ECG-Derived Respiratory Rate in Atrial Fibrillation

Relationship between central autonomic effective connectivity and heart rate variability: A Resting-state fMRI dynamic causal modeling study

The central autonomic network (CAN) serves as a regulatory hub with top-down regulatory control and integration of bottom-up physiological feedback via the autonomic nervous system. Heart rate variability (HRV)-the time variance of the heart's beat-to-beat intervals-is an index of the CAN's affective and behavioral regulatory capacity. Although neural functional connectivities that are associated with HRV and CAN have been well studied, no published report to date has studied effective (directional) connectivities (EC) that are associated with HRV and CAN. Better understanding of neural EC in the brain has the potential to improve our understanding of how the CAN sub-regions regulate HRV. To begin to address this knowledge gap, we employed resting-state functional magnetic resonance imaging and dynamic causal modeling (DCM) with parametric empirical Bayes analyses in 34 healthy adults (19 females; mean age= 32.68 years [SD= 14.09], age range 18-68 years) to examine the bottom-up and top-down neural circuits associated with HRV. Throughout the whole brain, we identified 12 regions associated with HRV. DCM analyses revealed that the ECs from the right amygdala to the anterior cingulate cortex and to the ventrolateral prefrontal cortex had a negative linear relationship with HRV and a positive linear relationship with heart rate. These findings suggest that ECs from the amygdala to the prefrontal cortex may represent a neural circuit associated with regulation of cardiodynamics.

Is breathing frequency a potential means for monitoring exercise intensity in people with atrial fibrillation and coronary heart disease when heart rate is mitigated?

PURPOSE

Moderate-intensity aerobic exercise is safe and beneficial in atrial fibrillation (AF) and coronary heart disease (CHD). Irregular or rapid heart rates (HR) in AF and other heart conditions create a challenge to using HR to monitor exercise intensity. The purpose of this study was to assess the potential of breathing frequency (BF) to monitor exercise intensity in people with AF and CHD without AF.

METHODS

This observational study included 30 AF participants (19 Male, 70.7 ± 8.7 yrs) and 67 non-AF CHD participants (38 Male, 56.9 ± 11.4 yrs). All performed an incremental maximal exercise test with pulmonary gas exchange.

RESULTS

Peak aerobic power in AF ( V ˙ O2peak; 17.8 ± 5.0 ml.kg-1.min-1) was lower than in CHD (26.7 ml.kg-1.min-1) (p < .001). BF responses in AF and CHD were similar (BF peak: AF 34.6 ± 5.4 and CHD 36.5 ± 5.0 breaths.min-1; p = .106); at the 1st ventilatory threshold (BF@VT-1: AF 23.2 ± 4.6; CHD 22.4 ± 4.6 breaths.min-1; p = .240). % V ˙ O2peak at VT-1 were similar in AF and CHD (AF: 59%; CHD: 57%; p = .656).

CONCLUSION

With the use of wearable technologies on the rise, that now include BF, this first study provides an encouraging potential for BF to be used in AF and CHD. As the supporting data are based on incremental ramp protocol results, further research is required to assess BF validity to manage exercise intensity during longer bouts of exercise.

An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep

This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.

ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation

Introduction: Information about autonomic nervous system (ANS) activity may offer insights about atrial fibrillation (AF) progression and support personalized AF treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in atrioventricular (AV) nodal refractory period and conduction delay. Methods: A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where an ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. Results: We demonstrated using synthetic data that the 1D-CNN can estimate the respiratory modulation from RR series alone with a Pearson sample correlation of r = 0.805 and that the addition of either respiration signal (r = 0.830), AFR (r = 0.837), or both (r = 0.855) improves the estimation. Discussion: Initial results from analysis of ECG data suggest that our proposed estimate of respiration-induced autonomic modulation, a resp, is reproducible and sufficiently sensitive to monitor changes and detect individual differences. However, further studies are needed to verify the reproducibility, sensitivity, and clinical significance of a resp.

Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration

Objective.We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal.Methods.We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection.Results.The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (γ-score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance.Conclusion.The sync-ensembled EDR provides robust respiratory information from electrocardiogram.Significance.Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.

Camera-Based Respiration Monitoring of Unconstrained Rodents

Animal research has always been crucial for various medical and scientific breakthroughs, providing information on disease mechanisms, genetic predisposition to diseases, and pharmacological treatment. However, the use of animals in medical research is a source of great controversy and ongoing debate in modern science. To ensure a high level of bioethics, new guidelines have been adopted by the EU, implementing the 3R principles to replace animal testing wherever possible, reduce the number of animals per experiment, and refine procedures to minimize stress and pain. Supporting these guidelines, this article proposes an improved approach for unobtrusive, continuous, and automated monitoring of the respiratory rate of laboratory rats. It uses the cyclical expansion and contraction of the rats' thorax/abdominal region to determine this physiological parameter. In contrast to previous work, the focus is on unconstrained animals, which requires the algorithms to be especially robust to motion artifacts. To test the feasibility of the proposed approach, video material of multiple rats was recorded and evaluated. High agreement was obtained between RGB imaging and the reference method (respiratory rate derived from electrocardiography), which was reflected in a relative error of 5.46%. The current work shows that camera-based technologies are promising and relevant alternatives for monitoring the respiratory rate of unconstrained rats, contributing to the development of new alternatives for a continuous and objective assessment of animal welfare, and hereby guiding the way to modern and bioethical research.

Feasibility Analysis and Implementation of Head-Mounted Electrical Impedance Respiratory Monitoring

The respiratory rate is one of the crucial indicators for monitoring human physiological health. The purpose of this paper was to introduce a head-mounted respiratory monitoring solution based on electrical impedance sensing. Firstly, we constructed a finite element model to analyze the feasibility of using head impedance for respiratory sensing based on the physiological changes in the pharynx. After that, we developed a circuit module that could be integrated into a head-mounted respiratory monitoring device using a bioelectrical impedance sensor. Furthermore, we combined adaptive filtering and respiratory tracking algorithms to develop an app for a mobile phone. Finally, we conducted controlled experiments to verify the effectiveness of this electrical impedance sensing system for extracting respiratory rate. We found that the respiration rates measured by the head-mounted electrical impedance respiratory monitoring system were not significantly different from those of commercial respiratory monitoring devices by a paired t-test (p > 0.05). The results showed that the respiratory rates of all subjects were within the 95% confidence interval. Therefore, the head-mounted respiratory monitoring scheme proposed in this paper was able to accurately measure respiratory rate, indicating the feasibility of this solution. In addition, this respiratory monitoring scheme helps to achieve real-time continuous respiratory monitoring, which can provide new insights for personalized health monitoring.

A subspace projection approach to quantify respiratory variations in the f-wave frequency trend

Background: The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such information.

Objective: This paper proposes a novel approach for improved estimation of such respiratory induced variations and investigates the impact of deep breathing on the f-wave frequency in AF patients.

Methods: A harmonic model is fitted to the f-wave signal to estimate a high-resolution f-wave frequency trend, and an orthogonal subspace projection approach is employed to quantify variations in the frequency trend that are linearly related to respiration using an ECG-derived respiration signal. The performance of the proposed approach is evaluated and compared to that of a previously proposed bandpass filtering approach using simulated f-wave signals. Further, the proposed approach is applied to analyze ECG data recorded for 5 min during baseline and 1 min deep breathing from 28 AF patients from the Swedish cardiopulmonary bioimage study (SCAPIS).

Results: The simulation results show that the estimates of respiratory variations obtained using the proposed approach are more accurate than estimates obtained using the previous approach. Results from the analysis of SCAPIS data show no significant differences between baseline and deep breathing in heart rate (75.5 ± 22.9 vs. 74 ± 22.3) bpm, atrial fibrillation rate (6.93 ± 1.18 vs. 6.94 ± 0.66) Hz and respiratory f-wave frequency variations (0.130 ± 0.042 vs. 0.130 ± 0.034) Hz. However, individual variations are large with changes in heart rate and atrial fibrillatory rate in response to deep breathing ranging from −9% to +5% and −8% to +6%, respectively and there is a weak correlation between changes in heart rate and changes in atrial fibrillatory rate (r = 0.38, p &lt; 0.03).

Conclusion: Respiratory induced f-wave frequency variations were observed at baseline and during deep breathing. No significant changes in the magnitude of these variations in response to deep breathing was observed in the present study population.

Towards Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals

The current COVID-19 pandemic highlights the critical importance of ubiquitous respiratory health monitoring. The two fundamental elements of monitoring respiration are respiration rate (the frequency of breathing) and tidal volume (TV, the volume of air breathed by the lungs in each breath). Wearable sensing systems have been demonstrated to provide accurate measurement of respiration rate, but TV remains challenging to measure accurately with wearable and unobtrusive technology. In this work, we leveraged electrocardiogram (ECG) and seismocardiogram (SCG) measurements obtained with a custom wearable sensing patch to derive an estimate of TV from healthy human participants. Specifically, we fused both ECG-derived and SCG-derived respiratory signals (EDR and SDR) and trained a machine learning model with gas rebreathing as the ground truth to estimate TV. The respiration cycle modulates ECG and SCG signals in multiple different ways that are synergistic. Thus, here we extract EDRs and SDRs using a multitude of different demodulation techniques. The extracted features are used to train a subject independent machine learning model to accurately estimate TV. By fusing the extracted EDRs and SDRs, we were able to estimate the TV with a root-mean-square error (RMSE) of 181.45 mL and Pearson correlation coefficient (r) of 0.61, with a global subject-independent model. We further show that SDRs are better TV estimators than EDRs. Among SDRs, amplitude modulated (AM) SCG features are the most correlated to TV. We demonstrated that fusing EDRs and SDRs can result in moderately accurate estimation of TV using a subject-independent model. Additionally, we highlight the most informative features for estimating TV. This work presents a significant step towards achieving continuous, calibration free, and unobtrusive TV estimation, which could advance the state of the art in wearable respiratory monitoring.

Respiratory Rate Estimation Using U-Net-Based Cascaded Framework From Electrocardiogram and Seismocardiogram Signals

OBJECTIVE

At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals-measured in less cumbersome contact form factors than the conventional sealed mask that measures respiratory air flow-are promising solutions for respiratory monitoring. In particular, respiratory rates (RR) can be estimated from ECG-derived respiratory (EDR) and SCG-derived respiratory (SDR) signals. Yet, non-respiratory artifacts might still be present in these surrogates of respiratory signals, hindering the accuracy of the RRs estimated.

METHODS

In this paper, we propose a novel U-Net-based cascaded framework to address this problem. The EDR and SDR signals were transformed to the spectro-temporal domain and subsequently denoised by a 2D U-Net to reduce the non-respiratory artifacts.

MAJOR RESULTS

We have shown that the U-Net that fused an EDR input and an SDR input achieved a low mean absolute error of 0.82 breaths per minute (bpm) and a coefficient of determination (R2) of 0.89 using data collected from our chest-worn wearable patch. We also qualitatively provided insights on the complementariness between EDR and SDR signals and demonstrated the generalizability of the proposed framework.

CONCLUSION

ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework.

SIGNIFICANCE

We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement.

QT interval Adaptation to Heart Rate Changes in Atrial Fibrillation as a Predictor of Sudden Cardiac Death

OBJECTIVE

The clinical significance of QT interval adaptation to heart rate changes has been poorly investigated in atrial fibrillation (AF), since QT delineation in the presence of f-waves is challenging. Therefore, the objective of the present study is to investigate new techniques for QT adaptation estimation in permanent AF.

METHODS

A multilead strategy based on generalized periodic component analysis is proposed for QT delineation, involving a spatial, linear transformation which emphasizes Twave periodicity and attenuates f-waves. QT adaptation is modeled by a linear, time-invariant filter, whose impulse response describes the dependence between the current QT interval and the preceding RR intervals, followed by a memoryless, possibly nonlinear, function. The QT adaptation time lag is determined from the estimated impulse response.

RESULTS

Using simulated ECGs in permanent AF, the transformed lead was found to offer more accurate QT delineation and time lag estimation than did the original ECG leads for a wide range of f-wave amplitudes (the time lag estimation error was found to be -0.2+/-0.6 s for SNR = 12 dB). In a population with chronic heart failure and permanent AF, the time lag estimated from the transformed lead was found to have the strongest, statistically significant association with sudden cardiac death (SCD) (hazard ratio = 3.49), whereas none of the original, orthogonal leads had any such association.

CONCLUSIONS

Periodic component analysis provides more accurate QT delineation and improves time lag estimation in AF. A prolonged adaptation time of the QT interval to heart rate changes is associated with a high risk for SCD.

SIGNIFICANCE

This study demonstrates that SCD risk markers, originally developed for sinus rhythm, can also be used in AF, provided that Twave periodicity is emphasized. The time lag is a potentially useful marker for identifying patients at high risk for SCD, guiding clinicians in adopting effective therapeutic decisions.

A Wearable Bioimpedance Chest Patch for Real-Time Ambulatory Respiratory Monitoring

OBJECTIVE

This paper aims to introduce a wearable solution and a low-complexity algorithm for real-time continuous ambulatory respiratory monitoring.

METHODS

A wearable chest-worn patch is designed using a bioimpedance (BioZ) sensor to measure the changes in chest impedance caused by breathing. Besides, a medical-grade infrared temperature sensor is utilized to monitor body temperature. The computing algorithm implemented on the patch enables computation of breath-by-breath respiratory rate and chest temperature in real-time. Two wireless communication protocols are included in the system, namely Bluetooth and Long Range (LoRa), which enable both short-range and long-range data transmission.

RESULTS

The breathing rate measured in static (i.e., standing, sitting, supine, and lateral lying) and dynamic (i.e., walking, running, and cycling) positions by our device yielded an accuracy of more than 97.8% and 98.5% to the ground truth, respectively. Additionally, the devices performance is evaluated in real-world scenarios both indoors and outdoors.

CONCLUSION

The proposed system is capable of measuring breathing rate throughout a variety of daily activities. To the best of our knowledge, this is the first BioZ-based wearable patch capable of detecting breath-by-breath respiratory rate in real-time remotely under unrestricted ambulatory conditions.

SIGNIFICANCE

This study establishes a strategy for continuous respiratory monitoring that could aid in the early detection of cardiopulmonary disorders in everyday life.

The Added Value of Nonlinear Cardiorespiratory Coupling Indices in the Assessment of Depression. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:5473-5476. [PMID: 34892364 DOI: 10.1109/embc46164.2021.9631096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The present study investigates the differences in autonomic nervous system (ANS) function and stress response between patients with major depressive disorder (MDD) and healthy subjects by measuring changes in ANS biomarkers. ANS-related parameters are derived from various biosignals during a mental stress protocol consisting of a basal, stress, and recovery phase. The feature set consists of ANS biomarkers such as the heart rate (HR) derived from the electrocardiogram, the respiratory rate derived from the respiration signal, vascular parameters obtained from a model-based photoplethysmographic pulse waveform analysis, and cardiorespiratory coupling indices derived from the joint analysis of the heart rate variability (HRV) and respiratory signals. In particular, linear cardiorespiratory interactions are quantified by means of time-frequency coherence, while interactions of quadratic nonlinear nature between HRV and respiration are quantified by means of real wavelet biphase. The intra-subject difference of a feature value between two phases of the protocol, the so-called autonomic reactivity, is considered as a ANS biomarker as well. The performance of ANS biomarkers on discriminating MDD patients is evaluated using a classification pipeline. The results show that the most discriminative ANS biomarkers are related with differences in HR and autonomic reactivity of both vascular and nonlinear cardiorespiratory coupling indices. Differences in autonomic reactivity imply that MDD and healthy subjects differ in their ability to cope with stress. Considering only HR and vascular characteristics a linear support-vector machine classifier yields to accuracy 72.5% and F1-score 73.2%. However, taking into account the nonlinear cardiorespiratory coupling indices, the classification performance improves, yielding to accuracy 77.5% and F1-score 78.0%.Clinical relevance- Changes in the nonlinear properties of the cardiorespiratory system during stress may yield additional information on the assessment of depression.

Respiratory Induced Modulation in f-Wave Characteristics During Atrial Fibrillation

The autonomic nervous system (ANS) is an important factor in cardiac arrhythmia, and information about ANS activity during atrial fibrillation (AF) may contribute to personalized treatment. In this study we aim to quantify respiratory modulation in the f-wave frequency trend from resting ECG. First, an f-wave signal is extracted from the ECG by QRST cancelation. Second, an f-wave model is fitted to the f-wave signal to obtain a high resolution f-wave frequency trend and an index for signal quality control ( S ). Third, respiratory modulation in the f-wave frequency trend is extracted by applying a narrow band-pass filter. The center frequency of the band-pass filter is determined by the respiration rate. Respiration rate is estimated from a surrogate respiration signal, obtained from the ECG using homomorphic filtering. Peak conditioned spectral averaging, where spectra of sufficient quality from different leads are averaged, is employed to obtain a robust estimate of the respiration rate. The envelope of the filtered f-wave frequency trend is used to quantify the magnitude of respiratory induced f-wave frequency modulation. The proposed methodology is evaluated using simulated f-wave signals obtained using a sinusoidal harmonic model. Results from simulated signals show that the magnitude of the respiratory modulation is accurately estimated, quantified by an error below 0.01 Hz, if the signal quality is sufficient ( S > 0 . 5 ). The proposed method was applied to analyze ECG data from eight pacemaker patients with permanent AF recorded at baseline, during controlled respiration, and during controlled respiration after injection of atropine, respectively. The magnitude of the respiratory induce f-wave frequency modulation was 0.15 ± 0.01, 0.18 ± 0.02, and 0.17 ± 0.03 Hz during baseline, controlled respiration, and post-atropine, respectively. Our results suggest that parasympathetic regulation affects the magnitude of respiratory induced f-wave frequency modulation.

Electrocardiogram Derived Respiratory Rate Using a Wearable Armband

A method for deriving respiratory rate from an armband, which records three-channel electrocardiogram (ECG) using three pairs of dry (no hydrogel) electrodes, is presented. The armband device is especially convenient for long-term (months-years) monitoring because it does not use obstructive leads nor hydrogels/adhesives, which cause skin irritation even after few days. An ECG-derived respiration (EDR) based on respiration-related modulation of QRS slopes and R-wave angle approach was used. Moreover, we modified the EDR algorithm to lower the computational cost. Respiratory rates were estimated with the armband-ECG and the reference plethysmography-based respiration signals from 15 subjects who underwent breathing experiment consisting of five stages of controlled breathing (at 0.1, 0.2, 0.3, 0.4, and 0.5 Hz) and one stage of spontaneous breathing. The respiratory rates from the armband obtained a relative error with respect to the reference (respiratory rate estimated from the plethysmography-based respiration signal) that was not higher than 2.26% in median nor interquartile range (IQR) for all stages of fixed and spontaneous breathing, and not higher than 3.57% in median nor IQR in the case when the low computational cost algorithm was applied. These results demonstrate that respiration-related modulation of the ECG morphology are also present in the armband ECG device. Furthermore, these results suggest that respiration-related modulation can be exploited by the EDR method based on QRS slopes and R-wave angles to obtain respiratory rate, which may have a wide range of applications including monitoring patients with chronic respiratory diseases, epileptic seizures detection, stress assessment, and sleep studies, among others.

ImpediBands: Body Coupled Bio-Impedance Patches for Physiological Sensing Proof of Concept

Continuous and robust monitoring of physiological signals is essential in improving the diagnosis and management of cardiovascular and respiratory diseases. The state-of-the-art systems for monitoring vital signs such as heart rate, heart rate variability, respiration rate, and other hemodynamic and respiratory parameters use often bulky and obtrusive systems or depend on wearables with limited sensing methods based on repetitive properties of the signals rather than the morphology. Moreover, multiple devices and modalities are typically needed for capturing various vital signs simultaneously. In this paper, we introduce ImpediBands: small-sized distributed smart bio-impedance (Bio-Z) patches, where the communication between the patches is established through the human body, eliminating the need for electrical wires that would create a common potential point between sensors. We use ImpediBands to collect instantaneous measurements from multiple locations over the chest at the same time. We propose a blind source separation (BSS) technique based on the second-order blind identification (SOBI) followed by signal reconstruction to extract heart and lung activities from the Bio-Z signals. Using the separated source signals, we demonstrate the performance of our system via providing strong confidence in the estimation of heart and respiration rates with low RMSE (HR_RMSE = 0.579 beats per minute, RR_RMSE = 0.285 breaths per minute), and high correlation coefficients (r_HR = 0.948, r_RR = 0.921).

A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG

Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems.

A novel acquisition platform for long-term breathing frequency monitoring based on inertial measurement units

Continuous monitoring of breathing frequency (f_B) could foster early prediction of adverse clinical effects and exacerbation of medical conditions. Current solutions are invasive or obtrusive and thus not suitable for prolonged monitoring outside the clinical setting. Previous studies demonstrated the feasibility of deriving f_B by measuring inclination changes due to breathing using accelerometers or inertial measurement units (IMU). Nevertheless, few studies faced the problem of motion artifacts that limit the use of IMU-based systems for continuous monitoring. Moreover, few attempts have been made to move towards real portability and wearability of such devices. This paper proposes a wearable IMU-based device that communicates via Bluetooth with a smartphone, uploading data on a web server to allow remote monitoring. Two IMU units are placed on thorax and abdomen to record breathing-related movements, while a third IMU unit records body/trunk motion and is used as reference. The performance of the proposed system was evaluated in terms of long-acquisition-platform reliability showing good performances in terms of duration and data loss amount. The device was preliminarily tested in terms of accuracy in breathing temporal parameter measurement, in static condition, during postural changes, and during slight indoor activities showing favorable comparison against the reference methods (mean error breathing frequency < 5%). Graphical abstract Proof of concept of a wearable, wireless, modular respiratory Holter based on inertial measurement units (IMUS) for the continuous breathing pattern monitoring through the detection of chest wall breathing-related movements.