Respiration can be monitored by photoplethysmography with high sensitivity and specificity regardless of anaesthesia and ventilatory mode

Rapid Vital Sign Extraction for Real-Time Opto-Physiological Monitoring at Varying Physical Activity Intensity Levels

Robustness of physiological parameters obtained from photoplethysmographic (PPG) signals is highly dependent on a signal quality that is often affected by the motion artefacts (MAs) generated during physical activity. This study aims to suppress MAs and obtain reliable physiological readings using the part of the pulsatile signal, captured by a multi-wavelength illumination optoelectronic patch sensor (mOEPS), that minimizes the residual between the measured signal and the motion estimates obtained from an accelerometer. The minimum residual (MR) method requires the simultaneous collection of (1) multiple wavelength data from the mOEPS, and (2) motion reference signals from a triaxial accelerometer attached to the mOEPS. The MR method suppresses those frequencies associated with motion in a manner that is easily embedded on a microprocessor. The performance of the method in reducing both in-band and out-of-band frequencies of MAs is evaluated through two protocols with 34 subjects engaged in the study. The MA-suppressed PPG signal, obtained through MR, enables the calculation of the heart rate (HR) with an average absolute error of 1.47 beats/min for the IEEE-SPC datasets, and the calculation of HR and respiration rate (RR) to 1.44 beats/min and 2.85 breaths/min respectively for our in-house datasets. Oxygen saturation (SpO 2) levels calculated from the minimum residual wave forms were consistently [Formula: see text]. The comparison with the reference HR and RR show errors with an absolute accuracy of [Formula: see text] and the Pearson correlation ( R) for HR and RR are 0.9976 and 0.9118, respectively. These outcomes demonstrate that MR is capable of effective suppression of MAs for a range of physical activity intensities and to achieve real-time signal processing for wearable health monitoring.

Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates

The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the “gold-standard” signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction.

Wavelet and pain rating index for inhalation anesthesia: A randomized controlled trial

BACKGROUND

Wavelet index (WLi) and pain rating index (PRi) are new parameters for regulating general anesthesia depth based on wavelet analysis.

AIM

To investigate the safety and efficacy of using WLi or PRi in sevoflurane anesthesia.

METHODS

This randomized controlled trial enrolled 66 patients scheduled for elective posterior lumbar interbody fusion surgery under sevoflurane anesthesia between September 2017 and February 2018. A random number generator was used to assign the eligible patients to three groups: Systolic blood pressure (SBP) monitoring group, WLi monitoring group, and PRi monitoring group. The main anesthesiologist was aware of the patient grouping and intervention used. The primary endpoint was anesthesia recovery time. Secondary endpoints included extubation time, sevoflurane consumption, number of unwanted events/ interventions, number of adverse events and postoperative visual analogue scale for pain.

RESULTS

A total of 62 patients were included in the final analysis (SBP group, n = 21; WLi group, n = 21; and PRi group, n = 20). There were no significant differences among the three groups in patient age, gender distribution, body mass index, American Society of Anesthesiologists class, duration of surgery, or duration of anesthesia. Anesthesia recovery time was shorter in the WLi and PRi groups than in the SBP group with no significant difference between the WLi and PRi groups. Extubation time was shorter in the WLi and PRi groups than in the SBP group. Sevoflurane consumption was lower in the WLi and PRi groups than in the SBP group. Nicardipine was more commonly needed to treat hypertension in the WLi and PRi groups than in the SBP group.

CONCLUSION

Regulation of sevoflurane anesthesia depth with WLi or PRi reduced anesthesia recovery time, extubation time and sevoflurane consumption without intraoperative unwanted events.

Oral Cavity Pressure Measurement-based Respiratory Monitoring System with Reduced Susceptibility to Motion Artifacts

In this paper, we propose a novel approach for respiratory monitoring through the direct measurement of oral cavity pressure. To measure the oral cavity pressure, a pressure sensor is placed inside the oral cavity. The intraorally obtained pressure signals are analyzed in the time-domain and validated against the conventional respiration monitoring belt (reference measurement). Tests have been performed on four subjects (four tests on each subject) in stationary and non-stationary conditions to evaluate the usage of the system in real life. Measurement from the proposed system shows that our approach can monitor the respiration rate with an accuracy of 99% when compared to the reference measurement. Moreover, the system can effectively track the respiration pattern and can detect breathing events independent of breathing routes, i.e., the nasal and oral. It has the minimum susceptibility to motion artifacts. Therefore, it has potential to be used as a wearable monitoring system for day to day life.

Measuring diaphragm movement and respiratory frequency using a novel ultrasound device in healthy volunteers

PURPOSE

To evaluate the ability of a novel ultrasound (US) device, DiaMon, to monitor diaphragm movement via its proxy liver movement, and compare it with the respired flow measured with a flowmeter, in awake and healthy volunteers. We wanted to (1) establish the optimal anatomical position for attaching the DiaMon device to the abdominal wall, and (2) evaluate the accuracy of continuous monitoring of respiratory frequency.

METHODS

Thirty healthy subjects were recruited. The DiaMon probe was applied subcostally in four different positions with the subjects in five different postures. The subjects breathed tidal volumes into a spirometer for 30-60 s with the DiaMon recording simultaneously.

RESULTS

The device detected a readable signal in 83-100% of the position/posture-combinations. The technical correlation between the two signals was highest in the anterior axillary-supine position (mean ± SD: 0.95 ± 0.03), followed by paramidline-supine (0.90 ± 0.09) and midclavicular-supine (0.89 ± 0.12). The frequency measurements yielded a mean difference of 0.03 (95% limits of agreement - 0.11, 0.16) breaths per minute in the anterior axillary-supine position.

CONCLUSION

The DiaMon device is able to detect liver movement in most subjects, and it measures breathing frequency accurately.

Continuous Distant Measurement of the User's Heart Rate in Human-Computer Interaction Applications

In real world scenarios, the task of estimating heart rate (HR) using video plethysmography (VPG) methods is difficult because many factors could contaminate the pulse signal (i.e., a subjects’ movement, illumination changes). This article presents the evaluation of a VPG system designed for continuous monitoring of the user’s heart rate during typical human-computer interaction scenarios. The impact of human activities while working at the computer (i.e., reading and writing text, playing a game) on the accuracy of HR VPG measurements was examined. Three commonly used signal extraction methods were evaluated: green (G), green-red difference (GRD), blind source separation (ICA). A new method based on an excess green (ExG) image representation was proposed. Three algorithms for estimating pulse rate were used: power spectral density (PSD), autoregressive modeling (AR) and time domain analysis (TIME). In summary, depending on the scenario being studied, different combinations of signal extraction methods and the pulse estimation algorithm ensure optimal heart rate detection results. The best results were obtained for the ICA method: average RMSE = 6.1 bpm (beats per minute). The proposed ExG signal representation outperforms other methods except ICA (RMSE = 11.2 bpm compared to 14.4 bpm for G and 13.0 bmp for GRD). ExG also is the best method in terms of proposed success rate metric (sRate).

A review on wearable photoplethysmography sensors and their potential future applications in health care

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal’s second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.

Multicenter Study Validating Accuracy of a Continuous Respiratory Rate Measurement Derived From Pulse Oximetry: A Comparison With Capnography

Published ahead of print January 17, 2017.

BACKGROUND:

Intermittent measurement of respiratory rate via observation is routine in many patient care settings. This approach has several inherent limitations that diminish the clinical utility of these measurements because it is intermittent, susceptible to human error, and requires clinical resources. As an alternative, a software application that derives continuous respiratory rate measurement from a standard pulse oximeter has been developed. We sought to determine the performance characteristics of this new technology by comparison with clinician-reviewed capnography waveforms in both healthy subjects and hospitalized patients in a low-acuity care setting.

METHODS:

Two independent observational studies were conducted to validate the performance of the Medtronic Nellcor^TM Respiration Rate Software application. One study enrolled 26 healthy volunteer subjects in a clinical laboratory, and a second multicenter study enrolled 53 hospitalized patients. During a 30-minute study period taking place while participants were breathing spontaneously, pulse oximeter and nasal/oral capnography waveforms were collected. Pulse oximeter waveforms were processed to determine respiratory rate via the Medtronic Nellcor Respiration Rate Software. Capnography waveforms reviewed by a clinician were used to determine the reference respiratory rate.

RESULTS:

A total of 23,243 paired observations between the pulse oximeter-derived respiratory rate and the capnography reference method were collected and examined. The mean reference-based respiratory rate was 15.3 ± 4.3 breaths per minute with a range of 4 to 34 breaths per minute. The Pearson correlation coefficient between the Medtronic Nellcor Respiration Rate Software values and the capnography reference respiratory rate is reported as a linear correlation, R, as 0.92 ± 0.02 (P < .001), whereas Lin’s concordance correlation coefficient indicates an overall agreement of 0.85 ± 0.04 (95% confidence interval [CI] +0.76; +0.93) (healthy volunteers: 0.94 ± 0.02 [95% CI +0.91; +0.97]; hospitalized patients: 0.80 ± 0.06 [95% CI +0.68; +0.92]). The mean bias of the Medtronic Nellcor Respiration Rate Software was 0.18 breaths per minute with a precision (SD) of 1.65 breaths per minute (healthy volunteers: 0.37 ± 0.78 [95% limits of agreement: –1.16; +1.90] breaths per minute; hospitalized patients: 0.07 ± 1.99 [95% limits of agreement: –3.84; +3.97] breaths per minute). The root mean square deviation was 1.35 breaths per minute (healthy volunteers: 0.81; hospitalized patients: 1.60).

CONCLUSIONS:

These data demonstrate the performance of the Medtronic Nellcor Respiration Rate Software in healthy subjects and patients hospitalized in a low-acuity care setting when compared with clinician-reviewed capnography. The observed performance of this technology suggests that it may be a useful adjunct to continuous pulse oximetry monitoring by providing continuous respiratory rate measurements. The potential patient safety benefit of using combined continuous pulse oximetry and respiratory rate monitoring warrants assessment.

Real-Time Robust Heart Rate Estimation From Wrist-Type PPG Signals Using Multiple Reference Adaptive Noise Cancellation

Heart rate (HR) monitoring using photoplethysmographic (PPG) signals recorded from wearers' wrist greatly facilitates design of wearable devices and maximizes user experience. However, placing PPG sensors in wrist causes much stronger and complicated motion artifacts (MA) due to loose interface between sensors and skin. Therefore, developing robust HR estimation algorithms for wrist-type PPG signals has significant commercial values. In this paper, we propose a robust HR estimation algorithm for wrist-type PPG signals using multiple reference adaptive noise cancellation (ANC) technique-termed here as "MURAD." The main challenge of using ANC for MA reduction is to devise a qualified reference noise signal (RNS) to the adaptive filter. We propose a novel solution by using four RNSs, namely, the three-axis accelerometer data and the difference signal between the two PPG signals. For each RNS, we get a different version of the cleaned PPG signal. Then, a set of probable HR values is estimated using all of the cleaned PPG signals, and then, the value that is closest to the estimated HR of the previous time window is chosen to be the HR estimate of the current window. Then, some peak verification techniques are employed to ensure accurate HR estimations. The proposed technique gives lower average absolute error compared to state-of-the art methods. So, MURAD method provides a promising solution to the challenge of HR monitoring using PPG in wearable devices during severe MA conditions.

Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters

Goal: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent “validation” datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. Methods: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. Results: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} $\text {th}$\end{document}–75\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\text {th}$\end{document} percentiles for a window size of 32 seconds) of 1.5 (0.3–3.3) and 4.0 (1.8–5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). Conclusion: Increased robustness of RR estimation by the proposed method was demonstrated. Significance: This work demonstrates that the use of large publicly available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice.

Determination of saturation, heart rate, and respiratory rate at forearm using a Nellcor™ forehead SpO2-saturation sensor

Alterations in arterial blood oxygen saturation, heart rate (HR), and respiratory rate (RR) are strongly associated with intra-hospital cardiac arrests and resuscitations. A wireless, easy-to-use, and comfortable method for monitoring these important clinical signs would be highly useful. We investigated whether the Nellcor™ OxiMask MAX-FAST forehead sensor could provide data for vital sign measurements when located at the distal forearm instead of its intended location at the forehead to provide improved comfortability and easy placement. In a prospective setting, we recruited 30 patients undergoing surgery requiring postoperative care. At the postoperative care unit, patients were monitored for two hours using a standard patient monitor and with a study device equipped with a Nellcor™ Forehead SpO₂ sensor. The readings were electronically recorded and compared in post hoc analysis using Bland-Altman plots, Spearman's correlation, and root-mean-square error (RMSE). Bland-Altman plot showed that saturation (SpO₂) differed by a mean of -0.2 % points (SD, 4.6), with a patient-weighted Spearman's correlation (r) of 0.142, and an RMSE of 4.2 points. For HR measurements, the mean difference was 0.6 bpm (SD, 2.5), r = 0.997, and RMSE = 1.8. For RR, the mean difference was -0.5 1/min (4.1), r = 0.586, and RMSE = 4.0. The SpO₂ readings showed a low mean difference, but also a low correlation and high RMSE, indicating that the Nellcor™ saturation sensor cannot reliably assess oxygen saturation at the forearm when compared to finger PPG measurements.

Estimation of respiratory rates based on photoplethysmographic measurements at the sternum

The respiratory rate (RR) is a clinically important vital sign and is a frequently used parameter in the general hospital wards. In current clinical practice, the monitoring of the RR is by manual count of the chest movement for one minute. This paper addresses a new approach where the respiratory rate is extracted using photoplethysmography (PPG) on the chest bone (sternum). Sternal PPG signals were acquired from 10 healthy subjects resting in a supine position. As reference signals, finger PPG, electrocardiogram (ECG), and capnography were simultaneously recorded during spontaneous and paced breathing. The sternal PPG signals were then compared with the reference signals in terms of Bland-Altman analysis, the power spectrum analysis and the magnitude squared coherence. The Bland-Altman analysis showed an average bias of 0.21 breaths/min between RR extracted from sternal PPG and capnography. The respiratory power content at the sternum was 78.8 (38) % in terms of the median and (the interquartile range). The cardiac content was 19 (18.4) % within the cardiac region. The results from the magnitude squared coherence analysis was 0.97 (0.09) in the respiratory region (6 to 27 breaths/min) and 0.98 (0.01) in the cardiac pulse region (30-120 beats/min). This preliminary study demonstrates the possibility of monitoring the RR from sternal PPG on a healthy group of subjects during rest.

Recording system and data fusion algorithm for enhancing the estimation of the respiratory rate from photoplethysmogram

The respiratory rate is a vital parameter that can provide valuable information about the health condition of a patient. The extraction of respiratory information from photoplethysmographic signal (PPG) was actually encouraged by the reported results, our main goal being to obtain accurate respiratory rate estimation from the PPG signal. We developed a fusion algorithm that identifies the best derived respiratory signals, from which is possible to extract the respiratory rate; based on these, a global respiratory rate is computed using the proposed fusion algorithm. The algorithm is qualitatively tested on real PPG signals recorded by an acquisition system we implemented, using a reflection pulse oximeter sensor. Its performance is also statistically evaluated using benchmark dataset publically available from CapnoBase.Org.

Monitoring of Heart and Breathing Rates Using Dual Cameras on a Smartphone

Some smartphones have the capability to process video streams from both the front- and rear-facing cameras simultaneously. This paper proposes a new monitoring method for simultaneous estimation of heart and breathing rates using dual cameras of a smartphone. The proposed approach estimates heart rates using a rear-facing camera, while at the same time breathing rates are estimated using a non-contact front-facing camera. For heart rate estimation, a simple application protocol is used to analyze the varying color signals of a fingertip placed in contact with the rear camera. The breathing rate is estimated from non-contact video recordings from both chest and abdominal motions. Reference breathing rates were measured by a respiration belt placed around the chest and abdomen of a subject; reference heart rates (HR) were determined using the standard electrocardiogram. An automated selection of either the chest or abdominal video signal was determined by choosing the signal with a greater autocorrelation value. The breathing rate was then determined by selecting the dominant peak in the power spectrum. To evaluate the performance of the proposed methods, data were collected from 11 healthy subjects. The breathing ranges spanned both low and high frequencies (6-60 breaths/min), and the results show that the average median errors from the reflectance imaging on the chest and the abdominal walls based on choosing the maximum spectral peak were 1.43% and 1.62%, respectively. Similarly, HR estimates were also found to be accurate.

Detection of respiration in central venous pressure using state machine

Reliable information from patient monitors enhances treatment for critically ill patients. Redundant sources for information would aid identification of faulty sensors and leads, and improve presentation of physiological data. Respiratory information can be obtained from several sources, including airway pressure and central venous pressure (CVP). CVP signals have been analyzed using frequency information to isolate the respiration related part of the signal or to obtain statistics about respiration. This study uses a state machine algorithm to detect the timing of each cycle of respiration. A state machine has advantages of enforcing a predictable cycle of expiration and inspiration. The detection of respiratory cycles can be done in real-time, allowing identification of irregular periods between inspirations and prolonged periods with no inspiration, for which an alert may be issued. The algorithm was tested on data obtain from the PhysioNet database of recordings from intensive care patients. The airway pressure signal was used to determine the "true values" of the timing of each respiratory cycle for checking the accuracy of the algorithm analyzing the CVP signal. Parameters of the algorithm were found that would result in a true positive value of above 98% for detection of each cycle of respiration from analysis of the CVP signal, compared to analysis of the RESP signal.

Noninvasive monitoring of peripheral microcirculatory hemodynamics under varying degrees of hypoxia

The effect of hypoxia on skin blood flow was examined in anesthetized rabbits during induction of various levels of hypoxia. Peripheral perfusion and oxygenation were monitoring using a combined system (LPT) composed of a laser Doppler flowmeter (LDF), a photoplatysmograph (PPG), and a transcutaneous oxygen tension monitor (tc-PO2). Central blood parameters (PaO2, HCO3(-), SaO2, pH, and lactate) were measured concomitantly throughout the experiment. A continuous decline was found in both peripheral and central values, depending on the severity of the hypoxia. The results clearly indicate that monitoring peripheral indices with the LPT system enables monitoring changes of vital blood parameters during hypoxia. The system has clinical potential for sensitive and noninvasive monitoring of vital variables during medical procedures in clinics, as well as for homecare for patients with respiratory diseases. Minimizing the system may be useful in various conditions of exposure to low oxygen levels, such as during mountain climbing.

Pulse oximetry-derived respiratory rate in general care floor patients

Respiratory rate is recognized as a clinically important parameter for monitoring respiratory status on the general care floor (GCF). Currently, intermittent manual assessment of respiratory rate is the standard of care on the GCF. This technique has several clinically-relevant shortcomings, including the following: (1) it is not a continuous measurement, (2) it is prone to observer error, and (3) it is inefficient for the clinical staff. We report here on an algorithm designed to meet clinical needs by providing respiratory rate through a standard pulse oximeter. Finger photoplethysmograms were collected from a cohort of 63 GCF patients monitored during free breathing over a 25-min period. These were processed using a novel in-house algorithm based on continuous wavelet-transform technology within an infrastructure incorporating confidence-based averaging and logical decision-making processes. The computed oximeter respiratory rates (RRoxi) were compared to an end-tidal CO2 reference rate (RRETCO2). RRETCO2 ranged from a lowest recorded value of 4.7 breaths per minute (brpm) to a highest value of 32.0 brpm. The mean respiratory rate was 16.3 brpm with standard deviation of 4.7 brpm. Excellent agreement was found between RRoxi and RRETCO2, with a mean difference of -0.48 brpm and standard deviation of 1.77 brpm. These data demonstrate that our novel respiratory rate algorithm is a potentially viable method of monitoring respiratory rate in GCF patients. This technology provides the means to facilitate continuous monitoring of respiratory rate, coupled with arterial oxygen saturation and pulse rate, using a single non-invasive sensor in low acuity settings.

A motion-tolerant adaptive algorithm for wearable photoplethysmographic biosensors

The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation ( SpO2) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO2 sensors during standing, walking, and running at different conditions for a single- and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO2 extraction) between measurements by reference sensors and our algorithm.

On the analysis of fingertip photoplethysmogram signals

Photoplethysmography (PPG) is used to estimate the skin blood flow using infrared light. Researchers from different domains of science have become increasingly interested in PPG because of its advantages as non-invasive, inexpensive, and convenient diagnostic tool. Traditionally, it measures the oxygen saturation, blood pressure, cardiac output, and for assessing autonomic functions. Moreover, PPG is a promising technique for early screening of various atherosclerotic pathologies and could be helpful for regular GP-assessment but a full understanding of the diagnostic value of the different features is still lacking. Recent studies emphasise the potential information embedded in the PPG waveform signal and it deserves further attention for its possible applications beyond pulse oximetry and heart-rate calculation. Therefore, this overview discusses different types of artifact added to PPG signal, characteristic features of PPG waveform, and existing indexes to evaluate for diagnoses.

Multiparameter respiratory rate estimation from the photoplethysmogram

We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.

Developing an algorithm for pulse oximetry derived respiratory rate (RR(oxi)): a healthy volunteer study

Objective The presence of respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring respiratory rate requires: (1) the recognition of the multi-faceted manifestations of respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported respiratory rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RR_OXI algorithm based on this principle and its performance on healthy subject trial data is described herein.

Methods Finger PPGs were collected from a cohort of 139 healthy adult volunteers monitored during free breathing over an 8-min period. These were subsequently processed using a novel in-house algorithm based on continuous wavelet transform technology within an infrastructure incorporating weighted averaging and logical decision making processes. The computed oximeter respiratory rates (RR_oxi) were then compared to an end-tidal CO₂ reference rate (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$\end{document}).

Results\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$\end{document} ranged from a lowest recorded value of 2.97 breaths per min (br/min) to a highest value of 28.02 br/min. The mean rate was 14.49 br/min with standard deviation of 4.36 br/min. Excellent agreement was found between RR_oxi and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$\end{document}, with a mean difference of −0.23 br/min and standard deviation of 1.14 br/min. The two measures are tightly spread around the line of agreement with a strong correlation observable between them (R² = 0.93).

Conclusions These data indicate that RR_oxi represents a viable technology for the measurement of respiratory rate of healthy individuals.

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.

Photoplethysmography and nociception

Photoplethysmography (PPG), i.e. pulse oximetric wave, is a non-invasive technique that is used in anaesthesia monitoring primarily to monitor blood oxygenation. The PPG waveform resembles that of the arterial blood pressure but instead of pressure it is related to the volume changes in the measurement site and hence contains information related to the peripheral blood circulation, including skin vasomotion, which is controlled by the sympathetic nervous system. Because of this link, skin vasomotor response and PPG amplitude response have been associated with nociception under general anaesthesia. Recently, there has been interest in monitoring nociception during general anaesthesia. In many of the published studies, PPG waveform information has been included. The focus of this topical review is to provide an overview on the information embedded in the PPG waveform especially in the context of the autonomic nervous system and analgesia monitoring.

Non-invasive measurement of respiratory rate in children using the photoplethysmogram

Respiratory rate is recognised as a valuable predictor of the severity of illness in children, but it is not currently feasible to measure this automatically in a triage environment. Autoregressive modelling on data from the pulse oximeter photoplethysmogram has the potential to introduce automated breathing measurement into the realm of paediatric triage. Using autoregressive modelling, it is shown that respiratory rate can be extracted from the paediatric photoplethysmogram with a mean error of 3.4 breaths per minute.

Low-frequency changes in finger volume in patients after surgery, related to respiration and venous pressure

BACKGROUND AND OBJECTIVE

In patients after surgery, we observed large-amplitude low-frequency changes in digital plethysmograph measurements when DC coupling of the signal was used. We set out to assess factors that might contribute to these events and in particular to test the possibility that low-frequency signals could be used to assess respiratory disturbances.

METHODS

We recorded values in 23 patients who had undergone gynaecological surgery. We measured nasal flow, abdominal pressure (by urinary catheter), venous pressure in the hand, and DC-coupled optical transmission plethysmography. Signals were replayed and analysed to assess the incidence of specific patterns of events.

RESULTS

Most patients received morphine for postoperative analgesia. Respiratory irregularity and expiratory muscle action were very frequent. Increases in abdominal pressure during expiration caused increases in venous pressure and pulsation. In 12 out of 23 patients, a characteristic response consistent with vasoconstriction was noted after increases in breath size, and, in seven patients, very-low-frequency (0.2-0.7 Hz) oscillations of finger volume were present that appeared unrelated to respiratory events. Patients who did not receive morphine had very different plethysmograph patterns, with significantly smaller pulse amplitude.

CONCLUSION

Low-frequency changes in finger volume can be simply obtained and provide considerable information about peripheral circulatory dynamics. Diverse patterns can be recognized, but the range of responses suggests that current techniques cannot be used alone to assess cardiorespiratory status. However, a combination of plethysmography with respiratory measurements shows characteristic events.

Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation

We present a new method that uses the pulse oximeter signal to estimate the respiratory rate. The method uses a recently developed time-frequency spectral estimation method, variable-frequency complex demodulation (VFCDM), to identify frequency modulation (FM) of the photoplethysmogram waveform. This FM has a measurable periodicity, which provides an estimate of the respiration period. We compared the performance of VFCDM to the continuous wavelet transform (CWT) and autoregressive (AR) model approaches. The CWT method also utilizes the respiratory sinus arrhythmia effect as represented by either FM or AM to estimate respiratory rates. Both CWT and AR model methods have been previously shown to provide reasonably good estimates of breathing rates that are in the normal range (12-26 breaths/min). However, to our knowledge, breathing rates higher than 26 breaths/min and the real-time performance of these algorithms are yet to be tested. Our analysis based on 15 healthy subjects reveals that the VFCDM method provides the best results in terms of accuracy (smaller median error), consistency (smaller interquartile range of the median value), and computational efficiency (less than 0.3 s on 1 min of data using a MATLAB implementation) to extract breathing rates that varied from 12-36 breaths/min.

Noninvasive respiratory monitoring system based on the piezoceramic transducer's pyroelectric effect

This paper presents a simple alternative method and system for noninvasive respiratory airflow monitoring. The proposed system uses a piezoceramic transducer to measure respiratory airflow. When a piezoceramic transducer is impacted by respiratory airflow, there is a piezoelectric and a pyroelectric response to pressure and thermal airflow fluctuations. In this study, the selected transducer's response output is dominated by the pyroelectricity factor. Therefore, the piezoelectric effect is not significant and can be ignored in this study. Using the transducer's pyroelectricity to measure thermal flow variations, a subject's respiratory rate and respiratory air volumetric flow rate can be monitored. The proposed system was evaluated for accuracy and response time using quiet and postphysical exertion breathing modes. Using the pneumotach system as a benchmark, the proposed system's respiratory rate measurement accuracy for the two breathing modes is approximately 98.78%. In addition, the proposed system's output voltage is highly correlated with the respiratory volumetric flow rate measured by the selected pneumotach (r2=0.9783). The average correlation coefficient between the pneumotach system's output waveform and the proposed system is approximately 0.9389. Moreover, the proposed system and the selected pneumotach have almost the same rapid response time to respiratory airflow. When compared to a temperature measurement thermistor system, the thermistor on average is approximately 25.3 ms slower than the proposed system. Furthermore, compared to the selected screen-type pneumotach system, the proposed system simplifies the respiration monitoring requirements. Instead of sensing the pressure drop across a mesh screen, like the screen-type pneumotach, it measures respiration at one point within the respiratory airflow. The proposed system benefits from simplified processing circuits and a mesh-free design. The advantages of this new respiratory airflow measurement method are fast response time, high accuracy, low cost, and ease of implementation.

Identifying airway obstructions using photoplethysmography (PPG)

OBJECTIVE

Central and obstructive apneas are sources of morbidity and mortality associated with primary patient conditions as well as secondary to medical care such as sedation/analgesia in post-operative patients. This research investigates the predictive value of the respirophasic variation in the noninvasive photoplethysmography (PPG) waveform signal in detecting airway obstruction.

METHODS

PPG data from 20 consenting healthy adults (12 male, 8 female) undergoing anesthesia were collected directly after surgery and before transfer to the Post Anesthesia Care Unit (PACU). Features of the PPG waveform were calculated and used in a neural network to classify normal and obstructive events.

RESULTS

During the postoperative period studied, the neural network classifier yielded an average (+/-standard deviation) 75.4 (+/-3.7)% sensitivity, 91.6 (+/-2.3)% specificity, 84.7 (+/-3.5)% positive predictive value, 85.9 (+/-1.8)% negative predictive value, and an overall accuracy of 85.4 (+/-2.0)%.

CONCLUSIONS

The accuracy of this method shows promise for use in real-time monitoring situations.

Combined photoplethysmographic monitoring of respiration rate and pulse: a comparison between different measurement sites in spontaneously breathing subjects

BACKGROUND

The non-invasive photoplethysmographic (PPG) signal reflects blood flow and volume in a tissue. The PPG signal shows variation synchronous with heartbeat (PPGc), as used in pulse oximetry, and variations synchronous with breathing (PPGr). PPGr has been used for non-invasive monitoring of respiration with promising results. Our aim was to investigate PPG signals recorded from different skin sites in order to find suitable locations for parallel monitoring of variations synchronous with heartbeat and breathing.

METHODS

PPG sensors were applied to the forearm, finger, forehead, wrist and shoulder on 48 awake healthy volunteers. From these sites, seven PPG signals were simultaneously recorded during normal spontaneous breathing over 10 min. Capnometry served as respiration and electrocardiogram (ECG) as pulse reference signals. PPG signals were compared with respect to power spectral content and squared coherence.

RESULTS

Forearm PPG measurement showed significantly higher power within the respiratory region of the power spectrum [median (quartile range) 42 (26)%], but significantly lower power within the cardiac region [9 (10)%] compared with the other skin sites. PPG finger measurement showed the opposite; in transmission mode, the power within the respiratory region was significantly lower [4 (10)%] and within the cardiac region significantly higher [45 (25)%] than the other sites. PPGc coherence values were generally high [>0.96 (0.08)], and PPGr coherence values lower [0.83 (0.35)-0.94 (0.17)].

CONCLUSION

Combined PPG respiration and pulse monitoring is possible, but there are significant differences between the respiratory and cardiac components of the PPG signal at different sites.

Photoplethysmography and its application in clinical physiological measurement

Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile ('AC') physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.

Measurement of respiratory rate from the photoplethysmogram in chest clinic patients

OBJECTIVE

We studied the application of our algorithm for the robust extraction of respiratory information from the pulse oximeter signal acquired from a selection of patients attending the chest clinic.

METHODS

Photoplethysmograms were obtained from 16 individuals: 13 patients with various conditions in the respiratory ward and three healthy subjects. Wavelet transforms were generated from which respiratory information was extracted to obtain a measure of respiratory rate. This measured rate was compared with the respiratory rate determined by one of a variety of other means (a digital end tidal CO(2) signal, the output from a non-invasive ventilation device, or a switch actuated by the patient or observer.)

RESULTS

Respiratory rates varied from 6.2 to 35.8 breaths per minute (bpm). The oximeter rate determined through our method matched the marker rate obtained for all patients to within 1 bpm.

CONCLUSION

The technique allows the measurement of respiratory rate directly from the photoplethysmogram of a pulse oximeter, and leads the way for development of a simple non-invasive combined respiration and saturation monitor useful for patients with all forms of breathlessness.

Age and gender do not influence the ability to detect respiration by photoplethysmography

OBJECTIVE

The non-invasive technique photoplethysmography (PPG) can detect changes in blood volume and perfusion in a tissue. Respiration causes variations in the peripheral circulation, making it possible to monitor breaths using an optical sensor attached to the skin. The respiratory-synchronous part of the PPG signal (PPGr) has been used to monitor respiration during anaesthesia, and in postoperative and neonatal care. Studies addressing possible differences in PPGr signal characteristics depending on gender or age are lacking.

METHODS

We studied three groups of 16 healthy subjects each during normal breathing; young males, old males and young females, and calculated the concordance between PPGr, derived from a reflection mode PPG sensor on the forearm, and a reference CO(2 )signal. The concordance was quantified by using a squared coherence analysis. Time delay between the two signals was calculated. In this process, we compared three different methods for calculating time delay.

RESULTS

Coherence values >or=0.92 were seen for all three groups without any significant differences depending on age or gender (p = 0.67). Comparison between the three different methods for calculating time delay showed a correlation r = 0.93.

CONCLUSIONS

These results demonstrate clinically important information implying the possibility to register qualitative PPGr signals for respiration monitoring, regardless of age and gender.

Continuous noninvasive measurement of pulsus paradoxus complements medical decision making in assessment of acute asthma severity

BACKGROUND

Pulsus paradoxus (PP) is a pathophysiologic parameter that is indicative of asthma severity. The ability of PP to categorize acutely asthmatic patients in accordance with the earlier National Asthma Education and Prevention Program (NAEPP) expert panel report 1 guidelines was determined.

METHODS

An arterial tonometric BP monitor, which was interfaced to an analog-digital converter, executed a periodic amplitude analysis algorithm, which computed PP in real time. The PP measurement was compared to the criterion standard of emergency physicians in determining the hospital admission vs hospital discharge disposition following the NAEPP standardized treatment. Receiver operating characteristics (ROCs) were calculated, and the PP threshold, which maximized sensitivity and specificity, was identified. In a separate laboratory investigation, PP was induced in a healthy volunteer by inspiration through a fixed resistance. Plethysmographic waveform changes, induced by PP, were measured by a second analog-to-digital converter that was connected to a pulse oximeter.

RESULTS

A total of 79 patients were enrolled in the study, of whom 63 met a priori inclusion criteria and had uninterrupted data acquisition. The mean PP for patients who were appropriately discharged from the hospital was 9.1 mm Hg (95% confidence interval [CI], 7.3 to 10.9 mm Hg) and differed from the PP of 17.6 mm Hg (95% CI, 13.5 to 21.8; p < 0.001) for patients admitted to the hospital/relapsed. The sensitivity and specificity for physician disposition were 0.83 and 0.89, respectively, and for PP values were 0.78 and 0.78, respectively. The Wilcoxon area under the ROC curve was 0.82 (95% CI, 0.64 to 0.99) following treatment. The risk ratio was 5.32 for hospital admission among patients with a PP of > 11.3 mm Hg. Changes in the photoplethysmography peak height were correlated to PP from the BP monitor by a regression line with a slope of 0.01 V/mm Hg.

CONCLUSIONS

Continuous PP can aid in determining disposition among emergency department (ED) patients with acute asthma. ED physicians equipped with a PP monitor would be able to objectify the work of breathing and would more closely adhere to NAEPP guidelines. The possibility that a PP detection algorithm could reside in a pulse oximeter warrants further investigation.

An automated algorithm for determining respiratory rate by photoplethysmogram in children

BACKGROUND

We have developed an automated algorithm to allow the measurement of respiratory rate directly from the photoplethysmogram (pulse oximeter waveform).

AIM

To test the algorithm's ability to determine respiratory rate in children.

METHODS

A convenience sample of patients attending a paediatric Accident and Emergency Department was monitored using a purpose-built pulse oximeter and the photoplethysmogram (PPG) recorded. Respiration was also recorded by an observer activating a push-button switch in synchronization with the child's breathing. The switch marker signals were processed to derive a manual respiratory rate that was compared with the wavelet-based oximeter respiratory rate derived from the PPG signal.

RESULTS

Photoplethysmograms were obtained from 18 children aged 18 mo to 12 y, breathing spontaneously at rates of 17 to 27 breaths per minute. There was close correspondence between the wavelet-based oximeter respiration rate and the manual respiratory rate, with the difference between them being less than one breath per minute in all children.

CONCLUSION

Our automated algorithm allows the accurate determination of respiratory rate from photoplethysmograms of a heterogeneous group of children. We believe that our automated wavelet-based signal-processing techniques could soon be easily incorporated into current pulse oximetry technology.

Estimation of breathing interval from the photoplethysmographic signals in children

Two important parameters that are generally under continual observation during clinical monitoring are heart rate (HR) variability and breathing interval (BI) of patients. Current HR monitoring during night-long childhood respiratory sleep studies is well tolerated but BI monitoring requires instrumentation, like nasal cannula, that can be less accommodating for children. In this study, BI was extracted from the photoplethysmographic (PPG) signals using a two-stage signal processing technique termed zero-phase digital filtering. Eight children (7 male) aged 8.6 +/- 2.6 years were recruited to perform two breathing activities: during tidal and with customized externally applied inspiratory resistive loading (IRL). The accuracy of BI derived from the PPG signals was compared with that estimated by a calibrated air pressure transducer in children. Statistical analysis revealed that mean BI attained from the PPG signals were significantly related during tidal breathing (r(2) = 0.76; range 0.61-0.83; p < 0.05) and with the IRL (r(2) = 0.79; range 0.68-0.85; p < 0.05) in the absence of motion artefacts. Preliminary findings herein suggest that besides having the capability to monitor HR and arterial blood oxygen saturation measurements, the PPG signals can be used to derive BI for children. This can be an attractive alternative for children who are more disturbed by intrusive techniques in prolonged clinical monitoring.