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Journal articles on the topic 'Respiratory signal processing'

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Author: Grafiati

Published: 11 March 2023

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1

Song, Ning, Lian Ying Ji, and Yong Peng Xu. "Denoising of the Respiratory Signal of Electrical Bio-Impedance." Advanced Materials Research 718-720 (July 2013): 1024–28. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1024.

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Human respiratory signal provides important information in modern medical care. In daily life, respiratory signal is usually captured under different motion states with the help of Electrical impedance pneumography (EIP). Consequently, the captured signal is easily corrupted by electronic/electromagnetic noise, internal mechanical vibration of the lung and motion artifacts. Because respiratory signal and interferences co-exist in an overlapping spectra manner, classical filtering method cannot work here. In this paper, we present a new signal processing method for eliminating the noise and interferences included in EIP signal, by separating the correlated motion artifacts from the raw EIP and 3-axis Acceleration (ACC) signals, restoring the rough respiration signal from the mixed signal, and further processing using wavelet analysis approach. Results are compared to traditional denosing algorithms by wiener filter, which indicates that the new signal processing method we presented is suitable for EIP signals under the motion states.

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2

Lorino, H., C. Mariette, M. Karouia, and A. M. Lorino. "Influence of signal processing on estimation of respiratory impedance." Journal of Applied Physiology 74, no. 1 (January 1, 1993): 215–23. http://dx.doi.org/10.1152/jappl.1993.74.1.215.

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Respiratory impedance was estimated between 4 and 30 Hz by spectral analysis of the mouth flow and pressure signals measured in spontaneously breathing subjects when applying a pseudorandom pressure excitation at the mouth. The signals were submitted to antialiasing low-pass filtering followed by digital preprocessing before the calculation of spectra by a fast Fourier transform algorithm. The effectiveness of signal preprocessing in eliminating the leakage error due to breathing noise was illustrated in both a mechanical analogue and a patient. Five preprocessing techniques that combined high-pass filtering and windowing were then compared in 32 randomly selected patients by examining the influence of these techniques on 1) the values of impedance at 5, 10, and 20 Hz, and 2) the parameters of linear models fitting the real (Zr) and imaginary (Zi) parts of impedance for coherence values higher than a preset threshold. The impedance values and derived parameters were either the mean of the estimates separately obtained in the three data recordings (PA) or the single estimate obtained from average spectra (SP). Small but significant differences between filtering and windowing, as well as between SP and PA, were evidenced for the Zr, whereas Zi was only slightly sensitive to the type of averaging technique. We conclude that the signal preprocessing and data averaging techniques selected in this study have similar effects on spectral estimation of respiratory impedance.

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3

Qi, Qingjie, Youxin Zhao, Liang Zhang, Zhen Yang, Lifeng Sun, and Xinlei Jia. "Research on Ultra-Wideband Radar Echo Signal Processing Method Based on P-Order Extraction and VMD." Sensors 22, no. 18 (September 6, 2022): 6726. http://dx.doi.org/10.3390/s22186726.

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As a new method to detect vital signs, Ultra-wideband (UWB) radar could continuously monitor human respiratory signs without contact. Aimed at addressing the problem of large interference and weak acquisition signal in radar echo signals from complex scenes, this paper adopts a UWB radar echo signal processing method that combines strong physical sign information extraction at P time and Variational Mode Decomposition (VMD) to carry out theoretical derivation. Using this novel processing scheme, respiration and heartbeat signals can be quickly reconstructed according to the selection of the appropriate intrinsic mode functions (IMFs), and the real-time detection accuracy of human respiratory signs is greatly improved. Based on an experimental platform, the data collected by the UWB radar module were first verified against the measured values obtained at the actual scene. The results of a validation test proved that our UWB radar echo signal processing method effectively eliminated the respiratory clutter signal and realized the accurate measurement of respiratory and heartbeat signals, which would prove the existence of life and further improve the quality of respiration and heartbeat signal and the robustness of detection.

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Kemper, Guillermo, Angel Oshita, Ricardo Parra, and Carlos Herrera. "An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (February 1, 2023): 358. http://dx.doi.org/10.11591/ijece.v13i1.pp358-373.

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<span lang="EN-US">This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 19 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm’s performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal’s envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.</span>

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Schulz, André, Thomas M. Schilling, Claus Vögele, Mauro F. Larra, and Hartmut Schächinger. "Respiratory modulation of startle eye blink: a new approach to assess afferent signals from the respiratory system." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1708 (November 19, 2016): 20160019. http://dx.doi.org/10.1098/rstb.2016.0019.

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Current approaches to assess interoception of respiratory functions cannot differentiate between the physiological basis of interoception, i.e. visceral-afferent signal processing, and the psychological process of attention focusing. Furthermore, they typically involve invasive procedures, e.g. induction of respiratory occlusions or the inhalation of CO 2 -enriched air. The aim of this study was to test the capacity of startle methodology to reflect respiratory-related afferent signal processing, independent of invasive procedures. Forty-two healthy participants were tested in a spontaneous breathing and in a 0.25 Hz paced breathing condition. Acoustic startle noises of 105 dB(A) intensity (50 ms white noise) were presented with identical trial frequency at peak and on-going inspiration and expiration, based on a new pattern detection method, involving the online processing of the respiratory belt signal. The results show the highest startle magnitudes during on-going expiration compared with any other measurement points during the respiratory cycle, independent of whether breathing was spontaneous or paced. Afferent signals from slow adapting phasic pulmonary stretch receptors may be responsible for this effect. This study is the first to demonstrate startle modulation by respiration. These results offer the potential to apply startle methodology in the non-invasive testing of interoception-related aspects in respiratory psychophysiology. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’.

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6

Zhao, Huayu. "Design and Application of Human Movement Respiratory and ECG Signal Acquisition System." Journal of Medical Imaging and Health Informatics 10, no. 4 (April 1, 2020): 890–97. http://dx.doi.org/10.1166/jmihi.2020.2950.

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To realize the design of mobile phone human movement breathing and electrocardiograph (ECG) signal acquisition system based on Bluetooth transmission, the principle of the generation and detection of ECG and respiratory signal and the guide system of signal acquisition are analyzed. Additionally, the hardware of the system is designed, including the hardware of the signal acquisition system, the design of ADS1292R ECG and respiratory signal acquisition module, the design of the main control chip and the design of the Bluetooth module. Then, the digital filtering processing of the ECG and respiratory signals is completed, including the baseline drift filtering and the suppression of the power frequency interference. The results show that the monitoring system runs well and it can effectively collect ECG and respiratory signals, calculate heart rate and respiratory frequency in real time, and display ECG waveform in real time. To sum up, the monitoring system is of great significance for real-time monitoring of the patient's condition.

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DE SILVA, CLARENCE W., SHAN XIAO, MAOQING LI, and CHERYL N. DE SILVA. "SENSORY SIGNAL PROCESSING ISSUES IN A TELEMEDICINE SYSTEM." International Journal of Information Acquisition 09, no. 02 (June 2013): 1350013. http://dx.doi.org/10.1142/s0219878913500137.

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A telemedicine system will provide sustainable, comprehensive, low-cost, fast, private, and convenient access to medical consultation and diagnosis for patients from remote locations. The telemedicine system addressed in this paper consists of a sensor jacket, which is worn by the patient for medical monitoring. The signals sensed through the jacket are processed and transmitted through a public telecommunication link, to a medical professional in a hospital at distance. The medical professional interacts with the patient through audio and video links, and simultaneously examines the data transmitted by the monitoring system. Medical assessment, diagnosis, and prescription are carried out on this basis. Sensing and signal processing are paramount to providing the patient data to the medical professional in an accurate and effective manner. This paper presents some relevant issues and techniques. Specific examples of electrocardiograms and respiratory signals are provided to illustrate the applicable signal conditioning approaches. Results are presented to demonstrate the feasibility and the effectiveness of these methods.

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De Meersman, R. E., A. S. Zion, S. Teitelbaum, J. P. Weir, J. Lieberman, and J. Downey. "Deriving respiration from pulse wave: a new signal-processing technique." American Journal of Physiology-Heart and Circulatory Physiology 270, no. 5 (May 1, 1996): H1672—H1675. http://dx.doi.org/10.1152/ajpheart.1996.270.5.h1672.

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Investigations of autonomic nervous system activity using spectral analysis of heart rate (HR) and blood pressure (BP) variability is very popular in many scientific disciplines, and yet only half of all studies involving spectral analysis control for respiration. Because respiration modulates HR and BP variability, knowledge of the respiratory rate is necessary for the proper interpretation of HR and BP power spectra. We devised and validated a new signal-processing technique to derive respiration from the blood pressure wave. This technique is based on the relationship between oscillations in the area under the dicrotic notch of the pulse wave and respiration. The results of our view signal-processing technique yielded significant correlations between protocols of the actual number of respiratory cycles and our blood pressure-derived respiratory cycles and their respective spectra for a number of standard autonomic tests (P < 0.05). Our method will allow retrospective extraction of the respiratory wave and as such afford a more precise interpretation of HR and BP spectra.

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9

Moreno, Silvia, Andres Quintero-Parra, Carlos Ochoa-Pertuz, Reynaldo Villarreal, and Isaac Kuzmar. "A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography." International Journal of Signal Processing, Image Processing and Pattern Recognition 11, no. 2 (April 30, 2018): 1–10. http://dx.doi.org/10.14257/ijsip.2018.11.2.01.

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10

Motamedi-Fakhr, Shayan, Mohamed Moshrefi-Torbati, Martyn Hill, David Simpson, Romola S. Bucks, Annette Carroll, and Catherine M. Hill. "Respiratory cycle related EEG changes: Modified respiratory cycle segmentation." Biomedical Signal Processing and Control 8, no. 6 (November 2013): 838–44. http://dx.doi.org/10.1016/j.bspc.2013.08.001.

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11

Park, Cheolhyeong, and Deokwoo Lee. "Classification of Respiratory States Using Spectrogram with Convolutional Neural Network." Applied Sciences 12, no. 4 (February 11, 2022): 1895. http://dx.doi.org/10.3390/app12041895.

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This paper proposes an approach to the classification of respiration states based on a neural network model by visualizing respiratory signals using a spectrogram. The analysis and processing of human biosignals are still considered some of the most crucial and fundamental research areas in both signal processing and medical applications. Recently, learning-based algorithms in signal and image processing for medical applications have shown significant improvement from both quantitative and qualitative perspectives. Human respiration is still considered an important factor for diagnosis, and it plays a key role in preventing fatal diseases in practice. This paper chiefly deals with a contactless-based approach for the acquisition of respiration data using an ultra-wideband (UWB) radar sensor because it is simple and easy for use in an experimental setup and shows high accuracy in distance estimation. This paper proposes the classification of respiratory states by using a feature visualization scheme, a spectrogram, and a neural network model. The proposed method shows competitive and promising results in the classification of respiratory states. The experimental results also show that the method provides better accuracy (precision: 0.86 and specificity: 0.90) than conventional methods that use expensive equipment for respiration measurement.

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12

King, Gregory G., Jason Bates, Kenneth I. Berger, Peter Calverley, Pedro L. de Melo, Raffaele L. Dellacà, Ramon Farré, et al. "Technical standards for respiratory oscillometry." European Respiratory Journal 55, no. 2 (November 26, 2019): 1900753. http://dx.doi.org/10.1183/13993003.00753-2019.

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Oscillometry (also known as the forced oscillation technique) measures the mechanical properties of the respiratory system (upper and intrathoracic airways, lung tissue and chest wall) during quiet tidal breathing, by the application of an oscillating pressure signal (input or forcing signal), most commonly at the mouth. With increased clinical and research use, it is critical that all technical details of the hardware design, signal processing and analyses, and testing protocols are transparent and clearly reported to allow standardisation, comparison and replication of clinical and research studies. Because of this need, an update of the 2003 European Respiratory Society (ERS) technical standards document was produced by an ERS task force of experts who are active in clinical oscillometry research.The aim of the task force was to provide technical recommendations regarding oscillometry measurement including hardware, software, testing protocols and quality control.The main changes in this update, compared with the 2003 ERS task force document are 1) new quality control procedures which reflect use of “within-breath” analysis, and methods of handling artefacts; 2) recommendation to disclose signal processing, quality control, artefact handling and breathing protocols (e.g. number and duration of acquisitions) in reports and publications to allow comparability and replication between devices and laboratories; 3) a summary review of new data to support threshold values for bronchodilator and bronchial challenge tests; and 4) updated list of predicted impedance values in adults and children.

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Kahl, Lorenz, and Ulrich Hofmann. "Removal of ECG Artifacts Affects Respiratory Muscle Fatigue Detection—A Simulation Study." Sensors 21, no. 16 (August 23, 2021): 5663. http://dx.doi.org/10.3390/s21165663.

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This work investigates elimination methods for cardiogenic artifacts in respiratory surface electromyographic (sEMG) signals and compares their performance with respect to subsequent fatigue detection with different fatigue algorithms. The analysis is based on artificially constructed test signals featuring a clearly defined expected fatigue level. Test signals are additively constructed with different proportions from sEMG and electrocardiographic (ECG) signals. Cardiogenic artifacts are eliminated by high-pass filtering (HP), template subtraction (TS), a newly introduced two-step approach (TSWD) consisting of template subtraction and a wavelet-based damping step and a pure wavelet-based damping (DSO). Each method is additionally combined with the exclusion of QRS segments (gating). Fatigue is subsequently quantified with mean frequency (MNF), spectral moments ratio of order five (SMR5) and fuzzy approximate entropy (fApEn). Different combinations of artifact elimination methods and fatigue detection algorithms are tested with respect to their ability to deliver invariant results despite increasing ECG contamination. Both DSO and TSWD artifact elimination methods displayed promising results regarding the intermediate, “cleaned” EMG signal. However, only the TSWD method enabled superior results in the subsequent fatigue detection across different levels of artifact contamination and evaluation criteria. SMR5 could be determined as the best fatigue detection algorithm. This study proposes a signal processing chain to determine neuromuscular fatigue despite the presence of cardiogenic artifacts. The results furthermore underline the importance of selecting a combination of algorithms that play well together to remove cardiogenic artifacts and to detect fatigue. This investigation provides guidance for clinical studies to select optimal signal processing to detect fatigue from respiratory sEMG signals.

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14

Li, Qiuping, Xing Zhang, Xin’an Wang, Tianxia Zhao, Changpei Qiu, and Bing Zhou. "Detection Method and System of the Human Body Characteristic Index Based on TCM." Journal of Healthcare Engineering 2021 (April 25, 2021): 1–10. http://dx.doi.org/10.1155/2021/5549842.

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As traditional Chinese medicine (TCM) has gained more and more recognition in the world, Chinese medicine has also played its important role. However, traditional Chinese medicine equipment is relatively deficient, with insufficient functions and low degree of digitalization. For example, existing auscultation equipment can obtain few human characteristic indicators, which is difficult to meet the needs of reference in traditional Chinese medicine diagnosis. Based on this, this paper designed a human body characteristic index detection system based on the principle of traditional Chinese medicine, which includes respiratory and heartbeat signal acquisition device, meridian and acupoint signal acquisition device, temperature signal acquisition device, pulse and blood pressure acquisition device, processing module, keyword module, and output module. The respiratory and heartbeat signal acquisition device is used to collect the respiratory and heartbeat signal of human body. Meridian acupoint signal acquisition device is used to collect human meridian acupoint radio signals. The temperature signal acquisition device is used to collect the infrared temperature light wave signal of human body. Pulse and blood pressure acquisition devices are used to collect pulse and blood pressure signals. The processing module is used to obtain one or more human body characteristic indicators according to one or more of the respiration and heartbeat signals, meridians and acupoints signals, temperature signals, pulse, and blood pressure, including Qi and blood characteristic indicators, viscera and six meridian characteristic indicators, and temperature characteristic indicators. The keyword corresponding module is used to obtain the corresponding keyword representing the physiological state information of human body according to the one or more human body characteristic indicators. The output module is used to output the human body characteristic index and the key words. It includes the key words of Qi and blood state information, the key words of viscera state information, the key words of Qi and blood state information, etc. The system can be used for serious disease screening, chronic disease management, and risk early warning.

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Scarpetta, M., M. Spadavecchia, G. Andria, M. A. Ragolia, and N. Giaquinto. "Accurate simultaneous measurement of heartbeat and respiratory intervals using a smartphone." Journal of Instrumentation 17, no. 07 (July 1, 2022): P07020. http://dx.doi.org/10.1088/1748-0221/17/07/p07020.

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Abstract In this paper, a method based on the usage of a smartphone for measuring simultaneously both heartbeat intervals and respiratory intervals is presented. In particular, the commodity accelerometer of a smartphone is used for measuring the seismocardiographic signal generated by heart activity and the acceleration due to breathing movements. The measurement is performed with the subject laying down, placing the smartphone on his/her xiphoid process. Signal processing algorithms are presented in the paper that can produce an accurate estimation of heartbeat and respiratory intervals from the measured acceleration signals. A metrological validation of the heartbeat and respiratory intervals estimates obtained with the proposed method is carried out by comparison with measurements obtained using an electrocardiograph and a spirometer. Two practical examples of applications of the measured quantities are finally reported, that are the measurement of the Heart Rate Variability from heartbeat intervals and of the Respiratory Sinus Arrhythmia from both the heartbeat intervals and the respiratory signal.

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Rudnitskii, A. G., M. A. Rudnytska, and L. V. Tkachenko. "SINGLE-CHANNEL PROCESSING OF AUSCULTATORY SIGNALS USING METHODS OF MATHEMATICAL MORPHOLOGY." Journal of Numerical and Applied Mathematics, no. 1 (135) (2021): 179–85. http://dx.doi.org/10.17721/2706-9699.2021.1.24.

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The paper considers a new method of separating respiratory sounds from heart sounds in a general signal registered on the surface of the human body. The proposed approach is based on a combination of Bayesian noise suppression techniques and methods of mathematical morphology. The proposed method was tested on real auscultatory signals. Evaluation of the efficiency of the algorithm using auditory, visual and numerical analysis shows that the developed approach is a promising alternative to existing techniques for separating auscultatory signals into its natural components.

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Fallatah, Anwar, Miodrag Bolic, Miller MacPherson, and Daniel J. La Russa. "Monitoring Respiratory Motion during VMAT Treatment Delivery Using Ultra-Wideband Radar." Sensors 22, no. 6 (March 16, 2022): 2287. http://dx.doi.org/10.3390/s22062287.

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The goal of this paper is to evaluate the potential of a low-cost, ultra-wideband radar system for detecting and monitoring respiratory motion during radiation therapy treatment delivery. Radar signals from breathing motion patterns simulated using a respiratory motion phantom were captured during volumetric modulated arc therapy (VMAT) delivery. Gantry motion causes strong interference affecting the quality of the extracted respiration motion signal. We developed an artificial neural network (ANN) model for recovering the breathing motion patterns. Next, automated classification into four classes of breathing amplitudes is performed, including no breathing, breath hold, free breathing and deep inspiration. Breathing motion patterns extracted from the radar signal are in excellent agreement with the reference data recorded by the respiratory motion phantom. The classification accuracy of simulated deep inspiration breath hold breathing was 94% under the worst case interference from gantry motion and linac operation. Ultra-wideband radar systems can achieve accurate breathing rate estimation in real-time during dynamic radiation delivery. This technology serves as a viable alternative to motion detection and respiratory gating systems based on surface detection, and is well-suited to dynamic radiation treatment techniques. Novelties of this work include detection of the breathing signal using radar during strong interference from simultaneous gantry motion, and using ANN to perform adaptive signal processing to recover breathing signal from large interference signals in real time.

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Kuwalek, Piotr, Bartlomiej Burlaga, Waldemar Jesko, and Patryk Konieczka. "Research on methods for detecting respiratory rate from photoplethysmographic signal." Biomedical Signal Processing and Control 66 (April 2021): 102483. http://dx.doi.org/10.1016/j.bspc.2021.102483.

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19

Khomenko, J. M., and S. V. Pavlov. "Biometric signal processing at radar remote diagnostics of cardio-respiratory human activity." Optoelectronic Information-Power Technologies 37, no. 1 (November 2019): 50–54. http://dx.doi.org/10.31649/1681-7893-2019-37-1-50-54.

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Mori, Vitor, Renato L. Vitorasso, Vitor A. Takeuchi, Wothan T. Lima, Maria A. Oliveira, and Henrique T. Moriya. "Signal processing to remove spurious contributions to the assessment of respiratory mechanics." Experimental Lung Research 48, no. 1 (December 22, 2021): 1–11. http://dx.doi.org/10.1080/01902148.2021.2019355.

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Noto, Torben, Guangyu Zhou, Stephan Schuele, Jessica Templer, and Christina Zelano. "Automated analysis of breathing waveforms using BreathMetrics: a respiratory signal processing toolbox." Chemical Senses 43, no. 8 (July 7, 2018): 583–97. http://dx.doi.org/10.1093/chemse/bjy045.

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Porta, Alberto, Federico Aletti, Frederic Vallais, and Giuseppe Baselli. "Multimodal signal processing for the analysis of cardiovascular variability." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1887 (October 22, 2008): 391–409. http://dx.doi.org/10.1098/rsta.2008.0229.

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Cardiovascular (CV) variability as a primary vital sign carrying information about CV regulation systems is reviewed by pointing out the role of the main rhythms and the various control and functional systems involved. The high complexity of the addressed phenomena fosters a multimodal approach that relies on data analysis models and deals with the ongoing interactions of many signals at a time. The importance of closed-loop identification and causal analysis is remarked upon and basic properties, application conditions and methods are recalled. The need of further integration of CV signals relevant to peripheral and systemic haemodynamics, respiratory mechanics, neural afferent and efferent pathways is also stressed.

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Machado Fernández, José Raúl, and Lesya Anishchenko. "Mental stress detection using bioradar respiratory signals." Biomedical Signal Processing and Control 43 (May 2018): 244–49. http://dx.doi.org/10.1016/j.bspc.2018.03.006.

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Aqueveque, Pablo, Macarena Díaz, Britam Gomez, Rodrigo Osorio, Francisco Pastene, Luciano Radrigan, and Anibal Morales. "Embedded Electronic Sensor for Monitoring of Breathing Activity, Fitting and Filter Clogging in Reusable Industrial Respirators." Biosensors 12, no. 11 (November 8, 2022): 991. http://dx.doi.org/10.3390/bios12110991.

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Millions of workers are required to wear reusable respirators in several industries worldwide. Reusable respirators include filters that protect workers against harmful dust, smoke, gases, and vapors. These hazards may cause cancer, lung impairment, and diseases. Respiratory protection is prone to failure or misuse, such as wearing respirators with filters out of service life and employees wearing respirators loosely. Currently, there are no commercial systems capable of reliably alerting of misuse of respiratory protective equipment during the workday shifts or provide early information about dangerous clogging levels of filters. This paper proposes a low energy and non-obtrusive functional building block with embedded electronics that enable breathing monitoring inside an industrial reusable respirator. The embedded electronic device collects multidimensional data from an integrated pressure, temperature, and relative humidity sensor inside a reusable industrial respirator in real time and sends it wirelessly to an external platform for further processing. Here, the calculation of instantaneous breathing rate and estimation of the filter’s respirator fitting and clogging level is performed. The device was tested with ten healthy subjects in laboratory trials. The subjects were asked to wear industrial reusable respirator with the embedded electronic device attached inside. The signals measured with the system were compared with airflow signals measured with calibrated transducers for validation purposes. The correlation between the estimated breathing rates using pressure, temperature, and relative humidity with the reference signal (airflow) is 0.987, 0.988 and 0.989 respectively, showing that instantaneous breathing rate can be calculated accurately using the information from the embedded device. Moreover, respirator fitting (well-fitted or loose condition) and filter’s clogging levels (≤60%, 80% and 100% clogging) also can be estimated using features extracted from absolute pressure measurements combined to statistical analysis ANOVA models. These experimental outputs represent promising results for further development of data-driven prediction models using machine learning techniques to determine filters end-of-service life. Furthermore, the proposed system would collect relevant data for real-time monitoring of workers’ breathing conditions and respirator usage, helping to improve occupational safety and health in the workplace.

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Ji, Zhenjie, and Menglun Zhang. "Highly sensitive and stretchable piezoelectric strain sensor enabled wearable devices for real-time monitoring of respiratory and heartbeat simultaneously." Nanotechnology and Precision Engineering 5, no. 1 (March 1, 2022): 013002. http://dx.doi.org/10.1063/10.0009365.

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The World Health Organization has declared COVID-19 a pandemic. The demand for devices or systems to diagnose and track COVID-19 infections noninvasively not only in hospitals but also in home settings has led to increased interest in consumer-grade wearables. A common symptom of COVID-19 is dyspnea, which may manifest as an increase in respiratory and heart rates. In this paper, a novel piezoelectric strain sensor is presented for real-time monitoring of respiratory and heartbeat signals. A highly sensitive and stretchable piezoelectric strain sensor is fabricated using a piezoelectric film with a serpentine layout. The thickness of the patterned PVDF flexible piezoelectric strain sensor is only 168 μm, and the voltage sensitivity reaches 0.97 mV/μ ɛ. The effective modulus is 13.5 MPa, which allows the device to fit to the skin and detect the small strain exhibited by the human body. Chest vibrations are captured by the piezoelectric sensor, which produces an electrical output voltage signal conformally mapped with respiratory–cardiac activities. The separate heart activity and respiratory signals are extracted from the mixed respiratory–cardiac signal by an empirical mode decomposition data processing algorithm. By detecting vital signals such as respiratory and heart rates, the proposed device can aid early diagnosis and monitoring of respiratory diseases such as COVID-19.

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Alabacy, Zina. "Applications of Wavelets for BVPs and Signal Processing." Journal of Kufa for Mathematics and Computer 7, no. 2 (November 1, 2021): 10–15. http://dx.doi.org/10.31642/jokmc/2018/070203.

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The transfer of information using the signal needs speed, which leads to the compression of the information. It is only possible through the process of using a mathematical technique at work. To demonstrate an increase in theory efficiency, it was used in signal processing, compression, and good results. In section 4 Matrix was used because M=3 was taken, where six functions were obtained, when these functions were integrated, the operations matrix of integration was added, which was added to solve Boundary Value Problems (BVPs) numerically. In addition to solving problems numerically, using the proposed technique, which is signal processing, to demonstrate the efficiency of the proposed theory as indicated in section 2, wavelets are built on the dependence of the four effects . In addition, the number of equations obtained is calculated based on the value of where six functions are obtained and the greater value of is obtained More functions, leading to greater accuracy in obtaining the best results.

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K, Chethana, Guru Prasad AS, Nagaraj SB, and Asokan S. "Cardiac and respiratory signal extraction methods from ballistocardiography signal sensed using fiber bragg grating sensor." MOJ Applied Bionics and Biomechanics 4, no. 1 (February 24, 2020): 15–19. http://dx.doi.org/10.15406/mojabb.2020.04.00125.

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This work presents a simple, efficient and an easy-to-build ballistocardiography sys-tem using Fiber Bragg Grating (FBG) sensor and a comprehensive assessment of relevant digital signal processing algorithms to simultaneously extract respiratory and cardiac frequency components from a cluttered mix Ballistocardiography (BCG) signal. The primary purpose of the current study is two-fold: first- to build an analog circuit for BCG signal amplification, and second to evaluate the performance of three state-of-the-art methods, namely: lowpass-highpass filter, weiner filter and ensemble empherical mode decomposition to simultaneously extract respiratory and cardiac frequency components from the BCG signal. BCG measurements from test subjects were used in this study and a commercial digital stethoscope was used to validate the performance of methods used in this study. In addition to the effective amplification of the BCG signal through proposed analog circuit configuration, we demonstrate that a simple low pass high pass filter configuration can be used for accurate measurement of cardiac and respiratory frequencies. Due to its simplicity, the proposed system can be suitably tailored to process BCG signal for simultaneous extraction of respiration and heart rate which can aid as an effective diagnostic tool for identifying critical disorders associated with lungs and heart dysfunction.

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Ohnishi, Takashi, Yuya Takano, Hideyuki Kato, Yoshihiko Ooka, and Hideaki Haneishi. "Respiratory-synchronized digital subtraction angiography based on a respiratory phase matching method." Signal, Image and Video Processing 12, no. 3 (October 6, 2017): 539–47. http://dx.doi.org/10.1007/s11760-017-1190-8.

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Ksenofontov, D. G., and V. N. Kostin. "Implementation of digital methods to analyze eddy-current signals based on the E14-440 module." Diagnostics, Resource and Mechanics of materials and structures, no. 6 (December 2021): 32–36. http://dx.doi.org/10.17804/2410-9908.2021.6.032-036.

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Nondestructive testing methods utilize various sensors, and different signal types require different processing methods. Digital implementation of signal processing methods can expand the variety of methods implemented by one system. An eddy-current test system based on the E14-440 module has been developed. Quadrature amplitude demodulation and fast Fourier transformation are implemented to analyze the signal. The amplitude, phase, and complex parts of the signal are calculated. It is shown that both methods are applicable and allow elimination of some analog circuits. However, digital signal processing significantly depends on conversion rates and synchronization between generation and pickup of the signal.

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Ma, Xiaoxiao, Shaoxing Zhang, Peikai Zou, Ruya Li, and Yubo Fan. "Paper-Based Humidity Sensor for Respiratory Monitoring." Materials 15, no. 18 (September 16, 2022): 6447. http://dx.doi.org/10.3390/ma15186447.

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Flexible respiratory monitoring devices have become available for outside-hospital application scenarios attributable to their improved system wearability. However, the complex fabrication process of such flexible devices results in high prices, limiting their applications in real-life scenarios. This study proposes a flexible, low-cost, and easy-processing paper-based humidity sensor for sleep respiratory monitoring. A paper humidity sensing model was established and sensors under different design parameters were processed and tested, achieving high sensitivity of 5.45 kΩ/%RH and good repeatability with a matching rate of over 85.7%. Furthermore, the sensor patch with a dual-channel 3D structure was designed to distinguish between oral and nasal breathing from origin signals proved in the simulated breathing signal monitoring test. The sensor patch was applied in the sleep respiratory monitoring of a healthy volunteer and an obstruct sleep apnea patient, demonstrating its ability to distinguish between different respiratory patterns as well as various breathing modes.

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Wichum, Felix, Christian Wiede, and Karsten Seidl. "Depth-Based Measurement of Respiratory Volumes: A Review." Sensors 22, no. 24 (December 10, 2022): 9680. http://dx.doi.org/10.3390/s22249680.

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Depth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showing further improvements for DPG by analyzing recent developments regarding the individual components of a DPG measurement. Starting from the advantages and application scenarios, measurement scenarios and recording devices, selection algorithms and location of a region of interest (ROI) on the upper body, signal processing steps, models for error minimization with a reference measurement device, and final evaluation procedures are presented and discussed. It is shown that ROI selection has an impact on signal quality. Adaptive methods and dynamic referencing of body points to select the ROI can allow more accurate placement and thus lead to better signal quality. Multiple different ROIs can be used to assess breathing mechanics and distinguish patient groups. Signal acquisition can be performed quickly using arithmetic calculations and is not inferior to complex 3D reconstruction algorithms. It is shown that linear models provide a good approximation of the signal. However, further dependencies, such as personal characteristics, may lead to non-linear models in the future. Finally, it is pointed out to focus developments with respect to single-camera systems and to focus on independence from an individual calibration in the evaluation.

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Ionescu, C., R. De Keyser, J. Sabatier, A. Oustaloup, and F. Levron. "Low frequency constant-phase behavior in the respiratory impedance." Biomedical Signal Processing and Control 6, no. 2 (April 2011): 197–208. http://dx.doi.org/10.1016/j.bspc.2010.10.005.

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33

Long, Xi, Jérôme Foussier, Pedro Fonseca, Reinder Haakma, and Ronald M. Aarts. "Analyzing respiratory effort amplitude for automated sleep stage classification." Biomedical Signal Processing and Control 14 (November 2014): 197–205. http://dx.doi.org/10.1016/j.bspc.2014.08.001.

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34

Saatci, Esra, and Ertugrul Saatci. "State-space analysis of fractional-order respiratory system models." Biomedical Signal Processing and Control 57 (March 2020): 101820. http://dx.doi.org/10.1016/j.bspc.2019.101820.

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35

Mateu-Mateus, M., F. Guede-Fernández, N. Rodriguez-Ibáñez, M. A. García-González, J. Ramos-Castro, and M. Fernández-Chimeno. "A non-contact camera-based method for respiratory rhythm extraction." Biomedical Signal Processing and Control 66 (April 2021): 102443. http://dx.doi.org/10.1016/j.bspc.2021.102443.

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36

Ionescu, Clara M., and D. Copot. "Monitoring respiratory impedance by wearable sensor device: Protocol and methodology." Biomedical Signal Processing and Control 36 (July 2017): 57–62. http://dx.doi.org/10.1016/j.bspc.2017.03.018.

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37

Ernst, Floris, Alexander Schlaefer, Sonja Dieterich, and Achim Schweikard. "A Fast Lane Approach to LMS prediction of respiratory motion signals." Biomedical Signal Processing and Control 3, no. 4 (October 2008): 291–99. http://dx.doi.org/10.1016/j.bspc.2008.06.001.

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38

Ge, Hao, Hui Qin, Shan Xue, Enkang Liu, Mingzhu Zhang, Zixuan Bai, and Yixin Ma. "Research on denoising algorithm of thoracic impedance signal for respiratory monitoring during running exercise." Biomedical Signal Processing and Control 70 (September 2021): 102941. http://dx.doi.org/10.1016/j.bspc.2021.102941.

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39

Zhang, Xiaorong, and Quan Ding. "Respiratory rate estimation from the photoplethysmogram via joint sparse signal reconstruction and spectra fusion." Biomedical Signal Processing and Control 35 (May 2017): 1–7. http://dx.doi.org/10.1016/j.bspc.2017.02.003.

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40

Charlton, Peter H., Timothy Bonnici, Lionel Tarassenko, David A. Clifton, Richard Beale, Peter J. Watkinson, and Jordi Alastruey. "An impedance pneumography signal quality index: Design, assessment and application to respiratory rate monitoring." Biomedical Signal Processing and Control 65 (March 2021): 102339. http://dx.doi.org/10.1016/j.bspc.2020.102339.

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41

Addison, Paul S., Rui Wang, Alberto A. Uribe, and Sergio D. Bergese. "Increasing signal processing sophistication in the calculation of the respiratory modulation of the photoplethysmogram (DPOP)." Journal of Clinical Monitoring and Computing 29, no. 3 (September 11, 2014): 363–72. http://dx.doi.org/10.1007/s10877-014-9613-3.

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42

Orphanidou, C., S. Fleming, S. A. Shah, and L. Tarassenko. "Data fusion for estimating respiratory rate from a single-lead ECG." Biomedical Signal Processing and Control 8, no. 1 (January 2013): 98–105. http://dx.doi.org/10.1016/j.bspc.2012.06.001.

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43

Docherty, Paul D., Christoph Schranz, Yeong-Shiong Chiew, Knut Möller, and J. Geoffrey Chase. "Reformulation of the pressure-dependent recruitment model (PRM) of respiratory mechanics." Biomedical Signal Processing and Control 12 (July 2014): 47–53. http://dx.doi.org/10.1016/j.bspc.2013.12.001.

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44

Lee, Yee Siong, and Pubudu N. Pathirana. "Suppression of interference in continuous wave Doppler radar based respiratory measurements." Biomedical Signal Processing and Control 25 (March 2016): 86–90. http://dx.doi.org/10.1016/j.bspc.2015.10.002.

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45

Sello, Stefano, Soo-kyung Strambi, Gennaro De Michele, and Nicolino Ambrosino. "Respiratory sound analysis in healthy and pathological subjects: A wavelet approach." Biomedical Signal Processing and Control 3, no. 3 (July 2008): 181–91. http://dx.doi.org/10.1016/j.bspc.2008.02.002.

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46

Khoma, Volodymyr, Halyna Kenyo, and Aleksandra Kawala-Sterniuk. "Advanced Computing Methods for Impedance Plethysmography Data Processing." Sensors 22, no. 6 (March 8, 2022): 2095. http://dx.doi.org/10.3390/s22062095.

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In this paper we are introducing innovative solutions applied in impedance plethysmography concerning improvement of the rheagraph characteristics and the efficiency increase of the developing rheograms using computer methods. The described methods have been developed in order to ensure the stability of parameters and to extend the functionality of the rheographic system based on digital signal processing, which applies to the compensation of the base resistance with a digital potentiometer, digital synthesis of quadrature excitation signals and the performance of digital synchronous detection. The emphasis was put on methods for determination of hemodynamic parameters by computer processing of the rheograms. As a result–three methods for respiratory artifacts elimination have been proposed: based on the discrete cosine transform, the discrete wavelet transform and the approximation of the zero line with spline functions. Additionally, computer methods for physiological indicators determination, including those based on wavelet decomposition, were also proposed and described in this paper. The efficiency of various rheogram compression algorithms was tested, evaluated and presented in this work.

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Zhang, Zhen, and Fang Liu. "Design of Multi-Channel Physiological Signal Monitor Based on Wireless Data Transmission." Applied Mechanics and Materials 496-500 (January 2014): 1207–10. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1207.

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In order to monitor important parameters of patient vital signal continuously in real-time manner for a long time, a multi-channel physiological signal monitor based on wireless data transmission was designed. The device can acquire 8-channel high-resolution signal of electromyography or electrocardiograph and transmit these signal to remote monitoring station using real-time wireless communication. The monitoring station implements data displaying, storing, analyzing and processing. Simulation analysis shows that the design of the monitor has a broad outlook of clinical applications, making possible to realize the telemetry wireless monitoring of respiratory disease.

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Rizal, Achmad, Risanuri Hidayat, and Hanung Adi Nugroho. "Comparison of Multiscale Entropy for Lung Sound Classification." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 3 (December 1, 2018): 984. http://dx.doi.org/10.11591/ijeecs.v12.i3.pp984-994.

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<p><em>Lung sound is a biological signal used to determine the health level of the respiratory tract. Various digital signal processing techniques have been developed for the automatic lung sound classification. Entropy is one of the parameters used to measure the biomedical signal complexity. Multiscale entropy is introduced to measure the entropy of a signal at a particular scale range. Over time, various multiscale entropy techniques are used to measure the signal complexity on biological signal and other physical signals. In this paper, a number of multiscale entropy techniques for the lung sound classification are discussed. The results showed that Multiscale Permutation Entropy (MPE) could produce the highest accuracy of 97.98% for five classes of lung sound data. Results achieved for the scale 1-10 producing ten features for each lung sound data. This result is better than other seven entropies. The use of Permutation entropy (PE) on a multiscale scheme was to obtain a better accuracy compared to PE on one scale only</em><em> </em></p>

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49

Sorokin, Anatoly, Alexander Borisov, Mikhail Reushev, Victor Ivanov, and Dmitriy Kharlamov. "The influence the horizontal structure of the forest on the passing the L1 range of navigation satellites signals." E3S Web of Conferences 333 (2021): 01014. http://dx.doi.org/10.1051/e3sconf/202133301014.

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Experimental data on the passing of signals from navigation satellites of the L1 range through forest plantations with an anisotropic horizontal tree arrangement structure are presented. A feature of the studied environment is an irregular distance between trees in rows and a constant distance between rows. Signals were recorded by an antenna located inside the forest at heights of 0.5 and 10 meters. Based on the results of processing the amplitude-time dependences of the recorded signal by means of fast Fourier transform, qualitative differences were revealed depending on the orientation of the probe signal path in the forest stand.

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Fu, Tianyu, Jingshu Li, Jiaju Zhang, Danni Ai, Jingfan Fan, Hong Song, Ping Liang, and Jian Yang. "Four-Dimensional Wide-Field Ultrasound Reconstruction System With Sparse Respiratory Signal Matching." IEEE Transactions on Computational Imaging 7 (2021): 234–47. http://dx.doi.org/10.1109/tci.2021.3054527.

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