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Ding L, Peng J, Song L, Zhang X. Automatically detecting OSAHS patients based on transfer learning and model fusion. Physiol Meas 2024; 45:055013. [PMID: 38722551 DOI: 10.1088/1361-6579/ad4953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 05/09/2024] [Indexed: 05/24/2024]
Abstract
Objective. Snoring is the most typical symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) that can be used to develop a non-invasive approach for automatically detecting OSAHS patients.Approach. In this work, a model based on transfer learning and model fusion was applied to classify simple snorers and OSAHS patients. Three kinds of basic models were constructed based on pretrained Visual Geometry Group-16 (VGG16), pretrained audio neural networks (PANN), and Mel-frequency cepstral coefficient (MFCC). The XGBoost was used to select features based on feature importance, the majority voting strategy was applied to fuse these basic models and leave-one-subject-out cross validation was used to evaluate the proposed model.Main results. The results show that the fused model embedded with top-5 VGG16 features, top-5 PANN features, and MFCC feature can correctly identify OSAHS patients (AHI > 5) with 100% accuracy.Significance. The proposed fused model provides a good classification performance with lower computational cost and higher robustness that makes detecting OSAHS patients at home possible.
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Affiliation(s)
- Li Ding
- Guangzhou Railway Polytechnic, Guangzhou 510430, People's Republic of China
- School of Physics and Optoelectronics, South China University of Technology, Guangzhou 510640, People's Republic of China
| | - Jianxin Peng
- School of Physics and Optoelectronics, South China University of Technology, Guangzhou 510640, People's Republic of China
| | - Lijuan Song
- State Key Laboratory of Respiratory Disease, Department of Otolaryngology-Head and Neck Surgery, Laboratory of ENT-HNS Disease, First Affiliated Hospital, Guangzhou Medical University, Guangzhou 510120, People's Republic of China
| | - Xiaowen Zhang
- State Key Laboratory of Respiratory Disease, Department of Otolaryngology-Head and Neck Surgery, Laboratory of ENT-HNS Disease, First Affiliated Hospital, Guangzhou Medical University, Guangzhou 510120, People's Republic of China
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2
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Exploring interpretable representations for heart sound abnormality detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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3
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Ding L, Peng J, Song L, Zhang X. Automatically detecting apnea-hypopnea snoring signal based on VGG19 + LSTM. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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4
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Qian K, Hu B, Yamamoto Y, Schuller BW. The Voice of the Body: Why AI Should Listen to It and an Archive. CYBORG AND BIONIC SYSTEMS 2023; 4:0005. [PMID: 37040282 PMCID: PMC10076047 DOI: 10.34133/cbsystems.0005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/20/2022] [Indexed: 01/27/2023] Open
Abstract
The sound generated by body carries important information about our health status physically and psychologically. In the past decades, we have witnessed a plethora of successes achieved in the field of body sound analysis. Nevertheless, the fundamentals of this young field are still not well established. In particular, publicly accessible databases are rarely developed, which dramatically restrains a sustainable research. To this end, we are launching and continuously calling for participation from the global scientific community to contribute to the Voice of the Body (VoB) archive. We aim to build an open access platform to collect the well-established body sound databases in a well standardized way. Moreover, we hope to organize a series of challenges to promote the development of audio-driven methods for healthcare via the proposed VoB. We believe that VoB can help break the walls between different subjects toward an era of Medicine 4.0 enriched by audio intelligence.
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Affiliation(s)
- Kun Qian
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yoshiharu Yamamoto
- Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan
| | - Björn W. Schuller
- GLAM—Group on Language, Audio, & Music, Imperial College London, London SW7 2AZ, UK
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, 86159 Augsburg, Germany
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Mutanu L, Gohil J, Gupta K, Wagio P, Kotonya G. A Review of Automated Bioacoustics and General Acoustics Classification Research. SENSORS (BASEL, SWITZERLAND) 2022; 22:8361. [PMID: 36366061 PMCID: PMC9658612 DOI: 10.3390/s22218361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 10/19/2022] [Accepted: 10/21/2022] [Indexed: 06/16/2023]
Abstract
Automated bioacoustics classification has received increasing attention from the research community in recent years due its cross-disciplinary nature and its diverse application. Applications in bioacoustics classification range from smart acoustic sensor networks that investigate the effects of acoustic vocalizations on species to context-aware edge devices that anticipate changes in their environment adapt their sensing and processing accordingly. The research described here is an in-depth survey of the current state of bioacoustics classification and monitoring. The survey examines bioacoustics classification alongside general acoustics to provide a representative picture of the research landscape. The survey reviewed 124 studies spanning eight years of research. The survey identifies the key application areas in bioacoustics research and the techniques used in audio transformation and feature extraction. The survey also examines the classification algorithms used in bioacoustics systems. Lastly, the survey examines current challenges, possible opportunities, and future directions in bioacoustics.
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Affiliation(s)
- Leah Mutanu
- Department of Computing, United States International University Africa, Nairobi P.O. Box 14634-0800, Kenya
| | - Jeet Gohil
- Department of Computing, United States International University Africa, Nairobi P.O. Box 14634-0800, Kenya
| | - Khushi Gupta
- Department of Computer Science, Sam Houston State University, Huntsville, TX 77341, USA
| | - Perpetua Wagio
- Department of Computing, United States International University Africa, Nairobi P.O. Box 14634-0800, Kenya
| | - Gerald Kotonya
- School of Computing and Communications, Lancaster University, Lacaster LA1 4WA, UK
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Aktuelle Entwicklungen in der Schlafforschung und Schlafmedizin – eine Einschätzung der AG „Chirurgische Therapieverfahren“. SOMNOLOGIE 2022. [DOI: 10.1007/s11818-022-00367-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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7
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Automatic classification of the obstruction site in obstructive sleep apnea based on snoring sounds. Am J Otolaryngol 2022; 43:103584. [DOI: 10.1016/j.amjoto.2022.103584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/02/2022] [Indexed: 11/22/2022]
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8
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Ren Z, Qian K, Dong F, Dai Z, Nejdl W, Yamamoto Y, Schuller BW. Deep attention-based neural networks for explainable heart sound classification. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2022.100322] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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9
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Rossi M, Alessandrelli G, Dombrovschi A, Bovio D, Salito C, Mainardi L, Cerveri P. Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks. SENSORS 2022; 22:s22072684. [PMID: 35408297 PMCID: PMC9003131 DOI: 10.3390/s22072684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/28/2022] [Accepted: 03/28/2022] [Indexed: 11/28/2022]
Abstract
Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation.
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Affiliation(s)
- Matteo Rossi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (G.A.); (A.D.); (L.M.)
- Correspondence: (M.R.); (P.C.)
| | - Giulia Alessandrelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (G.A.); (A.D.); (L.M.)
| | - Andra Dombrovschi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (G.A.); (A.D.); (L.M.)
| | - Dario Bovio
- Biocubica SRL, 20154 Milan, Italy; (D.B.); (C.S.)
| | | | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (G.A.); (A.D.); (L.M.)
| | - Pietro Cerveri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy; (G.A.); (A.D.); (L.M.)
- Correspondence: (M.R.); (P.C.)
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Qian K, Schmitt M, Zheng H, Koike T, Han J, Liu J, Ji W, Duan J, Song M, Yang Z, Ren Z, Liu S, Zhang Z, Yamamoto Y, Schuller BW. Computer Audition for Fighting the SARS-CoV-2 Corona Crisis-Introducing the Multitask Speech Corpus for COVID-19. IEEE INTERNET OF THINGS JOURNAL 2021; 8:16035-16046. [PMID: 35782182 PMCID: PMC8768988 DOI: 10.1109/jiot.2021.3067605] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 02/24/2021] [Accepted: 03/17/2021] [Indexed: 05/29/2023]
Abstract
Computer audition (CA) has experienced a fast development in the past decades by leveraging advanced signal processing and machine learning techniques. In particular, for its noninvasive and ubiquitous character by nature, CA-based applications in healthcare have increasingly attracted attention in recent years. During the tough time of the global crisis caused by the coronavirus disease 2019 (COVID-19), scientists and engineers in data science have collaborated to think of novel ways in prevention, diagnosis, treatment, tracking, and management of this global pandemic. On the one hand, we have witnessed the power of 5G, Internet of Things, big data, computer vision, and artificial intelligence in applications of epidemiology modeling, drug and/or vaccine finding and designing, fast CT screening, and quarantine management. On the other hand, relevant studies in exploring the capacity of CA are extremely lacking and underestimated. To this end, we propose a novel multitask speech corpus for COVID-19 research usage. We collected 51 confirmed COVID-19 patients' in-the-wild speech data in Wuhan city, China. We define three main tasks in this corpus, i.e., three-category classification tasks for evaluating the physical and/or mental status of patients, i.e., sleep quality, fatigue, and anxiety. The benchmarks are given by using both classic machine learning methods and state-of-the-art deep learning techniques. We believe this study and corpus cannot only facilitate the ongoing research on using data science to fight against COVID-19, but also the monitoring of contagious diseases for general purpose.
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Affiliation(s)
- Kun Qian
- Educational Physiology Laboratory, Graduate School of EducationThe University of TokyoTokyo113-0033Japan
| | - Maximilian Schmitt
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Huaiyuan Zheng
- Department of Hand SurgeryWuhan Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China
| | - Tomoya Koike
- Educational Physiology Laboratory, Graduate School of EducationThe University of TokyoTokyo113-0033Japan
| | - Jing Han
- Mobile Systems GroupUniversity of CambridgeCambridgeCB2 1TNU.K.
| | - Juan Liu
- Department of Plastic SurgeryCentral Hospital of Wuhan, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China
| | - Wei Ji
- Department of Plastic SurgeryWuhan Third Hospital and Tongren Hospital of Wuhan UniversityWuhan430072China
| | - Junjun Duan
- Department of Plastic SurgeryCentral Hospital of Wuhan, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China
| | - Meishu Song
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Zijiang Yang
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Zhao Ren
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Shuo Liu
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Zixing Zhang
- GLAM—the Group on Language, Audio, and MusicImperial College LondonLondonSW7 2BUU.K.
| | - Yoshiharu Yamamoto
- Educational Physiology Laboratory, Graduate School of EducationThe University of TokyoTokyo113-0033Japan
| | - Björn W. Schuller
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
- GLAM—the Group on Language, Audio, and MusicImperial College LondonLondonSW7 2BUU.K.
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Koike T, Qian K, Schuller BW, Yamamoto Y. Transferring Cross-Corpus Knowledge: An Investigation on Data Augmentation for Heart Sound Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1976-1979. [PMID: 34891674 DOI: 10.1109/embc46164.2021.9629714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Human auscultation has been regarded as a cheap, convenient and efficient method for the diagnosis of cardiovascular diseases. Nevertheless, training professional auscultation skills needs tremendous efforts and is time-consuming. Computer audition (CA) that leverages the power of advanced machine learning and signal processing technologies has increasingly attracted contributions to the field of automatic heart sound classification. While previous studies have shown promising results in CA based heart sound classification with the 'shuffle split' method, machine learning for heart sound classification decreases in accuracy with a cross-corpus test dataset. We investigate this problem with a cross-corpus evaluation using the PhysioNet CinC Challenge 2016 Dataset and propose a new combination of data augmentation techniques that leads to a CNN robust for such cross-corpus evaluation. Compared with the baseline, which is given without augmentation, our data augmentation techniques combined improve by 20.0 % the sensitivity and by 7.9 % the specificity on average across 6 databases, which is a significant difference on 4 out of these (p < .05 by one-tailed z-test).
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12
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Yang Z, Huang L, Jiang J, Hu B, Tang C, Li J. Opinions on Computer Audition for Bowel Sounds Analysis in Intestinal Obstruction: Opportunities and Challenges From a Clinical Point of View. Front Med (Lausanne) 2021; 8:655298. [PMID: 34124092 PMCID: PMC8192713 DOI: 10.3389/fmed.2021.655298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 04/22/2021] [Indexed: 02/05/2023] Open
Affiliation(s)
| | | | | | | | | | - Jing Li
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
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