Respiratory Signal Research Articles

A paper-based self-inductive folding displacement sensor for human respiration and motion signals measurement

Flexible sensors have recently attracted much attention for potential applications in motion measurement and wearable health monitoring. In this paper, an environment-friendly, economic, and high-performance paper-based self-inductive folding displacement sensor (PSIFS) was proposed, with displacement resolution of 20 μm and wide measurement range of 43.2 mm. The sensor sensitivity could reach 4.44% mm−1 with the maximum deviation of 0.00904 in experiments. The sensing mechanism based on inductance variation due to three-dimensional deformation of planar inductor coil and structural design principle under two dimensions of sensitivity and size were proposed and studied. The folding method to enhance sensitivity was proposed and 3 times sensitivity enhancement could be achieved by once folding. Multiple breathing states and motion states of the human joints could be recognized. The proposed simple paper-based sensor, folding method to increase sensitivity, and structural design model may provide a way for the research of flexible sensors.

Model-based assessment of cardiopulmonary autonomic regulation in paced deep breathing.

Autonomic dysfunction can lead to many physical and psychological diseases. The assessment of autonomic regulation plays an important role in the prevention, diagnosis, and treatment of these diseases. A physiopathological mathematical model for cardiopulmonary autonomic regulation, namely Respiratory-Autonomic-Sinus (RSA) regulation Model, is proposed in this study. A series of differential equations are used to simulate the whole process of RSA phenomenon. Based on this model, with respiration signal and ECG signal simultaneously acquired in paced deep breathing scenario, we manage to obtain the cardiopulmonary autonomic regulation parameters (CARP), including the sensitivity of respiratory-sympathetic nerves and respiratory-parasympathetic nerves, the time delay of sympathetic, the sensitivity of norepinephrine and acetylcholine receptor, as well as cardiac remodeling factor by optimization algorithm. An experimental study has been conducted in healthy subjects, along with subjects with hypertension and coronary heart disease. CARP obtained in the experiment have shown their clinical significance.

A Broadband MFCW Agile Radar Concept for Vital-Sign Detection Under Various Thoracic Movements

A multiple-frequency continuous-wave (MFCW) agile radar system has the ability to cope with various physiological conditions of a human subject. Following the mathematic model, a favorable carrier frequency for detection is pursued by analyzing the amplitude and composition of the baseband signal. In Experiment-1, a CMOS dual-band transceiver prototype emulate searching a subject from a distance. The frequencies of the dual-band system are 4.26–4.95 GHz and 17.06–19.79 GHz. During the experiment, the respiratory signal could be detected up to 12 m, and the heartbeat signal up to 6 m. Even with a wooden or a brick barrier in the path, the vital-sign detection remained satisfactory. Later, a concurrent MFCW detection is performed in Experiment-2 for more precise analyses. From the concurrent results, the breathing status can be inferred, and by cross-correlating the results after the ensemble empirical mode decomposition (EEMD) and principal component analysis (PCA), the nonlinear effects of respiratory harmonics or intermodulation tones are reduced, resulting in an accurate heart rate extraction. The receiver front-end and the signal source are manufactured using a 180-nm CMOS process, both packaged on an RO4003C printed-circuit board (PCB).

Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time.

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.

Development of Respiratory Signal Monitoring System Based on Photoplethysmography

Breathing is of great significance in the monitoring of patients with obstructive sleep apnea hypopnea syndrome, perioperative monitoring and intensive care. In this study, a respiratory monitoring and verification system based on optical capacitance product pulse wave (PPG) is designed, which can synchronously collect human PPG signals. Through algorithm processing, the characteristic parameters of PPG signal are calculated, and the respiratory signal and respiratory frequency can be extracted in real time. In order to verify the accuracy of extracting respiratory signal and respiratory rate by the algorithm, the system adds the nasal airflow respiratory signal acquisition module to synchronously collect the nasal airflow respiratory signal as the standard signal for comparison and verification. Finally, the root mean square error between the respiratory rate extracted by the algorithm from the pulse wave and the standard respiratory rate is only 1.05 times/min.

Emotion recognition method using millimetre wave radar based on deep learning

Non-contact emotion recognition is a new research field. Using millimetre-wave radar does not require users to wear any equipment, and there is no privacy violation. This study proposes a new emotion recognition method, which uses millimetre-wave radar to transmit frequency-modulated continuous wave signals and extracts and separates the time domain signals of heartbeat and respiration of subjects through the echo of human reflection. According to the characteristics of collected signals, a deep learning framework combining one-dimensional convolutional neural network and Bidirectional Long Short-Term Memory neural networks is designed for feature extraction and classification. Experiments show that this method has high average recognition accuracy for four emotions in the case of person-independent. The excellent range resolution, higher system integration, and lower power consumption of millimetre-wave radar will profoundly impact the development of human–computer interaction, modern medical care, and education.

Blind source separation of inspiration and expiration in respiratory sEMG signals

Objective. Surface electromyography (sEMG) is a noninvasive option for monitoring respiratory effort in ventilated patients. However, respiratory sEMG signals are affected by crosstalk and cardiac activity. This work addresses the blind source separation (BSS) of inspiratory and expiratory electrical activity in single- or two-channel recordings. The main contribution of the presented methodology is its applicability to the addressed muscles and the number of available channels. Approach. We propose a two-step procedure consisting of a single-channel cardiac artifact removal algorithm, followed by a single- or multi-channel BSS stage. First, cardiac components are removed in the wavelet domain. Subsequently, a nonnegative matrix factorization (NMF) algorithm is applied to the envelopes of the resulting wavelet bands. The NMF is initialized based on simultaneous standard pneumatic measurements of the ventilated patient. Main results. The proposed estimation scheme is applied to twelve clinical datasets and simulated sEMG signals of the respiratory system. The results on the clinical datasets are validated based on expert annotations using invasive pneumatic measurements. In the simulation, three measures evaluate the separation success: The distortion and the correlation to the known ground truth and the inspiratory-to-expiratory signal power ratio. We find an improvement across all SNRs, recruitment patterns, and channel configurations. Moreover, our results indicate that the initialization strategy replaces the manual matching of sources after the BSS. Significance. The proposed separation algorithm facilitates the interpretation of respiratory sEMG signals. In crosstalk affected measurements, the developed method may help clinicians distinguish between inspiratory effort and other muscle activities using only noninvasive measurements.

Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals

Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (Fr-WCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed Fr-WCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed Fr-WCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed Fr-WCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963, 0.932, and 0.948, respectively. Moreover, the testing accuracy value achieved by the Random Forest classifier, machine learning, DNN, CNN, WCSO-based HAN, and developed Fr-WCSO-based DRN is 0.753, 0.797, 0.844, 0.887, 0.929, and 0.948. While analyzing the results that are tabulated, it is clear that the developed Fr-WCSO-based DRN computed a higher TPR of 0.963, higher TNR of 0.932 using dataset-1, and higher testing accuracy of 0.948 using dataset-2, respectively. The effectual results are obtained as the model is well trained with the proposed Fr-WCSO and hence increasing the learning rate of the Deep Residual Network.

Application of Improved VMD-LSTM Model in Sports Artificial Intelligence.

In recent years, with the rapid development of a new generation of artificial intelligence technology, how to deeply apply artificial intelligence technology to physical education and break through the limitations of time-space scenarios and knowledge transfer methods in traditional models has become a key issue in intelligent physical education in the era of artificial intelligence. In order to realize the online monitoring of wearable devices with artificial intelligence in sports and overcome the problem of low recognition accuracy of electrocardiogram, blood oxygen, and respiratory signals in many cases, this paper proposes a combination of variational modal decomposition based on the maximum envelope kurtosis method. Long-short-term neural network (VMD-LSTM) monitoring method for wearable sports equipment. Through experimental analysis and verification, the current signal of the VMD model shows a trend of fluctuating from large to stable and then to large with motion, while the training accuracy of LSTM after the 150th iteration is 94.09%, which shows that the coupling model VMD LSTM can better predict the direction of sports artificial intelligence. In addition, although the training time of the BP neural network is shorter than that of the LSTM model, there is a large gap between the recognition effect and the LSTM, and there are also large differences between different neural network structures. This shows that the VMD-LSTM model has broad application prospects in such models.

Quantifying Respiration Effects on Cardiac Vibrations using Teager Energy Operator and Gradient Boosted Trees.

This work proposes a novel beat scoring system for quantifying the effects of exhalation and inhalation on the seismocardiogram (SCG) signals in rest and physiologically modulated conditions. Data from 19 subjects during rest, listening to classical music and recovery states were used. First, the SCG and electrocardiogram (ECG) signals were segmented into exhalation and inhalation phases using the respiration signal; and a representative SCG beat for each exhale and inhale phase was constructed using the ECG R-peak locations. Second, the significant differences across the exhalation- and inhalation-induced SCG beats were detected and extracted using the Teager- Kaiser energy operator. Finally, a gradient-based beat scoring system was developed using extreme gradient boosted trees and monotonic mapping. For the rest, classical music and recovery sessions, the area under the receiver operating characteristic curve was found to be 0.978, 0.874, 0.985, respectively. On the other hand, the kernel density estimation distributions of the inhalation and exhalation scores had an overlap of 14.2%, 41.2%, 10.6%, respectively. Overall, our results show that different physiological modulations directly change the effect of respiration on the SCG morphology, thus standardization across the beats should be studied for achieving more reliable and accurate investigation of cardiovascular parameters. Clinical relevance - Such a system can potentially allow for more informed and clinically useful SCG analysis by providing valuable insights regarding the intra-recording variability caused by the respiratory system.

Respiration and heart rates measurement using 77GHz FMCW radar with blind source separation algorithm.

This paper presents a blind source separation based signal processing method of measuring respiration and heartbeat rates using frequency modulated continues wave (FMCW) radar with working frequency range 77GHz-81GHz. To improve signal quality, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used in preprocessing stage to first decompose the phase signal into several intrinsic mode functions (IMFs) and then the phase signal is reconstructed by adding the selected IMFs. The accurate measurement results can be obtained by the proposed method both in separating respiratory and heartbeat signals of one person (the average deviations are 1.1 beat per minute and 6.8 beat per minute respectively), and in separating respiratory signals of two persons.

Cardiorespiratory Phase Synchronization in Elderly Patients with Periodic and non-Periodic Breathing Patterns.

Cardiorespiratory Phase Synchronization (CRPS) is the manifestation of the non-linear coupling between the cardiac and the respiratory systems, different to the Respiratory Sinus Arrythmia (RSA). This takes place when the heartbeats occur at the same relative phase of the breathing, during a succession of respiratory cycles. In this study, we investigated the CRPS in 45 elderly patients admitted to the semi-critical unit of a hospital. The patients were classified according to their respiratory state as non-Periodic Breathing (nPB), Periodic Breathing (PB) and Cheyne-Stokes Respiration (CSR). The phase synchrogram between the electrocardiographic and respiratory signals was computed using the Hilbert transform technique. A continuous measure of the CRPS was obtained from the synchrogram, and was characterized by the average duration of synchronized epochs (A vgDurSync), the percentage of synchronized time (%Sync), the number of synchronized epochs (NumSync), and the frequency ratio between the cardiac and respiratory oscillators (FreqRat). These measures were studied using two different thresholds (0.1 and 0.05) for the amplitude of the synchronization and a minimum duration threshold of 10s. According to the results, the AvgDurSync and %Sync had a decreasing trend in patients with breathing periodicity. In addition, CSR patients presented the lowest values A vgDurSync and %Sync. Therefore, the CRPS method could be a useful tool for characterizing periodic respiratory patterns in elderly patients, which might be related to chronic heart failure. Clinical Relevance- This study analyzes the synchronization between cardiac and respiratory systems in elderly patients with a possible progressive decompensation in the cardiac function.

The Use of Conductive Lycra Fabric in the Prototype Design of a Wearable Device to Monitor Physiological Signals.

Wearable technology has become commonplace for the measurement of heart rate, steps taken, and monitoring exercise regimes. However, wearables can also be used to enable or enhance the lives of persons living with disabilities. This paper discusses the design of a wearable device that aims to facilitate the assessment of physiological signals using conductive Lycra fabric. The device will be applicable for daily use within diverse contexts including the evaluation of emotional experiences of children with Severe Motor and Communication Impairment and the detection of Obstructive Sleep Apnea in children with Down Syndrome. The Lycra fabric sensors are used to acquire electrocardiographic signals, galvanic skin response, and respiratory signals. Articulated design requirements include constraints related to the ability to fit children of all sizes, and meeting medical device standards and biocompatibility, and criteria related to low costs, comfortability, and maintainability. Upon prototyping and preliminary testing, this device was found to offer an affordable, comfortable, and accessible solution to the monitoring of physiological signals. Clinical Relevance- This research provides initial knowledge and momentum towards an affordable wearable device using conductive Lycra to effectively monitor and assess physiological signals in children with disabilities.

Recurrence Plot-based Classification of Ischemic and Dilated Cardiomyopathy Patients.

A large portion of the elderly population are affected by cardiovascular diseases. The early prognosis of cardiomyopathies is still a challenge. The aim of this study was to classify cardiomyopathy patients by their etiology in function of significant indexes extracted from the characterization of the recurrence plot of the systems involved. Thirty-nine cardiomyopathy patients (CMP) classified as ischemic (ICM - 24 patients) and dilated (DCM-15 patients) were considered. In addition, thirty-nine control subjects (CON) were used as reference. The beat-to-beat (BBI) time series, from the electrocardiographic signal, the systolic (SBP), and diastolic (DBP) time series, from the blood pressure signal, and the respiratory time (FLW) from the respiratory flow signal, were extracted. The recurrence plot from each signal considered were calculated and characterized by a total of 12 indexes. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.3% accuracy, 95.8% sensitivity, and 86.6% specificity. When comparing CMP patients and CON subjects, the best model achieved 85.8% accuracy, 92.3% sensitivity, and 80.1% specificity. Our results suggest a more deterministic behavior in DCM patients. Clinical Relevance - This study explores the recurrence plot for the classification of ICM and DCM patients.

Validation of Dozee, a Ballistocardiography-based Device, for Contactless and Continuous Heart Rate and Respiratory Rate Measurement.

Long-term acquisition of respiratory and heart signals is useful in a variety of applications, including sleep analysis, monitoring of respiratory and heart disorders, and so on. Ballistocardiography (BCG), a non-invasive technique that measures micro-body vibrations caused by cardiac contractions as well as motion caused by breathing, snoring, and body movements, would be ideal for long-term vital parameter acquisition. Turtle Shell Technologies Pvt. Ltd.'s Dozee device, which is based on BCG, is a contactless continuous vital parameters monitoring system. It is designed to measure Heart Rate (HR) and Respiratory Rate (RR) continuously and without contact in a hospital setting or at home. A validation study for HR and RR was conducted using Dozee by comparing it to the vitals obtained from the FDA-approved Patient Monitor. This was done in a sleep laboratory setting over 110 nights in 51 subjects to evaluate HR and over 20 nights in 17 subjects to evaluate RR at the National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India. Approximately 789 hours data for HR and approximately 112 hours data for RR was collected. Dozee was able to achieve a mean absolute error of 1.72 bpm for HR compared to the gold standard ECG. A mean absolute error of ∼1.24 breaths/min was obtained in determining RR compared to currently used methods. Dozee is ideal for long-term contactless monitoring of vital parameters due to its low mean absolute errors in measuring both HR and RR. Clinical Relevance- Continuous and long-term vitals monitoring is known to enable early screening of clinical deterioration, improve patient outcomes and reduce mortality. Current methods of continuous monitoring are overly complex, costly, and rely heavily on patient compliance. The proposed remote vitals monitoring solution based on BCG was found to be at par with gold standard methods of recording HR and RR. As a result, clinicians can use it to effectively monitor patients in both the hospital and at home.

Causality in cardiorespiratory signals in pediatric cardiac patients.

Four different Granger causality-based methods - one linear and three nonlinear (Granger Causality, Kernel Granger Causality, large-scale Nonlinear Granger Causality, and Neural Network Granger Causality) were used for assessment and causal-based quantification of the respiratory sinus arrythmia (RSA) in the group of pediatric cardiac patients, based on the single-lead ECG and impedance pneumography signals (the latter as the tidal volume curve equivalent). Each method was able to detect the dependency (in terms of causal inference) between respiratory and cardiac signals. The correlations between quantified RSA and the demographic parameters were also studied, but the results differ for each method. Clinical relevance- The presented methods (among which NNGC seems to be the most valid) allow for quantification of RSA and study of dependency between tidal volume and RR intervals which may help to better understand association between respiratory and cardiovascular systems in different populations.

Multi-frequency Electrical Impedance Pneumography System as Point-Of-Care Device.

In this work we present the development of a multi-frequency electrical impedance pneumography (EIP) system based on a portable acquisition device and a mobile platform. This design is intended as an upgrade to our previous device for clinical use in the screening of patients with pulmonary diseases. The acquisition device uses the bioimpedance analog front end MAX30001, a mux/demux stage, Bluetooth 4.0 communication and an ESP32 microcontroller unit. It generates an excitation current of 8 μApp in a range of selectable frequencies from 1 kHz to 130 kHz. The mobile platform provides a real-time respiration signal at 64 S/s and allows the configuration of the device. Results show less error in higher frequencies, which are the most common in these applications, ensuring the feasibility of the system for use in humans. Some hardware constraints related to EIP integrated circuits are discussed, an their effect in the signal acquisition.

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.

Algorithmic insights of camera-based respiratory motion extraction

Objective. Measuring the respiratory signal from a video based on body motion has been proposed and recently matured in products for contactless health monitoring. The core algorithm for this application is the measurement of tiny chest/abdominal motions induced by respiration (i.e. capturing sub-pixel displacement caused by subtle motion between subsequent video frames), and the fundamental challenge is motion sensitivity. Though prior art reported on the validation with real human subjects, there is no thorough or rigorous benchmark to quantify the sensitivities and boundary conditions of motion-based core respiratory algorithms. Approach. A set-up was designed with a fully-controllable physical phantom to investigate the essence of core algorithms, together with a mathematical model incorporating two motion estimation strategies and three spatial representations, leading to six algorithmic combinations for respiratory signal extraction. Their promises and limitations are discussed and clarified through the phantom benchmark. Main results. With the variation of phantom motion intensity between 0.5 mm and 8 mm, the recommended approach obtains an average precision, recall, coverage and MAE of 88.1%, 91.8%, 95.5% and 2.1 bpm in the day-light condition, and 81.7%, 90.0%, 93.9% and 4.4 bpm in the night condition. Significance. The insights gained in this paper are intended to improve the understanding and applications of camera-based respiration measurement in health monitoring. The limitations of this study stem from the used physical phantom that does not consider human factors like body shape, sleeping posture, respiratory diseases, etc., and the investigated scenario is focused on sleep monitoring, not including scenarios with a sitting or standing patient like in clinical ward and triage.

Multitask Siamese Network for Remote Photoplethysmography and Respiration Estimation.

Heart and respiration rates represent important vital signs for the assessment of a person’s health condition. To estimate these vital signs accurately, we propose a multitask Siamese network model (MTS) that combines the advantages of the Siamese network and the multitask learning architecture. The MTS model was trained by the images of the cheek including nose and mouth and forehead areas while sharing the same parameters between the Siamese networks, in order to extract the features about the heart and respiratory information. The proposed model was constructed with a small number of parameters and was able to yield a high vital-sign-prediction accuracy, comparable to that obtained from the single-task learning model; furthermore, the proposed model outperformed the conventional multitask learning model. As a result, we can simultaneously predict the heart and respiratory signals with the MTS model, while the number of parameters was reduced by 16 times with the mean average errors of heart and respiration rates being 2.84 and 4.21. Owing to its light weight, it would be advantageous to implement the vital-sign-monitoring model in an edge device such as a mobile phone or small-sized portable devices.