A comparison of algorithms for estimation of a respiratory signal from the surface electrocardiogram

SolunumWear: A smart textile system for dynamic respiration monitoring across various postures

We introduce SolunumWear, a multi-sensory e-textile system designed for respiration in daily life settings, addressing the gap in continuous, real-world respiration event monitoring. Leveraging a textile pressure sensor belt to capture chest movements and a wireless data acquisition system, SolunumWear offers a promising solution for both medical and wellness applications. The system's efficacy was evaluated through a human study involving 10 healthy adults (six female and four male) across various breathing rates and postures, demonstrating a strong correlation (R value = 0.836) with the gold-standard system. The study highlights the system's computational and communication efficiencies, with latencies of approximately 4.84 s and 2.13 ms, respectively. These findings highlight the efficacy of SolunumWear as a wireless, wearable technology for respiration monitoring in daily settings. This research contributes to the expanding body of knowledge on smart textile-based health monitoring technologies, demonstrating its potential to provide reliable respiratory data in real-world environments.

Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning

Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds.

Open-source software for respiratory rate estimation using single-lead electrocardiograms

Respiratory rate (RR) is a critical vital sign used to assess pulmonary function. Currently, RR estimating instrumentation is specialized and bulky, therefore unsuitable for remote health monitoring. Previously, RR was estimated using proprietary software that extract surface electrocardiogram (ECG) waveform features obtained at several thoracic locations. However, developing a non-proprietary method that uses minimal ECG leads, generally available from mobile cardiac monitors is highly desirable. Here, we introduce an open-source and well-documented Python-based algorithm that estimates RR requiring only single-stream ECG signals. The algorithm was first developed using ECGs from awake, spontaneously breathing adult human subjects. The algorithm-estimated RRs exhibited close linear correlation to the subjects' true RR values demonstrating an R2 of 0.9092 and root mean square error of 2.2 bpm. The algorithm robustness was then tested using ECGs generated by the ischemic hearts of anesthetized, mechanically ventilated sheep. Although the ECG waveforms during ischemia exhibited severe morphologic changes, the algorithm-determined RRs exhibited high fidelity with a resolution of 1 bpm, an absolute error of 0.07 ± 0.07 bpm, and a relative error of 0.67 ± 0.64%. This optimized Python-based RR estimation technique will likely be widely adapted for remote lung function assessment in patients with cardiopulmonary disease.

Novel statistical time series data augmentation and machine learning based classification of unobtrusive respiration data for respiration Digital Twin model

Digital Twin (DT), a concept of Healthcare (4.0), represents the subject's biological properties and characteristics in a digital model. DT can help in monitoring respiratory failures, enabling timely interventions, personalized treatment plans to improve healthcare, and decision-support for healthcare professionals. Large-scale implementation of DT technology requires extensive patient data for accurate monitoring and decision-making with Machine Learning (ML) and Deep Learning (DL). Initial respiration data was collected unobtrusively with the ESP32 Wi-Fi Channel State Information (CSI) sensor. Due to limited respiration data availability, the paper proposes a novel statistical time series data augmentation method for generating larger synthetic respiration data. To ensure accuracy and validity in the augmentation method, correlation methods (Pearson, Spearman, and Kendall) are implemented to provide a comparative analysis of experimental and synthetic datasets. Data processing methodologies of denoising (smoothing and filtering) and dimensionality reduction with Principal Component Analysis (PCA) are implemented to estimate a patient's Breaths Per Minute (BPM) from raw respiration sensor data and the synthetic version. The methodology provided the BPM estimation accuracy of 92.3% from raw respiration data. It was observed that out of 27 supervised classifications with k-fold cross-validation, the Bagged Tree ensemble algorithm provided the best ML-supervised classification. In the case of binary-class and multi-class, the Bagged Tree ensemble showed accuracies of 89.2% and 83.7% respectively with combined real and synthetic respiration dataset with the larger synthetic dataset. Overall, this provides a blueprint of methodologies for the development of the respiration DT model.

Hybrid Mock Circulatory Loop Simulation of Extreme Cardiac Events

OBJECTIVE

This paper presents preliminary methods of incorporating the pathological conditions of cardiac arrhythmias and valvular stenosis in hybrid mock circulation loop (hMCL) operation for the enhanced verification and validation of mechanical circulatory support devices such as VADs.

METHODS

The MGH/MF Waveform datasets from PhysioNet database (including both nominal and clinically diagnosed arrhythmic ECG measurements) as well as cardiovascular system model updates are used to recreate arrhythmic events and valvular stenosis in vitro.

RESULTS

Preliminary results show the hMCL can recreate each tested cardiac event within 2% and 4% mean error for reference pressure tracking in the aortic and left ventricular pressure chambers, respectively. Further, frequency spectrum analysis comparisons using the magnitude-squared coherence analysis shows close alignment between measured arrhythmic and hMCL realized pressure frequency content.

CONCLUSION

The generation of cardiac arrhythmias and valvular stenosis around a VAD via both model and acute measurement based methods was achieved.

SIGNIFICANCE

Pathological conditions such as cardiac arrhythmias and valvular stenosis are limited in documentation despite the large percentage of patients who experience these events. This paper provides a means to begin incorporating these events into hardware-in-the-loop mock circulatory systems for next generation VAD validation and verification.

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.

A Pervasive Respiratory Monitoring Sensor for COVID-19 Pandemic

Goal: The SARS-CoV-2 viral infection could cause severe acute respiratory syndrome, disturbing the regular breathing and leading to continuous coughing. Automatic respiration monitoring systems could provide the necessary metrics and warnings for timely intervention, especially for those with mild symptoms. Current respiration detection systems are expensive and too obtrusive for any large-scale deployment. Thus, a low-cost pervasive ambient sensor is proposed. Methods: We will posit a barometer on the working desk and develop a novel signal processing algorithm with a sparsity-based filter to remove the similar-frequency noise. Three modes (coughing, breathing and others) will be conducted to detect coughing and estimate different respiration rates. Results: The proposed system achieved 97.33% accuracy of cough detection and 98.98% specificity of respiration rate estimation. Conclusions: This system could be used as an effective screening tool for detecting subjects suffering from COVID-19 symptoms and enable large scale monitoring of patients diagnosed with or recovering.

Respiration-related variations in Pd/Pa ratio and fractional flow reserve in resting conditions and during intravenous adenosine administration

Aims

We evaluated the occurrence and physiology of respiration‐related beat‐to‐beat variations in resting Pd/Pa and FFR during intravenous adenosine administration, and its impact on clinical decision‐making.

Methods and Results

Coronary pressure tracings in rest and at plateau hyperemia were analyzed in a total of 39 stenosis from 37 patients, and respiratory rate was calculated with ECG‐derived respiration (EDR) in 26 stenoses from 26 patients. Beat‐to‐beat variations in FFR occurred in a cyclical fashion and were strongly correlated with respiratory rate (R² = 0.757, p < 0.001). There was no correlation between respiratory rate and variations in resting Pd/Pa. When single‐beat averages were used to calculate FFR, mean ΔFFR was 0.04 ± 0.02. With averaging of FFR over three or five cardiac cycles, mean ΔFFR decreased to 0.02 ± 0.02, and 0.01 ± 0.01, respectively. Using a FFR ≤ 0.80 threshold, stenosis classification changed in 20.5% (8/39), 12.8% (5/39) and 5.1% (2/39) for single‐beat, three‐beat and five‐beat averaged FFR. The impact of respiration was more pronounced in patients with pulmonary disease (ΔFFR 0.05 ± 0.02 vs 0.03 ± 0.02, p = 0.021).

Conclusion

Beat‐to‐beat variations in FFR during plateau hyperemia related to respiration are common, of clinically relevant magnitude, and frequently lead FFR to cross treatment thresholds. A five‐beat averaged FFR, overcomes clinically relevant impact of FFR variation.

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.

Wearable Respiration Monitoring: Interpretable Inference With Context and Sensor Biomarkers

Continuous monitoring of breathing rate (BR), minute ventilation (VE), and other respiratory parameters could transform care for and empower patients with chronic cardio-pulmonary conditions, such as asthma. However, the clinical standard for measuring respiration, namely Spirometry, is hardly suitable for continuous use. Wearables can track many physiological signals, like ECG and motion, yet respiration tracking faces many challenges. In this work, we infer respiratory parameters from wearable ECG and wrist motion signals. We propose a modular and generalizable classification-regression pipeline to utilize available context information, such as physical activity, in learning context-conditioned inference models. Novel morphological and power domain features from the wearable ECG are extracted to use with these models. Exploratory feature selection methods are incorporated in this pipeline to discover application-driven interpretable biomarkers. Using data from 15 subjects, we evaluate two implementations of the proposed inference pipeline: for BR and VE. Each implementation compares generalized linear model, random forest, support vector machine, Gaussian process regression, and neighborhood component analysis as regression models. Permutation, regularization, and relevance determination methods are used to rank the ECG features to identify robust ECG biomarkers across models and activities. This work demonstrates the potential of wearable sensors not only in continuous monitoring, but also in designing biomarker-driven preventive measures.

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.

Chaos-Based Analysis of Heart Rate Variability Time Series in Obstructive Sleep Apnea Subjects

Obstructive sleep apnea (OSA) is a common disorder which can cause periodic fluctuations in heart rate. To diagnose sleep apnea, some studies analyze electrocardiogram (ECG) signals by adopting chaos-based analysis. This research is going to specifically focus on whether it is possible to use chaos-based analysis of heart rate variability (HRV) signals rather than using chaotic analysis of ECG signals to diagnose OSA. While conventional studies mostly use chaos-based analysis of ECG signals to detect OSA, here, we apply correlation dimension (CD) as a chaotic index to analyze HRV data in OSA patients. For this purpose, 17 patients with OSA and 9 healthy individuals referred to a sleep clinic in Isfahan/Iran are studied, and their HRV time series were extracted from 1-h ECG signals recorded overnight. The preliminary step to calculate CD is phase-space reconstruction of the system based on HRV time series. Corresponding parameters, including embedding dimension and lag time, are estimated optimally using enhanced related methods, and then CD is calculated using Grassberger–Procaccia algorithm. Moreover, to evaluate our results, detrended fluctuation analysis (DFA), one of the well-known nonlinear methods in HRV analysis to detect OSA, is also applied to our data and the result is compared with those obtained from CD analysis of HRV. CD index with P < 0.005 indicates a significant difference in nonlinear dynamics of HRV signals detected from OSA patients and healthy individuals.

Wearable Vital Signs Monitoring for Patients With Asthma: A Review

Worldwide,an estimated 461 000 people die from asthma attacks each year. While there remain treatments to alleviate asthma symptoms and reduce deaths, patient deterioration needs to be identified in sufficient time. To prevent asthma deterioration, patients need to be aware of personal and environmental triggers and monitor their asthma symptoms. The aim of this article is to provide a comprehensive review of the current state-of-the-art wearable sensors and devices that use vital signs for asthma patient monitoring and management. Among all vital signs, breathing rate and airflow sound are key indicators of asthmatic patients' health that can be measured directly using wearable sensors to provide continuous and constant patient monitoring or indirectly by estimations based on proven algorithms using electrocardiogram (ECG), photoplethysmogram (PPG), and chest movements. ECG and PPG signals are widely used in smart watches and chest bands, enabling easy integration of a more extensive body sensor framework for asthmatic exacerbation prediction. Other vital signs used in asthma patient monitoring include blood oxygen saturation, temperature, blood pressure, verbal sound, and pain responses. The use of wearable vital signs enabled a broad range of wearable sensor application scenarios for asthma monitoring and management.

Sleep apnea detection from ECG using variational mode decomposition

Sleep apnea is a pervasive breathing problem during night sleep, and its repetitive occurrence causes various health problems. Polysomnography is commonly used for apnea screening which is an expensive, time-consuming, and complex process. In this paper, a simple but efficient technique based on the variational mode decomposition (VMD) for automated detection of sleep apnea from single-lead ECG is proposed. The heart rate variability and ECG-derived respiration signals obtained from ECG are decomposed into different modes using the VMD, and these modes are used for extracting different features including spectral entropies, interquartile range, and energy. The principal component analysis is employed to reduce the dimension of the feature vector. The experiments are conducted using the Apnea-ECG dataset, and the classification performance of various classifiers is investigated. In per-segment classification, an accuracy of about 87.5% (Sens: 84.9%, Spec: 88.2%) is achieved using the K-nearest neighbor classifier. In per-recording classification, the proposed technique using the linear discriminant analysis model outperformed the existing apnea detection approaches by achieving the accuracy of 100%. The algorithm also provided the best agreement between the estimated and reference apnea-hypopnea index (AHI) values. These results show that the algorithm has the potential to be used for home-based apnea screening systems.

Comparing Different Methods of Hand-crafted HRV, EDR and CPC Features for Sleep Apnoea Detection

In this paper, we extracted hand crafted features from the ECG signals and evaluated the performance of different combination of features for sleep apnoea detection. We calculated the ECG derived respiratory (EDR) signal using three methods (QRS area, amplitude demodulation and fast PCA methods) and then calculated the cardiopulmonary coupling (CPC) spectrum using each EDR method. We then extracted features from the CPC spectrums and the time and frequency representations of the heart rate variability (HRV) and EDR signals Then, we compared the performance results of different combinations of the features used for automated sleep apnoea detection. We also applied a temporal optimisation method by averaging the features of every three adjacent epochs. Two classifiers were used to detect sleep apnoea: the extreme learning machine (ELM), and linear discriminant analysis. The features were evaluated on the MIT PhysioNet Apnea-ECG database. Apnoea detection was evaluated with leave-one-record-out cross-validation. The PCA CPC features obtained the highest accuracy of 86.5% and AUC of 0.94 using LDA classifier. The performance results of the combined features (of PCA method) obtained the same results. We conclude that for this study, the CPC features using fast PCA method are our best feature set for sleep apnoea detection.

Non-invasive Diagnosis of Sleep Apnoea Using ECG and Respiratory Bands

In this paper, we used ECG signals and repiratory inductance plethysmography (RIP) or respiratory bands. We evaluated the performance of the signals individually as well as different combinations of features and signals for sleep apnoea detection. We implemented two methods (QRS area, and fast principal component analysis (PCA) methods) for estimating the ECG derived respiratory (EDR) signal and the cardiopulmonary coupling (CPC) spectrum. We then extracted features from the time and frequency representations of the ECG and RIP signals. Finally, we applied different features sets to a linear discriminant analysis (LDA) for classification. The results were examined on the MIT PhysioNet Apnea-ECG database. Apnoea classification was carried out using leave-one-record-out crossvalidation approach. The highest performance of our algorithm was achieved using the RIP and RR-interval features as well as using the RIP and PCA CPC features with an accuracy of 90% and AUC of 0.97. The highest performance results of using only RIP or ECG features achieved an accuracy of 87% and AUC of 0.95. We conclude that although ECG sensors are more convenient for patients in sleep studies, using both RIP and ECG sensors enhances the performance results for automated diagnosis of sleep apnoea.

Recent development of respiratory rate measurement technologies

Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.

Heart rate variability and occupational stress-systematic review

The aim of this systematic review was to explore studies regarding association between occupational stress and heart rate variability (HRV) during work. We searched PubMed, Web of Science, Scopus, Cinahl and PsycINFO for peer-reviewed articles published in English between January 2005 and September 2017. A total of 10 articles met the inclusion criteria. The included articles were analyzed in terms of study design, study population, assessment of occupational stress and HRV, and the study limitations. Among the studies there were cross-sectional (n=9) studies and one longitudinal study design. Sample size varied from 19 to 653 participants and both females and males were included. The most common assessment methods of occupational stress were the Job Content Questionnaire (JCQ) and the Effort-Reward Imbalance (ERI) questionnaire. HRV was assessed using 24 h or longer Holter ECG or HR monitoring and analyzed mostly using standard time-domain and frequency-domain parameters. The main finding was that heightened occupational stress was found associated with lowered HRV, specifically with reduced parasympathetic activation. Reduced parasympathetic activation was seen as decreases in RMSSD and HF power, and increase in LF/HF ratio. The assessment and analysis methods of occupational stress and HRV were diverse.

A Fast Principal Component Analysis Method For Calculating The ECG Derived Respiration

In this paper, we present a principal component analysis (PCA) method for estimating the respiration from overnight ECG recording. In comparison to other published methods, our method is very fast to compute and has low memory requirements, which makes it suitable for processing long duration ECG recordings. We used our method to derive respiratory features for the ECG which were then used to identify epochs of sleep apnoea from the ECG. Three classifiers including the extreme learning machine (ELM), linear discriminant analysis, and support vector machine were used to detect sleep apnoea. The method was evaluated on the MIT PhysioNet Apnea-ECG database. Apnoea detection was evaluated with leave-one-record-out cross-validation. Our PCA method obtained the highest accuracy of 74% by ELM classifier. We conclude that the fast PCA method is useful to apply PCA to long ECG recordings.

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.

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.

Systolic Time Interval Estimation Using Continuous Wave Radar With On-Body Antennas

The estimation of systolic time intervals (STIs) is done using continuous wave (CW) radar at 2.45 GHz with an on-body antenna.

MOTIVATION

In the state of the art, typically bioimpedance, heart sounds and/or ultrasound are used to measure STIs. All three methods suffer from insufficient accuracy of STI estimation due to various reasons. CW radar is investigated for its ability to overcome the deficiencies in the state of the art.

METHODS

Ten healthy male subjects aged 25-45 were asked to lie down at a 30 incline. Recordings of 60 s were taken without breathing and with paced breathing. Heart sounds, electrocardiogram, respiration, and impedance cardiogram were measured simultaneously as reference. The radar antennas were placed at two positions on the chest. The antennas were placed directly on the body as well as with cotton textile in between. The beat to beat STIs have been determined from the reference signals as well as CW radar signals.

RESULTS

The results indicate that CW radar can be used to estimate STIs in ambulatory monitoring.

SIGNIFICANCE

The results pave way to a potentially more compact method of estimating STIs, which can be integrated into a wearable device.

A portable respiratory rate estimation system with a passive single-lead electrocardiogram acquisition module

BACKGROUND

Among vital signs of acutely ill hospital patients, respiratory rate (RR) is a highly accurate predictor of health deterioration.

OBJECTIVE

This study proposes a system that consists of a passive and non-invasive single-lead electrocardiogram (ECG) acquisition module and an ECG-derived respiratory (EDR) algorithm in the working prototype of a mobile application.

METHOD

Before estimating RR that produces the EDR rate, ECG signals were evaluated based on the signal quality index (SQI). The SQI algorithm was validated quantitatively using the PhysioNet/Computing in Cardiology Challenge 2011 training data set. The RR extraction algorithm was validated by adopting 40 MIT PhysioNet Multiparameter Intelligent Monitoring in Intensive Care II data set.

RESULTS

The estimated RR showed a mean absolute error (MAE) of 1.4 compared with the ``gold standard'' RR. The proposed system was used to record 20 ECGs of healthy subjects and obtained the estimated RR with MAE of 0.7 bpm.

CONCLUSION

Results indicate that the proposed hardware and algorithm could replace the manual counting method, uncomfortable nasal airflow sensor, chest band, and impedance pneumotachography often used in hospitals. The system also takes advantage of the prevalence of smartphone usage and increase the monitoring frequency of the current ECG of patients with critical illnesses.

Automated Real-Time Processing of Single Lead Electrocardiogram for Simultaneous Heart Rate and Respiratory Rate Monitoring

In this article, the design and development of a real-time heart rate (HR) and respiratory rate (RR) monitoring device is reported. The proposed device is designed to impose minimum data acquisition hazards on the subject. In standard bedside monitors, HR and RR are derived from electrocardiogram (ECG) and respiration signals, respectively, and different electrodes are required for capturing the 12-lead ECG and respiration via a chest belt, which is cumbersome for patients and healthcare providers. Respiration signal has an impact on ECG due to anatomical proximity of the heart and lung, and ECG is modulated by respiration, a phenomenon known as respiratory sinus arrhythmia (RSA). In the proposed method, the ECG signal is acquired using clip electrodes at the wrists and the respiration signal is extracted from the ECG using an Arduino Uno microcontroller-based real-time processing of ECG. RR is then derived from ECG-derived respiration (EDR). The prototype is tested on healthy subjects and compared to measurements taken using a standard MP45 data acquisition device associated with a Biopac Student Lab (BSL). A mean percentage error of 5.54 ± 8.48% was observed under normal breathing conditions and an error of −3.41 ± 3.27% was observed for a single subject tested under a variety of breathing conditions, such as resting, stair-climbing, and paced breathing. The proposed algorithm can also be used in combination with standard ECG monitoring systems to measure HR and RR, without any data acquisition hazard to the subject.

Physiological noise correction using ECG-derived respiratory signals for enhanced mapping of spontaneous neuronal activity with simultaneous EEG-fMRI

The study of spontaneous brain activity based on BOLD-fMRI may be seriously compromised by the presence of signal fluctuations of non-neuronal origin, most prominently due to cardiac and respiratory mechanisms. Methods used for modeling and correction of the so-called physiological noise usually rely on the concurrent measurement of cardiac and respiratory signals. In simultaneous EEG-fMRI recordings, which are primarily aimed at the study of spontaneous brain activity, the electrocardiogram (ECG) is typically measured as part of the EEG setup but respiratory data are not generally available. Here, we propose to use the ECG-derived respiratory (EDR) signal estimated by Empirical Mode Decomposition (EMD) as a surrogate of the respiratory signal, for retrospective physiological noise correction of typical simultaneous EEG-fMRI data. A physiological noise model based on these physiological signals (P-PNM) complemented with fMRI-derived noise regressors was generated, and evaluated, for 17 simultaneous EEG-fMRI datasets acquired from a group of seven epilepsy patients imaged at 3T. The respiratory components of P-PNM were found to explain BOLD variance significantly in addition to the cardiac components, suggesting that the EDR signal was successfully extracted from the ECG, and P-PNM outperformed an image-based model (I-PNM) in terms of total BOLD variance explained. Further, the impact of the correction using P-PNM on fMRI mapping of patient-specific epileptic networks and the resting-state default mode network (DMN) was assessed in terms of sensitivity and specificity and, when compared with an ICA-based procedure and a standard pre-processing pipeline, P-PNM achieved the best performance. Overall, our results support the feasibility and utility of extracting physiological noise models of the BOLD signal resorting to ECG data exclusively, with substantial impact on the simultaneous EEG-fMRI mapping of resting-state networks, and, most importantly, epileptic networks where sensitivity and specificity are still limited.

A REVIEW OF ECG-BASED DIAGNOSIS SUPPORT SYSTEMS FOR OBSTRUCTIVE SLEEP APNEA

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.

Combining different ECG derived respiration tracking methods to create an optimal reconstruction of the breathing pattern

ECG derived respiration (EDR) is a technique applied to estimate the respiration signal using only the electrocardiogram (ECG). Different approaches have been proposed in the past on how respiration could be gained from the ECG. However, in many applications only one of them is used while the others are not considered at all. In this paper, we propose a new algorithm for the optimal linear combination of different EDR methods in order to create a more accurate estimation. Using two well known databases, it was statistically shown that an optimally chosen fixed set of coefficients for the linear combination delivers a better estimation than each of the methods used solely.

Estimation of respiratory signal from thoracic impedance cardiography in low electrical current

A new method to estimate respiratory signal from thoracic impedance is proposed. To realize battery powered, wearable respiratory monitoring devices, low current impedance measurement techniques are desired. However, under low current conditions, conventional methods to separate cardiac and respiratory signals do not work well as the cardiac signal is much larger than the respiratory signal. In the proposed method, respiratory signal is estimated by calculating an envelope curve from the detected T waves of cardiac component. The results of the experiments show that the accuracy of proposed method is greater than conventional method.

Does disgust increase parasympathetic activation in individuals with a history of fainting? A psychophysiological analysis of disgust stimuli with and without blood-injection-injury association

People with blood-injection-injury fear can faint when being confronted with blood, injections or injuries. Page (1994) holds that people with blood-injury phobia faint, because they are disgust sensitive and disgust facilitates fainting by eliciting parasympathetic activity. We tested the following two hypotheses: (1) Disgusting pictures elicit more disgust in blood-injection-injury-anxious people with a history of fainting than they do in controls. (2) Disgust causes parasympathetic activation. Subjects were 24 participants with high blood-injection-injury fear and a history of fainting in anxiety relevant situations and 24 subjects with average blood-injection-injury fear and no fainting history. We analyzed self-reported feelings of disgust, anxiety and faintness and reactions in heart rate, skin conductance, blood pressure and respiratory sinus arrhythmia during the confrontation with disgusting pictures with and without blood content. We did not find any evidence that the blood-injection-injury anxious subjects were more disgust sensitive than the control subjects and we also did not find any evidence that disgust elicits parasympathetic activation.

Two proximal skin electrodes--a respiration rate body sensor

We propose a new body sensor for extracting the respiration rate based on the amplitude changes in the body surface potential differences between two proximal body electrodes. The sensor could be designed as a plaster-like reusable unit that can be easily fixed onto the surface of the body. It could be equipped either with a sufficiently large memory for storing the measured data or with a low-power radio system that can transmit the measured data to a gateway for further processing. We explore the influence of the sensor's position on the quality of the extracted results using multi-channel ECG measurements and considering all the pairs of two neighboring electrodes as potential respiration-rate sensors. The analysis of the clinical measurements, which also include reference thermistor-based respiration signals, shows that the proposed approach is a viable option for monitoring the respiration frequency and for a rough classification of breathing types. The obtained results were evaluated on a wireless prototype of a respiration body sensor. We indicate the best positions for the respiration body sensor and prove that a single sensor for body surface potential difference on proximal skin electrodes can be used for combined measurements of respiratory and cardiac activities.

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.

Respiration rate monitoring methods: a review

Respiration rate is an important indicator of a person's health, and thus it is monitored when performing clinical evaluations. There are different approaches for respiration monitoring, but generally they can be classed as contact or noncontact. For contact methods, the sensing device (or part of the instrument containing it) is attached to the subject's body. For noncontact approaches the monitoring is performed by an instrument that does not make any contact with the subject. In this article a review of respiration monitoring approaches (both contact and noncontact) is provided. Concerns related to the patient's recording comfort, recording hygiene, and the accuracy of respiration rate monitoring have resulted in the development of a number of noncontact respiration monitoring approaches. A description of thermal imaging based and vision based noncontact respiration monitoring approaches we are currently developing is provided.