Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure

Photoplethysmography (PPG) is a non-invasive optical way of measuring variations in blood volume and perfusion in the tissue, used in pulse oximetry for instance. Respiratory-induced intensity variations (RIIVs) in the PPG signal exist, but the physiological background is not fully understood. Respiration causes variations in the blood volume in the peripheral vascular bed. It was hypothesised that the filling of peripheral veins is one of the important factors involved. In 16 healthy subjects, the respiratory synchronous variations from a PPG reflection mode signal and the peripheral venous pressure (PVP) were recorded. Variations of tidal volume, respiratory rate and contribution from abdominal and thoracic muscles gave significant and similar amplitude changes in both RIIV and the respiratory variation of PVP (p<0.01). The highest amplitudes of both signals were found at the largest tidal volume, lowest respiratory rate and during mainly thoracic breathing, respectively. The coherence between PVP and RIIV signals was high, the median (quartile range) being 0.78 (0.42). Phase analysis showed that RIIV was usually leading PVP, but variations between subjects were large. Although respiratory-induced variations in PVP and PPG showed a close correlation in amplitude variation, a causal relationship between the signals could not be demonstrated.     

Photoplethysmographic derivation of respiratory rate: a review of relevant physiology

An abnormal respiratory rate is often the earliest sign of critical illness. A reliable estimate of respiratory rate is vital in the application of remote telemonitoring systems, which may facilitate early supported discharge from hospital or prompt recognition of physiological deterioration in high-risk patient groups. Traditional approaches use analysis of respiratory sinus arrhythmia from the electrocardiogram (ECG), but this phenomenon is predominantly limited to the young and healthy. Analysis of the photoplethysmogram (PPG) waveform offers an alternative means of non-invasive respiratory rate monitoring, but further development is required to enable reliable estimates. This review conceptualizes the challenge by discussing the effect of respiration on the PPG waveform and the key physiological mechanisms that underpin the derivation of respiratory rate from the PPG.     

Deriving respiration from photoplethysmographic pulse width

A method for deriving respiration from the pulse photoplethysmographic (PPG) signal is presented. This method is based on the pulse width variability (PWV), and it exploits the respiratory information present in the pulse wave velocity and dispersion. It allows to estimate respiration signal from only a pulse oximeter which is a cheap and comfortable sensor. Evaluation is performed over a database containing electrocardiogram (ECG), blood pressure (BP), PPG, and respiratory signals simultaneously recorded in 17 subjects during a tilt table test. Respiratory rate estimation error is computed obtaining of 1.27 ± 7.81 % (0.14 ± 14.78 mHz). For comparison purposes, we have also obtained a respiratory rate estimation from other known methods which involve ECG, BP, or also PPG signals. In addition, we have also combined respiratory information derived from different methods which involve only PPG signal, obtaining a respiratory rate error of ?0.17 ± 6.67 % (?2.16 ± 12.69 mHz). The presented methods, PWV and combination of PPG derived respiration methods, avoid the need of ECG to derive respiration without degradation of the obtained estimates, so it is possible to have reliable respiration rate estimates from just the PPG signal.     

Data-dependent filter characteristics of peak-valley respiratory sinus arrhythmia estimation: A cautionary note

Filter characteristics of the peak-valley respiratory sinus arrhythmia estimation method are described. To identify filter characteristics of this method, models were generated that combined signals of different frequencies with trends of varying slopes. These models simulate the influence of trend and changing respiratory frequency on the accuracy of peak-valley estimates. The transfer function of the peak-valley method, unlike that of other time domain filters, is not solely dependent upon signal frequency. Two factors interact to determine the relative accuracy of the peak-valley method: (a) slope of the signal component and (b) slope of the underlying trend. Combinations of these factors may result in significant distortion to the input signal. The direction of error is a function of the direction of the trend (i.e., overestimation with deceleration and underestimation with acceleration). In many situations when respiratory sinus arrhythmia amplitude is low in special populations (e.g., cardiovascular disorders, high-risk infants, or human fetuses) or under conditions that greatly reduce respiratory sinus arrhythmia amplitude (e.g., exercise, drugs, pharmacological manipulations), use of the peak-valley method may result in significant measurement error. The use of this method to evaluate respiratory sinus arrhythmia over short epochs (i.e., less than 2 min) or to quantify changes in respiratory sinus arrhythmia due to discrete stimulation (e.g., breath by breath) may result in inconsistent measurement error. Recommendations are made for detrending heart rate data prior to application of the peak-valley method.     

基于深度学习的指-桡端脉搏波信号转换方法

针对目前市面上大多数脉搏波检测仪器检测的是指端脉搏波信号,提出一种基于卷积神经网络的指-桡端脉搏波信号转换方法,在仅获取指端脉搏波信号的情况下得到对应的桡动脉脉搏波信号。该方法主要由一维卷积神经网络通过端到端的训练实现,模型包含编码器、解码器和跳跃连接3个部分,通过编码器网络提取指端脉搏波信号的特征,再通过解码器网络将特征图进行扩展,并且利用跳跃连接的方式实现特征图的融合。采集60份指端和桡端的脉搏波信号进行实验,并与传递函数模型和弹性腔模型进行对比。实验结果表明,该模型转换所得的桡端脉搏波信号在MAE和PRD的指标上分别达到1.4%±0.3%和3.6%±1.2%,优于其他模型。研究表明,该模型能够较精确地实现指端脉搏波信号到桡端脉搏波信号的转化。     

Estimation of respiratory volumes from the photoplethysmographic signal. Part 2: a model study

A Windkessel model has been constructed with the aim of investigating the respiratory-volume dependence of the photoplethysmographic (PPG) signal. Experimental studies show a correlation between respiratory volume and the peak-to-peak value of the respiratory-induced intensity variations (RIIV) in the PPG signal. The model compartments are organised in two closed chambers, representing the thorax and the abdomen, and in a peripheral part not directly influenced by respiration. Cardiac pulse and respiration are created by continuous adjustment of the pressures in the affected compartments. Together with the criteria for heart and venous valves, the model is based on a set of 17 differential equations. These equations are solved for varying thoracic and abdominal pressures corresponding to different respiratory volumes. Furthermore, a sensitivity analysis is performed to evaluate the properties of the model. The PPG signals are created as a combination of peripheral blood flow and pressure. From these signals, the respiratory synchronous parts are extracted and analysed. To study some important limitations of the model, respiratory type and rate are varied. From the simulations, it is possible to verify our earlier experimental results concerning the relationship between respiratory volume and the peak-to-peak value of the RIIV signal. An expected decrease in the amplitude of the respiratory signal with increased respiratory rate is also found, which is due to the lowpass characteristics of the vessel system. Variations in the relationship between thoracic and abdominal respiration also affect the RIIV signal. The simulations explain and verify what has been found previously in experimental studies.     

Estimation of respiratory volumes from the photoplethysmographic signal. Part I: experimental results

To evaluate the possibility of respiratory-volume measurement using photoplethysmography (PPG), PPG signals from 16 normal volunteers are collected, and the respiratory-induced intensity variations (RIIV) are digitally extracted. The RIIV signals are studied while reepiratory volume is varied. Furthermore, respiratory rate, body posture and type of respiration are varied. A Fleisch pneumotachograph is used as the inspired volume reference. The RIIV and pneumotachography signals are compared, and a statisical analysis is performed (linear regression and t-tests). The key idea is that the amplitude of the RIIV signal is related to the respiratory volume. The conclusion from the measurements is that there exists a relationship between the amplitude of the RIIV signal and the respiratory volume (R=0.842, s=0.428, p<0.005). Absolute measurements of the respiratory volume are not possible from the RIIV signal with the present set-up. The RIIV signal also seems to be affected by respiratory rate and type. More knowledge about respiratory parameters and improved sensor and filter design are required to make absolute measurements of volumes possible.     

基于脉搏波的呼吸频率计算方法

为了在可穿戴医疗领域中快速检测出人体的呼吸频率,提出一种基于光电容积脉搏波的呼吸频率计算方法。首 先,通过MIMIC Database数据集获取人体同时段的脉搏波信号与呼吸波信号;其次,通过对脉搏波信号运行经验模态分 解算法,从而获得脉搏波信号的有限个本征模态函数,再选取合适的本征模态函数重构呼吸波信号;最后,通过对重构的 呼吸波信号进行特征提取,计算出呼吸频率。结果表明:经过脉搏波分解得到的呼吸信号与原始呼吸信号的相对相干系 数在0.6以上,呼吸频率也十分接近,准确率高达0.9以上。说明通过光电容积脉搏波信号计算呼吸频率的可行性,这对于 可穿戴医疗领域、无创医疗诊断具有重要意义。     

Influence of inspiratory threshold load on cardiovascular responses to controlled breathing at 0.1 Hz

Slow, deep breathing is being used as a self‐management intervention for various health conditions including pain and hypertension. Stimulation of the arterial baroreceptors and increased vagal modulation are among the proposed mechanisms for the therapeutic effects of slow, deep breathing. We investigated whether adding inspiratory threshold load can enhance the cardiovascular responses to controlled breathing at the frequency of 0.1 Hz, a common form of slow, deep breathing. Healthy volunteers (N = 29) performed controlled breathing at 0.1 Hz (6 breaths/minute) without load and with inspiratory threshold loads of 5 cmH₂O and 10 cmH₂O. Respiratory airflow, heart rate, and blood pressure were continuously recorded. The amplitude of the systolic blood pressure variation during respiratory cycles increased with increasing loads. Respiratory sinus arrhythmia was higher during controlled breathing at 0.1 Hz with the load of 10 cmH₂O compared to without load. Baroreflex sensitivity was not affected by loads. The effect of loads on respiratory sinus arrhythmia was mediated by increasing the amplitude of systolic blood pressure variation during respiratory cycles. These results suggest that applying small inspiratory threshold loads during controlled breathing at 0.1 Hz increases cardiac vagal modulation by this breathing exercise. This effect seems to be mediated by stronger stimulation of the arterial baroreceptors because of larger systolic blood pressure swings along the respiratory cycle. The potential benefit of long‐term practice of controlled breathing at 0.1 Hz with inspiratory threshold loads on baroreflex function and cardiac vagal control needs to be investigated, particularly in pain and hypertension patients.     

Cardiac regulation in the socially monogamous prairie vole

Social experiences, both positive and negative, may influence cardiovascular regulation. Prairie voles (Microtus ochrogaster) are socially monogamous rodents that form social bonds similar to those seen in primates, and this species may provide a useful model for investigating neural and social regulation of cardiac function. Cardiac regulation has not been studied previously in the prairie vole. Radiotelemetry transmitters were implanted into adult female prairie voles under anesthesia, and electrocardiographic parameters were recorded. Autonomic blockade was performed using atenolol (8 mg/kg ip) and atropine methyl nitrate (4 mg/kg ip). Several variables were evaluated, including heart rate (HR), HR variability and the amplitude of respiratory sinus arrhythmia. Sympathetic blockade significantly reduced HR. Parasympathetic blockade significantly increased HR, and reduced HR variability and the amplitude of respiratory sinus arrhythmia. Combined autonomic blockade significantly increased HR, and reduced HR variability and respiratory sinus arrhythmia amplitude. The data indicate that autonomic function in prairie voles shares similarities with primates, with a predominant vagal influence on cardiac regulation. The current results provide a foundation for studying neural and social regulation of cardiac function during different behavioral states in this socially monogamous rodent model.     

Spectra of data sampled at frequency-modulated rates in application to cardiovascular signals: Part 1 analytical derivation of the spectra

Beat-to-beat cardiovascular signals, e.g. a series of systolic pressure values, can be considered as time series which are pulse amplitude modulated (PAM) and pulse frequency modulated (PFM). The latter process, due to variations in heart rate, causes the series to become non-uniformly spaced in time. If PAM is to be quantified by spectral analysis, the influence of PFM must be known. An analytical expression is therefore derived for the spectrum of sinusoids which are sampled according to the output event series of a linear integral pulse frequency modulator (IPFM). We conclude that two spectral components arise at the difference and sum of the PFM and PAM frequencies, f_p±f_x, with amplitudes proportional to the PFM modulation depth. These components appear as a DC component and as a first harmonic if both modulating frequencies are equal. In addition, a cluster of spectral components appears around the mean pulse frequency f_o (i.e. mean heart rate), at frequencies f_o-nf_p±f_x, which may leak into the signal band. From these theoretical considerations, we conclude that the amplitude spectrum of a sinusoidally varying systolic blood pressure series can contain up to 20–30% spurious components, owing to the heart rate modulation process.     

Cardio-respiratory coupling depends on the pons

Cardio-respiratory coupling is reciprocal; it is expressed as respiratory-modulated sympathetic nerve activity and pulse-modulated respiratory motor activity. In the brainstem, the neuraxis controlling cardio-respiratory functions forms a ventrolateral cell column which extends to the dorsolateral (dl) pons. Our general working hypothesis is that these control systems converge at points with the common purpose of gas exchange and that neural activity along this axis coordinates both arterial pulse pressure and breathing. Here, we review the data showing that pontine nuclei modulate heart rate, blood pressure and breathing, and present new results demonstrating a vagal influence on pontine activity modulated with both arterial pulse pressure and phrenic nerve activity in the decerebrate cat. Generally with the vagi intact, dl pontine activity was weakly modulated by both arterial pulse pressure and respiratory pattern. After bilateral vagotomy, the strength and consistency of respiratory modulation increased significantly, although the strength and consistency of arterial pulse pressure modulation did not change significantly for the group; a decrease in some (62%) was offset by an increase in others (36%) neurons. Thus, the vagus shapes the envelope of the cycle-triggered averages of neural activity for both the respiratory and cardiac cycles. These data provide insight into the neural substrate for the prominent vagal effect on the cardio-respiratory coupling pattern, in particular respiratory sinus arrhythmia. While these results support convergence of inputs to neural populations controlling breathing and cardiovascular functions, the physiologic role of balancing ventilation, vascular resistance, heart rate and blood flow for the benefit of tissue oxygenation, remains hypothetical.     

Investigation of photoplethysmographic signals and blood oxygen saturation values on healthy volunteers during cuff-induced hypoperfusion using a multimode PPG/SpO2 sensor

Photoplethysmography (PPG) is a technique widely used to monitor volumetric blood changes induced by cardiac pulsations. Pulse oximetry uses the technique of PPG to estimate arterial oxygen saturation values (SpO?). In poorly perfused tissues, SpO? readings may be compromised due to the poor quality of the PPG signals. A multimode finger PPG probe that operates simultaneously in reflectance, transmittance and a combined mode called "transreflectance" was developed, in an effort to improve the quality of the PPG signals in states of hypoperfusion. Experiments on 20 volunteers were conducted to evaluate the performance of the multimode PPG sensor and compare the results with a commercial transmittance pulse oximeter. A brachial blood pressure cuff was used to induce artificial hypoperfusion. Results showed that the amplitude of the transreflectance AC PPG signals were significantly different (p < 0.05) than the AC PPG signals obtained from the other two conventional PPG sensors (reflectance and transmittance). At induced brachial pressures between 90 and 135 mmHg, the reflectance finger pulse oximeter failed 25 times (failure rate 42.2 %) to estimate SpO? values, whereas the transmittance pulse oximeter failed 8 times (failure rate 15.5 %). The transreflectance pulse oximeter failed only 3 times (failure rate 6.8 %) and the commercial pulse oximeter failed 17 times (failure rate 29.4 %).     

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.     

Unconstrained detection of respiration rhythm and pulse rate with one under-pillow sensor during sleep

A completely non-invasive and unconstrained method is proposed to detect respiration rhythm and pulse rate during sleep. By employing wavelet transformation (WT), waveforms corresponding to the respiration rhythm and pulse rate can be extracted from a pulsatile pressure signal acquired by a pressure sensor under a pillow. The respiration rhythm was obtained by an upward zero-crossing point detection algorithm from the respiration-related waveform reconstructed from the WT 2⁶ scale approximation, and the pulse rate was estimated by a peak point detection algorithm from the pulse-related waveform reconstructed from the WT 2⁴ and 2⁵ scale details. The finger photo-electric plethysmogram (FPP) and nasal thermistor signals were recorded simultaneously as reference signals. The reference pulse rate and respiration rhythm were detected with the peak and upward zero-crossing point detection algorithm. This method was verified using about 24 h of data collected from 13 healthy subjects. The results showed that, compared with the reference data, the average error rates were 3.03% false negative and 1.47% false positive for pulse rate detection in the extracted pulse waveform. Similarly, 4.58% false negative and 3.07% false positive were obtained for respiration rhythm detection in the extracted respiration waveform. This study suggests that the proposed method is suitable, in sleep monitoring, for the diagnosis of sleep apnoea or sudden death syndrome.     

Automated recognition of corrupted arterial waveforms using neural network techniques.

A data acquisition system that automatically discards corrupted or undesirable signals would save untold hours of drudgery for researchers. Continuous recording of variables to provide detailed behavior patterns generates huge amounts of raw data. Unfortunately waveforms usually require visual inspection for isolating desired behavior or validating signal integrity. This tedious and time-consuming step can potentially be eliminated using a novel computer science technique. We have trained a simulated neural network to recognize corrupted arterial pressure waveforms. Our system can now evaluate the validity of the arterial waveform without human intervention with an average false positive error rate of 2.2% and an average false negative error rate of 12.6%.     

血压变异性和容积脉搏波特征参数变异性的关联分析

目的 分析血压变异性和容积脉搏波特征参数变异性的相关性,确定相关性较好的参数,为利用容积脉搏波特征参数的变异性来估计血压变异性提供理论依据.方法 同步连续采集运动前后19个样本的容积脉搏波信号和血压信号,根据容积脉搏波信号的波形特征,提取8个特征参数,利用皮尔森相关系数分析血压变异性和容积脉搏波特征参数变异性在时域的相...     

Assessment of autonomic function by the spectral analysis of heart rate variability: an examination in a mirror drawing task]

The effect of an experimental task on autonomic function was investigated by spectral analysis of heart rate variability in 13 male college students. Power spectral density of heart rate variability has been said to contain two significant components: respiratory sinus arrhythmia (RSA) as an index of cardiac vagal activity, and Mayer wave related sinus arrhythmia (MWSA) as an index of sympathetic activity with vagal modulation. Those two components were examined during a task of mirror drawing on the CRT. In order to eliminate the effect of respiratory rate on RSA, the respiratory rate was controlled at 15 breaths/min. Furthermore, the coefficient of variance of R-R interval (CV-RR) and the fluctuation of plethysmograph (PTG) were calculated simultaneously. Results indicated that, while RSA decreased significantly, MWSA did not change during the task. On the other hand, neither CV-RR nor PTG showed any significant differences during the task. These findings indicated that cardiac parasympathetic activity was diminished in the mirror drawing task. The significance of spectral analysis of heart rate variability were discussed.     

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。     

Respiratory rate estimation during triage of children in hospitals

Abstract

Accurate assessment of a child’s health is critical for appropriate allocation of medical resources and timely delivery of healthcare in Emergency Departments. The accurate measurement of vital signs is a key step in the determination of the severity of illness and respiratory rate is currently the most difficult vital sign to measure accurately. Several previous studies have attempted to extract respiratory rate from photoplethysmogram (PPG) recordings. However, the majority have been conducted in controlled settings using PPG recordings from healthy subjects. In many studies, manual selection of clean sections of PPG recordings was undertaken before assessing the accuracy of the signal processing algorithms developed. Such selection procedures are not appropriate in clinical settings. A major limitation of AR modelling, previously applied to respiratory rate estimation, is an appropriate selection of model order. This study developed a novel algorithm that automatically estimates respiratory rate from a median spectrum constructed applying multiple AR models to processed PPG segments acquired with pulse oximetry using a finger probe. Good-quality sections were identified using a dynamic template-matching technique to assess PPG signal quality. The algorithm was validated on 205 children presenting to the Emergency Department at the John Radcliffe Hospital, Oxford, UK, with reference respiratory rates up to 50 breaths per minute estimated by paediatric nurses. At the time of writing, the authors are not aware of any other study that has validated respiratory rate estimation using data collected from over 200 children in hospitals during routine triage.