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Hwang S, Lee HS, Park CH, Jung JY, Lee JC. Voice reduction in cardiac auscultation sounds with reference signals measured from vocal resonators. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2024; 155:3822-3832. [PMID: 38874464 DOI: 10.1121/10.0026237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 05/20/2024] [Indexed: 06/15/2024]
Abstract
This study proposes the use of vocal resonators to enhance cardiac auscultation signals and evaluates their performance for voice-noise suppression. Data were collected using two electronic stethoscopes while each study subject was talking. One collected auscultation signal from the chest while the other collected voice signals from one of the three voice resonators (cheek, back of the neck, and shoulder). The spectral subtraction method was applied to the signals. Both objective and subjective metrics were used to evaluate the quality of enhanced signals and to investigate the most effective vocal resonator for noise suppression. Our preliminary findings showed a significant improvement after enhancement and demonstrated the efficacy of vocal resonators. A listening survey was conducted with thirteen physicians to evaluate the quality of enhanced signals, and they have received significantly better scores regarding the sound quality than their original signals. The shoulder resonator group demonstrated significantly better sound quality than the cheek group when reducing voice sound in cardiac auscultation signals. The suggested method has the potential to be used for the development of an electronic stethoscope with a robust noise removal function. Significant clinical benefits are expected from the expedited preliminary diagnostic procedure.
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Affiliation(s)
- Soyun Hwang
- Department of Pediatrics, Severance Children's Hospital, Seoul 03722, Republic of Korea
- Department of Emergency Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea
- Department of Biomedical Engineering, College of Medicine and Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul 03080, Republic of Korea
| | - Hee Su Lee
- Interdisciplinary Program in Bioengineering, Seoul National University Graduate School, Seoul 03080, Republic of Korea
| | - Chan Hun Park
- Interdisciplinary Program in Bioengineering, Seoul National University Graduate School, Seoul 03080, Republic of Korea
| | - Jae Yun Jung
- Department of Emergency Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea
| | - Jung Chan Lee
- Department of Biomedical Engineering, College of Medicine and Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul 03080, Republic of Korea
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2
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Haider NS, Behera AK. Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction. BIOMED ENG-BIOMED TE 2024; 0:bmt-2023-0426. [PMID: 38507674 DOI: 10.1515/bmt-2023-0426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 03/06/2024] [Indexed: 03/22/2024]
Abstract
OBJECTIVES Computerized breath sound based diagnostic methods are one of the emerging technologies gaining popularity in terms of detecting respiratory disorders. However, the breath sound signal used in such automated systems used to be too noisy, which affects the quality of the diagnostic interpretations. To address this problem, the proposed work presents the new hybrid approach to reject the noises from breath sound. METHODS In this method, 80 chronic obstructive pulmonary disease (COPD), 75 asthmatics and 80 normal breath sounds were recorded from the participants of a hospital. Each of these breath sound data were decontaminated using hybrid method of Butterworth band-pass filter, transient artifact reduction algorithm and spectral subtraction algorithm. The study examined the algorithms noise rejection potential over each category of breath sound by estimating the noise rejection performance metrics, i.e., mean absolute error (MAE), mean square error (MSE), peak signal to noise ratio (PSNR), and signal to noise ratio (SNR). RESULTS Using this algorithm, the study obtained a high value of SNR of 70 dB and that of PSNR of 72 dB. CONCLUSIONS The study could definitely a suitable one to suppress noises and to produce noise free breath sound signal.
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Affiliation(s)
- Nishi Shahnaj Haider
- Department of Electronics and Instrumentation Engineering, 154018 Ramaiah Institute of Technology , Bangalore, Karnataka, India
| | - Ajoy K Behera
- Department of Pulmonary Medicine and TB, 417408 All India Institute of Medical Sciences - Raipur , Raipur, Chhattisgarh, India
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Jin Y, Liu Z, Hu C, Dong Z, Rong R, Liu H, Liang Z, Liu J, Chen L, Huang M, Cui H, Shen Y. Study on the flow mechanism and frequency characteristics of rales in lower respiratory tract. Biomech Model Mechanobiol 2024; 23:227-239. [PMID: 37831284 DOI: 10.1007/s10237-023-01769-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/21/2023] [Indexed: 10/14/2023]
Abstract
The frequency characteristics of lung sounds have great significance for noninvasive diagnosis of respiratory diseases. The rales in the lower respiratory tract region that can provide rich information about symptoms of respiratory diseases are not clear. In this paper, a three-dimensional idealized bifurcated lower respiratory tract geometric model, which contains 3rd to 13th generation (G3-G13) bronchi is constructed, where Re ∼ 10 1 - 10 3 , and then the large eddy simulation and volume of fluid are used to study the fluid flow characteristics. Ffowcs Williams and Hawkings model are subsequently used to study the frequency characteristics of rale of different generations of bronchi. The results showed that bronchial blockage and sputum movement will enhance the turbulence intensity and vortex shedding intensity of flow. The dominant frequency and highest value of sound pressure level (SPL) of rhonchi/moist crackles decrease with the increase of bronchial generation. The change rates of dominant frequency of rhonchi / moist crackles in adjacent generations were 5.0 ± 0.1 ~ 9.1 ± 0.2% and 3.1 ± 0.1 ~ 11.9 ± 0.3%, respectively, which is concentrated in 290 ~ 420 Hz and 200 ~ 300 Hz, respectively. The change rates of SPL of rhonchi/moist crackles were 8.8 ± 0.1 ~ 15.7 ± 0.1% and 7.1 ± 0.1 ~ 19.5 ± 0.2%, respectively, which is concentrated in 28 ~ 50 dB and 16 ~ 32 dB, respectively. In the same generation of bronchus (e.g., G8, G9) with the same degree of initial blockage, the dominant frequency and SPL of moist crackles can be 3.7 ± 0.2% and 4.5 ± 0.3% slightly higher than that of rhonchi, respectively. This research is conducive to the establishment of a rapid and accurate noninvasive diagnosis system for respiratory diseases.
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Affiliation(s)
- Yongjun Jin
- Department of Power Engineering, North China Electric Power University, Baoding, 071003, China
| | - Zhijian Liu
- Department of Power Engineering, North China Electric Power University, Baoding, 071003, China.
| | - Chenxing Hu
- School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing, 100081, China
| | - Zhijian Dong
- Department of Power Engineering, North China Electric Power University, Baoding, 071003, China
| | - Rui Rong
- Department of Power Engineering, North China Electric Power University, Baoding, 071003, China
| | - Haiyang Liu
- Department of Power Engineering, North China Electric Power University, Baoding, 071003, China
| | - Zhenyu Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China
| | - Jingwei Liu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China
| | - Li Chen
- School of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, China
| | - Minghua Huang
- School of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, China
| | - Haihang Cui
- School of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
| | - Yan Shen
- Henan Institute of Occupational Disease Prevention and Control, The Third People's Hospital of Henan Province, Zhengzhou, 450000, China
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4
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Huang DM, Huang J, Qiao K, Zhong NS, Lu HZ, Wang WJ. Deep learning-based lung sound analysis for intelligent stethoscope. Mil Med Res 2023; 10:44. [PMID: 37749643 PMCID: PMC10521503 DOI: 10.1186/s40779-023-00479-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023] Open
Abstract
Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .
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Affiliation(s)
- Dong-Min Huang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Jia Huang
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China
| | - Kun Qiao
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China
| | - Nan-Shan Zhong
- Guangzhou Institute of Respiratory Health, China State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China.
| | - Hong-Zhou Lu
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China.
| | - Wen-Jin Wang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
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Rennoll V, McLane I, Eisape A, Grant D, Hahn H, Elhilali M, West JE. Electrostatic Acoustic Sensor with an Impedance-Matched Diaphragm Characterized for Body Sound Monitoring. ACS APPLIED BIO MATERIALS 2023; 6:3241-3256. [PMID: 37470762 PMCID: PMC10804910 DOI: 10.1021/acsabm.3c00359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Acoustic sensors are able to capture more incident energy if their acoustic impedance closely matches the acoustic impedance of the medium being probed, such as skin or wood. Controlling the acoustic impedance of polymers can be achieved by selecting materials with appropriate densities and stiffnesses as well as adding ceramic nanoparticles. This study follows a statistical methodology to examine the impact of polymer type and nanoparticle addition on the fabrication of acoustic sensors with desired acoustic impedances in the range of 1-2.2 MRayls. The proposed method using a design of experiments approach measures sensors with diaphragms of varying impedances when excited with acoustic vibrations traveling through wood, gelatin, and plastic. The sensor diaphragm is subsequently optimized for body sound monitoring, and the sensor's improved body sound coherence and airborne noise rejection are evaluated on an acoustic phantom in simulated noise environments and compared to electronic stethoscopes with onboard noise cancellation. The impedance-matched sensor demonstrates high sensitivity to body sounds, low sensitivity to airborne sound, a frequency response comparable to two state-of-the-art electronic stethoscopes, and the ability to capture lung and heart sounds from a real subject. Due to its small size, use of flexible materials, and rejection of airborne noise, the sensor provides an improved solution for wearable body sound monitoring, as well as sensing from other mediums with acoustic impedances in the range of 1-2.2 MRayls, such as water and wood.
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Affiliation(s)
- Valerie Rennoll
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Ian McLane
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Adebayo Eisape
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Drew Grant
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Helena Hahn
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Mounya Elhilali
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - James E West
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
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6
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Yang C, Dai N, Wang Z, Cai S, Wang J, Hu N. Cardiopulmonary auscultation enhancement with a two-stage noise cancellation approach. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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7
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Pinezich MR, Mir SM, Reimer JA, Kaslow SR, Chen J, Guenthart BA, Bacchetta M, O'Neill JD, Vunjak‐Novakovic G, Kim J. Sound-guided assessment and localization of pulmonary air leak. Bioeng Transl Med 2023; 8:e10322. [PMID: 36684064 PMCID: PMC9842055 DOI: 10.1002/btm2.10322] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 03/08/2022] [Accepted: 03/15/2022] [Indexed: 01/25/2023] Open
Abstract
Pulmonary air leak is the most common complication of lung surgery, with air leaks that persist longer than 5 days representing a major source of post-surgery morbidity. Clinical management of air leaks is challenging due to limited methods to precisely locate and assess leaks. Here, we present a sound-guided methodology that enables rapid quantitative assessment and precise localization of air leaks by analyzing the distinct sounds generated as the air escapes through defective lung tissue. Air leaks often present after lung surgery due to loss of tissue integrity at or near a staple line. Accordingly, we investigated air leak sounds from a focal pleural defect in a rat model and from a staple line failure in a clinically relevant swine model to demonstrate the high sensitivity and translational potential of this approach. In rat and swine models of free-flowing air leak under positive pressure ventilation with intrapleural microphone 1 cm from the lung surface, we identified that: (a) pulmonary air leaks generate sounds that contain distinct harmonic series, (b) acoustic characteristics of air leak sounds can be used to classify leak severity, and (c) precise location of the air leak can be determined with high resolution (within 1 cm) by mapping the sound loudness level across the lung surface. Our findings suggest that sound-guided assessment and localization of pulmonary air leaks could serve as a diagnostic tool to inform air leak detection and treatment strategies during video-assisted thoracoscopic surgery (VATS) or thoracotomy procedures.
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Affiliation(s)
- Meghan R. Pinezich
- Department of Biomedical EngineeringColumbia UniversityNew YorkNew YorkUSA
| | - Seyed Mohammad Mir
- Department of Biomedical EngineeringStevens Institute of TechnologyHobokenNew JerseyUSA
| | - Jonathan A. Reimer
- Department of Biomedical EngineeringColumbia UniversityNew YorkNew YorkUSA
- Department of SurgeryColumbia University Medical CenterNew YorkNew YorkUSA
| | - Sarah R. Kaslow
- Department of Biomedical EngineeringColumbia UniversityNew YorkNew YorkUSA
- Department of SurgeryColumbia University Medical CenterNew YorkNew YorkUSA
| | - Jiawen Chen
- Department of Biomedical EngineeringStevens Institute of TechnologyHobokenNew JerseyUSA
| | | | - Matthew Bacchetta
- Department of Thoracic Surgery, Vanderbilt UniversityNashvilleTennesseeUSA
| | - John D. O'Neill
- Department of Cell BiologyState University of New York Downstate Medical CenterBrooklynNew YorkUSA
| | - Gordana Vunjak‐Novakovic
- Department of Biomedical EngineeringColumbia UniversityNew YorkNew YorkUSA
- Department of MedicineColumbia University Medical CenterNew YorkNew YorkUSA
| | - Jinho Kim
- Department of Biomedical EngineeringStevens Institute of TechnologyHobokenNew JerseyUSA
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Park DE, Watson NL, Focht C, Feikin D, Hammitt LL, Brooks WA, Howie SRC, Kotloff KL, Levine OS, Madhi SA, Murdoch DR, O'Brien KL, Scott JAG, Thea DM, Amorninthapichet T, Awori J, Bunthi C, Ebruke B, Elhilali M, Higdon M, Hossain L, Jahan Y, Moore DP, Mulindwa J, Mwananyanda L, Naorat S, Prosperi C, Thamthitiwat S, Verwey C, Jablonski KA, Power MC, Young HA, Deloria Knoll M, McCollum ED. Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study. BMJ Open Respir Res 2022; 9:9/1/e001144. [PMID: 35577452 PMCID: PMC9115042 DOI: 10.1136/bmjresp-2021-001144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 04/28/2022] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Diagnosis of pneumonia remains challenging. Digitally recorded and remote human classified lung sounds may offer benefits beyond conventional auscultation, but it is unclear whether classifications differ between the two approaches. We evaluated concordance between digital and conventional auscultation. METHODS We collected digitally recorded lung sounds, conventional auscultation classifications and clinical measures and samples from children with pneumonia (cases) in low-income and middle-income countries. Physicians remotely classified recordings as crackles, wheeze or uninterpretable. Conventional and digital auscultation concordance was evaluated among 383 pneumonia cases with concurrently (within 2 hours) collected conventional and digital auscultation classifications using prevalence-adjusted bias-adjusted kappa (PABAK). Using an expanded set of 737 cases that also incorporated the non-concurrently collected assessments, we evaluated whether associations between auscultation classifications and clinical or aetiological findings differed between conventional or digital auscultation using χ2 tests and logistic regression adjusted for age, sex and site. RESULTS Conventional and digital auscultation concordance was moderate for classifying crackles and/or wheeze versus neither crackles nor wheeze (PABAK=0.50), and fair for crackles-only versus not crackles-only (PABAK=0.30) and any wheeze versus no wheeze (PABAK=0.27). Crackles were more common on conventional auscultation, whereas wheeze was more frequent on digital auscultation. Compared with neither crackles nor wheeze, crackles-only on both conventional and digital auscultation was associated with abnormal chest radiographs (adjusted OR (aOR)=1.53, 95% CI 0.99 to 2.36; aOR=2.09, 95% CI 1.19 to 3.68, respectively); any wheeze was inversely associated with C-reactive protein >40 mg/L using conventional auscultation (aOR=0.50, 95% CI 0.27 to 0.92) and with very severe pneumonia using digital auscultation (aOR=0.67, 95% CI 0.46 to 0.97). Crackles-only on digital auscultation was associated with mortality compared with any wheeze (aOR=2.70, 95% CI 1.12 to 6.25). CONCLUSIONS Conventional auscultation and remotely-classified digital auscultation displayed moderate concordance for presence/absence of wheeze and crackles among cases. Conventional and digital auscultation may provide different classification patterns, but wheeze was associated with decreased clinical severity on both.
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Affiliation(s)
- Daniel E Park
- Department of Environmental and Occupational Health, The George Washington University, Washington, District of Columbia, USA
| | | | | | - Daniel Feikin
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA
| | - Laura L Hammitt
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA,Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
| | - W Abdullah Brooks
- International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka and Matlab, Bangladesh,Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Stephen R C Howie
- Medical Research Council Unit, Basse, Gambia,Department of Paediatrics, The University of Auckland, Auckland, New Zealand
| | - Karen L Kotloff
- Department of Pediatrics, University of Maryland Center for Vaccine Development, Baltimore, Maryland, USA
| | - Orin S Levine
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA,Bill & Melinda Gates Foundation, Seattle, Washington, USA
| | - Shabir A Madhi
- South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, University of the Witwatersrand, Johannesburg, Gauteng, South Africa,Department of Science and Innovation/National Research Foundation: Vaccine Preventable Diseases Unit, University of the Witwatersrand, Johannesburg, Gauteng, South Africa
| | - David R Murdoch
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand,Microbiology Unit, Canterbury Health Laboratories, Christchurch, New Zealand
| | - Katherine L O'Brien
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA
| | - J Anthony G Scott
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya,Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Donald M Thea
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA
| | | | - Juliet Awori
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
| | - Charatdao Bunthi
- Division of Global Health Protection, Thailand Ministry of Public Health – US CDC Collaboration, Royal Thai Government Ministry of Public Health, Bangkok, Thailand
| | - Bernard Ebruke
- Medical Research Council Unit, Basse, Gambia,International Foundation Against Infectious Disease in Nigeria, Abuja, Nigeria
| | - Mounya Elhilali
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Melissa Higdon
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA
| | - Lokman Hossain
- International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka and Matlab, Bangladesh
| | - Yasmin Jahan
- International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka and Matlab, Bangladesh
| | - David P Moore
- South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, University of the Witwatersrand, Johannesburg, South Africa,Department of Paediatrics and Child Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Justin Mulindwa
- Department of Paediatrics and Child Health, University Teaching Hospital, Lusaka, Zambia
| | - Lawrence Mwananyanda
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA,Right to Care - Zambia, Lusaka, Zambia
| | | | - Christine Prosperi
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA
| | - Somsak Thamthitiwat
- Division of Global Health Protection, Thailand Ministry of Public Health – US CDC Collaboration, Royal Thai Government Ministry of Public Health, Nonthaburi, Thailand
| | - Charl Verwey
- South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, University of the Witwatersrand, Johannesburg, Gauteng, South Africa,Department of Paediatrics and Child Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Melinda C Power
- Department of Epidemiology, The George Washington University, Washington, District of Columbia, USA
| | - Heather A Young
- Department of Epidemiology, The George Washington University, Washington, District of Columbia, USA
| | - Maria Deloria Knoll
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA
| | - Eric D McCollum
- Global Program in Respiratory Sciences, Eudowood Division of Pediatric Respiratory Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA,Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
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Haider NS, Behera A. Respiratory sound denoising using sparsity-assisted signal smoothing algorithm. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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10
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Ahmed S, Mitra DK, Nair H, Cunningham S, Khan AM, Islam AA, McLane IM, Chowdhury NH, Begum N, Shahidullah M, Islam MS, Norrie J, Campbell H, Sheikh A, Baqui AH, McCollum ED. Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study. BMJ Open 2022; 12:e059630. [PMID: 35140164 PMCID: PMC8830242 DOI: 10.1136/bmjopen-2021-059630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
INTRODUCTION The WHO's Integrated Management of Childhood Illnesses (IMCI) algorithm for diagnosis of child pneumonia relies on counting respiratory rate and observing respiratory distress to diagnose childhood pneumonia. IMCI case defination for pneumonia performs with high sensitivity but low specificity, leading to overdiagnosis of child pneumonia and unnecessary antibiotic use. Including lung auscultation in IMCI could improve specificity of pneumonia diagnosis. Our objectives are: (1) assess lung sound recording quality by primary healthcare workers (HCWs) from under-5 children with the Feelix Smart Stethoscope and (2) determine the reliability and performance of recorded lung sound interpretations by an automated algorithm compared with reference paediatrician interpretations. METHODS AND ANALYSIS In a cross-sectional design, community HCWs will record lung sounds of ~1000 under-5-year-old children with suspected pneumonia at first-level facilities in Zakiganj subdistrict, Sylhet, Bangladesh. Enrolled children will be evaluated for pneumonia, including oxygen saturation, and have their lung sounds recorded by the Feelix Smart stethoscope at four sequential chest locations: two back and two front positions. A novel sound-filtering algorithm will be applied to recordings to address ambient noise and optimise recording quality. Recorded sounds will be assessed against a predefined quality threshold. A trained paediatric listening panel will classify recordings into one of the following categories: normal, crackles, wheeze, crackles and wheeze or uninterpretable. All sound files will be classified into the same categories by the automated algorithm and compared with panel classifications. Sensitivity, specificity and predictive values, of the automated algorithm will be assessed considering the panel's final interpretation as gold standard. ETHICS AND DISSEMINATION The study protocol was approved by the National Research Ethics Committee of Bangladesh Medical Research Council, Bangladesh (registration number: 09630012018) and Academic and Clinical Central Office for Research and Development Medical Research Ethics Committee, Edinburgh, UK (REC Reference: 18-HV-051). Dissemination will be through conference presentations, peer-reviewed journals and stakeholder engagement meetings in Bangladesh. TRIAL REGISTRATION NUMBER NCT03959956.
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Affiliation(s)
- Salahuddin Ahmed
- Projahnmo Research Foundation, Dhaka, Bangladesh
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Dipak Kumar Mitra
- Projahnmo Research Foundation, Dhaka, Bangladesh
- Public Health, North South University, Dhaka, Bangladesh
| | - Harish Nair
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Steven Cunningham
- Department of Child Life and Health, Royal Hospital for Sick Children, Edinburgh, UK
| | - Ahad Mahmud Khan
- Projahnmo Research Foundation, Dhaka, Bangladesh
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | | | | | | | - Nazma Begum
- Projahnmo Research Foundation, Dhaka, Bangladesh
| | - Mohammod Shahidullah
- Department of Neonatology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
| | - Muhammad Shariful Islam
- Directorate General of Health Services, Ministry of Health and Family Welfare, Government of Bangladesh, Dhaka, Bangladesh
| | - John Norrie
- Usher Institute, Edinburgh Clinical Trials Unit, University of Edinburgh No. 9, Bioquarter, Edinburgh, UK
| | - Harry Campbell
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Aziz Sheikh
- Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Abdullah H Baqui
- Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Eric D McCollum
- Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Global Program in Pediatric Respiratory Sciences, Eudowood Division of Pediatric Respiratory Sciences, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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McLane I, Lauwers E, Stas T, Busch-Vishniac I, Ides K, Verhulst S, Steckel J. Comprehensive Analysis System for Automated Respiratory Cycle Segmentation and Crackle Peak Detection. IEEE J Biomed Health Inform 2021; 26:1847-1860. [PMID: 34705660 DOI: 10.1109/jbhi.2021.3123353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Digital auscultation is a well-known method for assessing lung sounds, but remains a subjective process in typical practice, relying on the human interpretation. Several methods have been presented for detecting or analyzing crackles but are limited in their real-world application because few have been integrated into comprehensive systems or validated on non-ideal data. This work details a complete signal analysis methodology for analyzing crackles in challenging recordings. The procedure comprises five sequential processing blocks: (1) motion artifact detection, (2) deep learning denoising network, (3) respiratory cycle segmentation, (4) separation of discontinuous adventitious sounds from vesicular sounds, and (5) crackle peak detection. This system uses a collection of new methods and robustness-focused improvements on previous methods to analyze respiratory cycles and crackles therein. To validate the accuracy, the system is tested on a database of 1000 simulated lung sounds with varying levels of motion artifacts, ambient noise, cycle lengths and crackle intensities, in which ground truths are exactly known. The system performs with average F-score of 91.07% for detecting motion artifacts and 94.43% for respiratory cycle extraction, and an overall F-score of 94.08% for detecting the locations of individual crackles. The process also successfully detects healthy recordings. Preliminary validation is also presented on a small set of 20 patient recordings, for which the system performs comparably. These methods provide quantifiable analysis of respiratory sounds to enable clinicians to distinguish between types of crackles, their timing within the respiratory cycle, and the level of occurrence. Crackles are one of the most common abnormal lung sounds, presenting in multiple cardiorespiratory diseases. These features will contribute to a better understanding of disease severity and progression in an objective, simple and non-invasive way.
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Xie Y, Xie K, Yang Q, Xie S. Reverberant blind separation of heart and lung sounds using nonnegative matrix factorization and auxiliary function technique. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102899] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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13
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McLane I, Emmanouilidou D, West JE, Elhilali M. Design and Comparative Performance of a Robust Lung Auscultation System for Noisy Clinical Settings. IEEE J Biomed Health Inform 2021; 25:2583-2594. [PMID: 33534721 PMCID: PMC8374873 DOI: 10.1109/jbhi.2021.3056916] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Chest auscultation is a widely used clinical tool for respiratory disease detection. The stethoscope has undergone a number of transformative enhancements since its invention, including the introduction of electronic systems in the last two decades. Nevertheless, stethoscopes remain riddled with a number of issues that limit their signal quality and diagnostic capability, rendering both traditional and electronic stethoscopes unusable in noisy or non-traditional environments (e.g., emergency rooms, rural clinics, ambulatory vehicles). This work outlines the design and validation of an advanced electronic stethoscope that dramatically reduces external noise contamination through hardware redesign and real-time, dynamic signal processing. The proposed system takes advantage of an acoustic sensor array, an external facing microphone, and on-board processing to perform adaptive noise suppression. The proposed system is objectively compared to six commercially-available acoustic and electronic devices in varying levels of simulated noisy clinical settings and quantified using two metrics that reflect perceptual audibility and statistical similarity, normalized covariance measure (NCM) and magnitude squared coherence (MSC). The analyses highlight the major limitations of current stethoscopes and the significant improvements the proposed system makes in challenging settings by minimizing both distortion of lung sounds and contamination by ambient noise.
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Respiratory sound denoising using Empirical Mode Decomposition, Hurst analysis and Spectral Subtraction. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102313] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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15
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McCollum ED, Park DE, Watson NL, Fancourt NSS, Focht C, Baggett HC, Brooks WA, Howie SRC, Kotloff KL, Levine OS, Madhi SA, Murdoch DR, Scott JAG, Thea DM, Awori JO, Chipeta J, Chuananon S, DeLuca AN, Driscoll AJ, Ebruke BE, Elhilali M, Emmanouilidou D, Githua LP, Higdon MM, Hossain L, Jahan Y, Karron RA, Kyalo J, Moore DP, Mulindwa JM, Naorat S, Prosperi C, Verwey C, West JE, Knoll MD, O'Brien KL, Feikin DR, Hammitt LL. Digital auscultation in PERCH: Associations with chest radiography and pneumonia mortality in children. Pediatr Pulmonol 2020; 55:3197-3208. [PMID: 32852888 PMCID: PMC7692889 DOI: 10.1002/ppul.25046] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 01/16/2023]
Abstract
BACKGROUND Whether digitally recorded lung sounds are associated with radiographic pneumonia or clinical outcomes among children in low-income and middle-income countries is unknown. We sought to address these knowledge gaps. METHODS We enrolled 1 to 59monthold children hospitalized with pneumonia at eight African and Asian Pneumonia Etiology Research for Child Health sites in six countries, recorded digital stethoscope lung sounds, obtained chest radiographs, and collected clinical outcomes. Recordings were processed and classified into binary categories positive or negative for adventitial lung sounds. Listening and reading panels classified recordings and radiographs. Recording classification associations with chest radiographs with World Health Organization (WHO)-defined primary endpoint pneumonia (radiographic pneumonia) or mortality were evaluated. We also examined case fatality among risk strata. RESULTS Among children without WHO danger signs, wheezing (without crackles) had a lower adjusted odds ratio (aOR) for radiographic pneumonia (0.35, 95% confidence interval (CI): 0.15, 0.82), compared to children with normal recordings. Neither crackle only (no wheeze) (aOR: 2.13, 95% CI: 0.91, 4.96) or any wheeze (with or without crackle) (aOR: 0.63, 95% CI: 0.34, 1.15) were associated with radiographic pneumonia. Among children with WHO danger signs no lung recording classification was independently associated with radiographic pneumonia, although trends toward greater odds of radiographic pneumonia were observed among children classified with crackle only (no wheeze) or any wheeze (with or without crackle). Among children without WHO danger signs, those with recorded wheezing had a lower case fatality than those without wheezing (3.8% vs. 9.1%, p = .03). CONCLUSIONS Among lower risk children without WHO danger signs digitally recorded wheezing is associated with a lower odds for radiographic pneumonia and with lower mortality. Although further research is needed, these data indicate that with further development digital auscultation may eventually contribute to child pneumonia care.
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Affiliation(s)
- Eric D McCollum
- Global Program in Respiratory Sciences, Eudowood Division of Pediatric Respiratory Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.,Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Daniel E Park
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA
| | | | - Nicholas S S Fancourt
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Henry C Baggett
- Global Disease Detection Center, US Centers for Disease Control and Prevention Collaboration, Thailand Ministry of Public Health, Mueang Nonthaburi, Nonthaburi, Thailand.,Division of Global Health Protection, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - W Abdullah Brooks
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka and Matlab, Bangladesh
| | - Stephen R C Howie
- Medical Research Council Unit, Basse, The Gambia.,Department of Paediatrics, University of Auckland, Auckland, New Zealand.,Centre for International Health, University of Otago, Dunedin, New Zealand
| | - Karen L Kotloff
- Division of Infectious Disease and Tropical Pediatrics, Department of Pediatrics, Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, Maryland
| | - Orin S Levine
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Bill & Melinda Gates Foundation, Seattle, Washington, USA
| | - Shabir A Madhi
- Medical Research Council: Respiratory and Meningeal Pathogens Research Unit, University of the Witwatersrand, Johannesburg, South Africa.,Department of Science and Technology/National Research Foundation: Vaccine Preventable Diseases Unite, University of the Witwatersrand, Johannesburg, South Africa
| | - David R Murdoch
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand.,Microbiology Unit, Canterbury Health Laboratories, Christchurch, New Zealand
| | - J Anthony G Scott
- Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya.,Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Donald M Thea
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Juliet O Awori
- Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
| | - James Chipeta
- Department of Paediatrics and Child Health, University Teaching Hospital, Lusaka, Zambia
| | - Somchai Chuananon
- Global Disease Detection Center, US Centers for Disease Control and Prevention Collaboration, Thailand Ministry of Public Health, Mueang Nonthaburi, Nonthaburi, Thailand
| | - Andrea N DeLuca
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Amanda J Driscoll
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Bernard E Ebruke
- Medical Research Council Unit, Basse, The Gambia.,International Foundation Against Infectious Disease in Nigeria, Abuja, Nigeria
| | - Mounya Elhilali
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Dimitra Emmanouilidou
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Melissa M Higdon
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Lokman Hossain
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka and Matlab, Bangladesh
| | - Yasmin Jahan
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka and Matlab, Bangladesh
| | - Ruth A Karron
- Department of International Health, Center for Immunization Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Joshua Kyalo
- Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
| | - David P Moore
- Medical Research Council: Respiratory and Meningeal Pathogens Research Unit, University of the Witwatersrand, Johannesburg, South Africa.,Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Justin M Mulindwa
- Department of Paediatrics and Child Health, University Teaching Hospital, Lusaka, Zambia
| | - Sathapana Naorat
- Global Disease Detection Center, US Centers for Disease Control and Prevention Collaboration, Thailand Ministry of Public Health, Mueang Nonthaburi, Nonthaburi, Thailand
| | - Christine Prosperi
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Charl Verwey
- Medical Research Council: Respiratory and Meningeal Pathogens Research Unit, University of the Witwatersrand, Johannesburg, South Africa.,Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - James E West
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Maria Deloria Knoll
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Katherine L O'Brien
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Daniel R Feikin
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Laura L Hammitt
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
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De La Torre Cruz J, Cañadas Quesada FJ, Ruiz Reyes N, Vera Candeas P, Carabias Orti JJ. Wheezing Sound Separation Based on Informed Inter-Segment Non-Negative Matrix Partial Co-Factorization. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2679. [PMID: 32397155 PMCID: PMC7249056 DOI: 10.3390/s20092679] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 05/01/2020] [Accepted: 05/05/2020] [Indexed: 11/16/2022]
Abstract
Wheezing reveals important cues that can be useful in alerting about respiratory disorders, such as Chronic Obstructive Pulmonary Disease. Early detection of wheezing through auscultation will allow the physician to be aware of the existence of the respiratory disorder in its early stage, thus minimizing the damage the disorder can cause to the subject, especially in low-income and middle-income countries. The proposed method presents an extended version of Non-negative Matrix Partial Co-Factorization (NMPCF) that eliminates most of the acoustic interference caused by normal respiratory sounds while preserving the wheezing content needed by the physician to make a reliable diagnosis of the subject's airway status. This extension, called Informed Inter-Segment NMPCF (IIS-NMPCF), attempts to overcome the drawback of the conventional NMPCF that treats all segments of the spectrogram equally, adding greater importance for signal reconstruction of repetitive sound events to those segments where wheezing sounds have not been detected. Specifically, IIS-NMPCF is based on a bases sharing process in which inter-segment information, informed by a wheezing detection system, is incorporated into the factorization to reconstruct a more accurate modelling of normal respiratory sounds. Results demonstrate the significant improvement obtained in the wheezing sound quality by IIS-NMPCF compared to the conventional NMPCF for all the Signal-to-Noise Ratio (SNR) scenarios evaluated, specifically, an SDR, SIR and SAR improvement equals 5.8 dB, 4.9 dB and 7.5 dB evaluating a noisy scenario with SNR = -5 dB.
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Affiliation(s)
- Juan De La Torre Cruz
- Departament of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, 23700 Linares, Jaen, Spain; (F.J.C.Q.); (N.R.R.); (P.V.C.); (J.J.C.O.)
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Sathesh K, Rajkumar S, Goyal NK. Least Mean Square (LMS) based neural design and metric evaluation for auscultation signal separation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Zhang Q, Francisco CO, Kabir M, Zhang J, Montazeri N, Taati B, Yadollahi A. Noise Removal of Tracheal Sound Recorded During CPET to Determine Respiratory Rate. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:4650-4653. [PMID: 31946900 DOI: 10.1109/embc.2019.8857738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This study aimed to extract respiratory signal from tracheal sound recordings during cardio-pulmonary exercise test (CPET), which is the state-of-the-art diagnosis of cardiovascular and respiratory diseases. However, the availability of CPET is limited due to its expense. Physiological signal analysis is a convenient alternative to measure clinical parameters. We collected data from 30 healthy adults and applied wavelet transform thresholding (WTT), empirical mode decomposition (EMD), and Synchrosqueezing transform filtering (SST) to de-noise the raw data. Signal to noise ratio (SNR) was calculated as a quantitative measure of signal quality. We observed that SST yielded the highest SNR and introduced lowest signal distortion by visual-auditory inspection. Respiratory rate was then determined using the signal extracted by SST. Discrepancy of respiratory rate determined from the signal and the gold standard CPET was within 1.2 breaths per minute. In conclusion, SST effectively removed noises in tracheal sound recorded during CPET and provided analyzable respiratory signal for clinical parameter determination.
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Pipelined Architecture of Multi-Band Spectral Subtraction Algorithm for Speech Enhancement. ELECTRONICS 2017. [DOI: 10.3390/electronics6040073] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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20
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Tabata H, Enseki M, Nukaga M, Hirai K, Matsuda S, Furuya H, Kato M, Mochizuki H. Changes in the breath sound spectrum during methacholine inhalation in children with asthma. Respirology 2017; 23:168-175. [PMID: 28960780 DOI: 10.1111/resp.13177] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 07/05/2017] [Accepted: 07/11/2017] [Indexed: 01/24/2023]
Abstract
BACKGROUND AND OBJECTIVE An effort-independent breath sound analysis is expected to be a safe and simple method for clinical assessment of changes in airway function. The effects of bronchoconstriction and bronchodilation on novel breath sound parameters in asthmatic children were investigated. METHODS The study population included 49 children with atopic asthma (male = 33; mean age: 10.2 years). We evaluated breath sound parameters of the highest frequency of the power spectrum (HFp), frequency limiting 50% and 99% of the power spectrum (F50 and F99 ) and roll-off from 600 Hz to the HFp (Slope). We also assessed new parameters obtained using the ratios of sound spectrum parameters (spectrum curve indices), such as the ratio of the third and fourth power area to the total power area (P3 /PT and P4 /PT ), the ratio of the third and fourth areas to the total area under the curve (A3 /AT and B4 /AT ) and the ratio of power and frequency at 75% of HFp and 50% of HFp (RPF75 and RPF50 ). This was measured before and after methacholine inhalation challenge and after β2 agonist inhalation. RESULTS The parameters, F50 and F99 , showed no changes after methacholine inhalation. Conversely, the A3 /AT (12.5-10.0%, P < 0.001), B4 /AT (7.6-5.5%, P < 0.001), RPF75 (6.7-4.0 dBm/Hz, P < 0.001) and RPF50 (5.8-4.3 dBm/Hz, P < 0.001) were significantly decreased. These values returned to the original level after β2 agonist inhalation. CONCLUSION Spectrum curve indices indicate bronchoconstriction and bronchodilation. These parameters may play a role in the assessment of airway narrowing in asthmatic children.
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Affiliation(s)
- Hideyuki Tabata
- Department of Pediatrics, Tokai University School of Medicine, Isehara, Japan
| | - Mayumi Enseki
- Department of Pediatrics, Tokai University School of Medicine, Isehara, Japan
| | - Mariko Nukaga
- Department of Pediatrics, Tokai University School of Medicine, Isehara, Japan
| | - Kota Hirai
- Department of Pediatrics, Tokai University School of Medicine, Isehara, Japan
| | - Shinichi Matsuda
- Department of Pediatrics, Tokai University School of Medicine, Isehara, Japan
| | - Hiroyuki Furuya
- Department of Basic Clinical Science and Public Health, Tokai University School of Medicine, Isehara, Japan
| | - Masahiko Kato
- Department of Pediatrics, Tokai University School of Medicine, Isehara, Japan
| | - Hiroyuki Mochizuki
- Department of Pediatrics, Tokai University School of Medicine, Isehara, Japan
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McCollum ED, Park DE, Watson NL, Buck WC, Bunthi C, Devendra A, Ebruke BE, Elhilali M, Emmanouilidou D, Garcia-Prats AJ, Githinji L, Hossain L, Madhi SA, Moore DP, Mulindwa J, Olson D, Awori JO, Vandepitte WP, Verwey C, West JE, Knoll MD, O'Brien KL, Feikin DR, Hammit LL. Listening panel agreement and characteristics of lung sounds digitally recorded from children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study. BMJ Open Respir Res 2017; 4:e000193. [PMID: 28883927 PMCID: PMC5531306 DOI: 10.1136/bmjresp-2017-000193] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 05/25/2017] [Accepted: 05/25/2017] [Indexed: 01/14/2023] Open
Abstract
INTRODUCTION Paediatric lung sound recordings can be systematically assessed, but methodological feasibility and validity is unknown, especially from developing countries. We examined the performance of acoustically interpreting recorded paediatric lung sounds and compared sound characteristics between cases and controls. METHODS Pneumonia Etiology Research for Child Health staff in six African and Asian sites recorded lung sounds with a digital stethoscope in cases and controls. Cases aged 1-59 months had WHO severe or very severe pneumonia; age-matched community controls did not. A listening panel assigned examination results of normal, crackle, wheeze, crackle and wheeze or uninterpretable, with adjudication of discordant interpretations. Classifications were recategorised into any crackle, any wheeze or abnormal (any crackle or wheeze) and primary listener agreement (first two listeners) was analysed among interpretable examinations using the prevalence-adjusted, bias-adjusted kappa (PABAK). We examined predictors of disagreement with logistic regression and compared case and control lung sounds with descriptive statistics. RESULTS Primary listeners considered 89.5% of 792 case and 92.4% of 301 control recordings interpretable. Among interpretable recordings, listeners agreed on the presence or absence of any abnormality in 74.9% (PABAK 0.50) of cases and 69.8% (PABAK 0.40) of controls, presence/absence of crackles in 70.6% (PABAK 0.41) of cases and 82.4% (PABAK 0.65) of controls and presence/absence of wheeze in 72.6% (PABAK 0.45) of cases and 73.8% (PABAK 0.48) of controls. Controls, tachypnoea, >3 uninterpretable chest positions, crying, upper airway noises and study site predicted listener disagreement. Among all interpretable examinations, 38.0% of cases and 84.9% of controls were normal (p<0.0001); wheezing was the most common sound (49.9%) in cases. CONCLUSIONS Listening panel and case-control data suggests our methodology is feasible, likely valid and that small airway inflammation is common in WHO pneumonia. Digital auscultation may be an important future pneumonia diagnostic in developing countries.
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Affiliation(s)
- Eric D McCollum
- Eudowood Division of Pediatric Respiratory Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA,Department of International Health, Johns Hopkins Bloomberg School of Public Health, Dhaka, Bangladesh,Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Daniel E Park
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - W Chris Buck
- Department of Pediatrics, University of California Los Angeles, Maputo, Mozambique
| | - Charatdao Bunthi
- International Emerging Infections Program, Global Disease Detection Center, Thailand Ministry of Public Health – US Centers for Disease Control and Prevention Collaboration, Nonthaburi, Thailand
| | | | | | - Mounya Elhilali
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Dimitra Emmanouilidou
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Anthony J Garcia-Prats
- Department of Paediatrics and Child Health, Stellenbosch University, Tygerberg, South Africa
| | - Leah Githinji
- Division of Paediatric Pulmonology, University of Cape Town, Cape Town, South Africa
| | - Lokman Hossain
- Respiratory Vaccines, Center for Vaccine Sciences, icddr,b, Dhaka, Bangladesh
| | - Shabir A Madhi
- Medical Research Council, Respiratory and Meningeal Pathogens Research Unit, University of the Witwatersrand, Johannesburg, South Africa,Department of Science and Technology/National Research Foundation, South African Research Chair: Vaccine Preventable Diseases, University of the Witwatersrand, Johannesburg, South Africa
| | - David P Moore
- Medical Research Council, Respiratory and Meningeal Pathogens Research Unit, University of the Witwatersrand, Johannesburg, South Africa,Department of Paediatrics, University of the Witwatersrand, Chris Hani Baragwanath Academic Hospital, Johannesburg, South Africa
| | - Justin Mulindwa
- Department of Paediatrics and Child Health, University Teaching Hospital, Lusaka, Zambia
| | - Dan Olson
- Department of Pediatrics, Section of Infectious Disease, Center for Global Health, University of Colorado, Colorado, USA
| | - Juliet O Awori
- Kenya Medical Research Institute Wellcome Trust Research Programme, Kilifi, Kenya
| | - Warunee P Vandepitte
- Queen Sirikit National Institute of Child Health, Rangsit University, Bangkok, Thailand
| | - Charl Verwey
- Medical Research Council, Respiratory and Meningeal Pathogens Research Unit, University of the Witwatersrand, Johannesburg, South Africa,Department of Paediatrics, University of the Witwatersrand, Chris Hani Baragwanath Academic Hospital, Johannesburg, South Africa
| | - James E West
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Maria D Knoll
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Katherine L O'Brien
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Daniel R Feikin
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA,Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Laura L Hammit
- Kenya Medical Research Institute Wellcome Trust Research Programme, Kilifi, Kenya
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22
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Emmanouilidou D, McCollum ED, Park DE, Elhilali M. Computerized Lung Sound Screening for Pediatric Auscultation in Noisy Field Environments. IEEE Trans Biomed Eng 2017. [PMID: 28641244 DOI: 10.1109/tbme.2017.2717280] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
GOAL Chest auscultations offer a non-invasive and low-cost tool for monitoring lung disease. However, they present many shortcomings, including inter-listener variability, subjectivity, and vulnerability to noise and distortions. This work proposes a computer-aided approach to process lung signals acquired in the field under adverse noisy conditions, by improving the signal quality and offering automated identification of abnormal auscultations indicative of respiratory pathologies. METHODS The developed noise-suppression scheme eliminates ambient sounds, heart sounds, sensor artifacts, and crying contamination. The improved high-quality signal is then mapped onto a rich spectrotemporal feature space before being classified using a trained support-vector machine classifier. Individual signal frame decisions are then combined using an evaluation scheme, providing an overall patient-level decision for unseen patient records. RESULTS All methods are evaluated on a large dataset with 1000 children enrolled, 1-59 months old. The noise suppression scheme is shown to significantly improve signal quality, and the classification system achieves an accuracy of 86.7% in distinguishing normal from pathological sounds, far surpassing other state-of-the-art methods. CONCLUSION Computerized lung sound processing can benefit from the enforcement of advanced noise suppression. A fairly short processing window size ( s) combined with detailed spectrotemporal features is recommended, in order to capture transient adventitious events without highlighting sharp noise occurrences. SIGNIFICANCE Unlike existing methodologies in the literature, the proposed work is not limited in scope or confined to laboratory settings: This work validates a practical method for fully automated chest sound processing applicable to realistic and noisy auscultation settings.
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Mondal A, Saxena I, Tang H, Banerjee P. A Noise Reduction Technique Based on Nonlinear Kernel Function for Heart Sound Analysis. IEEE J Biomed Health Inform 2017; 22:775-784. [PMID: 28207404 DOI: 10.1109/jbhi.2017.2667685] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information, which are useful for recognition of heart diseases. The unwanted signals are produced mainly by lungs and surrounding environment. In this paper, a novel heart sound denoising technique has been introduced based on a combined framework of wavelet packet transform and singular value decomposition (SVD). The most informative node of the wavelet tree is selected on the criteria of mutual information measurement. Next, the coefficient corresponding to the selected node is processed by the SVD technique to suppress noisy component from heart sound signal. To justify the efficacy of the proposed technique, several experiments have been conducted with heart sound dataset, including normal and pathological cases at different signal to noise ratios. The significance of the method is validated by statistical analysis of the results. The biological information preserved in denoised heart sound signal is evaluated by the k-means clustering algorithm. The overall results show that the proposed method is superior than the baseline methods.
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Leal A, Couceiro R, Chouvarda I, Maglaveras N, Henriques J, Paiva R, Carvalho P, Teixeira C. Detection of different types of noise in lung sounds. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:5977-5980. [PMID: 28269614 DOI: 10.1109/embc.2016.7592090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Lung sound signal processing has proven to be a great improvement to the traditional acoustic interpretation of lung sounds. However, that analysis can be seriously hindered by the presence of different types of noise originated in the acquisition environment or caused by physiological processes. Consequently, the diagnostic accuracy of pulmonary diseases can be severely affected, especially if the implementation of telemonitoring systems is considered. The present study is focused on the implementation of an algorithm able to identify noisy periods, either voluntarily (vocalizations, chest movement and background voices) or involuntarily produced during acquisitions of lung sounds. The developed approach also had to deal with the presence of simulated cough events, that carry important diagnostic information regarding several pulmonary diseases. Features such as Katz fractal dimension, Teager-Kaiser energy operator and normalized mutual information, were extracted from the time domain of healthy and a pathological lung signals. Noise detection was the result of a good discrimination between uncontaminated lung sounds and both cough and noise episodes and a slightly worse classification of cough events. In fact, detection of cough periods carrying diagnostic information was influenced by the presence of two other types of noise having similar signal characteristics.
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