少,经信号调理电路将电荷量转换成电压量。单片机通过模数转换器获得呼吸信号数据,通过蓝牙发送给上位机。上位机从获得的数据中提取呼吸波形,并进行平滑滤波、自适应双阈值来计算呼吸率。试验结果表明,本系统可以实时准确地检测出人体的呼吸波形,呼吸次数识别的准确率在90%以上,可以满足人体呼吸监护的需求。     

Algorithm for the classification of multi-modulating signals on the electrocardiogram

This article discusses the algorithm to measure electrocardiogram (ECG) and respiration simultaneously and to have the diagnostic potentiality for sleep apnoea from ECG recordings. The algorithm is composed by the combination with the three particular scale transform of a(j)(t), u(j)(t), o(j)(a(j)) and the statistical Fourier transform (SFT). Time and magnitude scale transforms of a(j)(t), u(j)(t) change the source into the periodic signal and tau(j) = o(j)(a(j)) confines its harmonics into a few instantaneous components at tau(j) being a common instant on two scales between t and tau(j). As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals by inverse transform. The algorithm is expected to get the partial ventilation and the heart rate variability from scale transforms among a(j)(t), a(j+1)(t) and u(j+1)(t) joining with each modulation. The algorithm has a high potentiality of the clinical checkup for the diagnosis of sleep apnoea from ECG recordings.     

Research of fetal ECG extraction using wavelet analysis and adaptive filtering

Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.     

基于心电和脉搏波数据融合的呼吸率估计

本研究提出了一种通过心电和脉搏波提取呼吸信号并基于卡尔曼滤波的多路数据融合估计呼吸率的算法。算法分别从心电的RR间期、R波的绝对高度和脉搏波搏动周期中提取呼吸信号,利用AR模型估计呼吸率,根据信号波形、节律和频谱特征获得反映信号质量高低的质量指数,然后基于信号质量指数和卡尔曼滤波残差进行数据融合,获得融合呼吸率。14名志愿者参加了实验。结果表明,融合呼吸率比单独从心电或脉搏信号提取的呼吸率更好地反映了呼吸率的变化。与压阻式呼吸传感器提供的参考呼吸率相比,融合呼吸率误差为(-0.03±2.78)次/min,而从心电RR间期、R波的绝对高度和脉搏法提取的呼吸率的误差分别为(0.62±3.30)、(0.42±3.47)和(-0.17±2.69)次/min。总体认为,基于多路数据融合的方法可以有效避免干扰的影响,较准确地估计呼吸率。     

An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) in home healthcare is proposed. The whole system consists of two-lead electrocardiogram acquisition using conductive textile electrodes located in bed, baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problems of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we are proposing a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 male subjects, and we obtained satisfactory respiration signals that showed high correlation (r ² > 0.8) with the signal acquired from the chest-belt respiration sensor.     

基于指端脉搏波视频信号的心率稳定检测算法

目的:研究一种基于脉搏波的稳定检测心率的算法。 方法:提出一种基于快速独立成分分析（FastICA）算法处理指端脉搏波视频信号。首先通过手机摄像头采集手指视频,在每帧图片中提取感兴趣区域（ROI）,根据每个区域中像素灰度值的变化得到血液容积变化的时序曲线;然后通过对ROI进行RGB通道分离和FastICA后,分别选取红、绿色分量与盲源分离后的估计信号进行相关性分析,筛选出相关性最大的作为后续提取心率的信号,并与波峰法测得的心率进行对比,得到一种稳定的心率检测算法,并利用SPSS软件做相关性分析。 结果:选取R、G通道信号的一致性在95%以上,基于FastICA的算法与统计波峰法获取心率的一致性在95%以上。 结论:FastICA算法能够有效地提高心率测量的稳定性,实验结果验证了该方法的可行性和有效性,对于基于脉搏波的人体生理参数获取具有重要意义。     

Contactless Multiple Wavelength Photoplethysmographic Imaging: A First Step Toward “SpO<Subscript>2</Subscript> Camera” Technology

We describe a route toward contactless imaging of arterial oxygen saturation (SpO₂) distribution within tissue, based upon detection of a two-dimensional matrix of spatially resolved optical plethysmographic signals at different wavelengths. As a first step toward SpO₂-imaging we built a monochrome CMOS-camera with apochromatic lens and 3λ-LED-ringlight (λ₁ = 660 nm, λ₂ = 810 nm, λ₃ = 940 nm; 100 LEDs λ⁻¹). We acquired movies at three wavelengths while simultaneously recording ECG and respiration for seven volunteers. We repeated this experiment for one volunteer at increased frame rate, additionally recording the pulse wave of a pulse oximeter. Movies were processed by dividing each image frame into discrete Regions of Interest (ROIs), averaging 10 × 10 raw pixels each. For each ROI, pulsatile variation over time was assigned to a matrix of ROI-pixel time traces with individual Fourier spectra. Photoplethysmograms correlated well with respiration reference traces at three wavelengths. Increased frame rates revealed weaker pulsations (main frequency components 0.95 and 1.9 Hz) superimposed upon respiration-correlated photoplethysmograms, which were heartbeat-related at three wavelengths. We acquired spatially resolved heartbeat-related photoplethysmograms at multiple wavelengths using a remote camera. This feasibility study shows potential for non-contact 2-D imaging reflection-mode pulse oximetry. Clinical devices, however, require further development.     

Stacking集成学习算法验证动脉损伤对糖尿病早期检测的意义

背景:糖尿病可引起广泛的动脉结构和功能病理变化,导致动脉僵硬度增加、顺应性降低和动脉弹性降低。本研究从动脉损伤的角度,实现对尚未出现临床表现但有动脉损伤的糖尿病患者的早期检测。方法:动脉损伤会导致血管的力学参数发生变化,而脉搏信号的波形变化与心血管系统的力学参数变化密切相关。通过9级小波对糖尿病患者脉搏信号进行分解,提取cD8、cD7、cD6系数（中高频成分,代表信号细节特征）,作为能够反映动脉损伤程度的特征,将特征矩阵输入到10折交叉验证模型的Stacking集成学习模型中,设置第一层的4个基学习器为SVM、Random Forest、XGBoost、Extra Trees,第二层的元学习器是KNN。结果:单个机器学习模型可以达到90%以上的准确率。Stacking集成学习算法的准确率比单一机器学习模型高4%~5%,ROC曲线下面积提高1%~6%。结论:小波分解得到的脉搏信号cD8、cD7、cD6系数可以有效反映糖尿病引起的动脉损伤程度,因此动脉损伤对糖尿病的早期检测具有一定的指导意义。Stacking 集成学习算法将多个模型的优势结合起来生成一个新模型,可以获得比单一模型更好的性能。     

基于小波变换的自适应滤波器消除脉搏波基线漂移

脉诊仪的研制中,由于呼吸运动和身体移位导致脉搏信号的基线漂移是必须克服的.本文提出了一种新的基于Meyer小波变换的自适应滤波器,其参考输入选择原始信号经小波分解后的高频分量.通过仿真和实际测量实验表明,该方法能有效消除脉搏波的基线漂移.     

Comparison of pulse rate variability with heart rate variability during obstructive sleep apnea

We investigate whether pulse rate variability (PRV) extracted from finger photo-plethysmography (Pleth) waveforms can be the substitute of heart rate variability (HRV) from RR intervals of ECG signals during obstructive sleep apnea (OSA). Simultaneous measurements (ECG and Pleth) were taken from 29 healthy subjects during normal (undisturbed sleep) breathing and 22 patients with OSA during OSA events. Highly significant (p<0.01) correlations (1.0>r>0.95) were found between heart rate (HR) and pulse rate (PR). Bland-Altman plot of HR and PR shows good agreement (<5% difference). Comparison of 2 min recording epochs demonstrated significant differences (p<0.01) in time, frequency domains and complexity analysis, between normal and OSA events using PRV as well as HRV measures. Results suggest that both HRV and PRV indices could be used to distinguish OSA events from normal breathing during sleep. However, several variability measures (SDNN, RMSSD, HF power, LF/HF and sample entropy) of PR and HR were found to be significantly (p<0.01) different during OSA events. Therefore, we conclude that PRV provides accurate inter-pulse variability to measure heart rate variability under normal breathing in sleep but does not precisely reflect HRV in sleep disordered breathing.     

基于小波变换与形态学运算的ECG综合检测算法的研究

针对心电波形检测中小波变换算法的缺点 ,在 ECG特征点检测中 ,将原始信号在 3尺度上的 haar小波分解的细节信号模极大值对检测法与数学形态学峰谷检测相结合 ,提出了一种新的心电波形特征点综合检测算法 ,该算法弥补了小波变换算法对信号振幅检测上的不足 ,有效地提高了心电信号特征点检测的准确度。     

一种非侵入式人体睡眠期的监护方法

本文介绍了目前在睡眠监护中已受到广泛关注的监测方法——“枕头”传感器方法。枕头传感器是一种安放在枕头底下的压力传感器,它利用颈后压力信号的变化与人体呼吸运动和心脏跳动一致的规律来进行检测,并将检测到的压力信号经过A／D转换再经微机处理,再采用适当的算法提取呼吸节律和脉搏速率。实验数据证明了利用枕头传感器对人体睡眠期呼吸节律和脉搏速率进行实时监控的有效性。