Instantaneous Respiratory Rate Research Articles

Clinical validation of a contactless respiration rate monitor

Respiratory rate (RR) is an often underestimated and underreported vital sign with tremendous clinical value. As a predictor of cardiopulmonary arrest, chronic obstructive pulmonary disease (COPD) exacerbation or indicator of health state for example in COVID-19 patients, respiratory rate could be especially valuable in remote long-term patient monitoring, which is challenging to implement. Contactless devices for home use aim to overcome these challenges. In this study, the contactless Sleepiz One+ respiration monitor for home use during sleep was validated against the thoracic effort belt. The agreement of instantaneous breathing rate and breathing rate statistics between the Sleepiz One+ device and the thoracic effort belt was initially evaluated during a 20-min sleep window under controlled conditions (no body movement) on a cohort of 19 participants and secondly in a more natural setting (uncontrolled for body movement) during a whole night on a cohort of 139 participants. Excellent agreement was shown for instantaneous breathing rate to be within 3 breaths per minute (Brpm) compared to thoracic effort band with an accuracy of 100% and mean absolute error (MAE) of 0.39 Brpm for the setting controlled for movement, and an accuracy of 99.5% with a MAE of 0.48 Brpm for the whole night measurement, respectively. Excellent agreement was also achieved for the respiratory rate statistics over the whole night with absolute errors of 0.43, 0.39 and 0.67 Brpm for the 10th, 50th and 90th percentiles, respectively. Based on these results we conclude that the Sleepiz One+ can estimate instantaneous respiratory rate and its summary statistics at high accuracy in a clinical setting. Further studies are required to evaluate the performance in the home environment, however, it is expected that the performance is at similar level, as the measurement conditions for the Sleepiz One+ device are better at home than in a clinical setting.

Motion- Based Respiratory Rate Estimation with Motion Artifact Removal Using Video of Face and Upper Body.

Respiratory rate (RR) is a significant indicator of health conditions. Remote contactless measurement of RR is gaining popularity with recent respiratory tract infection awareness. Among various methods of contactless RR measurement, a video of an individual can be used to obtain an instantaneous RR. In this paper, we introduce an RR estimation based on the subtle motion of the head or upper chest captured on an RGB camera. Motion-based respiratory monitoring allows us to acquire RR from individuals with partial face coverings, such as glasses or a face mask. However, motion-based RR estimation is vulnerable to the subject's voluntary movement. In this work, adaptive selection between face and chest regions plus a motion artifact removal technique enables us to obtain a much cleaner respiratory signal from the video recordings. The average mean absolute error (MAE) for controlled and natural breathing is 1.95 BPM using head motion only and 1.28 BPM using chest motion only. Our results demonstrate the possibility of continuous monitoring of breathing rate in real-time with any personal device equipped with an RGB camera, such as a laptop or a smartphone.

PPG-Based Respiratory Rate Monitoring Using Hybrid Vote-Aggregate Fusion Technique.

In this work, we present a low-complexity photoplethysmography-based respiratory rate monitoring (PPG-RRM) algorithm that achieves high accuracy through a novel fusion method. The proposed technique extracts three respiratory-induced variation signals, namely the maximum slope, the amplitude, and the frequency, from the PPG signal. The variation signals undergo time domain peak detection to identify the inter-breath intervals and produce three different instantaneous respiratory rate (IRR) estimates. The IRR estimates are combined through a hybrid vote-aggregate fusion scheme to generate the final RR estimate. We utilize the publicly available Capnobase data-sets [1] that contain both PPG and capnography signals to evaluate our RR monitoring algorithm. Compared to the reference capnography IRR, the proposed PPG-RRM algorithm achieves a mean absolute error (MAE) of 1.44 breaths per minute (bpm), a mean error (ME) of 0.70±2.54 bpm, a root mean square error (RMSE) of 2.63 bpm, and a Pearson correlation coefficient r = 0.95, p < .001.

Pulse transit time based respiratory rate estimation with singular spectrum analysis.

Respiratory rate (RR) is an important vital sign which can be difficult to measure accurately and unobtrusively in routine clinical practice. Pulse transit time (PTT), on the other hand, is unobtrusive to collect from electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Using PTT is a novel method to estimate and monitor blood pressure (BP) and RR. This study aimed to estimate continuous RR using PTT with singular spectrum analysis to extract respiratory components. The performance of this method was validated on 17 subjects who carried out spontaneous breathing and controlled deep breathing conditions. Three types of estimated RR parameters (average RR by power spectral density (PSD) (RRPSD), number of breaths (RR#), and instantaneous RR (RRinst)) were compared with the corresponding reference RR. The reference RR was collected using a respiratory belt. Our findings demonstrate that the PTT signal reliably tracked respiratory variation with a root mean square error of 0.84, 1.11, and 0.74 breaths/min for RRPSD, RR#, and RRinst estimations, respectively. Overall, RR estimated by PTT was more accurate than heart/pulse rate interval, QRS area, and PPG amplitude, which were also extracted from ECG and PPG. The results suggest that it may be feasible to use PTT as an estimation of RR and that ECG and PPG may be relied upon for monitoring not only RR but also BP and heart rate. Graphical abstract The Pulse Transit Time (PTT) based Respiratory Rate (RR) estimation with Singular Spectrum Analysis (SSA) provides a superior performance than the method with other respiratory indicators extracted from Electrocardiogram (ECG) or Photoplethysmogram (PPG).

Contactless Anesthesia Monitoring in Spontanously Breathing Rodents.

Laboratory animal science plays a crucial role in medical and biological research. In the last decades, stricter regulations were enforced to safeguard laboratory animals. Following the "3Rs" guiding principles, animal trials should be replaced, reduced and refined, whenever possible.A contactless modality capable of assessing the respiratory rate (RR) and additional breath related characteristics can potentially refine anesthetic interventions in rodents by continuously monitoring their anesthetic depth. This can reduce complications and thus the number of needed animals.We were able to extract the instantaneous RR in rodents with a sum squared error (SSE) of 0.26 breaths/min from color video. A correlation of 0.9781 compared to an Electrocardiography (ECG) based reference was achieved. Furthermore, additional temporal and morphological characteristics were extracted, which are sensitive for changes in the anesthetic depth.

Cardiorespiratory Temporal Causal Links and the Differences by Sport or Lack Thereof.

Fitness level, fatigue and adaptation are important factors for determining the optimal training schedule and predicting future performance. We think that adding analysis of the mutual relationships between cardiac and respiratory activity enables better athlete profiling and feedback for improving training. Therefore, the main objectives were (1) to apply several methods for temporal causality analysis to cardiorespiratory data; (2) to establish causal links between the signals; and (3) to determine how parameterized connections differed across various subgroups. One hundred elite athletes (31 female) and a control group of 20 healthy students (6 female) took part in the study. All were asked to follow a protocol comprising two 5-min sessions of free breathing - once supine, once standing. The data were collected using Pneumonitor 2. Respiratory-related curves were obtained through impedance pneumography, along with a single-lead ECG. Several signals (e.g., tidal volume, instantaneous respiratory rate, and instantaneous heart rate) were derived and stored as: (1) raw data down-sampled to 25Hz; (2) further down-sampled to 2.5Hz; and (3) beat-by-beat sequences. Granger causality frameworks (pairwise-conditional, spectral or extended), along with Time Series Models with Independent Noise (TiMINo), were studied. The connections enabling the best distinctions were found using recursive feature elimination with a random forest kernel. Temporal causal links are the most evident between tidal volume and instantaneous heart rate signals. Predictions of the “effect” variable were improved by adding preceding “cause” samples, by medians of 20.3% for supine and 14.2% for standing body positions. Parameterized causal link structures and directions distinguish athletes from non-athletes with 83.3% accuracy on average. They may also be used to supplement standard analysis and enable classification into groups exhibiting different static and dynamic components during performance. Physiological markers of training may be extended to include cardiorespiratory data, and causality analysis may improve the resolution of training profiling and the precision of outcome prediction.

Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions.

Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (ii) a commercial wrist-worn device (WaveletHealth Inc., Mountain View, CA, USA). Comparator reference data were obtained via a thermocouple placed under the nostrils, providing ground-truth information concerning respiration cycles. Tracking instantaneous frequencies was performed in the joint time-frequency spectrum of the (4 Hz re-sampled) respiratory-induced modulation using the WSST, from data obtained from 10 healthy subjects. The estimated instantaneous respiratory rates have shown to be highly correlated with breath-by-breath variations derived from the reference signals. The proposed method produced more accurate results compared to averaged RR obtained using 32 s windows investigated with overlap between successive windows of (i) zero and (ii) 28 s. For a set of five healthy subjects, the averaged similarity between reference RR and instantaneous RR, given by the longest common subsequence (LCSS) algorithm, was calculated as 0.69; this compares with averaged similarity of 0.49 using 32 s windows with 28 s overlap between successive windows. The results provide insight into estimation of IRR and show that upper body positions produced PPG signals from which a better respiration signal was extracted than for other body locations.

Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations.

In this study, we proposed a novel method for extracting the instantaneous respiratory rate (IRR) from the pulse oximeter photoplethysmogram (PPG). The method was performed in three main steps: (1) a time-frequency transform called synchrosqueezing transform (SST) was used to extract the respiratory-induced intensity, amplitude and frequency variation signals from PPG, (2) the second SST was applied to each extracted respiratory-induced variation signal to estimate the corresponding IRR, and (3) the proposed peak-conditioned fusion method then combined the IRR estimates to calculate the final IRR. We validated the implemented method with capnography and nasal/oral airflow as the reference RR using the limits of agreement (LOA) approach. Compared to simple fusion and single respiratory-induced variation estimations, peak-conditioned fusion shows better performance. It provided a bias of 0.28 bpm with the 95% LOAs ranging from −3.62 to 4.17, validated against capnography and a bias of 0.04 bpm with the 95% LOAs ranging from −5.74 to 5.82, validated against nasal/oral airflow. This algorithm would expand the functionality of a conventional pulse oximetry beyond the measurement of heart rate and oxygen saturation to measure the respiratory rate continuously and instantly.

Polymer-coated fiber optic probe for the monitoring of breathing pattern and respiratory rate.

In recent years, no-invasive and small size systems are meeting the demand of the new healthcare system, in which the vital signs monitoring is gaining in importance. In this context, Fiber Bragg grating (FBG) sensors are becoming very popular and FBG-based systems could be used for monitoring vital signs. At the same time, FBG could be able to sense chemical parameters by the polymer functionalization. The aim of our study was investigating the ability of a polymer-coated FBG-based probe for monitoring breathing patterns and respiratory rates. We tested the proposed FBG-based probe on 9 healthy volunteers during spirometry, the most common pulmonary function test. Results showed the high accuracy of the proposed probe to detect respiratory rate. The comparison between the respiratory rates estimated by the probe with the ones by the spirometer showed the absolute value of the percentage errors lower than 2.07% (in the 78% of cases <.91%). Lastly, a Bland Altman analysis was performed to compare the instantaneous respiratory rate values gathered by the spirometer and the FBG probe showing the feasibility of breath-by-breath monitoring by the proposed probe. Results showed a bias of 0.06± 2.90 $\mathrm{breaths}\square {\mathrm {min}}^{-1}$. Additionally, our system was able to follow the breathing activities and monitoring the breathing patterns.

How Nonlinear-Type Time-Frequency Analysis Can Help in Sensing Instantaneous Heart Rate and Instantaneous Respiratory Rate from Photoplethysmography in a Reliable Way.

Despite the population of the noninvasive, economic, comfortable, and easy-to-install photoplethysmography (PPG), it is still lacking a mathematically rigorous and stable algorithm which is able to simultaneously extract from a single-channel PPG signal the instantaneous heart rate (IHR) and the instantaneous respiratory rate (IRR). In this paper, a novel algorithm called deppG is provided to tackle this challenge. deppG is composed of two theoretically solid nonlinear-type time-frequency analyses techniques, the de-shape short time Fourier transform and the synchrosqueezing transform, which allows us to extract the instantaneous physiological information from the PPG signal in a reliable way. To test its performance, in addition to validating the algorithm by a simulated signal and discussing the meaning of “instantaneous,” the algorithm is applied to two publicly available batch databases, the Capnobase and the ICASSP 2015 signal processing cup. The former contains PPG signals relative to spontaneous or controlled breathing in static patients, and the latter is made up of PPG signals collected from subjects doing intense physical activities. The accuracies of the estimated IHR and IRR are compared with the ones obtained by other methods, and represent the state-of-the-art in this field of research. The results suggest the potential of deppG to extract instantaneous physiological information from a signal acquired from widely available wearable devices, even when a subject carries out intense physical activities.

Monitoring of nocturnal central sleep apnea in Heart failure patients using noncontact respiratory differences.

Monitoring of respiration patterns allows the early detection of various breathing disorders and may better identify those at risk for adverse acute outcomes in a variety of clinical settings. In this paper, we report on the use of SleepMinder (SM), a bedside non-contact Doppler-based biomotion recording sensor, to monitor remotely the nocturnal respiration patterns of 50 patients with systolic Heart failure (HF) while undergoing a lab based Polysomnography (PSG) test. A new respiration rate (RR) monitoring algorithm was developed based on the collected overnight radar signals. Two schemes of RR scoring were utilized: respiratory rate count (RRC) and instantaneous respiratory rates (IRR). Analysis of SM vs. PSG revealed that the mean/median IRR scored by SM is highly correlated with that scored on the nasal flow/effort signals from the corresponding PSG studies on all patients, with a significant correlation coefficient of 0.98 (average absolute difference of 0.31 breaths/min), and 0.97 (p<;0.01, average absolute difference of 0.38 breaths/min) for the median and mean of RR respectively. Our experimental results also show that the difference between the RR estimations from IRR and RRC schemes can be utilized to identify central sleep apnea (CSA)/Cheyne-Stokes respiration (CSR) sections without additional apnea detection modules. As a result, with a sensitivity and specificity of 71% and 88% respectively, and an accuracy of 86%, our CSA/CSR screener, plugged with our RR estimation, can play an important role in the remote management of HF patients.

Statistical analysis of the age dependence of the normal capnogram.

The age dependence of the time-based capnogram from normal, healthy subjects has not been quantitatively characterized. The existence of age dependence would impact the development and operation of automated quantitative capnographic tools. Here, we quantitatively assess the relationship between normal capnogram shape and age. Capnograms were collected from healthy subjects, and physiologically-based features (exhalation duration, end-tidal CO2 and time spent at this value, normalized time spent at end-tidal CO2, end-exhalation slope, and instantaneous respiratory rate) were computationally extracted. The mean values of the individual features over 30 exhalations were linearly regressed against subject age, accounting for inter-feature correlation. After data collection, 154 of 178 subjects were eligible for analysis, with an age range of 3-78 years (mean age 39, std. dev. 20 years). The Bonferroni-corrected joint 95% confidence intervals (CIs) of the regression line slopes contained the origin for five of six features (the remaining CI was only slightly offset from the origin). The associated individual r2 values for the regressions were all below 0.07. We conclude that age is not a significant explanatory factor in describing variations in the shape of the normal capnogram. This finding could be exploited in the design of automated methods for quantitative capnogram analysis across a range of ages.

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.

Estimating instantaneous respiratory rate from the photoplethysmogram.

The photoplethysmogram (PPG) obtained from pulse oximetry shows the local changes of blood volume in tissues. Respiration induces variation in the PPG baseline due to the variation in venous blood return during each breathing cycle. We have proposed an algorithm based on the synchrosqueezing transform (SST) to estimate instantaneous respiratory rate (IRR) from the PPG. The SST is a combination of wavelet analysis and a reallocation method which aims to sharpen the time-frequency representation of the signal and can provide an accurate estimation of instantaneous frequency. In this application, the SST was applied to the PPG and IRR was detected as the predominant ridge in the respiratory band (0.1 Hz - 1 Hz) in the SST plane. The algorithm was tested against the Capnobase benchmark dataset that contains PPG, capnography, and expert labelled reference respiratory rate from 42 subjects. The IRR estimation accuracy was assessed using the root mean square (RMS) error and Bland-Altman plot. The median RMS error was 0.39 breaths/min for all subjects which ranged from the lowest error of 0.18 breaths/min to the highest error of 13.86 breaths/min. A Bland-Altman plot showed an agreement between the IRR obtained from PPG and reference respiratory rate with a bias of -0.32 and limits agreement of -7.72 to 7.07. Extracting IRR from PPG expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.

Non-invasive respiratory volume monitoring in patients with traumatic thoracic injuries

Background Respiratory decompensation is common after traumatic thoracic injuries such as multiple rib fractures and pulmonary contusions. A continuous, non-invasive, impedance-based respiratory volume monitor generates right and left tidal volume measurements, reflecting air exchange in the lungs and derives an instantaneous respiratory rate. The feasibility of using unilateral respiratory volume monitor–based tidal impedance measurements to monitor respiratory status in trauma patients is evaluated. Methods Three intensive care unit patients with three or more rib fractures following blunt trauma had continuous respiratory volume monitor measurements with a novel non-invasive impedance-based device (ExSpiron, Respiratory Motion Inc., Waltham, MA) and corresponding clinical data to permit analysis. Tidal impedance measurements were collected from both the injured and non-injured sides and converted into bilateral respiratory volume monitor measurements using advanced algorithms. Results In Patient 1, following evacuation of a pneumothorax, the respiratory volume monitor showed a significant increase in tidal measurements coupled with a compensatory decrease in tidal measurements on the uninjured side and a decrease in respiratory rate. In Patient 2, tidal measurements were only slightly decreased on both the injured side and uninjured side; respiratory rate remained unchanged. This patient remained stable and required no intervention. Patient 3 demonstrated a sustained decrease in tidal measurements on the injured side that corresponded with radiograph findings and clinical deterioration leading to the need for endotracheal intubation. Conclusions The results from these cases demonstrate that respiratory volume monitor can generate unilateral respiratory tidal measurements and respiratory rate in patients with traumatic thoracic injuries. Continuous respiratory volume monitor in patients with thoracic trauma has strong potential for application in the military, aeromedical, and other austere environments where respiratory monitoring is problematic. Future studies to investigate the utility of this technology are warranted.

Respiratory rate estimation from the ECG using an instantaneous frequency tracking algorithm

Monitoring the respiratory rate (RR) is important in many clinical and non-clinical situations but it is difficult in practice, for existing devices are obtrusive, bulky and expensive. The extraction of the RR from the routinely acquired electrocardiogram (ECG) has been proposed lately. Two approaches exist, one exploiting the modulation of the heart rate by the respiration, known as the respiratory sinus arrhythmia (RSA) and the other using the modulation by the respiration of the R-peak amplitudes (RPA). In this study, the weighted multi-signal oscillator based band pass filtering (W-OSC) algorithm is applied to track the common frequency in the RSA and RPA waveforms simultaneously, as an estimate of the instantaneous RR. On the public PhysioNet Fantasia data set, it is shown that the presented method is automatic, instantaneous and comparable in accuracy to the state-of-the-art.

Neurons of the A5 region are required for the tachycardia evoked by electrical stimulation of the hypothalamic defence area in anaesthetized rats

In order to assess the possible interactions between the pontine A5 region and the hypothalamic defence area (HDA), we have examined the pattern of double staining for c-Fos protein immunoreactivity (c-Fos-ir) and tyrosine hydroxylase, throughout the rostrocaudal extent of the A5 region in spontaneously breathing anaesthetized male Sprague-Dawley rats during electrical stimulation of the HDA. Activation of the HDA elicited a selective increase in c-Fos-ir with an ipsilateral predominance in catecholaminergic and non-catecholaminergic A5 somata (P < 0.001 in both cases). A second group of experiments was done to examine the importance of the A5 region in modulating the cardiorespiratory response evoked from the HDA. Cardiorespiratory changes were analysed in response to electrical stimulation of the HDA before and after ipsilateral microinjection of muscimol within the A5 region. Stimulation of the HDA evoked an inspiratory facilitatory response, consisting of an increase in respiratory rate (P < 0.001) due to a decrease in expiratory time (P < 0.01). The respiratory response was accompanied by a pressor response (P < 0.001) and tachycardia (P < 0.001). After muscimol microinjection within the A5 region, pressor and heart rate responses to HDA stimulation were reduced (P < 0.01 and P < 0.001, respectively). The respiratory response persisted unchanged. Finally, to confirm functional interactions between the HDA and the A5 region, extracellular recordings of putative A5 neurones were obtained during HDA stimulation. Seventy-five A5 cells were recorded, 35 of which were affected by the HDA (47%). These results indicate that neurones of the A5 region participate in the cardiovascular response evoked from the HDA. The possible mechanisms involved in these interactions are discussed.

Validation of a new non-invasive automatic monitor of respiratory rate for postoperative subjects

Respiratory rate is an important measurement in patient care but frequently poorly assessed. We set out to develop a simple non-invasive device to reliably measure respiratory movements and estimate respiratory rate, in clinical circumstances. Respiratory movement was detected with an encapsulated tri-axial accelerometer (Orient speck) and the data transmitted wirelessly to a computer for analysis. We studied subjects after gynaecological surgery who received opioid analgesia, and compared the derived signal with a signal from nasal cannula using directly matched breaths and within the same 5 min epoch. We analysed the signals for 5 min epochs over a 15 h recording period. For matched breath analysis, the instantaneous respiratory rates matched within 2 bpm on 86% of occasions. A similar match was found between epoch averages of the respiratory rate. The mean absolute difference between the respiratory rate measured by nasal cannula and Orient speck was 0.6 bpm. The Orient speck generated reliable measures of respiratory rate every 5 min in 95.4% of epochs. The Orient speck provides a reliable measure of respiratory rate at frequent intervals in subjects receiving patient-controlled morphine analgesia after surgery.

Evaluation of the physiological data indicating the dynamic stress level of drivers

Bio-signal recordings that reflect the operating conditions of the physiological systems provide several useful metrics to determine the dynamics of internal states in human body. In this context, this study is aimed to identify the useful metrics indicating the real-time stress level of drivers. Totally 10 of drivers’ bio-signal datasets obtained from MIT-BIH PhysioNet Multi-parameter Database were used to quantify the stress level of drivers. Each dataset includes several bio-signal recordings gathered from drivers during the drives conducted in a set path where 3 different levels of stress likely occur through over 20 miles of open roads. The recordings have markings indicating the time intervals between the driving segments corresponding to a number of city and highway driving periods with initial and final rest periods. Evaluations have been completed by using the available segment based arrays of instantaneous heart rate (IHR), hand based skin conductance (HSC), foot based skin conductance (FSC) and electromyography (EMG) signals. For further evaluations, the segment based data arrays including instantaneous respiratory rate (IRR) and average number of contractions/minutes (CPM) have been derived from the respiratory and EMG signals, respectively by using a peak detection algorithm. Statistical comparisons using the overall mean and mean values of the IHR, HSC, FSC, EMG, IRR and CPM data arrays showed that with an optional exception of IRR all of these metrics can be identified as useful parameters in the future car technology to determine the dynamic stress level of drivers. Key words: Driving stress of drivers, dynamic stress level, electromyography, heart rate, respiration rate, skin conductance.

Estimation of Breathing Rate from Respiratory Sinus Arrhythmia: Comparison of Various Methods

Although respiratory sinus arrhythmia (RSA) is a well-known and often studied phenomenon, methods to estimate (average) respiratory rate from heart rate variability via RSA have been investigated and published only sparsely. We reinvestigate three published techniques and contrast them to our own approaches. All methods were also evaluated for respiration signals to yield approximations of the true breathing rate for comparison. Our analyses are based on physiological recordings available at PhysioNet, an online database. Results show that the RSA of young supine subjects yields good approximations of mean respiratory rate in the case of time series longer than 1 min, while the estimations become noticeably less accurate for elderly persons. Our own "advanced counting method" produced the best results, and in addition principally permits even the definition of instantaneous respiratory rates. Consequently, it is recommended for further investigations.