Developing computational detection of individual respiratory cycle phases from tracheal sound signal during sleep

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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Blind source separation method for heart sound signal

JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT ◽

10.3724/sp.j.1187.2012.00498 ◽

2013 ◽

Vol 26 (6) ◽

pp. 498-502 ◽

Cited By ~ 2

Author(s):

Kexue Sun ◽

Xiefeng Cheng

Keyword(s):

Blind Source Separation ◽

Heart Sound ◽

Source Separation ◽

Separation Method ◽

Sound Signal ◽

Heart Sound Signal

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Automated Heart Sound Signal Segmentation and Identification using Abrupt Changes and Peak Finding Detection

Procedia Computer Science ◽

10.1016/j.procs.2021.01.005 ◽

2021 ◽

Vol 179 ◽

pp. 260-267

Author(s):

Norezmi Jamal ◽

Nabilah Ibrahim ◽

MNAH Sha’abani ◽

Farhanahani Mahmud ◽

N. Fuad

Keyword(s):

Heart Sound ◽

Sound Signal ◽

Signal Segmentation ◽

Peak Finding ◽

Abrupt Changes ◽

Heart Sound Signal

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Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/0954411921998108 ◽

2021 ◽

pp. 095441192199810

Author(s):

Madhwendra Nath ◽

Subodh Srivastava ◽

Niharika Kulshrestha ◽

Dilbag Singh

Keyword(s):

Success Rate ◽

Heart Sound ◽

Heart Diseases ◽

Heart Sounds ◽

Sound Signal ◽

Ecg Signal ◽

Shannon Energy ◽

Detection And Localization ◽

Pcg Signals ◽

Heart Sound Signal

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15–30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

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Sound Localization for Ad-Hoc Microphone Arrays

Energies ◽

10.3390/en14123446 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3446

Author(s):

Muhammad Usman Liaquat ◽

Hafiz Suliman Munawar ◽

Amna Rahman ◽

Zakria Qadir ◽

Abbas Z. Kouzani ◽

...

Keyword(s):

Sound Localization ◽

Sound Source ◽

Ad Hoc ◽

Dimensional Space ◽

Computation Time ◽

Location Estimation ◽

Microphone Arrays ◽

Sound Signal ◽

Experimental Arrangement ◽

Advantages And Disadvantages

Sound localization is a field of signal processing that deals with identifying the origin of a detected sound signal. This involves determining the direction and distance of the source of the sound. Some useful applications of this phenomenon exists in speech enhancement, communication, radars and in the medical field as well. The experimental arrangement requires the use of microphone arrays which record the sound signal. Some methods involve using ad-hoc arrays of microphones because of their demonstrated advantages over other arrays. In this research project, the existing sound localization methods have been explored to analyze the advantages and disadvantages of each method. A novel sound localization routine has been formulated which uses both the direction of arrival (DOA) of the sound signal along with the location estimation in three-dimensional space to precisely locate a sound source. The experimental arrangement consists of four microphones and a single sound source. Previously, sound source has been localized using six or more microphones. The precision of sound localization has been demonstrated to increase with the use of more microphones. In this research, however, we minimized the use of microphones to reduce the complexity of the algorithm and the computation time as well. The method results in novelty in the field of sound source localization by using less resources and providing results that are at par with the more complex methods requiring more microphones and additional tools to locate the sound source. The average accuracy of the system is found to be 96.77% with an error factor of 3.8%.

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Relative tidal volume and respiratory airflow estimation using tracheal sound and movement during sleep

Journal of Sleep Research ◽

10.1111/jsr.13279 ◽

2021 ◽

Author(s):

Nasim Montazeri Ghahjaverestan ◽

Muammar M. Kabir ◽

Shumit Saha ◽

Bojan Gavrilovic ◽

Kaiyin Zhu ◽

...

Keyword(s):

Tidal Volume ◽

Tracheal Sound ◽

Respiratory Airflow

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Dynamic Characteristics of Heat Capacity of the Human Nasal Cavity during a Respiratory Cycle

Respiratory Physiology & Neurobiology ◽

10.1016/j.resp.2021.103674 ◽

2021 ◽

pp. 103674

Author(s):

Seung-Kyu Chung ◽

Yang Na

Keyword(s):

Heat Capacity ◽

Nasal Cavity ◽

Dynamic Characteristics ◽

Respiratory Cycle

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Diagnostic characteristics of 11 formulae for calculating corrected flow time as measured by a wearable Doppler patch

Intensive Care Medicine Experimental ◽

10.1186/s40635-020-00339-7 ◽

2020 ◽

Vol 8 (1) ◽

Author(s):

Jon-Émile S. Kenny ◽

Igor Barjaktarevic ◽

David C. Mackenzie ◽

Andrew M. Eibl ◽

Matthew Parrotta ◽

...

Keyword(s):

Healthy Volunteers ◽

Sensitivity And Specificity ◽

Doppler Ultrasound ◽

Flow Time ◽

Cardiac Preload ◽

Respiratory Cycle ◽

Quiet Breathing ◽

Diagnostic Characteristics ◽

Corrected Flow Time ◽

Corrected Flow

Abstract Background Change of the corrected flow time (Ftc) is a surrogate for tracking stroke volume (SV) in the intensive care unit. Multiple Ftc equations have been proposed; many have not had their diagnostic characteristics for detecting SV change reported. Further, little is known about the inherent Ftc variability induced by the respiratory cycle. Materials and methods Using a wearable Doppler ultrasound patch, we studied the clinical performance of 11 Ftc equations to detect a 10% change in SV measured by non-invasive pulse contour analysis; 26 healthy volunteers performed a standardized cardiac preload modifying maneuver. Results One hundred changes in cardiac preload and 3890 carotid beats were analyzed. Most of the 11 Ftc equations studied had similar diagnostic attributes. Wodeys’ and Chambers’ formulae had identical results; a 2% change in Ftc detected a 10% change in SV with a sensitivity and specificity of 96% and 93%, respectively. Similarly, a 3% change in Ftc calculated by Bazett’s formula displayed a sensitivity and specificity of 91% and 93%. FtcWodey had 100% concordance and an R2 of 0.75 with change in SV; these values were 99%, 0.76 and 98%, 0.71 for FtcChambers and FtcBazetts, respectively. As an exploratory analysis, we studied 3335 carotid beats for the dispersion of Ftc during quiet breathing using the equations of Wodey and Bazett. The coefficient of variation of Ftc during quiet breathing for these formulae were 0.06 and 0.07, respectively. Conclusions Most of the 11 different equations used to calculate carotid artery Ftc from a wearable Doppler ultrasound patch had similar thresholds and abilities to detect SV change in healthy volunteers. Variation in Ftc induced by the respiratory cycle is important; measuring a clinically significant change in Ftc with statistical confidence requires a large sample of beats.

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