Respiratory Signal Research Articles

Clinical applicability of deep learning-based respiratory signal prediction models for four-dimensional radiation therapy.

For accurate respiration gated radiation therapy, compensation for the beam latency of the beam control system is necessary. Therefore, we evaluate deep learning models for predicting patient respiration signals and investigate their clinical feasibility. Herein, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and the Transformer are evaluated. Among the 540 respiration signals, 60 signals are used as test data. Each of the remaining 480 signals was spilt into training and validation data in a 7:3 ratio. A total of 1000 ms of the signal sequence (Ts) is entered to the models, and the signal at 500 ms afterward (Pt) is predicted (standard training condition). The accuracy measures are: (1) root mean square error (RMSE) and Pearson correlation coefficient (CC), (2) accuracy dependency on Ts and Pt, (3) respiratory pattern dependency, and (4) error for 30% and 70% of the respiration gating for a 5 mm tumor motion for latencies of 300, 500, and 700 ms. Under standard conditions, the Transformer model exhibits the highest accuracy with an RMSE and CC of 0.1554 and 0.9768, respectively. An increase in Ts improves accuracy, whereas an increase in Pt decreases accuracy. An evaluation of the regularity of the respiratory signals reveals that the lowest predictive accuracy is achieved with irregular amplitude patterns. For 30% and 70% of the phases, the average error of the three models is <1.4 mm for a latency of 500 ms and >2.0 mm for a latency of 700 ms. The prediction accuracy of the Transformer is superior to LSTM and Bi-LSTM. Thus, the three models have clinically applicable accuracies for a latency <500 ms for 10 mm of regular tumor motion. The clinical acceptability of the deep learning models depends on the inherent latency and the strategy for reducing the irregularity of respiration.

Flexible Optical Fiber-Based Smart Textile Sensor for Human–Machine Interaction

Flexible sensors are the main components of wearable devices. Compared with flexible substrate-based skin-like sensors, flexible textile sensors have the intrinsic advantages of excellent breathability, compliance with the human body and comfort to human skin. In this article, a flexible optical fiber-based smart textile (FOFT) sensor has been proposed for the application of intelligent human–machine interaction (HMI) and healthcare. The FOFT sensor integrated fiber Bragg gratings (FBGs) with yarns in the way of the plain weave. A double-layer flexible optical fiber crossover structure is proposed to improve the measurement accuracy of the sensor for human body signals. Such a structure leads to a highly flexible textile and wearing comfort. The weak characteristic information of the human body can be extracted by the sensor based on the wavelength demodulation of double FBGs. Combined with the backpropagation (BP) neural network, an FOFT sensor-based gesture recognition application with an off-line accuracy of 97.02% has been developed. Furthermore, the FOFT sensor is used to monitor the human respiratory signals under different postures and obtain the respiratory rate. A series of experiments demonstrate that the FOFT sensor has a huge potential in HMI and healthcare.

Breathing is coupled with voluntary initiation of mental imagery

Previous research has suggested that bodily signals from internal organs are associated with diverse cortical and subcortical processes involved in sensory-motor functions, beyond homeostatic reflexes. For instance, a recent study demonstrated that the preparation and execution of voluntary actions, as well as its underlying neural activity, are coupled with the breathing cycle. In the current study, we investigated whether such breathing-action coupling is limited to voluntary motor action or whether it is also present for mental actions not involving any overt bodily movement. To answer this question, we recorded electroencephalography (EEG), electromyography (EMG), and respiratory signals while participants were conducting a voluntary action paradigm including self-initiated motor execution (ME), motor imagery (MI), and visual imagery (VI) tasks. We observed that the voluntary initiation of ME, MI, and VI are similarly coupled with the respiration phase. In addition, EEG analysis revealed the existence of readiness potential (RP) waveforms in all three tasks (i.e., ME, MI, VI), as well as a coupling between the RP amplitude and the respiratory phase. Our findings show that the voluntary initiation of both imagined and overt action is coupled with respiration, and further suggest that the breathing system is involved in preparatory processes of voluntary action by contributing to the temporal decision of when to initiate the action plan, regardless of whether this culminates in overt movements.

Surface-guided DIBH radiotherapy for left breast cancer: impact of different thresholds on intrafractional motion monitoring and DIBH stability.

To compare two left breast cancer patient cohorts (tangential vs. locoregional deep-inspiration breath-hold- DIBH treatment) with different predefined beam gating thresholds and to evaluate their impact on motion management and DIBH stability. AnSGRT-based clinical workflow was adopted for the DIBH treatment. Intrafractional monitoring was performed by tracking both the respiratory signal and the real-time displacement between the isocenter on the daily reference surface and on the live surface ("SGRT shift"). Beam gating tolerances were 5 mm/4 mm for the SGRT shifts and 5 mm/3 mm for the gating window amplitude for breast tangential and breast + lymph nodes locoregional treatments, respectively. Atotal of 24patients, 12treated with atangential technique and 12with alocoregional technique, were evaluated for atotal number of 684 fractions. Statistical distributions of SGRT shift and respiratory signal for each treatment fraction, for each patient treatment, and for the two population samples were generated. Lateral cumulative distributions of SGRT shifts for both locoregional and tangential samples were consistent with anull shift, whereas longitudinal and vertical ones were slightly negative (mean values < 1 mm). The distribution of the percentage of beam on time with SGRT shift > 3 mm, > 4 mm, or > 5 mm was extended toward higher values for the tangential sample than for the locoregional sample. The variability in the DIBH respiration signal was significantly greater for the tangential sample. Different beam gating thresholds for surface-guided DIBH treatment of left breast cancer can impact motion management and DIBH stability by reducing the frequency of the maximum SGRT shift and increasing respiration signal stability when tighter thresholds are adopted.

Modeling of Respiratory Motion to Support the Minimally Invasive Destruction of Liver Tumors.

Objective: Respiratory movements are a significant factor that may hinder the use of image navigation systems during minimally invasive procedures used to destroy focal lesions in the liver. This article aims to present a method of estimating the displacement of the target point due to respiratory movements during the procedure, working in real time. Method: The real-time method using skin markers and non-rigid registration algorithms has been implemented and tested for various classes of transformation. The method was validated using clinical data from 21 patients diagnosed with liver tumors. For each patient, each marker was treated as a target and the remaining markers as target position predictors, resulting in 162 configurations and 1095 respiratory cycles analyzed. In addition, the possibility of estimating the respiratory phase signal directly from intraoperative US images and the possibility of synchronization with the 4D CT respiratory sequence are also presented, based on ten patients. Results: The median value of the target registration error (TRE) was 3.47 for the non-rigid registration method using the combination of rigid transformation and elastic body spline curves, and an adaptation of the assessing quality using image registration circuits (AQUIRC) method. The average maximum distance was 3.4 (minimum: 1.6, maximum 6.8) mm. Conclusions: The proposed method obtained promising real-time TRE values. It also allowed for the estimation of the TRE at a given geometric margin level to determine the estimated target position. Directions for further quantitative research and the practical possibility of combining both methods are also presented.

Multi-year observations reveal a larger than expected autumn respiration signal across northeast Eurasia

Abstract. Site-level observations have shown pervasive cold season CO2 release across Arctic and boreal ecosystems, impacting annual carbon budgets. Still, the seasonality of CO2 emissions are poorly quantified across much of the high latitudes due to the sparse coverage of site-level observations. Space-based observations provide the opportunity to fill some observational gaps for studying these high-latitude ecosystems, particularly across poorly sampled regions of Eurasia. Here, we show that data-driven net ecosystem exchange (NEE) from atmospheric CO2 observations implies strong summer uptake followed by strong autumn release of CO2 over the entire cold northeastern region of Eurasia during the 2015–2019 study period. Combining data-driven NEE with satellite-based estimates of gross primary production (GPP), we show that this seasonality implies less summer heterotrophic respiration (Rh) and greater autumn Rh than would be expected given an exponential relationship between respiration and surface temperature. Furthermore, we show that this seasonality of NEE and Rh over northeastern Eurasia is not captured by the TRENDY v8 ensemble of dynamic global vegetation models (DGVMs), which estimate that 47 %–57 % (interquartile range) of annual Rh occurs during August–April, while the data-driven estimates suggest 59 %–76 % of annual Rh occurs over this period. We explain this seasonal shift in Rh by respiration from soils at depth during the zero-curtain period, when sub-surface soils remain unfrozen up to several months after the surface has frozen. Additional impacts of physical processes related to freeze–thaw dynamics may contribute to the seasonality of Rh. This study confirms a significant and spatially extensive early cold season CO2 efflux in the permafrost-rich region of northeast Eurasia and suggests that autumn Rh from subsurface soils in the northern high latitudes is not well captured by current DGVMs.

Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram.

The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be −16.74%, −20.32%, and −15.78%, respectively (p-value = 1.31 × 10−12).

Real-Time Non-Contact Millimeter Wave Radar-Based Vital Sign Detection

In this paper, the extraction of the life activity spectrum based on the millimeter (mm) wave radar is designed to realize the detection of target objects and the threshold trigger module. The maximum likelihood estimation method is selected to complete the design of the average early warning probability trigger function. The threshold trigger module is designed for the echo signal of static objects in the echo signal. It will interfere with the extraction of Doppler frequency shift results. The moving target detection method is selected, and the filter is designed. The static clutter interference is filtered without affecting the phase difference between the detection sequences, and the highlight target signal is improved. The frequency and displacement of thoracic movement are used as the detection data. Through the Fourier transform calculation of the sequence, the spectrum value is extracted within the estimated range of the heartbeat and respiration spectrum, and the heartbeat and respiration signals are picked up. The proposed design uses Modelsim and Quartus for CO-simulation to complete the simulation verification of the function, extract the number of logical units occupied by computing resources, and verify the algorithm with the vital signs experiment. The heartbeat and respiration were detected using the sports bracelet; the relative errors of heartbeat detection were 0–6.3%, the respiration detection was 0–9.5%, and the relative errors of heartbeat detection were overwhelmingly less than 5%.

Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis.

Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respiratory diseases. In this work, a smart face mask is proposed with an air-permeable and biodegradable self-powered breath sensor as the key component. This smart face mask is easily fabricated, comfortable to use, eco-friendly, and has sensitive and stable output performances in real wearable conditions. To verify the practicability, we use smart face masks to record respiratory signals of patients with chronic respiratory diseases when the patients do not have obvious symptoms. With the assistance of the machine learning algorithm of the bagged decision tree, the accuracy for distinguishing the healthy group and three groups of chronic respiratory diseases (asthma, bronchitis, and chronic obstructive pulmonary disease) is up to 95.5%. These results indicate that the strategy of this work is feasible and may promote the development of wearable health monitoring systems.

IMPLEMENTING BREATH-BY-BREATH VOLUMETRIC CAPNOGRAPHY DEAD-SPACE COMPONENT CALCULATION DURING INCREMENTAL CYCLE EXERCISE

IMPLEMENTING BREATH-BY-BREATH VOLUMETRIC CAPNOGRAPHY DEAD-SPACE COMPONENT CALCULATION DURING INCREMENTAL CYCLE EXERCISE

Offset Compensation in Resistive Stretch Sensors Using Low-Frequency Feedback Topology

Respiration monitoring systems play an important role in healthcare and fitness. For this purpose, resistive stretch sensors are frequently used, which are cheap and simple in operation. However, they are not free from drawbacks. Varying offset due to patient movement, low signal amplitude, as well as susceptibility to interference, can all pose serious challenges. In this paper, a novel signal conditioning circuit for a resistive respiration sensor is proposed that alleviates some of the above problems. Namely, the proposed low-frequency feedback topology improves the dynamic range by offset compensation, sustaining a high signal amplification. Further advantages of the new configuration are the phase shift of 0.5 degrees in the band of interest and higher gain for the respiration signal than for the offset. The topology was proved to correctly represent signal amplitude changes, as well as to be able to sample human respiration in the home environment. However, the circuit shows some nonlinear behavior around resistance discontinuity points–settling time after body position change of the patient, which can be as long as 40 s. The circuit was tested both in bench tests and in the prototype of a respiratory polygraphy device during actual sleep apnea examinations. The results indicate that resistive stretch sensors, along with low-frequency feedback topology, are a promising development path for future respiration monitoring devices.

Remote sensing of vital signs by medical radar time-series signal using cardiac peak extraction and adaptive peak detection algorithm: Performance validation on healthy adults and application to neonatal monitoring at an NICU

Background and objectiveContinuous monitoring of vital signs plays a pivotal role in neonatal intensive care units (NICUs). In this paper, we present a system for monitoring fully non-contact medical radar-based vital signs to measure the respiratory rate (RR), heart rate (HR), I:E ratio, and heart rate variability (HRV). In addition, we evaluated its performance in a physiological laboratory and examined its adaptability in an NICU. MethodsA non-contact medical radar-based vital sign monitoring system that includes 24 GHz radar installed in an incubator was developed. To enable reliable monitoring, an advanced signal processing algorithm (i.e., a nonlinear filter to separate respiration and heartbeat signals from the output of radar), template matching to extract cardiac peaks, and an adaptive peak detection algorithm to estimate cardiac peaks in time-series were proposed and implemented in the system. Nine healthy subjects comprising five males and four females (24 ± 5 years) participated in the laboratory test. To evaluate the adaptability of the system in an NICU setting, we tested it with three hospitalized infants, including two neonates. ResultsThe results indicate strong agreement in healthy subjects between the non-contact system and reference contact devices for RR, HR, and inter-beat interval (IBI) measurement, with correlation coefficients of 0.83, 0.96, and 0.94, respectively. As anticipated, the template matching and adaptive peak detection algorithms outperformed the conventional approach. These showed a more accurate IBI close to the reference Bland–Altman analysis (proposed: bias of -3 ms, and 95% limits of agreement ranging from -73 to 67 ms; conventional: bias of -11 ms, and 95% limits of agreement ranging from -229 to 207 ms). Moreover, in the NICU clinical setting, the IBI correlation coefficient and 95% limit of agreement in the conventional method are 0.31 and 91 ms. The corresponding values obtained using the proposed method are 0.93 and 21 ms. ConclusionThe proposed system introduces a novel approach for NICU monitoring using a non-contact medical radar sensor. The signal processing method combining cardiac peak extraction algorithm with the adaptive peak detection algorithm shows high adaptability in detecting IBI the time series in various application settings.

Transcutaneous Cervical Vagus Nerve Stimulation Reduces Respiratory Variability in the Context of Opioid Withdrawal.

Opioid withdrawal's physiological effects are a major impediment to recovery from opioid use disorder (OUD). Prior work has demonstrated that transcutaneous cervical vagus nerve stimulation (tcVNS) can counteract some of opioid withdrawal's physiological effects by reducing heart rate and perceived symptoms. The purpose of this study was to assess the effects of tcVNS on respiratory manifestations of opioid withdrawal - specifically, respiratory timings and their variability. Patients with OUD (N = 21) underwent acute opioid withdrawal over the course of a two-hour protocol. The protocol involved opioid cues to induce opioid craving and neutral conditions for control purposes. Patients were randomly assigned to receive double-blind active tcVNS (n = 10) or sham stimulation (n = 11) throughout the protocol. Respiratory effort and electrocardiogram-derived respiration signals were used to estimate inspiration time (Ti), expiration time (Te), and respiration rate (RR), along with each measure's variability quantified via interquartile range (IQR). Comparing the active and sham groups, active tcVNS significantly reduced IQR(Ti) - a variability measure - compared to sham stimulation (p = .02). Relative to baseline, the active group's median change in IQR(Ti) was 500 ms less than the sham group's median change in IQR(Ti). Notably, IQR(Ti) was found to be positively associated with post-traumatic stress disorder symptoms in prior work. Therefore, a reduction in IQR(Ti) suggests that tcVNS downregulates the respiratory stress response associated with opioid withdrawal. Although further investigations are necessary, these results promisingly suggest that tcVNS - a non-pharmacologic, non-invasive, readily implemented neuromodulation approach - can serve as a novel therapy to mitigate opioid withdrawal symptoms.

Optical Monitoring of Breathing Patterns and Tissue Oxygenation: A Potential Application in COVID-19 Screening and Monitoring.

The worldwide outbreak of the novel Coronavirus (COVID-19) has highlighted the need for a screening and monitoring system for infectious respiratory diseases in the acute and chronic phase. The purpose of this study was to examine the feasibility of using a wearable near-infrared spectroscopy (NIRS) sensor to collect respiratory signals and distinguish between normal and simulated pathological breathing. Twenty-one healthy adults participated in an experiment that examined five separate breathing conditions. Respiratory signals were collected with a continuous-wave NIRS sensor (PortaLite, Artinis Medical Systems) affixed over the sternal manubrium. Following a three-minute baseline, participants began five minutes of imposed difficult breathing using a respiratory trainer. After a five minute recovery period, participants began five minutes of imposed rapid and shallow breathing. The study concluded with five additional minutes of regular breathing. NIRS signals were analyzed using a machine learning model to distinguish between normal and simulated pathological breathing. Three features: breathing interval, breathing depth, and O2Hb signal amplitude were extracted from the NIRS data and, when used together, resulted in a weighted average accuracy of 0.87. This study demonstrated that a wearable NIRS sensor can monitor respiratory patterns continuously and non-invasively and we identified three respiratory features that can distinguish between normal and simulated pathological breathing.

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 < 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.

Dataset of Psychological Scales and Physiological Signals Collected for Anxiety Assessment Using a Portable Device

Portable and wearable devices are becoming increasingly common in our daily lives. In this study, we examined the impact of anxiety-inducing videos on biosignals, particularly electrocardiogram (ECG) and respiration (RES) signals, that were collected using a portable device. Two psychological scales (Beck Anxiety Inventory and Hamilton Anxiety Rating Scale) were used to assess overall anxiety before induction. The data were collected at Simon Fraser University from participants aged 18–56, all of whom were healthy at the time. The ECG and RES signals were collected simultaneously while participants continuously watched video clips that stimulated anxiety-inducing (negative experience) and non-anxiety-inducing events (positive experience). The ECG and RES signals were recorded simultaneously at 500 Hz. The final dataset consisted of psychological scores and physiological signals from 19 participants (14 males and 5 females) who watched eight video clips. This dataset can be used to explore the instantaneous relationship between ECG and RES waveforms and anxiety-inducing video clips to uncover and evaluate the latent characteristic information contained in these biosignals.

Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System.

Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non-invasive device for quantitative and real-time evaluation of human respiration. This device simultaneously captures respiration and temperature signatures using customized capacitive and resistive sensors, encapsulated by a breathable layer, and does not limit the user's daily life. Further a machine learning-based respiration classification algorithm with a set of carefully studied features as inputs is proposed and it is deployed into mobile clients. The body status of users, such as being quiet, active and coughing, can be accurately recognized by the algorithm and displayed on clients. Moreover, multiple devices can be linked to a server network to monitor a group of users and provide each user with the statistical duration of physiological activities, coughing alerts, and body health advice. With these devices, individual and group respiratory health status can be quantitatively collected, analyzed, and stored for daily physiological signal detections as well as medical assistance.

Noise Reduction of Lung Sounds based on Singular Spectrum Analysis combined with Discrete Cosine Transform

Lung sound signals are sensitive to environmental noise. Much research has been conducted on the enhancement of pulmonary sound. This study investigated the normal lung sounds as bronchovesicular (BV) and vesicular (V) signals and proposed a novel denoising method called SSA-DCT. Using Singular Spectrum Analysis (SSA), the noise-related components were separated from respiratory information components. An algorithm was also proposed to determine the information and noise time interval, and by applying Discrete Cosine Transform (DCT), the signal energy was attenuated in the noise intervals. Moreover, the concept of safety component, which is secure against noise, was introduced. Then, an algorithm for automatic identification of BV and V signals using the safety component was presented. The error of this algorithm at SNR of 10 dB was 5%. Lung sounds were recorded from 12 healthy subjects using four channels over the posterior chest wall. The signals were recorded in an acoustic laboratory and then contaminated with additive white Gaussian noise with different levels of SNR. The respiratory signals were also recorded in a relatively quiet environment with real ambient noise and denoised by the proposed method. The proposed method was compared with Coiflet wavelet decomposition with hard SureShrink thresholding. The denoising performance of both methods was evaluated using qualitative and quantitative approaches. The SSA-DCT method (e.g., at an SNR level of 10 dB) with average segmental SNR improvements of 2.52 and 3.44 dB for the BV and V signals, respectively, is significantly superior to the wavelet analysis with average segmental SNR improvements of 0.89 and 1.53 dB for the BV and V signals, respectively.

Design of pulse and respiration monitoring system based on fiber optic sensing and VMD-FPR processing algorithm

We propose a pulse and respiration monitoring system based on fiber optic sensing, to achieve accurate monitoring of human pulse and respiration rate with wearable and high comfort. We designed a flexible packaging structure -- to embed the fiber Bragg grating sensors (FBGs) fiber optic sensor into a nylon bandage, which constitutes a wearable real-time monitoring system that can acquire pulse and respiration rates simultaneously. The system uses the Variational Mode Decomposition - Feature Point Recognition (VMD-FPR) signal processing algorithm to demodulate and analyze the FBG echo signal to invert the pulse and respiration rate. The experiments were conducted on human heart rate and respiration rate in different states. The experimental results showed that the error range of respiration rate was within ±0.2 bpm, and the accuracy of the respiration experiment feature point recognition algorithm was 93.29 %. The error range of heart rate was within ±2 bpm, and the accuracy of the pulse experiment main wave feature point recognition algorithm was 99.28 %. Thus, the system can accurately collect and analyze the feature points in the respiratory and pulse wave signals. The information obtained from the experiments is used to build an information base, which provides data support for establishing the analysis model of pulse characteristics and respiratory characteristics of the detectors, and to achieve the function of real-time monitoring of human physiological parameters.