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Chen Z, Liang N, Li H, Zhang H, Li H, Yan L, Hu Z, Chen Y, Zhang Y, Wang Y, Ke D, Shi N. Exploring explainable AI features in the vocal biomarkers of lung disease. Comput Biol Med 2024; 179:108844. [PMID: 38981214 DOI: 10.1016/j.compbiomed.2024.108844] [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: 01/02/2024] [Revised: 05/15/2024] [Accepted: 06/04/2024] [Indexed: 07/11/2024]
Abstract
This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.
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Affiliation(s)
- Zhao Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Liang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haoyuan Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haili Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Huizhen Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Lijiao Yan
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ziteng Hu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yaxin Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yujing Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanping Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dandan Ke
- Special Disease Clinic, Huaishuling Branch of Beijing Fengtai Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China.
| | - Nannan Shi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
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2
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Sang B, Wen H, Junek G, Neveu W, Di Francesco L, Ayazi F. An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning. BIOSENSORS 2024; 14:118. [PMID: 38534225 DOI: 10.3390/bios14030118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/17/2024] [Accepted: 02/20/2024] [Indexed: 03/28/2024]
Abstract
Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient's chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time-frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set-outperforming the deterministic time-frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.
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Affiliation(s)
- Brian Sang
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Haoran Wen
- StethX Microsystems Inc., Atlanta, GA 30308, USA
| | | | - Wendy Neveu
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Lorenzo Di Francesco
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Farrokh Ayazi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- StethX Microsystems Inc., Atlanta, GA 30308, USA
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Garcia-Mendez JP, Lal A, Herasevich S, Tekin A, Pinevich Y, Lipatov K, Wang HY, Qamar S, Ayala IN, Khapov I, Gerberi DJ, Diedrich D, Pickering BW, Herasevich V. Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review. Bioengineering (Basel) 2023; 10:1155. [PMID: 37892885 PMCID: PMC10604310 DOI: 10.3390/bioengineering10101155] [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: 08/09/2023] [Revised: 09/15/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.
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Affiliation(s)
- Juan P. Garcia-Mendez
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Amos Lal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Svetlana Herasevich
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Aysun Tekin
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Yuliya Pinevich
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
- Department of Cardiac Anesthesiology and Intensive Care, Republican Clinical Medical Center, 223052 Minsk, Belarus
| | - Kirill Lipatov
- Division of Pulmonary Medicine, Mayo Clinic Health Systems, Essentia Health, Duluth, MN 55805, USA
| | - Hsin-Yi Wang
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
- Department of Anesthesiology, Taipei Veterans General Hospital, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320317, Taiwan
| | - Shahraz Qamar
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Ivan N. Ayala
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Ivan Khapov
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | | | - Daniel Diedrich
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Brian W. Pickering
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, USA (Y.P.); (H.-Y.W.); (I.K.); (V.H.)
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Long Y, Liu Z, Ayazi F. 4H-Silicon Carbide as an Acoustic Material for MEMS. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2023; 70:1189-1200. [PMID: 37276110 DOI: 10.1109/tuffc.2023.3282920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This article discusses the potential of 4H-silicon carbide (SiC) as a superior acoustic material for microelectromechanical systems (MEMS), particularly for high-performance resonator and extreme environments applications. Through a comparison of the crystalline structure along with the mechanical, acoustic, electrical, and thermal properties of 4H with respect to other SiC polytypes and silicon, it is shown that 4H-SiC possesses salient properties for MEMS applications, including its transverse isotropy and small phonon scattering dissipation. The utility and implementation of bonded SiC on insulator (4H-SiCOI) substrates as an emerging MEMS technology platform are presented. Additionally, this article reports on the temperature-dependent mechanical properties of 4H-SiC, including the temperature coefficient of frequency (TCF) and quality factor ( Q -factor) for Lamé mode resonators. Finally, the 4H-SiC MEMS fabrication including its deep reactive ion etching is discussed. This article provides valuable insights into the potential of 4H-SiC as a mechanoacoustic material and provides a foundation for future research in the field.
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Shokouhmand A, Wen H, Khan S, Puma JA, Patel A, Green P, Ayazi F, Ebadi N. Diagnosis of Coexisting Valvular Heart Diseases Using Image-to-Sequence Translation of Contact Microphone Recordings. IEEE Trans Biomed Eng 2023; 70:2540-2551. [PMID: 37028021 DOI: 10.1109/tbme.2023.3253381] [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: 03/08/2023]
Abstract
OBJECTIVE Development of a contact microphone-driven screening framework for the diagnosis of coexisting valvular heart diseases (VHDs). METHODS A sensitive accelerometer contact microphone (ACM) is employed to capture heart-induced acoustic components on the chest wall. Inspired by the human auditory system, ACM recordings are initially transformed into Mel-frequency cepstral coefficients (MFCCs) and their first and second derivatives, resulting in 3-channel images. An image-to-sequence translation network based on the convolution-meets-transformer (CMT) architecture is then applied to each image to find local and global dependencies in images, and predict a 5-digit binary sequence, where each digit corresponds to the presence of a specific type of VHD. The performance of the proposed framework is evaluated on 58 VHD patients and 52 healthy individuals using a 10-fold leave-subject-out cross-validation (10-LSOCV) approach. RESULTS Statistical analyses suggest an average sensitivity, specificity, accuracy, positive predictive value, and F1 score of 93.28%, 98.07%, 96.87%, 92.97%, and 92.4% respectively, for the detection of coexisting VHDs. Furthermore, areas under the curve (AUC) of 0.99 and 0.98 are respectively reported for the validation and test sets. CONCLUSION The high performances achieved prove that local and global features of ACM recordings effectively characterize heart murmurs associated with valvular abnormalities. SIGNIFICANCE Limited access of primary care physicians to echocardiography machines has resulted in a low sensitivity of 44% when using a stethoscope for the identification of heart murmurs. The proposed framework provides accurate decision-making on the presence of VHDs, thus reducing the number of undetected VHD patients in primary care settings.
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6
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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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Lalouani W, Younis M, Emokpae RN, Emokpae LE. Enabling effective breathing sound analysis for automated diagnosis of lung diseases. SMART HEALTH 2022; 26:100329. [PMID: 36275046 PMCID: PMC9576264 DOI: 10.1016/j.smhl.2022.100329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/21/2022] [Accepted: 09/29/2022] [Indexed: 10/29/2022]
Abstract
With the emergence of the COVID-19 pandemic, early diagnosis of lung diseases has attracted growing attention. Generally, monitoring the breathing sound is the traditional means for assessing the status of a patient's respiratory health through auscultation; for that a stethoscope is one of the clinical tools used by physicians for diagnosis of lung disease and anomalies. On the other hand, recent technological advances have made telehealth systems a practical and effective option for health status assessment and remote patient monitoring. The interest in telehealth solutions have further grown with the COVID-19 pandemic. These telehealth systems aim to provide increased safety and help to cope with the massive growth in healthcare demand. Particularly, employing acoustic sensors to collect breathing sound would enable real-time assessment and instantaneous detection of anomalies. However, existing work focuses on autonomous determination of respiratory rate which is not suitable for anomaly detection due to inability to deal with noisy data recording. This paper presents a novel approach for effective breathing sound analysis. We promote a new segmentation mechanism of the captured acoustic signals to identify breathing cycles in recorded sound signals. A scoring scheme is applied to qualify the segment based on the targeted respiratory illness by the overall breathing sound analysis. We demonstrate the effectiveness of our approach via experiments using published COPD datasets.
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Affiliation(s)
- Wassila Lalouani
- Department of Computer and Information Science, Towson University, USA
| | - Mohamed Younis
- CSEE Dept., Univ. of Maryland, Baltimore County, Baltimore, MD, USA
| | | | - Lloyd E. Emokpae
- LASARRUS Clinic and Research Center Inc., Baltimore, MD, USA,Corresponding author
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8
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Yentes JM, Liu WY, Zhang K, Markvicka E, Rennard SI. Updated Perspectives on the Role of Biomechanics in COPD: Considerations for the Clinician. Int J Chron Obstruct Pulmon Dis 2022; 17:2653-2675. [PMID: 36274993 PMCID: PMC9585958 DOI: 10.2147/copd.s339195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/24/2022] [Indexed: 11/05/2022] Open
Abstract
Patients with chronic obstructive pulmonary disease (COPD) demonstrate extra-pulmonary functional decline such as an increased prevalence of falls. Biomechanics offers insight into functional decline by examining mechanics of abnormal movement patterns. This review discusses biomechanics of functional outcomes, muscle mechanics, and breathing mechanics in patients with COPD as well as future directions and clinical perspectives. Patients with COPD demonstrate changes in their postural sway during quiet standing compared to controls, and these deficits are exacerbated when sensory information (eg, eyes closed) is manipulated. If standing balance is disrupted with a perturbation, patients with COPD are slower to return to baseline and their muscle activity is differential from controls. When walking, patients with COPD appear to adopt a gait pattern that may increase stability (eg, shorter and wider steps, decreased gait speed) in addition to altered gait variability. Biomechanical muscle mechanics (ie, tension, extensibility, elasticity, and irritability) alterations with COPD are not well documented, with relatively few articles investigating these properties. On the other hand, dyssynchronous motion of the abdomen and rib cage while breathing is well documented in patients with COPD. Newer biomechanical technologies have allowed for estimation of regional, compartmental, lung volumes during activity such as exercise, as well as respiratory muscle activation during breathing. Future directions of biomechanical analyses in COPD are trending toward wearable sensors, big data, and cloud computing. Each of these offers unique opportunities as well as challenges. Advanced analytics of sensor data can offer insight into the health of a system by quantifying complexity or fluctuations in patterns of movement, as healthy systems demonstrate flexibility and are thus adaptable to changing conditions. Biomechanics may offer clinical utility in prediction of 30-day readmissions, identifying disease severity, and patient monitoring. Biomechanics is complementary to other assessments, capturing what patients do, as well as their capability.
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Affiliation(s)
- Jennifer M Yentes
- Department of Kinesiology & Sport Management, Texas A&M University, College Station, TX, USA
| | - Wai-Yan Liu
- Department of Orthopaedic Surgery & Trauma, Máxima MC, Eindhoven, the Netherlands
- Department of Orthopaedic Surgery & Trauma, Catharina Hospital, Eindhoven, the Netherlands
| | - Kuan Zhang
- Department of Electrical & Computer Engineering, University of Nebraska at Lincoln, Lincoln, NE, USA
| | - Eric Markvicka
- Department of Electrical & Computer Engineering, University of Nebraska at Lincoln, Lincoln, NE, USA
- Department of Mechanical & Materials Engineering, University of Nebraska at Lincoln, Lincoln, NE, USA
| | - Stephen I Rennard
- Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, University of Nebraska Medical Center, Omaha, NE, USA
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9
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Hidayat SN, Julian T, Dharmawan AB, Puspita M, Chandra L, Rohman A, Julia M, Rianjanu A, Nurputra DK, Triyana K, Wasisto HS. Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose. Artif Intell Med 2022; 129:102323. [PMID: 35659391 PMCID: PMC9110307 DOI: 10.1016/j.artmed.2022.102323] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 05/05/2022] [Accepted: 05/12/2022] [Indexed: 01/31/2023]
Abstract
Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.
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Affiliation(s)
- Shidiq Nur Hidayat
- PT Nanosense Instrument Indonesia, Umbulharjo, Yogyakarta 55167, Indonesia,Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, BLS 21, Yogyakarta 55281, Indonesia
| | - Trisna Julian
- PT Nanosense Instrument Indonesia, Umbulharjo, Yogyakarta 55167, Indonesia
| | - Agus Budi Dharmawan
- PT Nanosense Instrument Indonesia, Umbulharjo, Yogyakarta 55167, Indonesia,Faculty of Information Technology, Universitas Tarumanagara, Jl. Letjen S. Parman No. 1, Jakarta 11440, Indonesia
| | - Mayumi Puspita
- PT Nanosense Instrument Indonesia, Umbulharjo, Yogyakarta 55167, Indonesia
| | - Lily Chandra
- RS Bhayangkara Polda Daerah Istimewa Yogyakarta, Jl. Raya Solo-Yogyakarta KM. 14, Sleman 55571, Indonesia
| | - Abdul Rohman
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Jl. Farmako Sekip Utara, Yogyakarta 55281, Indonesia
| | - Madarina Julia
- Department of Child Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Farmako Sekip Utara, Yogyakarta 55281, Indonesia
| | - Aditya Rianjanu
- Department of Materials Engineering, Institut Teknologi Sumatera, Terusan Ryacudu, Way Hui, Jati Agung, Lampung 35365, Indonesia
| | - Dian Kesumapramudya Nurputra
- Department of Child Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Farmako Sekip Utara, Yogyakarta 55281, Indonesia
| | - Kuwat Triyana
- Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, BLS 21, Yogyakarta 55281, Indonesia,Corresponding author
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Abstract
COPD remains one of the leading causes of death worldwide. The impact of smoking and air pollution remain important causative factors. Traditional classification of COPD includes the overlap of emphysema, chronic bronchitis and asthma. More recently the COPDGene definition includes the terms possible, probable and definite COPD. These are supported by findings from exposures, symptoms, spirometry and radiologic imaging. This new approach can lead to earlier diagnosis and modification of risk factors, earlier therapeutic intervention and improved treatments of established disease.
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Affiliation(s)
- David M Mannino
- Department of Medicine, University of Kentucky College of Medicine, Lexington, Kentucky.
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