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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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Crisdayanti IAPA, Nam SW, Jung SK, Kim SE. Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:383-392. [PMID: 38899013 PMCID: PMC11186653 DOI: 10.1109/ojemb.2024.3402139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/24/2024] [Accepted: 05/13/2024] [Indexed: 06/21/2024] Open
Abstract
Goal: In light of the COVID-19 pandemic, the early diagnosis of respiratory diseases has become increasingly crucial. Traditional diagnostic methods such as computed tomography (CT) and magnetic resonance imaging (MRI), while accurate, often face accessibility challenges. Lung auscultation, a simpler alternative, is subjective and highly dependent on the clinician's expertise. The pandemic has further exacerbated these challenges by restricting face-to-face consultations. This study aims to overcome these limitations by developing an automated respiratory sound classification system using deep learning, facilitating remote and accurate diagnoses. Methods: We developed a deep convolutional neural network (CNN) model that utilizes spectrographic representations of respiratory sounds within an image classification framework. Our model is enhanced with attention feature fusion of low-to-high-level information based on a knowledge propagation mechanism to increase classification effectiveness. This novel approach was evaluated using the ICBHI benchmark dataset and a larger, self-collected Pediatric dataset comprising outpatient children aged 1 to 6 years. Results: The proposed CNN model with knowledge propagation demonstrated superior performance compared to existing state-of-the-art models. Specifically, our model showed higher sensitivity in detecting abnormalities in the Pediatric dataset, indicating its potential for improving the accuracy of respiratory disease diagnosis. Conclusions: The integration of a knowledge propagation mechanism into a CNN model marks a significant advancement in the field of automated diagnosis of respiratory disease. This study paves the way for more accessible and precise healthcare solutions, which is especially crucial in pandemic scenarios.
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Affiliation(s)
- Ida A. P. A. Crisdayanti
- Department of Applied Artificial IntelligenceSeoul National University of Science and TechnologySeoul01811South Korea
| | - Sung Woo Nam
- Woorisoa Children's HospitalSeoul08291South Korea
| | | | - Seong-Eun Kim
- Department of Applied Artificial IntelligenceSeoul National University of Science and TechnologySeoul01811South Korea
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3
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Al-Zaben A, Al-Fahoum A, Ababneh M, Al-Naami B, Al-Omari G. Improved recovery of cardiac auscultation sounds using modified cosine transform and LSTM-based masking. Med Biol Eng Comput 2024:10.1007/s11517-024-03088-x. [PMID: 38627355 DOI: 10.1007/s11517-024-03088-x] [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: 09/18/2023] [Accepted: 04/02/2024] [Indexed: 04/24/2024]
Abstract
Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum. The proposed strategy's performance in maintaining the clinical relevance of heart sounds continues to demonstrate effectiveness, even in environments marked by increased noise and complex disruptions. The present work highlights the clinical significance and reliability of the suggested methodology through in-depth signal visualization and rigorous statistical performance evaluations. In comparative assessments, the proposed approach has demonstrated superior performance compared to recent algorithms, such as LU-Net and PC-DAE. Furthermore, the system's adaptability to various datasets enhances its reliability and practicality. The suggested method is a potential way to improve the accuracy of cardiovascular diagnostics in an era of rapid advancement in medical signal processing. The proposed approach showed an enhancement in the average signal-to-noise ratio (SNR) by 9.6 dB at an input SNR of - 6 dB and by 3.3 dB at an input SNR of 10 dB. The average signal distortion ratio (SDR) achieved across a variety of input SNR values was 8.56 dB.
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Affiliation(s)
- Awad Al-Zaben
- Biomedical Engineering Department, Engineering Faculty, Hashemite University, Zarqa, Jordan.
- Biomedical Systems and Medical Informatics Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan.
| | - Amjad Al-Fahoum
- Biomedical Systems and Medical Informatics Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan
| | - Muhannad Ababneh
- Faculty of Medicine, Interventional Cardiologist, Jordan University of Science and Technology, Irbid, Jordan
| | - Bassam Al-Naami
- Biomedical Engineering Department, Engineering Faculty, Hashemite University, Zarqa, Jordan
| | - Ghadeer Al-Omari
- Biomedical Systems and Medical Informatics Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan
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4
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Al-Anazi S, Al-Omari A, Alanazi S, Marar A, Asad M, Alawaji F, Alwateid S. Artificial intelligence in respiratory care: Current scenario and future perspective. Ann Thorac Med 2024; 19:117-130. [PMID: 38766378 PMCID: PMC11100474 DOI: 10.4103/atm.atm_192_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.
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Affiliation(s)
- Saad Al-Anazi
- Lead Clincial Appliaction AzeerTrade (Lowenstein Medical Company), Riyadh, Saudi Arabia
| | - Awad Al-Omari
- Department of Intensive Care, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Safug Alanazi
- Intensivist, Al Hammadi Hospital, Riyadh, Saudi Arabia
| | - Aqeelah Marar
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Mohammed Asad
- Department of Emergency, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Fadi Alawaji
- Ar Rass General Hospital, Qassim Health Cluster, Senior Laboratory Specialist, Rass Region, Qassim City, Saudi Arabia
| | - Salman Alwateid
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
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5
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Gul S, Khan MS, Ur-Rehman A. DEW: A wavelet approach of rare sound event detection. PLoS One 2024; 19:e0300444. [PMID: 38547253 PMCID: PMC10977878 DOI: 10.1371/journal.pone.0300444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/27/2024] [Indexed: 04/01/2024] Open
Abstract
This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called 'chunks' are cropped from the input audio signal corresponding to the estimated locations of the significant transitions. Features from these chunks are extracted by the wavelet scattering network (WSN) and are given as input to a support vector machine (SVM) classifier, which classifies them. The proposed SED framework produces an error rate comparable to the SED systems based on convolutional neural network (CNN) architecture. Also, the proposed algorithm is computationally efficient and lightweight as compared to deep learning models, as it has no learnable parameter. It requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on CNNs and deep neural networks (DNNs), CNN with long short-term memory (LSTM) network, convolutional recurrent neural network (CRNN), and CNN respectively. The proposed model neither requires concatenation with previous frames for anomaly detection nor any additional training data creation needed for other comparative deep learning models. It needs to check almost 360 times fewer chunks for the presence of rare events than the other baseline systems used for comparison in this paper. All these characteristics make the proposed system suitable for real-time applications on resource-limited devices.
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Affiliation(s)
- Sania Gul
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan
- Intelligent Information Processing Lab, National Center of Artificial Intelligence, University of Engineering and Technology, Peshawar, Pakistan
| | - Muhammad Salman Khan
- Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar
| | - Ata Ur-Rehman
- Department of Electrical Engineering (MCS), NUST, Islamabad, Pakistan
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6
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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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7
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Razvadauskas H, Vaičiukynas E, Buškus K, Arlauskas L, Nowaczyk S, Sadauskas S, Naudžiūnas A. Exploring classical machine learning for identification of pathological lung auscultations. Comput Biol Med 2024; 168:107784. [PMID: 38042100 DOI: 10.1016/j.compbiomed.2023.107784] [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: 06/12/2023] [Revised: 11/17/2023] [Accepted: 11/28/2023] [Indexed: 12/04/2023]
Abstract
The use of machine learning in biomedical research has surged in recent years thanks to advances in devices and artificial intelligence. Our aim is to expand this body of knowledge by applying machine learning to pulmonary auscultation signals. Despite improvements in digital stethoscopes and attempts to find synergy between them and artificial intelligence, solutions for their use in clinical settings remain scarce. Physicians continue to infer initial diagnoses with less sophisticated means, resulting in low accuracy, leading to suboptimal patient care. To arrive at a correct preliminary diagnosis, the auscultation diagnostics need to be of high accuracy. Due to the large number of auscultations performed, data availability opens up opportunities for more effective sound analysis. In this study, digital 6-channel auscultations of 45 patients were used in various machine learning scenarios, with the aim of distinguishing between normal and abnormal pulmonary sounds. Audio features (such as fundamental frequencies F0-4, loudness, HNR, DFA, as well as descriptive statistics of log energy, RMS and MFCC) were extracted using the Python library Surfboard. Windowing, feature aggregation, and concatenation strategies were used to prepare data for machine learning algorithms in unsupervised (fair-cut forest, outlier forest) and supervised (random forest, regularized logistic regression) settings. The evaluation was carried out using 9-fold stratified cross-validation repeated 30 times. Decision fusion by averaging the outputs for a subject was also tested and found to be helpful. Supervised models showed a consistent advantage over unsupervised ones, with random forest achieving a mean AUC ROC of 0.691 (accuracy 71.11%, Kappa 0.416, F1-score 0.675) in side-based detection and a mean AUC ROC of 0.721 (accuracy 68.89%, Kappa 0.371, F1-score 0.650) in patient-based detection.
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8
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Luo Y, Xiao Y, Liu J, Wu Y, Zhao Z. Design and application of a flexible nano cardiac sound sensor based on P(VDF-TrFE)/KNN/GR composite piezoelectric film for heart disease diagnosis. NANOTECHNOLOGY 2023; 35:075502. [PMID: 37857282 DOI: 10.1088/1361-6528/ad0502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/19/2023] [Indexed: 10/21/2023]
Abstract
The paper proposes a flexible micro-nano composite piezoelectric thin film. This flexible piezoelectric film is fabricated through electrospinning process, utilizing a combination of 12 wt% poly(vinylidene fluoride-co-trifluoroethylene)(P(VDF-TrFE)), 8 wt% potassium sodium niobate (KNN) nanoparticles, and 0.5 wt% graphene (GR). Under cyclic loading, the composite film demonstrates a remarkable increase in open-circuit voltage and short-circuit current, achieving values of 36.1 V and 163.7 uA, respectively. These values are 5.8 times and 3.6 times higher than those observed in the pure P(VDF-TrFE) film. The integration of this piezoelectric film into a wearable flexible heartbeat sensor, coupled with the RepMLP classification model, facilitates heartbeat acquisition and real-time automated diagnosis. After training and validation on a dataset containing 2000 heartbeat samples, the system achieved an accuracy of approximately 99% in two classification of heart sound signals (normal and abnormal). This research substantially enhances the output performance of the piezoelectric film, offering a novel and valuable solution for the application of flexible piezoelectric films in physiological signal detection.
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Affiliation(s)
- Yi Luo
- School of Electronics and Information Engineering, Hangzhou DIANZI University, Hangzhou 310018, People's Republic of China
| | - Yu Xiao
- School of Communication Engineering, Hangzhou DIANZI University, Hangzhou 310018, People's Republic of China
| | - Jian Liu
- School of Communication Engineering, Hangzhou DIANZI University, Hangzhou 310018, People's Republic of China
| | - Ying Wu
- Academic Affairs Office, Hangzhou DIANZI University, Hangzhou 310018, People's Republic of China
| | - Zhidong Zhao
- School of Cyberspace Security, Hangzhou DIANZI University, Hangzhou 310018, People's Republic of China
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9
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Martins ML, Coimbra MT, Renna F. Markov-Based Neural Networks for Heart Sound Segmentation: Using Domain Knowledge in a Principled Way. IEEE J Biomed Health Inform 2023; 27:5357-5368. [PMID: 37672365 DOI: 10.1109/jbhi.2023.3312597] [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: 09/08/2023]
Abstract
This work considers the problem of segmenting heart sounds into their fundamental components. We unify statistical and data-driven solutions by introducing Markov-based Neural Networks (MNNs), a hybrid end-to-end framework that exploits Markov models as statistical inductive biases for an Artificial Neural Network (ANN) discriminator. We show that an MNN leveraging a simple one-dimensional Convolutional ANN significantly outperforms two recent purely data-driven solutions for this task in two publicly available datasets: PhysioNet 2016 (Sensitivity: 0.947 ±0.02; Positive Predictive Value : 0.937 ±0.025) and the CirCor DigiScope 2022 (Sensitivity: 0.950 ±0.008; Positive Predictive Value: 0.943 ±0.012). We also propose a novel gradient-based unsupervised learning algorithm that effectively makes the MNN adaptive to unseen datum sampled from unknown distributions. We perform a cross dataset analysis and show that an MNN pre-trained in the CirCor DigiScope 2022 can benefit from an average improvement of 3.90% Positive Predictive Value on unseen observations from the PhysioNet 2016 dataset using this method.
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10
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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: 1] [Impact Index Per Article: 1.0] [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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11
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Cococcetta C, Coutant T, Bagur S, Wernick MB, Huynh M. Use of Digital Stethoscope to Measure Heart Rate in Birds: Comparison of Different Counting Methods Using Phonocardiograms. J Avian Med Surg 2023; 37:108-117. [PMID: 37733450 DOI: 10.1647/22-00047] [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] [Indexed: 09/23/2023]
Abstract
The high cardiac contractility of birds poses a challenge to traditional cardiac auscultation, particularly for the accurate determination of heart rate (HR). The objectives of this study were to 1) evaluate the feasibility of using phonocardiograms of adequate length and quality to assess HR in different avian species with a commercially available digital stethoscope, 2) compare 5 counting methods, including 2 direct reading methods (manual counting and using a semiautomatic computerized algorithm as a reference method) and 3 listening methods (progressive mental counting, counting by 10s, and counting with a smartphone application tap counter), and 3) obtain the HR in selected birds and identify a correlation between body weight and HR in different avian species. An inverse correlation on a logarithmic scale was identified between the mean body weight and HR in 60 different bird species (n = 211; R = -0.72, P < 0.0001). Manual reading of phonocardiograms was the most reliable method and had the highest agreement with the reference method; this was followed by the counting by 10s method, the tapping method, and the progressive mental counting method, which was the least reliable. The agreement levels for the different methods were comparable for HRs <200 beats per minute (bpm) in birds weighing >1 kg. For HRs >500 bpm in birds weighing <150 g, only the reading method maintained a good agreement level. A digital stethoscope can be a useful tool for accurately determining the HR in birds, including very small species with high HRs.
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Affiliation(s)
- Ciro Cococcetta
- Service des Nouveaux Animaux de Compagnie Centre Hospitalier Vétérinaire Frégis, 94250 Gentilly, France, cirocococ@ gmail.com
| | - Thomas Coutant
- Service des Nouveaux Animaux de Compagnie Centre Hospitalier Vétérinaire Frégis, 94250 Gentilly, France
| | - Sophie Bagur
- Institut de l'Audition, Institut Pasteur, 75012 Paris, France
| | | | - Minh Huynh
- Service des Nouveaux Animaux de Compagnie Centre Hospitalier Vétérinaire Frégis, 94250 Gentilly, France
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12
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Jaros R, Koutny J, Ladrova M, Martinek R. Novel phonocardiography system for heartbeat detection from various locations. Sci Rep 2023; 13:14392. [PMID: 37658080 PMCID: PMC10474097 DOI: 10.1038/s41598-023-41102-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 08/22/2023] [Indexed: 09/03/2023] Open
Abstract
The paper presents evaluation of the proposed phonocardiography (PCG) measurement system designed primarily for heartbeat detection to estimate heart rate (HR). Typically, HR estimation is performed using electrocardiography (ECG) or pulse wave as one of the fundamental diagnostic methodologies for assessing cardiac function. The system includes novel both sensory part and data processing procedure, which is based on signal preprocessing using Wavelet Transform (WT) and Shannon energy computation and heart sounds classification using K-means. Due to the lack of standardization in the placement of PCG sensors, the study focuses on evaluating the signal quality obtained from 7 different sensor locations on the subject's chest and investigates which locations are most suitable for recording heart sounds. The suitability of sensor localization was examined in 27 subjects by detecting the first two heart sounds (S1, S2). The HR detection sensitivity related to reference ECG from all sensor positions reached values over 88.9 and 77.4% in detection of S1 and S2, respectively. The placement in the middle of sternum showed the higher signal quality with median of the proper S1 and S2 detection sensitivity of 98.5 and 97.5%, respectively.
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Affiliation(s)
- Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, 708 00, Ostrava, Czechia.
| | - Jiri Koutny
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, 708 00, Ostrava, Czechia
| | - Martina Ladrova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, 708 00, Ostrava, Czechia
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, 708 00, Ostrava, Czechia
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13
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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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14
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Arjoune Y, Nguyen TN, Salvador T, Telluri A, Schroeder JC, Geggel RL, May JW, Pillai DK, Teach SJ, Patel SJ, Doroshow RW, Shekhar R. StethAid: A Digital Auscultation Platform for Pediatrics. SENSORS (BASEL, SWITZERLAND) 2023; 23:5750. [PMID: 37420914 PMCID: PMC10304273 DOI: 10.3390/s23125750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/18/2023] [Accepted: 06/15/2023] [Indexed: 07/09/2023]
Abstract
(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.
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Affiliation(s)
- Youness Arjoune
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA
| | - Trong N. Nguyen
- AusculTech Dx, 2601 University Blvd West #301, Silver Spring, MD 20902, USA
| | - Tyler Salvador
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA
| | - Anha Telluri
- School of Medicine and Health Sciences, George Washington University, Washington, DC 20052, USA
| | - Jonathan C. Schroeder
- Division of Pulmonary and Sleep Medicine, Children’s National Hospital, Washington, DC 20010, USA
| | - Robert L. Geggel
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Joseph W. May
- Department of Pediatrics, Walter Reed National Military Medical Center, Bethesda, MD 20814, USA
| | - Dinesh K. Pillai
- Division of Pulmonary and Sleep Medicine, Children’s National Hospital, Washington, DC 20010, USA
| | - Stephen J. Teach
- Department of Pediatrics, Children’s National Hospital, Washington, DC 20010, USA
| | - Shilpa J. Patel
- Division of Emergency Medicine, Children’s National Hospital, Washington, DC 20010, USA
| | - Robin W. Doroshow
- AusculTech Dx, 2601 University Blvd West #301, Silver Spring, MD 20902, USA
- Department of Cardiology, Children’s National Hospital, Washington, DC 20010, USA
| | - Raj Shekhar
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA
- AusculTech Dx, 2601 University Blvd West #301, Silver Spring, MD 20902, USA
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15
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Azmeen A, Vakilzadian H, Haider H, Mathers DH, Zimmerman R, Bedi S, O'Leary EL. Heart sounds: Past, present, and future from a technological and clinical perspective - a systematic review. Proc Inst Mech Eng H 2023:9544119231172858. [PMID: 37139865 DOI: 10.1177/09544119231172858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The high prevalence of cardiac diseases around the world has created a need for quick, easy and cost effective approaches to diagnose heart disease. The auscultation and interpretation of heart sounds using the stethoscope is relatively inexpensive, requires minimal to advanced training, and is widely available and easily carried by healthcare providers working in urban environments or medically underserved rural areas. Since René-Théophile-Hyacinthe Laennec's simple, monoaural design, the capabilities of modern-day, commercially available stethoscopes and stethoscope systems have radically advanced with the integration of electronic hardware and software tools, however these systems are largely confined to the metropolitan medical centers. The purpose of this paper is to review the history of stethoscopes, compare commercially available stethoscope products and analytical software, and discuss future directions. Our review includes a description of heart sounds and how modern software enables the measurement and analysis of time intervals, teaching auscultation, remote cardiac examination (telemedicine) and, more recently, spectrographic evaluation and electronic storage. The basic methodologies behind modern software algorithms and techniques for heart sound preprocessing, segmentation and classification are described to provide awareness.
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Affiliation(s)
- Ayesha Azmeen
- University of Nebraska Medical Center, Omaha, NE, USA
| | | | - Hani Haider
- University of Nebraska Medical Center, Omaha, NE, USA
| | | | | | - Shine Bedi
- Univeristy of Nebraska-Lincoln, Lincoln, NE, USA
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16
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Seah JJ, Zhao J, Wang DY, Lee HP. Review on the Advancements of Stethoscope Types in Chest Auscultation. Diagnostics (Basel) 2023; 13:diagnostics13091545. [PMID: 37174938 PMCID: PMC10177339 DOI: 10.3390/diagnostics13091545] [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: 03/11/2023] [Revised: 04/16/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
Stethoscopes were originally designed for the auscultation of a patient's chest for the purpose of listening to lung and heart sounds. These aid medical professionals in their evaluation of the cardiovascular and respiratory systems, as well as in other applications, such as listening to bowel sounds in the gastrointestinal system or assessing for vascular bruits. Listening to internal sounds during chest auscultation aids healthcare professionals in their diagnosis of a patient's illness. We performed an extensive literature review on the currently available stethoscopes specifically for use in chest auscultation. By understanding the specificities of the different stethoscopes available, healthcare professionals can capitalize on their beneficial features, to serve both clinical and educational purposes. Additionally, the ongoing COVID-19 pandemic has also highlighted the unique application of digital stethoscopes for telemedicine. Thus, the advantages and limitations of digital stethoscopes are reviewed. Lastly, to determine the best available stethoscopes in the healthcare industry, this literature review explored various benchmarking methods that can be used to identify areas of improvement for existing stethoscopes, as well as to serve as a standard for the general comparison of stethoscope quality. The potential use of digital stethoscopes for telemedicine amidst ongoing technological advancements in wearable sensors and modern communication facilities such as 5G are also discussed. Based on the ongoing trend in advancements in wearable technology, telemedicine, and smart hospitals, understanding the benefits and limitations of the digital stethoscope is an essential consideration for potential equipment deployment, especially during the height of the current COVID-19 pandemic and, more importantly, for future healthcare crises when human and resource mobility is restricted.
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Affiliation(s)
- Jun Jie Seah
- Department of Otolaryngology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Jiale Zhao
- Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore
| | - De Yun Wang
- Department of Otolaryngology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Infectious Diseases Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117545, Singapore
| | - Heow Pueh Lee
- Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore
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17
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Chen X, Guo X, Zheng Y, Lv C. Heart function grading evaluation based on heart sounds and convolutional neural networks. Phys Eng Sci Med 2023; 46:279-288. [PMID: 36625996 DOI: 10.1007/s13246-023-01216-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 01/11/2023]
Abstract
Accurate and rapid cardiac function assessment is critical for disease diagnosis and treatment strategy. However, the current cardiac function assessment methods have their adaptability and limitations. Heart sounds (HS) can reflect changes in heart function. Therefore, HS signals were proposed to assess cardiac function, and a specially designed pruning convolutional neural network (CNN) was applied to recognize subjects' cardiac function at different levels in this paper. Firstly, the adaptive wavelet denoising algorithm and logistic regression based hidden semi-Markov model were utilized for signal denoising and segmentation. Then, the continuous wavelet transform (CWT) was employed to convert the preprocessed HS signals into spectra as input to the convolutional neural network, which can extract features automatically. Finally, the proposed method was compared with AlexNet, Resnet50, Xception, GhostNet and EfficientNet to verify the superiority of the proposed method. Through comprehensive comparison, the proposed approach achieves the best classification performance with an accuracy of 94.34%. The study indicates HS analysis is a non-invasive and effective method for cardiac function classification, which has broad research prospects.
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Affiliation(s)
- Xiao Chen
- Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, 400044, Chongqing, China
| | - Xingming Guo
- Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, 400044, Chongqing, China.
| | - Yineng Zheng
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, 400016, Chongqing, China
| | - Chengcong Lv
- Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, 400044, Chongqing, China
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18
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Lenz I, Rong Y, Bliss D. Contactless Stethoscope Enabled by Radar Technology. Bioengineering (Basel) 2023; 10:bioengineering10020169. [PMID: 36829662 PMCID: PMC9952308 DOI: 10.3390/bioengineering10020169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/21/2023] [Accepted: 01/24/2023] [Indexed: 01/31/2023] Open
Abstract
Contactless vital sign measurement technologies have the potential to greatly improve patient experiences and practitioner safety while creating the opportunity for comfortable continuous monitoring. We introduce a contactless alternative for measuring human heart sounds. We leverage millimeter wave frequency-modulated continuous wave radar and multi-input multi-output beamforming techniques to capture fine skin vibrations that result from the cardiac movements that cause heart sounds. We discuss contact-based heart sound measurement techniques and directly compare the radar heart sound technique with these contact-based approaches. We present experimental cases to test the strengths and limitations of both the contact-based measurement techniques and the contactless radar measurement. We demonstrate that the radar measurement technique is a viable and potentially superior method for capturing human heart sounds in many practical settings.
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Affiliation(s)
| | - Yu Rong
- Correspondence: (I.L.); (Y.R.)
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19
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Development of a Multi-Channel Wearable Heart Sound Visualization System. J Pers Med 2022; 12:jpm12122011. [PMID: 36556232 PMCID: PMC9782199 DOI: 10.3390/jpm12122011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
A multi-channel wearable heart sound visualization system based on novel heart sound sensors for imaging cardiac acoustic maps was developed and designed. The cardiac acoustic map could be used to detect cardiac vibration and heart sound propagation. The visualization system acquired 72 heart sound signals and one ECG signal simultaneously using 72 heart sound sensors placed on the chest surface and one ECG analog front end. The novel heart sound sensors had the advantages of high signal quality, small size, and high sensitivity. Butterworth filtering and wavelet transform were used to reduce noise in the signals. The cardiac acoustic map was obtained based on the cubic spline interpolation of the heart sound signals. The results showed the heart sound signals on the chest surface could be detected and visualized by this system. The variations of heart sounds were clearly displayed. This study provided a way to select optimal position for auscultation of heart sounds. The visualization system could provide a technology for investigating the propagation of heart sound in the thoracic cavity.
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20
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Huang PK, Yang MC, Wang ZX, Huang YJ, Lin WC, Pan CL, Guo MH. Augmented detection of septal defects using advanced optical coherence tomography network-processed phonocardiogram. Front Cardiovasc Med 2022; 9:1041082. [PMID: 36523363 PMCID: PMC9744752 DOI: 10.3389/fcvm.2022.1041082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 11/14/2022] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Cardiac auscultation is a traditional method that is most frequently used for identifying congenital heart disease (CHD). Failure to diagnose CHD may occur in patients with faint murmurs or obesity. We aimed to develop an intelligent diagnostic method of detecting heart murmurs in patients with ventricular septal defects (VSDs) and atrial septal defects (ASDs). MATERIALS AND METHODS Digital recordings of heart sounds and phonocardiograms of 184 participants were obtained. All participants underwent echocardiography by pediatric cardiologists to determine the type of CHD. The phonocardiogram data were classified as normal, ASD, or VSD. Then, the phonocardiogram signal was used to extract features to construct diagnostic models for disease classification using an advanced optical coherence tomography network (AOCT-NET). Cardiologists were asked to distinguish normal heart sounds from ASD/VSD murmurs after listening to the electronic sound recordings. Comparisons of the cardiologists' assessment and AOCT-NET performance were performed. RESULTS Echocardiography results revealed 88 healthy participants, 50 with ASDs, and 46 with VSDs. The AOCT-NET had no advantage in detecting VSD compared with cardiologist assessment. However, AOCT-NET performance was better than that of cardiologists in detecting ASD (sensitivity, 76.4 vs. 27.8%, respectively; specificity, 90 vs. 98.5%, respectively). CONCLUSION The proposed method has the potential to improve the ASD detection rate and could be an important screening tool for patients without symptoms.
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Affiliation(s)
- Po-Kai Huang
- Department of Electronic Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Ming-Chun Yang
- Department of Pediatrics, E-Da Hospital, Kaohsiung, Taiwan
- Department of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Zi-Xuan Wang
- Department of Applied Mathematics, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Yu-Jung Huang
- Department of Electronic Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Wei-Chen Lin
- Department of Medical Research, E-DA Hospital, Kaohsiung, Taiwan
| | - Chung-Long Pan
- Department of Electronic Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Mei-Hui Guo
- Department of Applied Mathematics, National Sun Yat-sen University, Kaohsiung, Taiwan
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21
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Sethi Y, Patel N, Kaka N, Desai A, Kaiwan O, Sheth M, Sharma R, Huang H, Chopra H, Khandaker MU, Lashin MMA, Hamd ZY, Emran TB. Artificial Intelligence in Pediatric Cardiology: A Scoping Review. J Clin Med 2022; 11:jcm11237072. [PMID: 36498651 PMCID: PMC9738645 DOI: 10.3390/jcm11237072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 11/22/2022] [Accepted: 11/26/2022] [Indexed: 12/05/2022] Open
Abstract
The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.
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Affiliation(s)
- Yashendra Sethi
- PearResearch, Dehradun 248001, India
- Department of Medicine, Government Doon Medical College, Dehradun 248001, India
| | - Neil Patel
- PearResearch, Dehradun 248001, India
- Department of Medicine, GMERS Medical College, Himmatnagar 383001, India
| | - Nirja Kaka
- PearResearch, Dehradun 248001, India
- Department of Medicine, GMERS Medical College, Himmatnagar 383001, India
| | - Ami Desai
- Department of Medicine, SMIMER Medical College, Surat 395010, India
| | - Oroshay Kaiwan
- PearResearch, Dehradun 248001, India
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH 44272, USA
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
| | - Mili Sheth
- Department of Medicine, GMERS Gandhinagar, Gandhinagar 382012, India
| | - Rupal Sharma
- Department of Medicine, Government Medical College, Nagpur 440003, India
| | - Helen Huang
- Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, India
| | - Mayeen Uddin Khandaker
- Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway 47500, Malaysia
| | - Maha M. A. Lashin
- Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. 84428, Riyadh 11671, Saudi Arabia
| | - Zuhal Y. Hamd
- Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
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22
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Evaluation of Internet-Connected Real-Time Remote Auscultation: An Open-Label Randomized Controlled Pilot Trial. J Pers Med 2022; 12:jpm12121950. [PMID: 36556171 PMCID: PMC9783264 DOI: 10.3390/jpm12121950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 11/26/2022] Open
Abstract
The utility of remote auscultation was unknown. This study aimed to evaluate internet-connected real-time remote auscultation using cardiopulmonary simulators. In this open-label randomized controlled trial, the physicians were randomly assigned to the real-time remote auscultation group (intervention group) or the classical auscultation group (control group). After the training session, the participants had to classify the ten cardiopulmonary sounds in random order as the test session. In both sessions, the intervention group auscultated with an internet-connected electronic stethoscope. The control group performed direct auscultation using a classical stethoscope. The total scores for correctly identified normal or abnormal cardiopulmonary sounds were 97/100 (97%) in the intervention group and 98/100 (98%) in the control group with no significant difference between the groups (p > 0.99). In cardiac auscultation, the test score in the control group (94%) was superior to that in the intervention group (72%, p < 0.05). Valvular diseases were not misclassified as normal sounds in real-time remote cardiac auscultation. The utility of real-time remote cardiopulmonary auscultation using an internet-connected electronic stethoscope was comparable to that of classical auscultation. Classical cardiac auscultation was superior to real-time remote auscultation. However, real-time remote cardiac auscultation is useful for classifying valvular diseases and normal sounds.
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23
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Azam FB, Ansari MI, Nuhash SISK, McLane I, Hasan T. Cardiac anomaly detection considering an additive noise and convolutional distortion model of heart sound recordings. Artif Intell Med 2022; 133:102417. [PMID: 36328670 DOI: 10.1016/j.artmed.2022.102417] [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: 10/29/2021] [Revised: 09/17/2022] [Accepted: 10/02/2022] [Indexed: 12/13/2022]
Abstract
Cardiac auscultation is an essential point-of-care method used for the early diagnosis of heart diseases. Automatic analysis of heart sounds for abnormality detection is faced with the challenges of additive noise and sensor-dependent degradation. This paper aims to develop methods to address the cardiac abnormality detection problem when both of these components are present in the cardiac auscultation sound. We first mathematically analyze the effect of additive noise and convolutional distortion on short-term mel-filterbank energy-based features and a Convolutional Neural Network (CNN) layer. Based on the analysis, we propose a combination of linear and logarithmic spectrogram-image features. These 2D features are provided as input to a residual CNN network (ResNet) for heart sound abnormality detection. Experimental validation is performed first on an open-access, multiclass heart sound dataset where we analyzed the effect of additive noise by mixing lung sound noise with the recordings. In noisy conditions, the proposed method outperforms one of the best-performing methods in the literature achieving an Macc (mean of sensitivity and specificity) of 89.55% and an average F-1 score of 82.96%, respectively, when averaged over all noise levels. Next, we perform heart sound abnormality detection (binary classification) experiments on the 2016 Physionet/CinC Challenge dataset that involves noisy recordings obtained from multiple stethoscope sensors. The proposed method achieves significantly improved results compared to the conventional approaches on this dataset, in the presence of both additive noise and channel distortion, with an area under the ROC (receiver operating characteristics) curve (AUC) of 91.36%, F-1 score of 84.09%, and Macc of 85.08%. We also show that the proposed method shows the best mean accuracy across different source domains, including stethoscope and noise variability, demonstrating its effectiveness in different recording conditions. The proposed combination of linear and logarithmic features along with the ResNet classifier effectively minimizes the impact of background noise and sensor variability for classifying phonocardiogram (PCG) signals. The method thus paves the way toward developing computer-aided cardiac auscultation systems in noisy environments using low-cost stethoscopes.
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Affiliation(s)
- Farhat Binte Azam
- mHealth Lab, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, Bangladesh
| | - Md Istiaq Ansari
- mHealth Lab, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, Bangladesh
| | - Shoyad Ibn Sabur Khan Nuhash
- mHealth Lab, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, Bangladesh
| | - Ian McLane
- Sonavi Labs Inc., Baltimore, 21230, MD, USA
| | - Taufiq Hasan
- mHealth Lab, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, Bangladesh.
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24
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Real-world evaluation of the Stemoscope electronic tele-auscultation system. Biomed Eng Online 2022; 21:63. [PMID: 36068509 PMCID: PMC9446597 DOI: 10.1186/s12938-022-01032-4] [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: 05/16/2022] [Accepted: 08/24/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND With the spread of COVID-19, telemedicine has played an important role, but tele-auscultation is still unavailable in most countries. This study introduces and tests a tele-auscultation system (Stemoscope) and compares the concordance of the Stemoscope with the traditional stethoscope in the evaluation of heart murmurs. METHODS A total of 57 patients with murmurs were recruited, and echocardiographs were performed. Three cardiologists were asked to correctly categorize heart sounds (both systolic murmur and diastolic murmur) as normal vs. abnormal with both the Stemoscope and a traditional acoustic stethoscope under different conditions. Firstly, we compared the in-person auscultation agreement between Stemoscope and the conventional acoustic stethoscope. Secondly, we compared tele-auscultation (recorded heart sounds) agreement between Stemoscope and acoustic results. Thirdly, we compared both the Stemoscope tele-auscultation results and traditional acoustic stethoscope in-person auscultation results with echocardiography. Finally, ten other cardiologists were asked to complete a qualitative questionnaire to assess their experience using the Stemoscope. RESULTS For murmurs detection, the in-person auscultation agreement between Stemoscope and the acoustic stethoscope was 91% (p = 0.67). The agreement between Stemoscope tele-auscultation and the acoustic stethoscope in-person auscultation was 90% (p = 0.32). When using the echocardiographic findings as the reference, the agreement between Stemoscope (tele-auscultation) and the acoustic stethoscope (in-person auscultation) was 89% vs. 86% (p = 1.00). The system evaluated by ten cardiologists is considered easy to use, and most of them would consider using it in a telemedical setting. CONCLUSION In-person auscultation and tele-auscultation by the Stemoscope are in good agreement with manual acoustic auscultation. The Stemoscope is a helpful heart murmur screening tool at a distance and can be used in telemedicine.
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Luo H, Lamata P, Bazin S, Bautista T, Barclay N, Shahmohammadi M, Lubrecht JM, Delhaas T, Prinzen FW. Smartphone as an electronic stethoscope: factors influencing heart sound quality . EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:473-480. [PMID: 36712168 PMCID: PMC9708017 DOI: 10.1093/ehjdh/ztac044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 08/11/2022] [Indexed: 02/01/2023]
Abstract
Aims Smartphones are equipped with a high-quality microphone which may be used as an electronic stethoscope. We aim to investigate the factors influencing quality of heart sound recorded using a smartphone by non-medical users. Methods and results An app named Echoes was developed for recording heart sounds using iPhone. Information on phone version and users' characteristics including sex, age, and body mass index (BMI) was collected. Heart sound quality was visually assessed and its relation to phone version and users' characteristics was analysed. A total of 1148 users contributed to 7597 heart sound recordings. Over 80% of users were able to make at least one good-quality recording. Good-, unsure- and bad-quality recordings amounted to 5647 (74.6%), 466 (6.2%) and 1457 (19.2%), respectively. Most good recordings were collected in the first three attempts of the users. Phone version did not significantly change the users' success rate of making a good recording, neither was sex in the first attempt (P = 0.41) or the first three attempts (P = 0.21). Success rate tended to decrease with age in the first attempt (P = 0.06) but not the first three attempts (P = 0.70). BMI did not significantly affect the heart sound quality in a single attempt (P = 0.73) or in three attempts (P = 0.14). Conclusion Smartphone can be used by non-medical users to record heart sounds in good quality. Age may affect heart sound recording, but hardware, sex, and BMI do not alter the recording.
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Affiliation(s)
- Hongxing Luo
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Pablo Lamata
- School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Salomé Bazin
- Cellule Studio, Leyton Studios, 15 Argall Avenue, London E107QE, UK
| | - Thea Bautista
- School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Natsuki Barclay
- School of Biomedical Engineering and Imaging Sciences, King's College London, 5th Floor Becket House, Lambeth Palace Road, London SE1 7EU, UK
| | - Mehrdad Shahmohammadi
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Jolijn M Lubrecht
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
| | - Frits W Prinzen
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
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Banning S, Höglinger M, Meyer D, Reich O. Evaluation of the Effect of a Multifunctional Telemedicine Device on Health Care Use and Costs: A Nonrandomized Pragmatic Trial. Telemed J E Health 2022; 29:510-517. [PMID: 36037076 DOI: 10.1089/tmj.2022.0166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Telemedicine health insurance models are highly prevalent in the Swiss health care system. Nevertheless, the potential of telemedicine is only partly being achieved, since current telemedicine health insurance models are limited to an initial contact by telephone and a gatekeeper role that organizes access to health care providers such as general practitioners, specialists, or hospitals. Against this background, a telemedicine device with diverse visual and auscultatory examination functions was made available to 2,000 telemedicine-insured clients. This device allowed diagnostic information to be sent to a medical care provider and used for telemedical consultation. Objective: To determine whether the additional implementation of a multifunctional telemedicine examination device resulted in fewer physical consultations, reduced service utilization, and lower health care expenditures among telemedicine-insured clients. Methods: Our analysis is based on claims data from 135,636 clients insured in a telemedicine call center model covering the years 2019 and 2020. We compare the use of health care and health care costs of clients who received a telemedicine device with those without such a device, using multivariable regression to adjust for group differences due to self-selection. Results: We found lower total health care expenditures of -229 (Swiss Francs) and lower inpatient costs of -160 (Swiss Francs) on the part of clients with the telemedicine device. However, the implementation of the telemedicine device did not lead to a statistically significant reduction in service utilization. Conclusions: The treatment of telemedicine-insured clients was on average more cost-effective when they received the multifunctional telemedicine device. Accordingly, complementing the existing telemedicine model with telemedicine devices that allow for improved telemedical consultations has the potential to increase the cost-saving potential of the existing telemedicine call center models.
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Affiliation(s)
- Stefan Banning
- Department of Health Sciences, University of Applied Sciences, Fulda, Germany
| | - Marc Höglinger
- Health Services Research, Winterthur Institute of Health Economics, Zurich University of Applied Sciences, Winterthur, Switzerland
| | - Delia Meyer
- Controlling and Swica Health Insurance, Winterthur, Switzerland
| | - Oliver Reich
- Santé24, Swica Health Insurance, Winterthur, Switzerland
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Yazıcıoğlu B, Bakkaloğlu SA. Impact of coronavirus disease-2019 on pediatric nephrology practice and education: an ESPN survey. Pediatr Nephrol 2022; 37:1867-1875. [PMID: 34971403 PMCID: PMC8929721 DOI: 10.1007/s00467-021-05226-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 07/02/2021] [Accepted: 07/02/2021] [Indexed: 01/31/2023]
Abstract
BACKGROUND Coronavirus disease-2019 (COVID-19) has been challenging for patients and medical staff. Radical changes have been needed to prevent disruptions in patient care and medical education. METHODS A web-based survey was sent to European Society for Pediatric Nephrology (ESPN) members via the ESPN mailing list to evaluate the effects of the COVID-19 pandemic on delivery of pediatric nephrology (PN) care and educational activities. There were ten questions with subheadings. RESULTS Seventy-six centers from 24 countries completed the survey. The time period was between the beginning of the pandemic and May 30, 2020. The number of patients admitted in PN wards and outpatient clinics were significantly decreased (2.2 and 4.5 times, respectively). Telemedicine tools, electronic prescriptions, online applications for off-label drugs, and remote access to laboratory/imaging results were used in almost half of the centers. Despite staff training and protective measures, 33% of centers reported COVID-19 infected staff, and 29% infected patients. Difficulties in receiving pharmaceuticals were reported in 25% of centers. Sixty percent of centers suspended living-related kidney transplantation, and one-third deceased-donor kidney transplantation. Hands-on education was suspended in 91% of medical schools, and face-to-face teaching was replaced by online systems in 85%. Multidisciplinary training in PN was affected in 54% of the centers. CONCLUSIONS This survey showed a sharp decline in patient admissions and a significant decrease in kidney transplantation. Telemedicine and online teaching became essential tools, requiring integration into the current system. The prolonged and fluctuating course of the pandemic may pose additional challenges necessitating urgent and rational solutions.
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Affiliation(s)
- Burcu Yazıcıoğlu
- Department of Pediatric Nephrology, Gazi University School of Medicine, Ankara, Turkey
| | - Sevcan A Bakkaloğlu
- Department of Pediatric Nephrology, Gazi University School of Medicine, Ankara, Turkey.
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Barua PD, Karasu M, Kobat MA, Balık Y, Kivrak T, Baygin M, Dogan S, Demir FB, Tuncer T, Tan RS, Acharya UR. An accurate valvular heart disorders detection model based on a new dual symmetric tree pattern using stethoscope sounds. Comput Biol Med 2022; 146:105599. [DOI: 10.1016/j.compbiomed.2022.105599] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/28/2022] [Accepted: 05/06/2022] [Indexed: 02/02/2023]
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Koerber RM, Vaccarello L, Ho A. The Intelligibility of the Reversed-Stethoscope Technique in Age-Related Hearing Loss. Can Geriatr J 2022; 25:127-133. [PMID: 35747410 PMCID: PMC9156421 DOI: 10.5770/cgj.25.527] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background This study evaluated the effectiveness of the reverse stethoscope technique in improving speech intelligibility. In this technique, a clinician places the earpieces of their stethoscope into the ears of a hearing-impaired patient and speaks into the chest piece. Methods The International Speech Test Signal was presented to four Littman® stethoscope models and a Pocketalker® personal voice amplifier using an Audioscan® hearing instrument test box. The acoustic outputs of the stethoscopes and voice amplifier were measured across the frequency spectrum of speech. The Speech Intelligibility Index of the resulting speech was calculated for natural speech and for each device in relation to 10 standardized hearing losses representing the population of older adults. Results For each of the 10 hearing losses, the speech signal emitted by the stethoscopes was quieter and yielded lower speech intelligibility scores than regular speech. In contrast, the voice amplifier provided mid- and high-frequency amplification and improved speech intelligibility scores for all but the mildest hearing losses. Conclusions The reverse stethoscope technique worsens the clarity of speech and should not be used with older, hearing-impaired patients. Instead, clinicians should use regular speech or, preferably, personal voice amplifiers.
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Affiliation(s)
- Raphaelle M Koerber
- Michael G. DeGroote School of Medicine, McMaster University, Waterloo Regional Campus, Kitchener, ON
| | | | - Allan Ho
- Division of Otolaryngology Head and Neck Surgery, University of Alberta Faculty of Medicine and Dentistry, Edmonton, AB
- Edmonton Ear Clinic, Edmonton, AB
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Ahmed S, Sultana S, Khan AM, Islam MS, Habib GMM, McLane IM, McCollum ED, Baqui AH, Cunningham S, Nair H. Digital auscultation as a diagnostic aid to detect childhood pneumonia: A systematic review. J Glob Health 2022; 12:04033. [PMID: 35493777 PMCID: PMC9024283 DOI: 10.7189/jogh.12.04033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background Frontline health care workers use World Health Organization Integrated Management of Childhood Illnesses (IMCI) guidelines for child pneumonia care in low-resource settings. IMCI guideline pneumonia diagnostic criterion performs with low specificity, resulting in antibiotic overtreatment. Digital auscultation with automated lung sound analysis may improve the diagnostic performance of IMCI pneumonia guidelines. This systematic review aims to summarize the evidence on detecting adventitious lung sounds by digital auscultation with automated analysis compared to reference physician acoustic analysis for child pneumonia diagnosis. Methods In this review, articles were searched from MEDLINE, Embase, CINAHL Plus, Web of Science, Global Health, IEEExplore database, Scopus, and the ClinicalTrial.gov databases from the inception of each database to October 27, 2021, and reference lists of selected studies and relevant review articles were searched manually. Studies reporting diagnostic performance of digital auscultation and/or computerized lung sound analysis compared against physicians’ acoustic analysis for pneumonia diagnosis in children under the age of 5 were eligible for this systematic review. Retrieved citations were screened and eligible studies were included for extraction. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. All these steps were independently performed by two authors and disagreements between the reviewers were resolved through discussion with an arbiter. Narrative data synthesis was performed. Results A total of 3801 citations were screened and 46 full-text articles were assessed. 10 studies met the inclusion criteria. Half of the studies used a publicly available respiratory sound database to evaluate their proposed work. Reported methodologies/approaches and performance metrics for classifying adventitious lung sounds varied widely across the included studies. All included studies except one reported overall diagnostic performance of the digital auscultation/computerised sound analysis to distinguish adventitious lung sounds, irrespective of the disease condition or age of the participants. The reported accuracies for classifying adventitious lung sounds in the included studies varied from 66.3% to 100%. However, it remained unclear to what extent these results would be applicable for classifying adventitious lung sounds in children with pneumonia. Conclusions This systematic review found very limited evidence on the diagnostic performance of digital auscultation to diagnose pneumonia in children. Well-designed studies and robust reporting are required to evaluate the accuracy of digital auscultation in the paediatric population.
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Affiliation(s)
- Salahuddin Ahmed
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Projahnmo Research Foundation, Dhaka, Bangladesh
| | | | - Ahad M Khan
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Projahnmo Research Foundation, Dhaka, Bangladesh
| | - Mohammad S Islam
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Child Health Research Foundation, Dhaka, Bangladesh
| | - GM Monsur Habib
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Bangladesh Primary Care Respiratory Society, Khulna, Bangladesh
| | | | - Eric D McCollum
- Global Program for Pediatric Respiratory Sciences, Eudowood Division of Paediatric Respiratory Sciences, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Abdullah H Baqui
- Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Steven Cunningham
- Department of Child Life and Health, Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
| | - Harish Nair
- Usher Institute, University of Edinburgh, Edinburgh, UK
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A Neural Network-Based Method for Respiratory Sound Analysis and Lung Disease Detection. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: Respiratory sound analysis represents a research topic of growing interest in recent times. In fact, in this area, there is the potential to automatically infer the abnormalities in the preliminary stages of a lung dysfunction. Methods: In this paper, we propose a method to analyse respiratory sounds in an automatic way. The aim is to show the effectiveness of machine learning techniques in respiratory sound analysis. A feature vector is gathered directly from breath audio and, thus, by exploiting supervised machine learning techniques, we detect if the feature vector is related to a patient affected by a lung disease. Moreover, the proposed method is able to characterise the lung disease in asthma, bronchiectasis, bronchiolitis, chronic obstructive pulmonary disease, pneumonia, and lower or upper respiratory tract infection. Results: A retrospective experimental analysis on 126 patients with 920 recording sessions showed the effectiveness of the proposed method. Conclusion: The experimental analysis demonstrated that it is possible to detect lung disease by exploiting machine learning techniques. We considered several supervised machine learning algorithms, obtaining the most interesting performance with the neural network model, with an F-Measure of 0.983 in lung disease detection and equal to 0.923 in lung disease characterisation, increasing the state-of-the-art performance.
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Heart sound classification using signal processing and machine learning algorithms. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2021.100206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Mohamadou Y, Kapen PT, Foutse M, Kamga ALK, Docna O, Mohammad M, Ahmad M, Rabbani KSE. Design and development of a phonocardiograph for telemedicine applications. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00646-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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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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Design of a Novel Medical Acoustic Sensor Based on MEMS Bionic Fish Ear Structure. MICROMACHINES 2022; 13:mi13020163. [PMID: 35208288 PMCID: PMC8880548 DOI: 10.3390/mi13020163] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 02/01/2023]
Abstract
High-performance medical acoustic sensors are essential in medical equipment and diagnosis. Commercially available medical acoustic sensors are capacitive and piezoelectric types. When they are used to detect heart sound signals, there is attenuation and distortion due to the sound transmission between different media. This paper proposes a new bionic acoustic sensor based on the fish ear structure. Through theoretical analysis and finite element simulation, the optimal parameters of the sensitive structure are determined. The sensor is fabricated using microelectromechanical systems (MEMS) technology, and is encapsulated in castor oil, which has an acoustic impedance close to the human body. An electroacoustic test platform is built to test the performance of the sensor. The results showed that the MEMS bionic sensor operated with a bandwidth of 20–2k Hz. Its linearity and frequency responses were better than the electret microphone. In addition, the sensor was tested for heart sound collection application to verify its effectiveness. The proposed sensor can be effectively used in clinical auscultation and has a high SNR.
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Nileshwar A, Ahuja V, Kini P. Evaluation of the electronic stethoscope (FONODOC) as a cardiac screening tool during the preoperative evaluation of children. Indian J Anaesth 2022; 66:625-630. [PMID: 36388445 PMCID: PMC9662099 DOI: 10.4103/ija.ija_305_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/17/2022] [Accepted: 08/31/2022] [Indexed: 11/23/2022] Open
Abstract
Background and Aims: An electronic stethoscope with an inbuilt phonocardiogram is a potentially useful tool for paediatric cardiac evaluation in a resource-limited setting. We aimed to compare the acoustic and electronic stethoscopes with respect to the detection of murmurs as compared to the transthoracic echocardiogram (TTE). Methods: This was an observational study. Fifty children aged 0–12 years with congenital heart diseases (CHDs) and 50 without CHD scheduled for echocardiography were examined using both stethoscopes. The findings were corroborated with clinical findings and compared with the echocardiography report. Results: Among the 50 cases without CHD, no murmur was detected using either of the stethoscopes. This was in agreement with TTE findings. The calculated specificity of both stethoscopes was 100%. Amongst the 50 cases with CHD, the electronic stethoscope picked up murmurs in 32 cases and missed 18 cases. The acoustic stethoscope picked up murmurs in 29 cases and missed 21 cases. Thus, the sensitivity of electronic and acoustic stethoscopes as compared to TTE was calculated to be 64% and 58%, respectively. The positive predictive value of the electronic stethoscope as compared to TTE was 100% while the negative predictive value was 73%. The kappa statistic was 0.93 suggesting agreement in 93%. Mc-Nemar’s test value was 0.24 suggesting that the electronic stethoscope did not offer any advantage over the acoustic stethoscope for the detection of CHD in children. Conclusion: A comparison of the electronic stethoscope with an acoustic stethoscope suggests that the rate of detection of CHD with both stethoscopes is similar and echocardiography remains the gold standard.
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A simple proposition for heart sound signal de-noising for effective components identification in normal and abnormal cases. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Wearable Bluetooth Triage Healthcare Monitoring System. SENSORS 2021; 21:s21227586. [PMID: 34833659 PMCID: PMC8619240 DOI: 10.3390/s21227586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/09/2021] [Accepted: 11/11/2021] [Indexed: 11/17/2022]
Abstract
Triage is the first interaction between a patient and a nurse/paramedic. This assessment, usually performed at Emergency departments, is a highly dynamic process and there are international grading systems that according to the patient condition initiate the patient journey. Triage requires an initial rapid assessment followed by routine checks of the patients’ vitals, including respiratory rate, temperature, and pulse rate. Ideally, these checks should be performed continuously and remotely to reduce the workload on triage nurses; optimizing tools and monitoring systems can be introduced and include a wearable patient monitoring system that is not at the expense of the patient’s comfort and can be remotely monitored through wireless connectivity. In this study, we assessed the suitability of a small ceramic piezoelectric disk submerged in a skin-safe silicone dome that enhances contact with skin, to detect wirelessly both respiration and cardiac events at several positions on the human body. For the purposes of this evaluation, we fitted the sensor with a respiratory belt as well as a single lead ECG, all acquired simultaneously. To complete Triage parameter collection, we also included a medical-grade contact thermometer. Performances of cardiac and respiratory events detection were assessed. The instantaneous heart and respiratory rates provided by the proposed sensor, the ECG and the respiratory belt were compared via statistical analyses. In all considered sensor positions, very high performances were achieved for the detection of both cardiac and respiratory events, except for the wrist, which provided lower performances for respiratory rates. These promising yet preliminary results suggest the proposed wireless sensor could be used as a wearable, hands-free monitoring device for triage assessment within emergency departments. Further tests are foreseen to assess sensor performances in real operating environments.
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Lee SH, Kim YS, Yeo WH. Advances in Microsensors and Wearable Bioelectronics for Digital Stethoscopes in Health Monitoring and Disease Diagnosis. Adv Healthc Mater 2021; 10:e2101400. [PMID: 34486237 DOI: 10.1002/adhm.202101400] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/19/2021] [Indexed: 12/13/2022]
Abstract
Acoustic stethoscopes have demonstrated beneficial factors aiding diagnosis from the doctors with accurate body sounds. Still, the conventional acoustic stethoscopes require a substantial amount of clinical experience and hearing skills for the physicians to accurately diagnose symptoms from abnormal sounds. Especially for cardiopulmonary systems, it is crucial to collect sounds with precision since they contain valuable information in specific frequency ranges for various sounds. This review paper summarizes recent advances and technical developments in microsensors, circuits, chips, and integrated electronics for fabricating different digital stethoscopes that offer portable detection of body sounds. They solve the limitations of conventional stethoscopes, aiming for wireless auscultation in digitized medicine. Overall, this comprehensive review will help researchers design and develop new wearable electronics and digital stethoscopes for advancing human healthcare, continuous monitoring, and better diagnosis.
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Affiliation(s)
- Sung Hoon Lee
- School of Electrical and Computer Engineering and Center for Human-Centric Interfaces and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Yun-Soung Kim
- George W. Woodruff School of Mechanical Engineering and Center for Human-Centric Interfaces and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering and Center for Human-Centric Interfaces and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Parker H. Petit Institute for Bioengineering and Biosciences, Neural Engineering Center, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, 30332, USA
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40
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Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review. INFORMATICS 2021. [DOI: 10.3390/informatics8040073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.
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Abstract
Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive. Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes. We present the literature review of non-invasive sound acquisition devices and techniques. The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope. Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.
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Mohamed N, Kim HS, Kang KM, Mohamed M, Kim SH, Kim JG. Heart and Lung Sound Measurement Using an Esophageal Stethoscope with Adaptive Noise Cancellation. SENSORS 2021; 21:s21206757. [PMID: 34695968 PMCID: PMC8540990 DOI: 10.3390/s21206757] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 09/26/2021] [Accepted: 09/29/2021] [Indexed: 01/23/2023]
Abstract
In surgeries where general anesthesia is required, the auscultation of heart and lung sounds is essential to provide information on the patient's cardiorespiratory system. Heart and lung sounds can be recorded using an esophageal stethoscope; however, there is huge background noise when this device is used in an operating room. In this study, a digital esophageal stethoscope system was designed. A 3D-printed case filled with Polydimethylsiloxane material was designed to hold two electret-type microphones. One of the microphones was placed inside the printed case to collect the heart and lung sound signals coming out from the patient through the esophageal catheter, the other was mounted on the surface of the case to collect the operating room sounds. A developed adaptive noise canceling algorithm was implemented to remove the operating room noise corrupted with the main heart and lung sound signals and the output signal was displayed on software application developed especially for this study. Using the designed case, the noise level of the signal was reduced to some extent, and by adding the adaptive filter, further noise reduction was achieved. The designed system is lightweight and can provide noise-free heart and lung sound signals.
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Affiliation(s)
- Nourelhuda Mohamed
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Korea; (N.M.); (M.M.)
| | - Hyun-Seok Kim
- Biomedical Engineering Research Center, Asan Institute for Life Science, Asan Medical Center, Seoul 05505, Korea;
| | - Kyu-Min Kang
- Department of Anesthesiology and Pain Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea;
| | - Manal Mohamed
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Korea; (N.M.); (M.M.)
| | - Sung-Hoon Kim
- Department of Anesthesiology and Pain Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea;
- Correspondence: (S.-H.K.); (J.G.K.); Tel.: +82-02-3010-3868 (S.-H.K.); +82-10-9384-7296 (J.G.K.)
| | - Jae Gwan Kim
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Korea; (N.M.); (M.M.)
- Correspondence: (S.-H.K.); (J.G.K.); Tel.: +82-02-3010-3868 (S.-H.K.); +82-10-9384-7296 (J.G.K.)
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Kim Y, Hyon Y, Jung SS, Lee S, Yoo G, Chung C, Ha T. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci Rep 2021; 11:17186. [PMID: 34433880 PMCID: PMC8387488 DOI: 10.1038/s41598-021-96724-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 08/12/2021] [Indexed: 11/09/2022] Open
Abstract
Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of respiratory sounds requires clinician's considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. To overcome such limitations, we tried to develop an automated classification of breath sounds. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy; the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases.
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Affiliation(s)
- Yoonjoo Kim
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - YunKyong Hyon
- Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Sung Soo Jung
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Sunju Lee
- Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Geon Yoo
- Clinical Research Division, National Institute of Food and Drug Safety Evaluation, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Chaeuk Chung
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Republic of Korea. .,Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon, 35015, Republic of Korea.
| | - Taeyoung Ha
- Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea.
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Hoodbhoy Z, Jiwani U, Sattar S, Salam R, Hasan B, Das JK. Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis. Front Artif Intell 2021; 4:708365. [PMID: 34308341 PMCID: PMC8297386 DOI: 10.3389/frai.2021.708365] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 06/28/2021] [Indexed: 12/23/2022] Open
Abstract
Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD. Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to generate the hierarchical Summary ROC (HSROC) curve. Results: We included 16 studies (1217 participants) that used ML algorithm to diagnose CHD. Neural networks were used in seven studies with overall sensitivity of 90.9% (95% CI 85.2-94.5%) and specificity was 92.7% (95% CI 86.4-96.2%). Other ML models included ensemble methods, deep learning and clustering techniques but did not have sufficient number of studies for a meta-analysis. Majority (n=11, 69%) of studies had a high risk of patient selection bias, unclear bias on index test (n=9, 56%) and flow and timing (n=12, 75%) while low risk of bias was reported for the reference standard (n=10, 62%). Conclusion: ML models such as neural networks have the potential to diagnose CHD accurately without the need for trained personnel. The heterogeneity of the diagnostic modalities used to train these models and the heterogeneity of the CHD diagnoses included between the studies is a major limitation.
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Affiliation(s)
- Zahra Hoodbhoy
- Department of Pediatrics and Child Health at the Aga Khan University, Karachi, Pakistan
| | - Uswa Jiwani
- Department of Pediatrics and Child Health at the Aga Khan University, Karachi, Pakistan
| | - Saima Sattar
- Department of Pediatrics and Child Health at the Aga Khan University, Karachi, Pakistan
| | - Rehana Salam
- Department of Pediatrics and Child Health at the Aga Khan University, Karachi, Pakistan
| | - Babar Hasan
- Department of Pediatrics and Child Health at the Aga Khan University, Karachi, Pakistan
| | - Jai K Das
- Department of Pediatrics and Child Health at the Aga Khan University, Karachi, Pakistan
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45
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Balogh M, Koch F, Siver L, Krima A, Vörös K. Digital phonocardiography of cardiac arrhythmias in dogs - Preliminary experiences. Acta Vet Hung 2021; 69:116-124. [PMID: 34270460 DOI: 10.1556/004.2021.00024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/09/2021] [Indexed: 11/19/2022]
Abstract
Electronic stethoscopes and digital phonocardiograms (DPCGs) can be applied when diagnosing cardiac murmurs, but their use for cardiac arrhythmias is not described in veterinary medicine. Data of 10 dogs are presented in this preliminary study, demonstrating the applicability of these techniques. Although the number of artefacts and the amount of baseline noise produced by the two digitising systems used did not differ, the Welch Allyn Meditron system or similar ones capable of simultaneous recording of electrocardiograms (ECGs) and DPCGs provide a better option for clinical research and education, whilst the 3M Littmann 3200 system might be more suitable for everyday clinical settings. A combined system with simultaneous phonocardiogram and ECG, especially with wireless transmission, might be a solution in the future.
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Affiliation(s)
- Márton Balogh
- 1Department and Clinic of Internal Medicine, University of Veterinary Medicine Budapest, István u. 2, H-1078 Budapest, Hungary
| | | | | | | | - Károly Vörös
- 1Department and Clinic of Internal Medicine, University of Veterinary Medicine Budapest, István u. 2, H-1078 Budapest, Hungary
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46
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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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47
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Jani V, Danford DA, Thompson WR, Schuster A, Manlhiot C, Kutty S. The discerning ear: cardiac auscultation in the era of artificial intelligence and telemedicine. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:456-466. [PMID: 36713594 PMCID: PMC9707892 DOI: 10.1093/ehjdh/ztab059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 06/19/2021] [Indexed: 02/01/2023]
Abstract
Heart murmur, a thoracic auscultatory finding of cardiovascular origin, is extremely common in childhood and can appear at any age from premature newborn to late adolescence. The objective of this review is to provide a modern examination and update of cardiac murmur auscultation in this new era of artificial intelligence (AI) and telemedicine. First, we provide a comprehensive review of the causes and differential diagnosis, clinical features, evaluation, and long-term management of paediatric heart murmurs. Next, we provide a brief history of computer-assisted auscultation and murmur analysis, along with insight into the engineering design of the digital stethoscope. We conclude with a discussion of the paradigm shifting impact of deep learning on murmur analysis, AI-assisted auscultation, and the implications of these technologies on telemedicine in paediatric cardiology. It is our hope that this article provides an updated perspective on the impact of AI on cardiac auscultation for the modern paediatric cardiologist.
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Affiliation(s)
- Vivek Jani
- Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine, M2315, 1800 Orleans St, Baltimore, MD 21287, USA
| | - David A Danford
- Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine, M2315, 1800 Orleans St, Baltimore, MD 21287, USA
| | - W Reid Thompson
- Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine, M2315, 1800 Orleans St, Baltimore, MD 21287, USA
| | - Andreas Schuster
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, 37077 Göttingen, Germany
| | - Cedric Manlhiot
- Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine, M2315, 1800 Orleans St, Baltimore, MD 21287, USA
| | - Shelby Kutty
- Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine, M2315, 1800 Orleans St, Baltimore, MD 21287, USA,Corresponding author. Tel: +1 410 502 3350, Fax: +1 410 955 9897,
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48
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Gupta P, Wen H, Di Francesco L, Ayazi F. Detection of pathological mechano-acoustic signatures using precision accelerometer contact microphones in patients with pulmonary disorders. Sci Rep 2021; 11:13427. [PMID: 34183695 PMCID: PMC8238985 DOI: 10.1038/s41598-021-92666-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 06/11/2021] [Indexed: 11/09/2022] Open
Abstract
Monitoring pathological mechano-acoustic signals emanating from the lungs is critical for timely and cost-effective healthcare delivery. Adventitious lung sounds including crackles, wheezes, rhonchi, bronchial breath sounds, stridor or pleural rub and abnormal breathing patterns function as essential clinical biomarkers for the early identification, accurate diagnosis and monitoring of pulmonary disorders. Here, we present a wearable sensor module comprising of a hermetically encapsulated, high precision accelerometer contact microphone (ACM) which enables both episodic and longitudinal assessment of lung sounds, breathing patterns and respiratory rates using a single integrated sensor. This enhanced ACM sensor leverages a nano-gap transduction mechanism to achieve high sensitivity to weak high frequency vibrations occurring on the surface of the skin due to underlying lung pathologies. The performance of the ACM sensor was compared to recordings from a state-of-art digital stethoscope, and the efficacy of the developed system is demonstrated by conducting an exploratory research study aimed at recording pathological mechano-acoustic signals from hospitalized patients with a chronic obstructive pulmonary disease (COPD) exacerbation, pneumonia, and acute decompensated heart failure. This unobtrusive wearable system can enable both episodic and longitudinal evaluation of lung sounds that allow for the early detection and/or ongoing monitoring of pulmonary disease.
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Affiliation(s)
- Pranav Gupta
- Georgia Institute of Technology, Atlanta, GA, 30308, USA.
| | - Haoran Wen
- StethX Microsystems, Atlanta, GA, 30308, USA
| | - Lorenzo Di Francesco
- Department of Medicine, Division of General Internal Medicine, Emory University, Atlanta, GA, 30303, USA
| | - Farrokh Ayazi
- Ken Byers Professor in Microsystems, Georgia Institute of Technology, Atlanta, GA, 30308, USA.
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Rennoll V, McLane I, Emmanouilidou D, West J, Elhilali M. Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope. IEEE J Biomed Health Inform 2021; 25:1542-1549. [PMID: 32870803 PMCID: PMC7917155 DOI: 10.1109/jbhi.2020.3020494] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Electronic stethoscopes offer several advantages over conventional acoustic stethoscopes, including noise reduction, increased amplification, and ability to store and transmit sounds. However, the acoustical characteristics of electronic and acoustic stethoscopes can differ significantly, introducing a barrier for clinicians to transition to electronic stethoscopes. This work proposes a method to process lung sounds recorded by an electronic stethoscope, such that the sounds are perceived to have been captured by an acoustic stethoscope. The proposed method calculates an electronic-to-acoustic stethoscope filter by measuring the difference between the average frequency responses of an acoustic and an electronic stethoscope to multiple lung sounds. To validate the method, a change detection experiment was conducted with 51 medical professionals to compare filtered electronic, unfiltered electronic, and acoustic stethoscope lung sounds. Participants were asked to detect when transitions occurred in sounds comprising several sections of the three types of recordings. Transitions between the filtered electronic and acoustic stethoscope sections were detected, on average, by chance (sensitivity index equal to zero) and also detected significantly less than transitions between the unfiltered electronic and acoustic stethoscope sections ( ), demonstrating the effectiveness of the method to filter electronic stethoscopes to mimic an acoustic stethoscope. This processing could incentivize clinicians to adopt electronic stethoscopes by providing a means to shift between the sound characteristics of acoustic and electronic stethoscopes in a single device, allowing for a faster transition to new technology and greater appreciation for the electronic sound quality.
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50
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Soto-Murillo MA, Galván-Tejada JI, Galván-Tejada CE, Celaya-Padilla JM, Luna-García H, Magallanes-Quintanar R, Gutiérrez-García TA, Gamboa-Rosales H. Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods. Healthcare (Basel) 2021; 9:317. [PMID: 33809283 PMCID: PMC7999739 DOI: 10.3390/healthcare9030317] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 02/25/2021] [Accepted: 03/04/2021] [Indexed: 11/16/2022] Open
Abstract
The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.
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Affiliation(s)
- Manuel A. Soto-Murillo
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Jorge I. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Carlos E. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Jose M. Celaya-Padilla
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Huizilopoztli Luna-García
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Rafael Magallanes-Quintanar
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
| | - Tania A. Gutiérrez-García
- Departamento de Ciencias Computacionales, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara, Jalisco 44430, Mexico;
| | - Hamurabi Gamboa-Rosales
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (M.A.S.-M.); (C.E.G.-T.); (J.M.C.-P.); (H.L.-G.); (R.M.-Q.); (H.G.-R.)
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