Respiratory Signal Research Articles

The Derivation of Epigastric Motion to Assess Neonatal Breathing and Sleep: An Exploratory Study.

New non-medical monitors are offered for respiration monitoring of neonates. Epigastric motion during sleep was investigated by means of a wearable tracker in parallel to clinical monitoring. 23 hospitalised neonates ready for discharge. A 3-axes-accelerometer and -gyroscope was placed in a standard epigastric position. Between two routine care rounds signals were recorded in parallel to monitoring of impedance pneumography (IP), ECG and pulse oximetry. Motion signals vs. time charts were evaluated using 10-min episodes and semiquantitatively assigned to breathing signal quality, regular breathing, periodic breathing and confounding artefacts. The results were compared with the impedance pneumographic data. 26 recordings (mean duration: 210 min/infant) were conducted without bradycardia or apnea alarm. The gestational age at birth ranged 28.9 to 41.1 and at recording from 35.6 to 42.3 postmenstrual weeks. Motion patterns of quiet sleep with regular breathing, periodic breathing and active sleep with confounding body movements were found. The longitudinal and transversal gyroscope axes resulted in best signal quality. Periodic breathing was found in up to 80% of episodes and decreased inversely with gestational age showing significantly more periodic breathing in preterm infants. Respiration signals of the gyroscope vs. IP showed a low bias and highly variating frequencies. Standardized motion trackers may detect typical neonatal breathing and body-motion-patterns, that could help to classify neonatal sleep. Respiratory rates can only be determined during quiet sleep.

Changes in the autonomic cardiorespiratory activity in parturient women with severe and moderate features of preeclampsia.

Cardiorespiratory coupling (CRC) is a physiological phenomenon that reflects the mutual interaction between the cardiac and respiratory control systems. It is mainly associated with efferent vagal activity from the central autonomic network. Few studies have explored the autonomic changes of CRC in preeclampsia, a critical obstetric complication related to possible autonomic dysfunctions and inflammatory disturbances. This study examined the autonomic mechanisms of CRC in women with severe and moderate preeclampsia and healthy controls by applying nonlinear methods based on information theory, such as mutual information (MI) and Renyi's mutual information (RMI) and the linear and nonlinear analysis of the Pulse-Respiration Quotient (PRQ). We studied three groups of parturient women in the third trimester of pregnancy with a clinical diagnosis of preeclampsia without severe symptoms (P, 38.5 ± 1.4 weeks of pregnancy, n=19), preeclampsia with severe symptoms (SP, 37.5 ± 0.9 weeks of pregnancy, n=22), and normotensive control women (C, 39.1 ± 1.3 weeks of pregnancy, n=20). 10-minutes of abdominal electrocardiograms (ECG) and respiratory signals (RESP) were recorded in all the participants. Subsequently, we obtained the maternal beat-to-beat (RR) and breath-to-breath (BB) time series from ECG and RESP, respectively. The CRC between RR and BB was quantified by nonlinear methods based on information theory, such as MI and RMI, along with the analysis of the novel index of PRQ. Subsequently, we computed the mean PRQ (mPRQ) and the normalized permutation entropy (nPermEn_PRQ) from the PRQ time series generated from BB and RR. In addition, we examined the vagal activity in the three groups by the logarithm of the median of the distribution of the absolute values of successive RR differences (logRSA). The MI and RMI values were significantly lower (p<0.05) in the preeclamptic groups compared to the control group. However, no significant differences were found between the preeclamptic groups. The logRSA and nPermEn_PRQ indices were significantly lower (p<0.05) in SP compared to C and P. Our data suggest that parturient women with severe and mild preeclampsia may manifest an altered cardiorespiratory coupling compared with normotensive control women. Disrupted CRC in severe preeclampsia could be associated with vagal withdrawal and less complex cardiorespiratory dynamics. The difference in vagal activity between the preeclamptic groups may suggest a further reduction in vagal activity associated with the severity of the disease.

Utilising Machine Learning to Predict Myocardial Infarction by Electrocardiogram Derived Respiration

Myocardial Infarction (MI) is one of the leading causes of death. Electrocardiogram (ECG) is a non-invasive tool that is commonly used as a diagnostic tool to assess cardiac conditions. A dataset consisting ECG signals of healthy individuals and MI patients was subjected to pre-processing techniques like normalization and application of a bandpass filter. R-R peak intervals from the pre-processed ECG signals are extracted to generate the respiratory signal. The features extracted from the respiratory signal are used to predict MI. The objective of this study is to evaluate the potential of ECG derived respiratory signal (EDR) in predicting MI by utilizing machine learning techniques like random forest, linear regression, Convolutional Neural Network(CNN), Multilayer perceptron(MLP). The results of the study will examine the feasibility of using EDR in predicting MI and provide insight into the most effective machine learning technique for this application. This study will contribute to the development of new and efficient prediction methods for MI patients.

Antibacterial, conductive nanocomposite hydrogel based on dextran, carboxymethyl chitosan and chitosan oligosaccharide for diabetic wound therapy and health monitoring

Diabetic severe wound healing is challenging and also carries a high risk of bacterial infection and may be accompanied by serious complications. Electrical stimulation (ES) can effectively promote wound healing, but its effectiveness is often limited by incomplete contact between the electrodes and the wound site. In order to improve the efficiency of electrical stimulation utilization and to avoid wound infection, a multi-dynamically crosslinked nanocomposite hydrogel was prepared from dextran modified with aldehyde groups and phenylboronic acid esters (Dex-FA-BA), carboxymethyl chitosan (CMCS), polyaniline grafted chitosan oligosaccharide (CP), and Epigallocatechin Gallate/Ca2+ modified melanin-like nanoparticles (CEMNPs), based on dynamic Schiff base bonds, phenylboronic acid/diol interactions, and hydrogen bonding. The CEMNPs have good photothermal conversion properties and antioxidant activity and can also enhance the mechanical properties of the hydrogel system. The CP endows the hydrogel with good electrical conductivity and sensing properties and can record the respiratory and heart rate of rats in real time. Based on the convolutional neural networks (CNN) algorithm constructed by ResNet9, the respiratory and heart rate signals can be distinguished with 93.9 % accuracy. This multifunctional nanocomposite hydrogel can provide a new strategy to promote chronic wound healing and achieve health monitoring effectively.

Combined Cardiac and Respiratory Monitoring from a Single Signal: A Case Study Employing the Fantasia Database.

This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device.

Smart cushion-based non-invasive mental fatigue assessment of construction equipment operators: A feasible study

Mental fatigue has a considerable influence on the completion of work as well as the safety and health of construction equipment operators who have been exposed to human–machine interaction for an extended period of time. Existing techniques of monitoring and measuring mental fatigue among construction equipment operators are either invasive or rely on subjective judgements, limiting their applicability on construction sites. Therefore, this paper developed a non-invasive smart cushion to evaluate the mental fatigue of construction equipment operators through real-time continuous monitoring of the heartbeat and respiration signals. A laboratory experiment was designed, and twelve participants were recruited to conduct the simulated excavator operation for data acquisition. First, wearable sensors were used as a reference to verify the monitoring accuracy of heartbeat and respiration signals. Excellent correlations were realized between the results of the two monitoring methods. Second, the relevant time domain and frequency domain features were extracted to construct a random forest classification model to discover the relationship between vital signs and subjectively reported mental fatigue status. The results demonstrated that the random forest model had a classification accuracy of 92%, better than other commonly used machine learning algorithms. A combination of heartbeat and respiration features is more effective than a single feature in identifying and classifying mental fatigue among construction equipment operators. The study's findings indicate that it is feasible to apply the smart cushion to identify and classify the mental fatigue of construction equipment operators, therefore contributing to the improvement of their health and safety.

Low-cost magnetic eddy current respiratory monitoring based on fast digital lock-in algorithm combining undersampling and oversampling

To design a low-cost non-contact respiratory monitoring system based on the magnetic eddy current sensing principle, a fast digital lock-in algorithm combining undersampling and oversampling is proposed in this paper. This algorithm enables direct digital sampling and fast demodulation of high-frequency respiratory modulated signals, decreasing the performance requirements of the analog-to-digital converter (ADC) and the microcontroller (MCU). In addition, a current source combined with the load characteristic is designed to reduce the cost of waveform generation. The experimental results show that the low-cost amplitude modulation (AM) system constructed based on the above studies can detect respiratory signals in different states with the signal-to-noise ratio (SNR) higher than 92 dB. Compared with the results of the medical monitor, the relative error range of the respiratory rate (RR) measured by the AM system is 1.15%–3.12%, and the measurement accuracy is higher than 98.9%.

Wavelet Analysis of Respiratory Muscle sEMG Signals during the Physiological Breakpoint of Static Dry End-Expiratory Breath-Holding in Naive Apneists: A Pilot Study.

The wavelet spectral characteristics of three respiratory muscle signals (scalenus (SC), parasternal intercostal (IC), and rectus abdominis (RA)) and one locomotor muscle (brachioradialis (BR)) were analyzed in the time-frequency (T-F) domain during voluntary breath-holding (BH), with a focus on the physiological breakpoint that is commonly considered the first involuntary breathing movement (IBM) that signals the end of the easy-going phase of BH. The study was performed for an end-expiratory BH physiological breaking point maneuver on twelve healthy, physically active, naive breath-holders/apneists (six professional athletes; six recreational athletes, and two individuals in the post-COVID-19 period) using surface electromyography (sEMG). We observed individual effects that were dependent on muscle oxygenation and each person's fitness, which were consistent with the mechanism of motor unit (MU) recruitment and the transition of slow-twitch oxidative (type 1) to fast-twitch glycolytic (type 2) muscle fibers. Professional athletes had longer BH durations (BHDs) and strong hypercapnic responses regarding the expiratory RA muscle, which is activated abruptly at higher BHDs in a person-specific range below 250 Hz and is dependent on the BHD. This is in contrast with recreational athletes, who had strong hypoxic responses regarding inspiratory IC muscle, which is activated faster and gradually in the frequency range of 250-450 Hz (independent of the person and BHD). This pilot study preliminarily indicates that it is possible to noninvasively assess the physiological characteristics of skeletal muscles, especially oxygenation, and improve physical fitness tests by determining the T-F features of elevated myoelectric IC and RA activity during BH.

Assessments of perioperative respiratory pattern with non-contact vital sign monitor in children undergoing minor surgery: a prospective observational study.

Nurses routinely assess respiration of hospitalized children; however, respiratory rate measurements are technically difficult due to rapid and small chest wall movements. The aim of this study is to reveal the respiratory status of small children undergoing minor surgery with load cells placed under the bed legs, and to test the hypothesis that respiratory rate (primary variable) is slower immediately after arrival to the ward and recovers in 2h. Continuous recordings of the load cell signals were performed and stable respiratory waves within the 10 discriminative perioperative timepoints were used for respiratory rate measurements. Apnea frequencies were calculated at pre and postoperative nights and 2h immediately after returning to the ward after surgery. Continuous recordings of the load cell signals were successfully performed in 18 children (13 to 119months). Respiratory waves were appraisable for more than 70% of nighttime period and 40% of immediate postoperative period. There were no statistically significant differences of respiratory rate in any timepoint comparisons (p = 0.448), thereby not supporting the study hypothesis. Respiratory rates changed more than 5 breaths per minute postoperatively in 5 out of 18 children (28%) while doses of fentanyl alone did not explain the changes. Apnea frequencies significantly decreased 2h immediately after returning to the ward and during the operative night compared to the preoperative night. Respiratory signal extracted from load cell sensors under the bed legs successfully revealed various postoperative respiratory pattern change in small children undergoing minor surgery. UMIN (University Hospital Information Network) Clinical Registry: UMIN000045579 ( https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000052039 ).

Lung anomaly detection from respiratory sound database (sound signals)

Chest or upper body auscultation has long been considered a useful part of the physical examination going back to the time of Hippocrates. However, it did not become a prevalent practice until the invention of the stethoscope by Rene Laennec in 1816, which made the practice suitable and hygienic. Pulmonary disease is a kind of sickness that affects the lungs and various parts of the respiratory system. Lung diseases are the third largest cause of death in the world. According to the World Health Organization (WHO), the five major respiratory diseases, namely chronic obstructive pulmonary disease (COPD), tuberculosis, acute lower respiratory tract infection (LRTI), asthma, and lung cancer, cause the death of more than 3 million people each year worldwide. 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(96.3%), 0.932,(93.2%) and 0.948(94.8%), respectively.

Flexible Inductance Pressure Sensor for Wearable Electronic Devices

Currently, wearable devices have higher requirements for flexible sensors based on environmental adaptability. Therefore, in order to solve the problem of low sensitivity of current flexible sensors, a new type of flexible inductance pressure sensor is proposed by introducing a ferrite film with high permeability into traditional flexible sensors. The validity and practicability of this method are verified by the research. The results show that the correlation curve of theoretical prediction shows the same trend as the actual curve. Sensor A showed the highest sensitivity of 1.61 kPa−1; Under different bending conditions, the sensitivity difference is 0 ∼ 0.20. When the actual external pressure is increased from 0 to 13.6 Pa, the actual change of inductance is about 0.88%. When the excitation voltage is 0.5 V, the actual inductance output value of sensor A increases from the initial 13.22 μH to 13.26 μH, and the actual change rate is less than 0.30%. In the application of wearable devices, the keys of wearable electronic keyboard have the maximum output voltage variation value, which is greater than 0.15 V, improving the high stability and practicality of the sensor. It is also practical in wearable human respiratory signal detection equipment. In summary, the flexible inductance pressure sensor designed in this study has high performance and practicability in the application of wearable electronic devices, which is of great significance to the development of actual wearable electronic devices.

Respiratory signal estimation for cardiac perfusion SPECT using deep learning.

Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting. We aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation. A modified U-Net was implemented that takes temporally finely sampled SPECT sub-projection data (100ms) as input. These sub-projections are obtained by reframing the 20-s list-mode data, resulting in 200 sub-projections, at each projection angle for each SPECT camera head. The network outputs a 200-time-point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo-camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data-driven approach based on the center-of-mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub-projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation. The Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de-blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12mm, compared to 2.6% for the CoM method and 8.5% for no correction. We demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo-camera VTS signals. This may enable fully automatic data-driven respiratory motion correction without relying on external motion tracking devices.

Contactless methods to acquire heart and respiratory signals—A review

&lt;p align="justify"&gt;The vital sign is the most important parameter for the internal health status of any subject in time. Every person is witnessed of COVID-19 global pandemic viruses. The world population has faced this problem globally. Collecting the infected person’s sample data in a contact-based approach may lead to the spreading of the disease. On the other hand, if we use a non-contact-based approach for the collection, it is somehow far better and breaks the chain of virus spreading. This radar-based technique is preferred in non-contact vital sign detection so that any person gets to their health status prior and according to that doctor can diagnose the proper treatment. The radar-based signal is targeted to the subject’s chest. Due to the chest wall displacement main vital sign parameters of the heart and respiration of the individual’s health are being captured. These captured signals are called vital signs, with this it is very helpful that the pre-diagnosis and treatment can be recommended by doctors or health service providers. Some patients due to their movement may be older or children for a long-time use skin irritation or allergy type of problems may face. On the other hand, some patients may be COVID-19 infected disease and burn patients. Hence, it is not possible to connect as both cases are unexpected for the required purpose. For constant and continuous measurement, existing contact-based methods are not fruitful hence non-contact-based approach is adopted. Non-contact-based vital sign detection is preferably due to several problems occurring. This paper presents a state-of-the-art review of recent monitoring methods and techniques for health monitoring in medical fields of operations. These methods and techniques are used as a tool to acquire, visualize and analyze the sampled data collected in any environment either indoor or outdoor.&lt;/p&gt;

A Point-Process Approach for Tracking Valence using a Respiration Belt.

Emotional valence is difficult to be inferred since it is related to several psychological factors and is affected by inter- and intra-subject variability. Changes in emotional valence have been found to cause a physiological response in respiration signals. In this study, we propose a state-space model and decode the valence by analyzing a person's respiration pattern. Particularly, we generate a binary point process based on features that are indicative of changes in respiration pattern as a result of an emotional valence response. High valence is typically associated with faster and deeper breathing. As a result, (i)depth of breath, (ii)rate of respiration, and (iii) breathing cycle time are indicators of high valence and used to generate the binary point process representing underlying neural stimuli associated with changes in valence. We utilize an expectation-maximization (EM) framework to decode a hidden valence state and the associated valence index. This predicted valence state is compared to self-reported valence ratings to optimize the parameters and determine the accuracy of the model. The accuracy of the model in predicting high and low valence events is found to be 77% and 73%, respectively. Our study can be applied towards the long term analysis of valence. Additionally, it has applications in a closed-loop system procedures and wearable design paradigm to track and regulate the emotional valence.

Information-Theoretic Analysis of Cardio-Respiratory Interactions in Heart Failure Patients: Effects of Arrhythmias and Cardiac Resynchronization Therapy.

The properties of cardio-respiratory coupling (CRC) are affected by various pathological conditions related to the cardiovascular and/or respiratory systems. In heart failure, one of the most common cardiac pathological conditions, the degree of CRC changes primarily depend on the type of heart-rhythm alterations. In this work, we investigated CRC in heart-failure patients, applying measures from information theory, i.e., Granger Causality (GC), Transfer Entropy (TE) and Cross Entropy (CE), to quantify the directed coupling and causality between cardiac (RR interval) and respiratory (Resp) time series. Patients were divided into three groups depending on their heart rhythm (sinus rhythm and presence of low/high number of ventricular extrasystoles) and were studied also after cardiac resynchronization therapy (CRT), distinguishing responders and non-responders to the therapy. The information-theoretic analysis of bidirectional cardio-respiratory interactions in HF patients revealed the strong effect of nonlinear components in the RR (high number of ventricular extrasystoles) and in the Resp time series (respiratory sinus arrhythmia) as well as in their causal interactions. We showed that GC as a linear model measure is not sensitive to both nonlinear components and only model free measures as TE and CE may quantify them. CRT responders mainly exhibit unchanged asymmetry in the TE values, with statistically significant dominance of the information flow from Resp to RR over the opposite flow from RR to Resp, before and after CRT. In non-responders this asymmetry was statistically significant only after CRT. Our results indicate that the success of CRT is related to corresponding information transfer between the cardiac and respiratory signal quantified at baseline measurements, which could contribute to a better selection of patients for this type of therapy.

MetaPhys: Contactless Physiological Sensing of Multiple Subjects Using RIS-Based 4-D Radar

Contactless physiological signal sensing is an emerging technology for routine health monitoring, ambient-assisted living, automotive, search and rescue, and public security. In this work, we propose MetaPhys, a reconfigurable intelligent surface (RIS)-based contactless physiological signal monitoring system that can simultaneously sense multiple persons and their respiratory and cardiac signals. The proposed system localizes the human targets by utilizing RIS to dynamically manipulate the electromagnetic waves in the environment for beamforming and 3D radar imaging in the spatial dimension. Then it extracts the respiration and cardiac signals from the sequential echoes in the temporal dimension. The high-resolution information in the four dimensions as “3D space + 1D time" makes it comprehensively perceive the targets and environment. The beamforming allows the radiated energy to focus on the thoraxes, which helps reduce interference and distortion caused by stationary reflectors (i.e., clutters, static body parts, and multipath), thereby improving the signal-to-noise ratio and enabling long-range measurements. We also verify the measured data in a real indoor environment, and the experimental results show that the proposed system can accurately monitor the physiological signals of multiple subjects. The root mean square errors of respiratory rate and heart rate at a distance of 3 meters are 0.2 breaths per minute and 1.1 beats per minute, respectively.

Bayesian optimized GoogLeNet based respiratory signal prediction model from empirically decomposed gammatone visualization

Discrimination of normal and adventitious respiratory sounds from stethoscope auscultation by human ears is challenging owing to low frequency characteristics and varying frequency range for inspiration and expiration. This makes the diagnosis of pulmonary disorders a subjective one relying on the experience and hearing capability of the physician. Most computer assisted diagnosis systems formulated to address these limitations fail to capture the inherent acoustic property of respiratory sounds close to human auditory system. To circumvent this problem, this study exploits the gammatone filter banks to evaluate the distribution of all frequency components present in the signal. To categorize the respiratory signals, GoogLeNet based Convolutional Neural Networks (CNN) prediction model is developed through Time-Frequency (TF) visualization of gammatone cepstral coefficients acquired from decomposed Intrinsic Mode Functions (IMFs) in an empirical manner. As the performance of the CNN model is greatly dependent on the learning environment, this study also tends to optimize the values of the hyper parameters to enhance the classification performance of the CNN model. Accordingly, the optimal values of initial learning rate, L2 regularization and momentum are identified for both Stochastic Gradient Descent Momentum (SGDM) and Adaptive Momentum (ADAM) optimizer using Bayesian optimization technique. Experimentation is carried out for three TF visualization methods viz. spectrograms, scalograms and Constant Q spectrograms. Evaluated results show that scalogram method of classification yields high accuracy of 87.37% and 88.32% for default selection of hyperparameters using SGDM and ADAM optimizers and with optimal selection of hyperparameters based on objective function reveal an improved accuracy of 93.68 % and 95.67% for SGDM and ADAM optimizers.

Current status and prospects of automatic sleep stages scoring: Review.

The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.

Hybrid RF and PCA method: The number and Posture of piezoresistive sensors in a multifunctional technology for respiratory monitoring

The motivation of the research work is Respiratory Rate (RR) monitoring which is susceptible to environmental and physiological stimuli, knowing it may help in assessing the health of patients. In this work, at rest, six trials on a treadmill were to be completed by 10 healthy males, while low-speed jogging in order to evaluate a new approach based on Principal Component Analysis (PCA). Random Forest (RF) with PCA for sensory selection using a special wearable system with six piezoresistive sensors. For instance, a single sensor is needed for a breathing evaluation while at rest, three sensors are needed for a low-speed walk, and four sensors are needed for a high-speed walk and run assessment. The findings may be helpful in the deployment of specialized algorithms to continuously and accurately monitor RR, as well as the creation of the best instrumented wearable devices for RR monitoring both while the subject is at rest and when they are engaging in physical activity. A smart garment was utilized to obtain the breathing information delivering six respiration signals in the three compartmental parts, and a data gathering board (DAQ NI USB6002 from National Instruments) was used to gather the reference respiratory data. The performance measurements are often calculated using it (e.g., accuracy, sensitivity, specificity, precision and F1 score).

Contactless Sensing-Aided Respiration Signal Acquisition Using Improved Empirical Wavelet Transform for Rhythm Detection.

Respiration is one of the most important vital signs indicating physical condition, while the signal detection is challenging due to the complex rhythm and effort in practical scenarios. In this paper, we propose a contactless sensing-aided respiration signal acquisition technique, which can adaptively extract the desired signal under time-varying respiration rhythms within a wide range. To be specific, respiration is perceived by piezoelectric ceramics sensors along with ballistocardiography and other interference in a contactless manner, and the proposed improved empirical wavelet transform (IEWT) performs spectrum division and recognition based on upper envelop and principal component criteria, respectively, to adaptively extract the respiration spectrum for signal reconstruction. For validations, we extracted respiration signals from 8 healthy individuals in lab breathing at specified rhythms from 0.2 Hz to 0.6 Hz as well as 38 in-patients suffering from sleep-disordered-breathing with reference of polysomnogram in practical clinic scenario. The results showed that the detected respiration rhythms perfectly fitted the ones in experimental lab dataset with a correlation coefficient of 0.98, which validated the effectiveness of the respiration spectrum extraction of the proposed IEWT method. Besides, in practical clinical dataset, the proposed IEWT method could yield mean absolute and relative errors of respiration intervals of 0.4 and 0.05 seconds, respectively, achieving significant improvement in comparison with conventional ones. Meanwhile, the performance of IEWT was robust to rhythm variation, individual difference and breathing cycle detection techniques, which demonstrated the feasibility and superiority of the proposed IEWT method for practical respiration monitoring.