Respiratory-induced Intensity Variations Research Articles

A novel approach for decomposition of biomedical signals in different applications based on data-adaptive Gaussian average filtering

The analysis of biomedical signals plays a crucial role in modern medicine and physiological research. Due to exogenous or endogenous interferences and complex interaction among physiological systems, biomedical signals usually possess nonstationary characteristics. To extract the features buried in such kind of signals, the signal decomposition algorithm that is data-adaptive and stable is highly demanded. This paper introduces a novel decomposition algorithm termed as data-adaptive Gaussian average filtering (DAGAF) that is new and potential in biomedical applications. Six biomedical scenarios, including finger photoplethysmography (PPG) signal with obscure respiratory-induced intensity variation (RIIV) component, wrist PPG signal with apparent RIIV component, seismocardiography (SCG) signal with implicit respiration component, electrocardiogram (ECG) with baseline wander (BW), ECG with power-line interference (PLI), and R-R intervals (RRI) sequence with implicit low-frequency trend wave, are adopted as examples for computer experiments. The results of computer experiments verify that the DAGAF algorithm can satisfy the specified requirement in different biomedical scenarios. DAGAF algorithm possesses the advantages of mathematical formulation and computational efficiency. It can be an alternative choice besides the commonly used empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD).

Design and Implementation of Respiration Rate Measurement System Using an Information Filter on an Embedded Device.

In this work, an algorithm was developed to measure respiration rate for an embedded device that can be used by a field robot for relief operations. With this algorithm, the rate measurement was calculated based on direct influences of respiratory-induced intensity variation (RIIV) on blood flow in cardiovascular pathways. For this, a photoplethysmogram (PPG) sensor was used to determine changes in heartbeat frequencies. The PPG sensor readings were filtered using an Information Filter and a fast Fourier transform (FFT) to determine the state of RIIV. With a relatively light initialization, the information filter can estimate unknown variables based on a series of measurements containing noise and other inaccuracies. Therefore, this filter is suitable for application in an embedded device. For faster calculation time in the implementation, the FFT analysis was calculated only for a major peak in frequency domain. Test and measurement of respiration rate was conducted based on the device algorithm and spirometer. Heartbeat measurements were also evaluated by comparing the heartbeat data of the PPG sensor and pulse-oximeter. Based on the test, the implemented algorithm can measure the respiration rate with approximately 80% accuracy compared with the spirometer.

Respiratory Rate Estimation by Using ECG, Impedance, and Motion Sensing in Smart Clothing

The needs for light-weight and soft smart clothing in homecare have been rising since the past decade. Many smart textile sensors have been developed and applied to automatic physiological and user-centered environmental status recognition. In the present study, we propose wearable multi-sensor smart clothing for homecare monitoring based on an economic fabric electrode with high elasticity and low resistance. The wearable smart clothing integrated with heterogeneous sensors is capable to measure multiple human biosignals (ECG and respiration), acceleration, and gyro information. Five independent respiratory signals (electric impedance plethysmography, respiratory induced frequency variation, respiratory induced amplitude variation, respiratory induced intensity variation, and respiratory induced movement variation) are obtained. The smart clothing can provide accurate respiratory rate estimation by using three different techniques (Naïve Bayes inference, static Kalman filter, and dynamic Kalman filter). During the static sitting experiments, respiratory induced frequency variation has the best performance; whereas during the running experiments, respiratory induced amplitude variation has the best performance. The Naïve Bayes inference and dynamic Kalman filter have shown good results. The novel smart clothing is soft, elastic, and washable and it is suitable for long-term monitoring in homecare medical service and healthcare industry.

Wavelet-based embedded algorithm for respiratory rate estimation from PPG signal

Photoplethysmography (PPG) is a popular technique utilized in pulse oximeter. Several researches have shown that PPG signal possesses the respiratory-induced intensity variation (RIIV) component, which implies that arterial oxygen saturation, heart rate (HR) and respiratory rate (RR) can be acquired by a single device. The commercial pulse oximeter generally provides the values of arterial oxygen saturation (SpO2) and HR. To successfully add the function of RR estimation to pulse oximeter, an algorithm requiring fewer resources plays a critical role. This paper presents a wavelet-based algorithm for RR estimation from PPG signal that can be implemented in the micro-controller (MCU) of pulse oximeter. The algorithm has been coded in C language and tested in a 32-bit MCU. The estimation results derived by the algorithm agree well with those from usual spectrum analysis methods. The RR estimations derived by PPG and respiratory signal are analyzed by Bland–Altman method. The RR estimations for long-term trace, breath-holding and paced-breathing experiments are also conducted to verify the performance of the proposed algorithm. The experimental results indicate that the proposed algorithm is highly reliable and is feasible to be incorporated in the commercial pulse oximeter.

Respiration Signals from Photoplethysmography

Pulse oximetry is based on the technique of photoplethysmography (PPG) wherein light transmitted through tissues is modulated by the pulse. In addition to variations in light modulation by the cardiac cycle, the PPG signal contains a respiratory modulation and variations associated with changing tissue blood volume of other origins. Cardiovascular, respiratory, and neural fluctuations in the PPG signal are of different frequencies and can all be characterized according to their sinusoidal components. PPG was described in 1937 to measure blood volume changes. The technique is today increasingly used, in part because of developments in semiconductor technology during recent decades that have resulted in considerable advances in PPG probe design. Artificial neural networks help to detect complex nonlinear relationships and are extensively used in electronic signal analysis, including PPG. Patient and/or probe-tissue movement artifacts are sources of signal interference. Physiologic variations such as vasoconstriction, a deep gasp, or yawn also affect the signal. Monitoring respiratory rates from PPG are often based on respiratory-induced intensity variations (RIIVs) contained in the baseline of the PPG signal. Qualitative RIIV signals may be used for monitoring purposes regardless of age, gender, anesthesia, and mode of ventilation. Detection of breaths in adult volunteers had a maximal error of 8%, and in infants the rates of overdetected and missed breaths using PPG were 1.5% and 2.7%, respectively. During central apnea, the rhythmic RIIV signals caused by variations in intrathoracic pressure disappear. PPG has been evaluated for detecting airway obstruction with a sensitivity of 75% and a specificity of 85%. The RIIV and the pulse synchronous PPG waveform are sensitive for detecting hypovolemia. The respiratory synchronous variation of the PPG pulse amplitude is an accurate predictor of fluid responsiveness. Pleth variability index is a continuous measure of the respiratory modulation of the pulse oximeter waveform and has been shown to predict fluid responsiveness in mechanically ventilated patients including infants. The pleth variability index value depends on the size of the tidal volume and on positive end-expiratory pressure. In conclusion, the respiration modulation of the PPG signal can be used to monitor respiratory rate. It is probable that improvements in neural network technology will increase sensitivity and specificity for detecting both central and obstructive apnea. The size of the PPG respiration variation can predict fluid responsiveness in mechanically ventilated patients.

Macrocirculation is not the sole determinant of respiratory induced variations in the reflection mode photoplethysmographic signal

Photoplethysmography (PPG) is a non-invasive optical technique sensitive to variations in blood volume and perfusion in the tissue. Reflection mode PPG may have clinical advantages over transmission mode PPG. To improve clinical usefulness and further development of the reflection mode PPG, studies on factors that modify the signal are warranted. We studied the coherence between the respiratory induced intensity variations (RIIV) of the PPG signal and respiratory synchronous pressure variations in central venous pressure (CVP), peripheral venous pressure (PVP) and arterial blood pressure (ABP) during positive pressure ventilation on 12 patients under anaesthesia and on 12 patients with spontaneous breathing. During positive pressure ventilation the coherence between all signals was high. Inspiration was followed first by an increase in CVP, then by increases in ABP and PVP and lastly by RIIV indicating less back-scattered light. In spontaneously breathing patients the coherence was high, but the phases between the signals were changed. During inspiration, ABP decreased slightly before CVP, followed by a decrease in RIIV and PVP. The phase relation between RIIV and respiratory induced variation in macrocirculation changed with ventilatory mode, but not in a uniform way, indicating the influence of mechanisms other than macrocirculation involved in generating the RIIV signal.

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.

Influence of tidal volume and thoraco-abdominal separation on the respiratory induced variation of the photoplethysmogram.

The present study was aimed at determining the relative influences of tidal volume and thoraco-abdominal separation (relative thoracic and abdominal contribution to the tidal volume) on the respiratory induced intensity variation (RIIV) of the photoplethysmographic signal. The effects were studied in two body positions. Respiratory inductive plethysmography was used for quantifying thoraco-abdominal separation and for assessing tidal volumes. 10 subjects were trained to perform widely varying degrees of thoraco-abdominal separation at different tidal volumes. The relationship between the RIIV signal peak-to-peak value (measured at the forearm), and the tidal volume and thoraco-abdominal separation was investigated in two body positions with the use of multiple linear regression. Larger tidal volume and more thoracic contribution to respiration were found to increase the RIIV peak-to-peak value (p < 0.0005). In the supine position, the tidal volume influence was stronger than that of thoraco-abdominal separation, and in the sitting position, the opposite was seen. The effects on the RIIV signal following changes in thoraco-abdominal separation and tidal volume are of the same order of magnitude. In the supine position, the influence of thoracic versus abdominal contribution to the tidal volume is not as significant as in the sitting position. Photoplethysmography is a promising technique for combined monitoring of several respiratory parameters, including tidal volume. In situations where the relative thoracic and abdominal contributions are likely to vary, the tidal volume information becomes less reliable.

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 respiratory 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 pneumotachograph signals are compared, and a statistical 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.

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.