Comparison of respiratory rates derived from heart rate variability, ECG amplitude, and nasal/oral airflow

Use of Intrinsic Entropy to Assess the Instantaneous Complexity of Thoracoabdominal Movement Patterns to Indicate the Effect of the Iso-Volume Maneuver Trial on the Performance of the Step Test

The recent surge in interest surrounds the analysis of physiological signals with a non-linear dynamic approach. The measurement of entropy serves as a renowned method for indicating the complexity of a signal. However, there is a dearth of research concerning the non-linear dynamic analysis of respiratory signals. Therefore, this study employs a novel method known as intrinsic entropy (IE) to assess the short-term dynamic changes in thoracoabdominal movement patterns, as measured by respiratory inductance plethysmography (RIP), during various states such as resting, step test, recovery, and iso-volume maneuver (IVM) trials. The findings reveal a decrease in IE of thoracic wall movement (TWM) and an increase in IE of abdominal wall movement (AWM) following the IVM trial. This suggests that AWM may dominate the breathing exercise after the IVM trial. Moreover, due to the high temporal resolution of IE, it proves to be a suitable measure for assessing the complexity of thoracoabdominal movement patterns under non-stationary states such as the step test and recovery. The results also demonstrate that the instantaneous complexity of TWM and AWM can effectively capture instantaneous changes during non-stationary states, which may prove valuable in understanding the respiratory mechanism for healthcare purposes in daily life.

EEG Evaluation of Stress Exposure on Healthcare Workers During COVID-19 Emergency: Not Just an Impression

Psychological distress among healthcare professionals, although already a common condition, was exacerbated by the COVID-19 pandemic. This effect has been generally self-reported or assessed through questionnaires. We aimed to identify potential abnormalities in the electrical activity of the brain of healthcare workers, operating in different roles during the pandemic. Cortical activity, cognitive performances, sleep, and burnout were evaluated two times in 20 COVID-19 frontline operators (FLCO, median age 29.5 years) and 20 operators who worked in COVID-19-free units (CFO, median 32 years): immediately after the outbreak of the pandemic (first session) and almost 6 months later (second session). FLCO showed higher theta relative power over the entire scalp (FLCO = 19.4%; CFO = 13.9%; p = 0.04) and lower peak alpha frequency of electrodes F7 (FLCO = 10.4 Hz; CFO = 10.87 Hz; p = 0.017) and F8 (FLCO = 10.47 Hz; CFO = 10.87 Hz; p = 0.017) in the first session. FLCO parietal interhemispheric coherence of theta (FLCO I = 0.607; FLCO II = 0.478; p = 0.025) and alpha (FLCO I = 0.578; FLCO II = 0.478; p = 0.007) rhythms decreased over time. FLCO also showed lower scores in the global cognitive assessment test (FLCO = 22.72 points; CFO = 25.56; p = 0.006) during the first session. The quantitative evaluation of the cortical activity might therefore reveal early signs of changes secondary to stress exposure in healthcare professionals, suggesting the implementation of measures to prevent serious social and professional consequences.

Assessment of long-range cross-correlations in cardiorespiratory and cardiovascular interactions

We propose higher-order detrending moving-average cross-correlation analysis (DMCA) to assess the long-range cross-correlations in cardiorespiratory and cardiovascular interactions. Although the original (zeroth-order) DMCA employs a simple moving-average detrending filter to remove non-stationary trends embedded in the observed time series, our approach incorporates a Savitzky-Golay filter as a higher-order detrending method. Because the non-stationary trends can adversely affect the long-range correlation assessment, the higher-order detrending serves to improve accuracy. To achieve a more reliable characterization of the long-range cross-correlations, we demonstrate the importance of the following steps: correcting the time scale, confirming the consistency of different order DMCAs, and estimating the time lag between time series. We applied this methodological framework to cardiorespiratory and cardiovascular time series analysis. In the cardiorespiratory interaction, respiratory and heart rate variability (HRV) showed long-range auto-correlations; however, no factor was shared between them. In the cardiovascular interaction, beat-to-beat systolic blood pressure and HRV showed long-range auto-correlations and shared a common long-range, cross-correlated factor. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Cardiorespiratory Interaction and Autonomic Sleep Quality Improve during Sleep in Beds Made from Pinus cembra (Stone Pine) Solid Wood

Cardiorespiratory interactions (CRIs) reflect the mutual tuning of two important organismic oscillators—the heartbeat and respiration. These interactions can be used as a powerful tool to characterize the self-organizational and recreational quality of sleep. In this randomized, blinded and cross-over design study, we investigated CRIs in 15 subjects over a total of 253 nights who slept in beds made from different materials. One type of bed, used as control, was made of melamine faced chipboard with a wood-like appearance, while the other type was made of solid wood from stone pine (Pinus cembra). We observed a significant increase of vagal activity (measured by respiratory sinus arrhythmia), a decrease in the heart rate (as an indicator of energy consumption during sleep) and an improvement in CRIs, especially during the first hours of sleep in the stone pine beds as compared to the chipboard beds. Subjective assessments of study participants’ well-being in the morning and sub-scalar assessments of their intrapsychic stability were significantly better after they slept in the stone pine bed than after they slept in the chipboard bed. Our observations suggest that CRIs are sensitive to detectable differences in indoor settings that are relevant to human health. Our results are in agreement with those of other studies that have reported that exposure to volatile phytochemical ingredients of stone pine (α-pinene, limonene, bornyl acetate) lead to an improvement in vagal activity and studies that show a reduction in stress parameters upon contact with solid wood surfaces.

An integrated framework for evaluation on typical ECG-derived respiration waveform extraction and respiration

OBJECTIVE

ECG-derived respiration (EDR) methods have been developed during the past decades to obtain respiration-relevant information. However, it is still necessary to compare the performance of these methods under uniform conditions for reasonable application.

APPROACH

In this paper, the performance of 10 feature-based EDR methods was evaluated comprehensively on three aspects: sampling rate, noise, and window length. The Fantasia database was used in this study, as it contained ECG signals and simultaneously measured respiration signals. The performance was quantified by two parameters: waveform correlation and breathing rate (BR) errors.

MAIN RESULTS

The BR errors of AM_area, AM_QR, AM_R were all below 2 beats per minute (bpm) when the sampling rate was above 150 Hz, while they decreased sharply by about 60% when the sampling rate was below 150 Hz. FM_RR presented stable performance with an error below 2 bpm at different sampling rates. The effect of noise was obviously found in amplitude-based EDR methods, with the maximum decreased by about 40% in waveform correlation. For all EDR methods, significant increase of BR errors occurred with the window shorting from 32 s to 16 s in the frequency-based technique. In addition, about 30%-40% of the window cannot obtain the BR error, calculated based on the time-based technique, within an 8 s window.

SIGNIFICANCE

We proposed a comprehensive and integrated evaluation on typical ECG-derived respiration waveform extraction and respiration rate calculation, providing references for algorithm selection based on different requirements.

A framework to quantify controlled directed interactions in network physiology applied to cognitive function assessment

The complex nature of physiological systems where multiple organs interact to form a network is complicated by direct and indirect interactions, with varying strength and direction of influence. This study proposes a novel framework which quantifies directional and pairwise couplings, while controlling for the effect of indirect interactions. Simulation results confirm the superiority of this framework in uncovering directional primary links compared to previous published methods. In a practical application of cognitive attention and alertness tasks, the method was used to assess controlled directed interactions between the cardiac, respiratory and brain activities (prefrontal cortex). It revealed increased interactions during the alertness task between brain wave activity on the left side of the brain with heart rate and respiration compared to resting phases. During the attention task, an increased number of right brain wave interactions involving respiration was also observed compared to rest, in addition to left brain wave activity with heart rate. The proposed framework potentially assesses directional interactions in complex network physiology and may detect cognitive dysfunctions associated with altered network physiology.

R-wave Amplitude Changes and Atypical Heart Rate Changes Accompanying Breath Hold During Low Breathing Rates

This paper reports an interesting phenomenon that the amplitude of the QRS complex reduces during inhalation and increases during exhalation and the variation can exceed even 100% during very slow breathing rates (BR). The phenomenon has been consistent in all the nine normal male subjects we have studied with age ranging from 23 to 61 years. Further, at very low respiration rates which included breath holds both after inhalation and exhalation, there are highly significant second and third harmonics of the respiration frequency in the heart rate variability spectrum. On the other hand, the R-wave amplitude changes do not have any noticeable higher harmonics of the BR. Thus, the observed changes in the R-wave amplitude are neither connected to the movement of the heart nor changes in its relative position with respect to the recording electrodes nor the fluctuations in the stroke volume.

Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis

The sleep apnea-hypopnea syndrome (SAHS) is a chronic respiratory disorder of high prevalence among children (up to 4%). Nocturnal polysomnography (PSG) is the gold standard method to diagnose SAHS, which is a complex, expensive, and time-consuming test. Consequently, alternative simplified methods are demanded. We propose the analysis of the respiratory rate variability (RRV) signal, directly obtained from the airflow (AF) signals. The aim of our study is to evaluate the usefulness of the spectral information obtained from RRV in the diagnosis of pediatric SAHS. A database composed of 946 AF and blood oxygen saturation (SpO2) recordings from children between 0 and 13 years old was used. Our database was divided into four severity groups according to the apnea-hipopnea index (AHI): no-SAHS (AHI <; 1 events/h), mild (1 events/h ≤ AHI <; 5 events/h), moderate (5 events/h ≤ AHI <; 10 events/h), and severe SAHS (AHI ≥ 10 events/h). RRV and 3% oxygen desaturation index (ODI3) were obtained from AF and SpO2 recordings, respectively. A spectral band of interest was determined (0.09-0.20 Hz.) and a total of 12 spectral features were extracted. Nine of these features showed statistically significant differences (p-value <; 0.05) among the four severity groups. The spectral features from RRV along with ODI3 were used as inputs to binary logistic regression (LR) classifiers. The diagnostic performance of LR models were evaluated for the AHI cut-off points of 1, 5, and 10 e/h, achieving 66.5%, 84.0%, and 88.5% accuracy, respectively. These results outperformed those obtained by single ODI3. The joint use of the spectral information from RRV and ODI3 achieved a high diagnostic capability in the most severely-affected children, thus showing their complementarity. These results suggest that the information contained in RRV spectrum together with ODI3 is useful to help identify moderate-to-severe SAHS.

The critical role of respiratory sinus arrhythmia on temporal cardiac dynamics

Temporal cardiac properties provide alternative information in analyzing heart rate variability (HRV), which may be disregarded by the standard HRV analyses. Patients with congestive heart failure (CHF) are known to have distinct temporal features from the healthy individuals. However, the underlying mechanism leading to the variation remains unclear. Whether or not these parameters can finely classify the severity for CHF patients is uncertain as well. In this work, an electrocardiogram was monitored in advanced CHF patients using 24-h Holter in four conditions, including baseline, one and three months after atenolol therapy, and healthy individuals. Slope and area under the curve (AUC) of multiscale entropy (MSE) curve over short (scales 1-5) and long (scales 6-20) scales, and detrended fluctuation analysis (DFA) scaling exponents at short (4-11 beats) and intermediate (>11 beats) window sizes were calculated. The results show that short-time scale MSE-derived parameters (slope: -0.08 ± 0.10, -0.03 ± 0.10, 0.02 ± 0.06, 0.08 ± 0.06; AUC: 4.03 ± 2.11, 4.69 ± 1.28, 4.73 ± 0.94, and 6.17 ± 1.23) and short-time scale DFA exponent (0.79 ± 0.16, 0.95 ± 0.22, 1.11 ± 0.19, and 1.35 ± 0.20) can hierarchically classify all four conditions. More importantly, simulated R-R intervals with different fractions and amplitude of respiratory sinus arrhythmia (RSA) components were examined to validate our hypothesis regarding the essentiality of RSA in the improvement of cardiovascular function, and its tight association with unpredictability and fractal property of HRV, which is in line with our hypothesis that RSA contributes significantly to the generation of the unpredictability and fractal behavior of HR dynamics.NEW & NOTEWORTHY Temporal cardiac properties provide useful diagnostic parameters for patients with congestive heart failure (CHF). Our study hierarchically classified CHF patients with β-blocker treatment by using multiscale entropy and detrended fluctuation analysis. Also, we provided the evidence to validate the critical role of respiratory sinus arrhythmia in the fractal properties of heart rate variability.

A novel approach towards non-obstructive detection and classification of COPD using ECG derived respiration

The alarming rate of mortality and disability due to Chronic Obstructive Pulmonary Disease (COPD) has become a serious health concern worldwide. The progressive nature of this disease makes it inevitable to detect this disease in its early stages, leads to a greater demand for developing non-obstructive and reliable technology for COPD detection. The use of highly patient-effort dependent, time-consuming, and expensive methods are some major inherent limitations of previous techniques. Lack of knowledge about the disease and inadequacy of proper diagnostic tool for early detection of COPD is another reason behind the 3rd leading cause of death worldwide. For this reason, this study aims to explore the utility of ECG Derived Respiration (EDR) for classification between COPD patients and normal healthy subjects as EDR can be easily extracted from ECG. ECG and respiration signals collected from 30 normal and 30 COPD subjects were analysed. Error calculation and statistical analysis were performed to observe the similarity between original respiration and EDR signal. The morphological pattern changes of respiration and EDR signals were analysed and three different features were extracted from those. Classification was performed by different classifiers employing Decision Tree, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Apart from obtaining comparable classification performance it was seen that EDR has better potential than the original respiration signal for classification of COPD from normal population.

Introduction of Application of Gini Coefficient to Heart Rate Variability Spectrum for Mental Stress Evaluation

Background

The Gini coefficient is a statistical tool generally used by economists to quantify income inequality. However, it can be applied to any kind of data with unequal distribution, including heart rate variability (HRV).

Objectives

To assess the application of the Gini coefficient to measure inequality in power spectral density of RR intervals, and to use this application as a psychophysiological indicator of mental stress.

Methods

Thirteen healthy subjects (19 ± 1.5 years) participated in this study, and their RR intervals were obtained by electrocardiogram during rest (five minutes) and during mental stress (arithmetic challenge; five minutes). These RR intervals were used to obtain the estimates of power spectral densities (PSD). The limits for the PSD bands were defined from 0.15 to 0.40 Hz for high frequency band (HF), from 0.04 to 0.15 Hz for low frequency band (LF), from 0.04 to 0.085 Hz for first low frequency sub-band (LF1) and from 0.085 to 0.15 Hz for second low frequency sub-band (LF2). The spectral Gini coefficient (SpG) was proposed to measure the inequality in the power distribution of the RR intervals in each of above-mentioned HRV bands. SpG from each band was compared with its respective traditional index of HRV during the conditions of rest and mental stress. All the differences were considered statistically significant for p < 0.05.

Results

There was a significant decrease in HF power (p = 0.046), as well as significant increases in heart rate (p = 0.004), LF power (p = 0.033), LF2 power (p = 0.019) and LF/HF (p = 0.002) during mental stress. There was also a significant increase in SpG(LF) (p = 0.009) and SpG(LF2) (p = 0.033) during mental stress. Coefficient of variation showed SpG has more homogeneity compared to the traditional index of HRV during mental stress.

Conclusions

This pilot study suggested that spectral inequality of Heart Rate Variability analyzed using the Gini coefficient seems to be an independent and homogeneous psychophysiological indicator of mental stress. Also, HR, LF/HF, SpG(LF) of HRV are possibly important, reliable and valid indicators of mental stress.

Assessment of Signal Processing Methods for Measuring the Respiratory Rate in the Neonatal Intensive Care Unit

Knowledge of the pathological instabilities in the breathing pattern can provide valuable insights into the cardiorespiratory status of the critically-ill infant as well as their maturation level. This paper is concerned with the measurement of respiratory rate in premature infants. We compare the rates estimated from the chest impedance pneumogram, the ECG-derived respiratory rhythms, and the PPG-derived respiratory rhythms against those measured in the reference standard of breath detection provided by attending clinical staff during 165 manual breath counts. We demonstrate that accurate RR estimates can be produced from all sources for RR in the 40-80 bpm (breaths per min) range. We also conclude that the use of indirect methods based on the ECG or the PPG poses a fundamental challenge in this population due to their poor behavior at fast breathing rates (upward of 80 bpm).

Spectral fusion-based breathing frequency estimation; experiment on activities of daily living

BACKGROUND

We study the estimation of breathing frequency (BF) derived from wearable single-channel ECG signal in the context of mobile daily life activities. Although respiration effects on heart rate variability and ECG morphology have been well established, studies on ECG-derived respiration in daily living settings are scarce; possibly due to considerable amount of disturbances in such data. Yet, unobtrusive BF estimation during everyday activities can provide vital information for both disease management and athletic performance optimization.

METHOD AND DATA

For robust ECG-derived BF estimation, we combine the respiratory information derived from R-R interval (RRI) variability and morphological scale variation of QRS complexes (MSV), acquired from ECG signals. Two different fusion techniques are applied on MSV and RRI signals: cross-power spectral density (CPSD) estimation and power spectrum multiplication (PSM). The algorithms were tested on large sets of data collected from 67 participants during office, household and sport activities, simulating daily living activities. We use spirometer reference BF to evaluate and compare our estimations made by different models.

RESULTS AND CONCLUSION

PSM acquires the least average error of BF estimation, [Formula: see text] and [Formula: see text], compared to the reference spirometer values. PSM offers approximately 25 and 75% less error in comparison with the CPSD fusion estimation and the estimation by those two exclusive sources, respectively. Our results demonstrate the superiority of both of the fusion approaches, compared to the estimation derived from either of RRI or MSV signals exclusively.

ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition

Monitoring of the respiration using the electrocardiogram (ECG) is desirable for the simultaneous study of cardiac activities and the respiration in the aspects of comfort, mobility, and cost of the healthcare system. This paper proposes a new approach for deriving the respiration from single-lead ECG based on the iterated Hilbert transform (IHT) and the Hilbert vibration decomposition (HVD). The ECG signal is first decomposed into the multicomponent sinusoidal signals using the IHT technique. Afterward, the lower order amplitude components obtained from the IHT are filtered using the HVD to extract the respiration information. Experiments are performed on the Fantasia and Apnea-ECG datasets. The performance of the proposed ECG-derived respiration (EDR) approach is compared with the existing techniques including the principal component analysis (PCA), R-peak amplitudes (RPA), respiratory sinus arrhythmia (RSA), slopes of the QRS complex, and R-wave angle. The proposed technique showed the higher median values of correlation (first and third quartile) for both the Fantasia and Apnea-ECG datasets as 0.699 (0.55, 0.82) and 0.57 (0.40, 0.73), respectively. Also, the proposed algorithm provided the lowest values of the mean absolute error and the average percentage error computed from the EDR and reference (recorded) respiration signals for both the Fantasia and Apnea-ECG datasets as 1.27 and 9.3%, and 1.35 and 10.2%, respectively. In the experiments performed over different age group subjects of the Fantasia dataset, the proposed algorithm provided effective results in the younger population but outperformed the existing techniques in the case of elderly subjects. The proposed EDR technique has the advantages over existing techniques in terms of the better agreement in the respiratory rates and specifically, it reduces the need for an extra step required for the detection of fiducial points in the ECG for the estimation of respiration which makes the process effective and less-complex. The above performance results obtained from two different datasets validate that the proposed approach can be used for monitoring of the respiration using single-lead ECG.

Cardiorespiratory coupling in young healthy subjects

OBJECTIVE

To quantify the presence of cardiorespiratory interaction in a group of 41 healthy subjects performing a subset of the Ewing test battery.

APPROACH

We measure the empirical distribution of the cardiorespiratory coupling time (RI), defined as the time from inspiration onset to R peaks in the ECG. The study protocol is a subset of the Ewing test battery. The respiratory function was measured with a thoracic belt and heart rate was obtained from a two channel ECG measurement. Both series of fiducial points were determined using custom software. Additionally, we determine the presence of cardiorespiratory coupling (CRC) and cardiorespiratory interaction (CRI) using Shannon entropy, synchrograms and coordigrams.

MAIN RESULTS

We observe that the RI distribution is asymmetric and nonuniform. These features are a manifestation of the causal relation between heart action and respiration. The preceding R peak strongly affects a position of inspiration onset. From the asymmetry of the RI distribution we conclude that this relation is stronger than the relation between inspiration onset and the following R peak. We use a suitable choice of surrogate data to prove that the result cannot be falsified. We observe a dual structure of the RI histograms, which may be related to the respiratory rhythmogenesis. We compare the sensitivity of RI histograms with other measures of CRI and CRC. In 46% of subjects, CRC appears in at least one stage of the examination, most often in resting states. In states of increased stress-orthostasis or physical (exercise)-the strength of coupling is visibly diminished. The nonuniform structure of the RI histogram is more sensitive to the presence of CRI than synchrograms or coordigrams are, as is well visible in the group averages. We also refer to the question of the most proper mathematical description of cardiorespiratory dynamics (phase domain or time domain). Finally, we formulate the hypothesis that the arterial blood pressure is a common driver of cardiac and respiratory rhythms.

SIGNIFICANCE

Analysis of the asymmetry of RI histograms is an interesting and sensitive method to study cardiorespiratory interaction and autonomic balance, in order to assess physical and mental health. The dual structure of the RI histograms which we have observed suggests the possible presence of a twofold mechanism for respiratory rhythmogenesis, as proposed by Galletly and Larsen.

A body position influence on ECG derived respiration

An influence of a human body position on ECG derived respiration (EDR) signal is presented in the paper. Examinations were performed during deep, suspended and normal breathing for eight people in four different body positions. EDR and thoracic impedance signals were compared using correlation and standard deviation coefficients. Obtained results have shown that it is possible to monitor breath activity of people being in different position, however a precise interpretation of the obtained signal is limited.

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.

Variational Mode Extraction: A New Efficient Method to Derive Respiratory Signals from ECG

ECG-derived respiratory (EDR) signal is an effective and inexpensive method to monitor the respiration. Previous studies have shown that the empirical mode decomposition (EMD) techniques can satisfactorily extract the EDR signal, however, their performances are degraded at the presence of noise. On the other hand, variational mode decomposition (VMD) performs good robustness against noise. In applications such as EDR extraction, where a specific mode is in interest, VMD imposes unnecessary computational cost. In this paper, we consider the extraction of EDR as a problem of obtaining a specific mode of a signal and suggest a new method named as variational mode extraction (VME). The method is established on the similar basis as VMD, with a new criterion: The residual signal after extracting the specific mode should have no or less energy at the center frequency of the mode. In this regard, VME is capable of solving the EDR problem by considering the EDR signal as a mode with approximate center frequency of zero. For verification, the respiratory rate signal is detected from EDR signal extracted by VME and compared it with those obtained by VMD, EMD-based methods, and bandpass filtering. The results confirm that the new method can extract the EDR signal with a better accuracy, while performing much lower computational cost and higher convergence rate.

Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants

OBJECTIVE

Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals.

APPROACH

Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient.

MAIN RESULTS

Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of  <250 Hz and  <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender.

SIGNIFICANCE

Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available.

A method for extracting respiratory frequency during blood pressure measurement, from oscillometric cuff pressure pulses and Korotkoff sounds recorded during the measurement

Respiratory frequency is an important physiological feature commonly used to assess health. However, the current measurements involve dedicated devices which not only increase the medical cost but also make health monitoring inconvenient. Earlier studies have shown that respiratory frequency could be extracted from electrocardiography (ECG) signal, but little was done to assess the possibility of extracting respiratory frequency from oscillometric cuff pressure pulses (OscP) or Korotkoff sounds (KorS), which are normally used for measuring blood pressure and more easily accessible than the ECG signal. This study presented a method to extract respiratory frequency from OscP and KorS during clinical blood pressure measurement. The method was evaluated with clinical data collected from 15 healthy participants, and its measurement accuracy was compared with a reference respiratory rate obtained with a magnetometer. Experimental results showed small non-significant mean absolute bias (0.019 Hz for OscP and 0.024 Hz for KorS) and high correlation (0.7 for both OscP and KorS) between the reference respiratory frequency and respiratory frequency extracted from OscP or KorS, indicating the high reliability of extracting respiratory frequency from OscP and KorS during normal blood pressure measurement.

Respiratory rate estimation from multilead directions, based on ECG delineation

Estimating the instantaneous respiratory rate (Rr) from the electrocardiogram (ECG) is of interest as respiration direct measurement in clinical situations is often cumbersome. In this study, the Rr was estimated from the same Final Directions of maximum projection (FD) used for multi lead ECG automatic delineation. Power spectral analysis over the directions based on QRS complex main peak and T wave onset, peak and end spatial loops was used for Rr estimation. On a subset of the Physionet MGH/MF dataset, the proposed method yielded more accurate Rr estimates (minimum mean absolute error (MAE), 2.82 bpm) than the frequency tracking algorithm (minimum MAE, 4.53 bpm) and Fourier-based frequency estimation (minimum MAE, 4.94 bpm) using each lead alone, outperforming also the weighted multi-signal oscillator-based algorithm estimates for two or three lead (minimum MAE, 3.04 bpm). It was also shown that the FD of the three orthogonalized leads from Principal Component algorithm, improve the performance of Rr estimation.

Instantaneous phase difference analysis between thoracic and abdominal movement signals based on complementary ensemble empirical mode decomposition

BACKGROUND

Thoracoabdominal asynchrony is often adopted to discriminate respiratory diseases in clinics. Conventionally, Lissajous figure analysis is the most frequently used estimation of the phase difference in thoracoabdominal asynchrony. However, the temporal resolution of the produced results is low and the estimation error increases when the signals are not sinusoidal. Other previous studies have reported time-domain procedures with the use of band-pass filters for phase-angle estimation. Nevertheless, the band-pass filters need calibration for phase delay elimination.

METHODS

To improve the estimation, we propose a novel method (named as instantaneous phase difference) that is based on complementary ensemble empirical mode decomposition for estimating the instantaneous phase relation between measured thoracic wall movement and abdominal wall movement. To validate the proposed method, experiments on simulated time series and human-subject respiratory data with two breathing types (i.e., thoracic breathing and abdominal breathing) were conducted. Latest version of Lissajous figure analysis and automatic phase estimation procedure were compared.

RESULTS

The simulation results show that the standard deviations of the proposed method were lower than those of two other conventional methods. The proposed method performed more accurately than the two conventional methods. For the human-subject respiratory data, the results of the proposed method are in line with those in the literature, and the correlation analysis result reveals that they were positively correlated with the results generated by the two conventional methods. Furthermore, the standard deviation of the proposed method was also the smallest.

CONCLUSIONS

To summarize, this study proposes a novel method for estimating instantaneous phase differences. According to the findings from both the simulation and human-subject data, our approach was demonstrated to be effective. The method offers the following advantages: (1) improves the temporal resolution, (2) does not introduce a phase delay, (3) works with non-sinusoidal signals, (4) provides quantitative phase estimation without estimating the embedded frequency of breathing signals, and (5) works without calibrated measurements. The results demonstrate a higher temporal resolution of the phase difference estimation for the evaluation of thoracoabdominal asynchrony.

Quantifying the Interactions between Maternal and Fetal Heart Rates by Transfer Entropy

Evidence of the short term relationship between maternal and fetal heart rates has been found in previous studies. However there is still limited knowledge about underlying mechanisms and patterns of the coupling throughout gestation. In this study, Transfer Entropy (TE) was used to quantify directed interactions between maternal and fetal heart rates at various time delays and gestational ages. Experimental results using maternal and fetal electrocardiograms showed significant coupling for 63 out of 65 fetuses, by statistically validating against surrogate pairs. Analysis of TE showed a decrease in transfer of information from fetus to the mother with gestational age, alongside the maturation of the fetus. On the other hand, maternal to fetal TE was significantly greater in mid (26-31 weeks) and late (32-41 weeks) gestation compared to early (16-25 weeks) gestation (Mann Whitney Wilcoxon (MWW) p<0.05). TE further increased from mid to late, for the fetuses with RMSSD of fetal heart rate being larger than 4 msec in the late gestation. This difference was not observed for the fetuses with smaller RMSSD, which could be associated with the quiet sleep state. Delay in the information transfer from mother to fetus significantly decreased (p = 0.03) from mid to late gestation, implying a decrease in fetal response time. These changes occur concomitant with the maturation of the fetal sensory and autonomic nervous systems with advancing gestational age. The effect of maternal respiratory rate derived from maternal ECG was also investigated and no significant relationship was found between breathing rate and TE at any lag. In conclusion, the application of TE with delays revealed detailed information on the fetal-maternal heart rate coupling strength and latency throughout gestation, which could provide novel clinical markers of fetal development and well-being.

Detection of sleep apnea events via tracking nonlinear dynamic cardio-respiratory coupling from electrocardiogram signals

Obstructive sleep apnea (OSA) is a common sleep disorder that causes increasing risk of mortality and affects quality of life of approximately 6.62% of the total US population. Timely detection of sleep apnea events is vital for the treatment of OSA. In this paper, we present a novel approach based on extracting the quantifiers of nonlinear dynamic cardio-respiratory coupling from electrocardiogram (ECG) signals to detect sleep apnea events. The quantifiers of the cardio-respiratory dynamic coupling were extracted based on recurrence quantification analysis (RQA), and a battery of statistical data mining techniques were to enhance OSA detection accuracy. This approach would lead to a cost-effective and convenient means for screening of OSA, compared to traditional polysomnography (PSG) methods. The results of tests conducted using data from PhysioNets Sleep Apnea database suggest excellent quality of the OSA detection based on a thorough comparison of multiple models, using model selection criteria of validation data: Sensitivity (91.93%), Specificity (85.84%), Misclassification (11.94%) and Lift (2.7).

The extraction of the new components from electrogastrogram (EGG), using both adaptive filtering and electrocardiographic (ECG) derived respiration signal

Electrogastrographic examination (EGG) is a noninvasive method for an investigation of a stomach slow wave propagation. The typical range of frequency for EGG signal is from 0.015 to 0.15 Hz or (0.015–0.3 Hz) and the signal usually is captured with sampling frequency not exceeding 4 Hz. In this paper a new approach of method for recording the EGG signals with high sampling frequency (200 Hz) is proposed. High sampling frequency allows collection of signal, which includes not only EGG component but also signal from other organs of the digestive system such as the duodenum, colon as well as signal connected with respiratory movements and finally electrocardiographic signal (ECG). The presented method allows improve the quality of analysis of EGG signals by better suppress respiratory disturbance and extract new components from high sampling electrogastrographic signals (HSEGG) obtained from abdomen surface. The source of the required new signal components can be inner organs such as the duodenum and colon. One of the main problems that appear during analysis the EGG signals and extracting signal components from inner organs is how to suppress the respiratory components. In this work an adaptive filtering method that requires a reference signal is proposed. In the present research, the respiratory component is obtained from non standard ECG (NSECG) signal. For purposes of this paper non standard ECG (namely NSECG) is used, because ECG signal was recorded by other than the standard electrodes placement on the surface of the abdomen. The electrocardiographic derived respiration signal (EDR) is extracted using the phenomena of QRS complexes amplitude modulation by respiratory movements. The main idea of extracting the EDR signal from electrocardiographic signal is to obtain the modulating signal. Adaptive filtering is done in the discrete cosine transform domain. Next the resampled HSEGG signal with attenuated respiratory components is low pass filtered and as a result the extended electrogastrographic signals, included EGG signal and components from other inner organs of digestive system is obtained. One of additional features of the proposed method is a possibility to obtain simultaneously recorded signals, such as: non-standard derivation of ECG, heart rate variability signal, respiratory signal, and EGG signal that allow investigating mutual interferences among internal human systems.

The matching of sinus arrhythmia to respiration: are trauma patients without serious injury comparable to healthy laboratory subjects?

We sought to better understand the physiology underlying the metrics of heart rate variability (HRV) in trauma patients without serious injury, compared to healthy laboratory controls. In trauma patients without serious injury (110 subjects, 470 2-min data segments), we studied the correlation between sinus arrhythmia (SA) rate, heart rate (HR), and respiratory rate (RR). Most segments with 2.4 < HR/RR < 4.8 exhibited SA-RR matching, whereas rate matching was absent in 81% of the segments with HR/RR < 2.4 and in 86% of the segments with HR/RR > 4.8. The findings were comparable, in some cases remarkably so, to previous reports from healthy laboratory subjects. The presence (or absence) of SA-RR matching, when SA is largely controlled by respiration, can be anticipated in this trauma population. This work provides a valuable step towards the definition of patterns of HRV found in trauma patients with and without life-threatening injury.

Adaption of cardio-respiratory balance during day-rest compared to deep sleep--an indicator for quality of life?

Heart rate and breathing rate fluctuations represent interacting physiological oscillations. These interactions are commonly studied using respiratory sinus arrhythmia (RSA) of heart rate variability (HRV) or analyzing cardiorespiratory synchronization. Earlier work has focused on a third type of relationship, the temporal ratio of respiration rate and heart rate (HRR). Each method seems to reveal a specific aspect of cardiorespiratory interaction and may be suitable for assessing states of arousal and relaxation of the organism. We used HRR in a study with 87 healthy subjects to determine the ability to relax during 5 day-resting periods in comparison to deep sleep relaxation. The degree to which a person during waking state could relax was compared to somatic complaints, health-related quality of life, anxiety and depression. Our results show, that HRR is barely connected to balance (LF/HF) in HRV, but significantly correlates to the perception of general health and mental well-being as well as to depression. If relaxation, as expressed in HRR, during day-resting is near to deep sleep relaxation, the subjects felt healthier, indicated better mental well-being and less depressive moods.

Aerobic exercise during pregnancy and presence of fetal-maternal heart rate synchronization

It has been shown that short-term direct interaction between maternal and fetal heart rates may take place and that this interaction is affected by the rate of maternal respiration. The aim of this study was to determine the effect of maternal aerobic exercise during pregnancy on the occurrence of fetal-maternal heart rate synchronization.

Spectral Boundary Adaptation on Heart Rate Variability for Sleep and Wake Classification

A method of adapting the boundaries when extracting the spectral features from heart rate variability (HRV) for sleep and wake classification is described. HRV series can be derived from electrocardiogram (ECG) signals obtained from single-night polysomnography (PSG) recordings. Conventionally, the HRV spectral features are extracted from the spectrum of an HRV series with fixed boundaries specifying bands of very low frequency (VLF), low frequency (LF), and high frequency (HF). However, because they are fixed, they may fail to accurately reflect certain aspects of autonomic nervous activity which in turn may limit their discriminative power, e.g. in sleep and wake classification. This is in part related to the fact that the sympathetic tone (partially reflected in the LF band) and the respiratory activity (modulated in the HF band) vary over time. In order to minimize the impact of these variations, we adapt the HRV spectral boundaries using time-frequency analysis. Experiments were conducted on a data set acquired from two groups with 15 healthy and 15 insomnia subjects each. Results show that adapting the HRV spectral features significantly increased their discriminative power when classifying sleep and wake. Additionally, this method also provided a significant improvement of the overall classification performance when used in combination with other HRV non-spectral features. Furthermore, compared with the use of actigraphy, the classification performed better when combining it with the HRV features.

In vivo cardiac phase response curve elucidates human respiratory heart rate variability

Recovering interaction of endogenous rhythms from observations is challenging, especially if a mathematical model explaining the behaviour of the system is unknown. The decisive information for successful reconstruction of the dynamics is the sensitivity of an oscillator to external influences, which is quantified by its phase response curve. Here we present a technique that allows the extraction of the phase response curve from a non-invasive observation of a system consisting of two interacting oscillators--in this case heartbeat and respiration--in its natural environment and under free-running conditions. We use this method to obtain the phase-coupling functions describing cardiorespiratory interactions and the phase response curve of 17 healthy humans. We show for the first time the phase at which the cardiac beat is susceptible to respiratory drive and extract the respiratory-related component of heart rate variability. This non-invasive method for the determination of phase response curves of coupled oscillators may find application in many scientific disciplines.

Pattern recognition in airflow recordings to assist in the sleep apnoea-hypopnoea syndrome diagnosis

This paper aims at detecting sleep apnoea-hypopnoea syndrome (SAHS) from single-channel airflow (AF) recordings. The study involves 148 subjects. Our proposal is based on estimating the apnoea-hypopnoea index (AHI) after global analysis of AF, including the investigation of respiratory rate variability (RRV). We exhaustively characterize both AF and RRV by extracting spectral, nonlinear, and statistical features. Then, the fast correlation-based filter is used to select those relevant and non-redundant. Multiple linear regression, multi-layer perceptron (MLP), and radial basis functions are fed with the features to estimate AHI. A conventional approach, based on scoring apnoeas and hypopnoeas, is also assessed for comparison purposes. An MLP model trained with AF and RRV selected features achieved the highest agreement with the true AHI (intra-class correlation coefficient = 0.849). It also showed the highest diagnostic ability, reaching 92.5 % sensitivity, 89.5 % specificity and 91.5 % accuracy. This suggests that AF and RRV can complement each other to estimate AHI and help in SAHS diagnosis.

Pulmonary and heart rate responses to wearing N95 filtering facepiece respirators

BACKGROUND

Filtering facepiece respirators are the most common respirator worn by US health care and industrial workers, yet little is known on the physiologic impact of wearing this protective equipment.

METHODS

Twenty young, healthy subjects exercised on a treadmill at a low-moderate (5.6 km/h) work rate while wearing 4 different models of N95 filtering facepiece respirators for 1 hour each, 2 models of which were equipped with exhalation valves, while being monitored for physiologic variables.

RESULTS

Compared with controls, respirator use was associated with mean 1 hour increases in heart rate (range, 5.7-10.6 beats per minute, P < .001), respiratory rate (range, 1.4-2.4 breaths per minute, P < .05), and transcutaneous carbon dioxide (range, 1.7-3.0 mm Hg, P < .001). No significant differences in oxygen saturation between controls and respirators were noted (P > .05).

CONCLUSION

The pulmonary and heart rate responses to wearing a filtering facepiece respirator for 1 hour at a low-moderate work rate are relatively small and should generally be well tolerated by healthy persons.

Application of kernel principal component analysis for single-lead-ECG-derived respiration

Recent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA (kPCA) is presented. KPCA can be seen as a generalization of PCA where nonlinearities in the data are taken into account by nonlinear mapping of the data, using a kernel function, into a higher dimensional space in which PCA is carried out. The comparison of several kernels suggests that a radial basis function (RBF) kernel performs the best when deriving EDR signals. Further improvement is carried out by tuning the parameter σ(2) that represents the variance of the RBF kernel. The performance of kPCA is assessed by comparing the EDR signals to a reference respiratory signal, using the correlation and the magnitude squared coherence coefficients. When comparing the coefficients of the tuned EDR signals using kPCA to EDR signals obtained using PCA and the algorithm based on the R peak amplitude, statistically significant differences are found in the correlation and coherence coefficients (both p<0.0001), showing that kPCA outperforms PCA and R peak amplitude in the extraction of a respiratory signal from single-lead ECGs.

Feasibility of continuous multiorgan variability analysis in the intensive care unit

PURPOSE

The aim of the study was to evaluate the feasibility of continuous heart and respiratory rate variability (HRV and RRV, respectively) monitoring in critically ill patients derived from electrocardiogram (ECG) and end-tidal capnography (etCO(2)) waveforms.

METHODS

Thirty-four patients (age, 56.5 ± 15.9 years; Acute Physiology and Chronic Health Evaluation II score, 22.8 ± 6.7) underwent continuous recording of ECG and etCO(2) waveforms from intensive care unit admission and intubation to discharge or maximum of 14 days. Overlapping 5-minute windows were analyzed with a wide range of variability measures (time, frequency, entropy, and scale-invariant and nonlinear domains). Waveform data quality, presence of disconnections and arrhythmias, quality of beat and breath detection, and subsequent variability computations were evaluated.

RESULTS

Patients were enrolled for 11.0 ± 3.6 days. The proportion of missing waveform data among all patients was (median [interquartile range, maximum]) 2.9% (1.3%-9.7%, 36.4%) for ECG and 3.1% (1.1%-11.4%, 84.5%) for etCO(2). Heart rate variability data loss (ie, proportion of windows removed) was 1.3% (1.0%-2.1%, 5.9%) due to disconnection, 0.6% (0.1%-3.9%, 39.5%) due to atrial fibrillation, and 6.6% (1.4%-17.9%, 89.0%) due to data cleaning. Respiratory rate variability data loss was 7.3% (2.9%-11.6%, 47.7%) due to disconnection (or apnea) and 5.5% (2.9%-8.4%, 56.4%) due to cleaning. Continuous individualized multiorgan variability analysis processing resulted in HRV and RRV computations for 81.2% ± 25.0% and 87.5% ± 11.9% of available ECG and etCO(2) waveform data, respectively.

CONCLUSIONS

The quality of continuously recorded ECG and etCO(2) waveforms in critically ill patients is adequate for subsequent continuous variability monitoring in this pilot study. The clinical utility of continuous variability analysis merits further investigation.