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Manshadi OD, Mihandoost S. Murmur identification and outcome prediction in phonocardiograms using deep features based on Stockwell transform. Sci Rep 2024; 14:7592. [PMID: 38555390 PMCID: PMC10981708 DOI: 10.1038/s41598-024-58274-6] [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: 12/17/2023] [Accepted: 03/27/2024] [Indexed: 04/02/2024] Open
Abstract
Traditionally, heart murmurs are diagnosed through cardiac auscultation, which requires specialized training and experience. The purpose of this study is to predict patients' clinical outcomes (normal or abnormal) and identify the presence or absence of heart murmurs using phonocardiograms (PCGs) obtained at different auscultation points. A semi-supervised model tailored to PCG classification is introduced in this study, with the goal of improving performance using time-frequency deep features. The study begins by investigating the behavior of PCGs in the time-frequency domain, utilizing the Stockwell transform to convert the PCG signal into two-dimensional time-frequency maps (TFMs). A deep network named AlexNet is then used to derive deep feature sets from these TFMs. In feature reduction, redundancy is eliminated and the number of deep features is reduced to streamline the feature set. The effectiveness of the extracted features is evaluated using three different classifiers using the CinC/Physionet challenge 2022 dataset. For Task I, which focuses on heart murmur detection, the proposed approach achieved an average accuracy of 93%, sensitivity of 91%, and F1-score of 91%. According to Task II of the CinC/Physionet challenge 2022, the approach showed a clinical outcome cost of 5290, exceeding the benchmark set by leading methods in the challenge.
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Affiliation(s)
| | - Sara Mihandoost
- Department of Electrical Engineering, Urmia University of Technology, Urmia, Iran.
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Zhang L, Cheng Z, Xu D, Wang Z, Cai S, Hu N, Ma J, Mei X. Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study. BMJ Open 2024; 14:e074288. [PMID: 38553085 PMCID: PMC10982737 DOI: 10.1136/bmjopen-2023-074288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 03/11/2024] [Indexed: 04/02/2024] Open
Abstract
INTRODUCTION Mitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for large-scale MR screening. Cardiac auscultation with electronic stethoscope and artificial intelligence (AI) can be a fast and economical modality for assessing MR severity. Our objectives are (1) to establish a deep neural network (DNN)-based cardiac auscultation method for assessing the severity of MR; and (2) to quantitatively measure the performance of the developed AI-based MR assessment method by virtual clinical trial. METHODS AND ANALYSIS In a cross-sectional design, phonocardiogram will be recorded at the mitral valve auscultation area of outpatients. The enrolled patients will be checked by echocardiography to confirm the diagnosis of MR or no MR. Echocardiographic parameters will be used as gold standard to assess the severity of MR, classified into four levels: none, mild, moderate and severe. The study consists of two stages. First, an MR-related cardiac sound database will be created on which a DNN-based MR severity classifier will be trained. The automatic MR severity classifier will be integrated with the Smartho-D2 electronic stethoscope. Second, the performance of the developed smart device will be assessed in an independent clinical validation data set. Sensitivity, specificity, precision, accuracy and F1 score of the developed smart MR assessment device will be evaluated. Agreement on the performance of the smart device between cardiologist users and patient users will be inspected. The interpretability of the developed model will also be studied with statistical comparisons of occlusion map-guided variables among the four severity groups. ETHICS AND DISSEMINATION The study protocol was approved by the Medical Ethics Committee of Huzhou Central Hospital, China (registration number: 202302009-01). Informed consent is required from all participants. Dissemination will be through conference presentations and peer-reviewed journals. TRIAL REGISTRATION NUMBER ChiCTR2300069496.
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Affiliation(s)
- Li Zhang
- Department of Cardiology, Huzhou Central Hospital, The Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, The Affiliated Central Hospital of Huzhou University, Huzhou, Zhejiang, China
| | - Zhenfeng Cheng
- Department of Cardiology, Huzhou Central Hospital, The Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, The Affiliated Central Hospital of Huzhou University, Huzhou, Zhejiang, China
| | - Dongyang Xu
- Center for Intelligent Acoustics and Signal Processing, Huzhou Institute of Zhejiang University, Huzhou, Zhejiang, China
| | - Zhi Wang
- Center for Intelligent Acoustics and Signal Processing, Huzhou Institute of Zhejiang University, Huzhou, Zhejiang, China
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shengsheng Cai
- Center for Intelligent Acoustics and Signal Processing, Huzhou Institute of Zhejiang University, Huzhou, Zhejiang, China
- Suzhou Melodicare Medical Technology Co., Ltd, Suzhou, Jiangsu, China
| | - Nan Hu
- School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China
| | - Jianming Ma
- Administration Office, Huzhou Central Hospital, The Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, The Affiliated Central Hospital of Huzhou University, Huzhou, Zhejiang, China
| | - Xueqin Mei
- Department of Medical Engineering, Huzhou Central Hospital, The Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, The Affiliated Central Hospital of Huzhou University, Huzhou, Zhejiang, China
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Jiang H, Zong D, Song Q, Gao K, Shao H, Liu Z, Tian J. Coal-gangue recognition via multi-branch convolutional neural network based on MFCC in noisy environment. Sci Rep 2023; 13:6541. [PMID: 37085691 PMCID: PMC10121578 DOI: 10.1038/s41598-023-33351-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 04/12/2023] [Indexed: 04/23/2023] Open
Abstract
Traditional coal-gangue recognition methods usually do not consider the impact of equipment noise, which severely limits its adaptability and recognition accuracy. This paper mainly studies the more accurate recognition of coal-gangue in the noise site environment with the operation of shearer, conveyor, transfer machine and other device in the process of top coal caving. Mel Frequency Cepstrum Coefficients (MFCC) smoothing method was introduced to express the intrinsic feature of sound pressure more clearly in the coal-gangue recognition site. Then, a multi-branch convolution neural network (MBCNN) model with three branches was developed, and the smoothed MFCC feature was incorporated into this model to realize the recognition of falling coal and gangue in noisy environment. The sound pressure signal datasets under the operation of different device were constructed through a great deal of laboratory and site data acquisition. Comparative experiments were carried out on noiseless dataset, single noise dataset and simulated site dataset, and the results show that our method can provide higher correct recognition accuracy and better robustness. The proposed coal-gangue recognition approach based on MBCNN and MFCC smoothing can not only recognize the state of falling coal or gangue, but also recognize the operational state of site device.
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Affiliation(s)
- HaiYan Jiang
- Department of Intelligent Equipment, Shandong University of Science & Technology, Taian, 271000, China
| | - DaShuai Zong
- Department of Intelligent Equipment, Shandong University of Science & Technology, Taian, 271000, China
| | - QingJun Song
- Department of Intelligent Equipment, Shandong University of Science & Technology, Taian, 271000, China.
| | - KuiDong Gao
- Shandong Province Key Laboratory of Mine Mechanical Engineering, Shandong University of Science & Technology, Qingdao, 266590, China
| | - HuiZhi Shao
- Hong Kong Baptist University, Hong Kong, China
| | - ZhiJiang Liu
- Department of Intelligent Equipment, Shandong University of Science & Technology, Taian, 271000, China
| | - Jing Tian
- Taihe Electric Power Co. Ltd, Taian, 271000, Shandong, China
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Wang W, Wang S, Qin D, Fang Y, Zheng Y. Heart-lung sound separation by nonnegative matrix factorization and deep learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Tuncer T, Dogan S, Tan RS, Acharya UR. Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.01.088] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals. ENTROPY 2021; 23:e23060642. [PMID: 34064025 PMCID: PMC8224099 DOI: 10.3390/e23060642] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/14/2021] [Accepted: 05/15/2021] [Indexed: 11/17/2022]
Abstract
Heart sound signals reflect valuable information about heart condition. Previous studies have suggested that the information contained in single-channel heart sound signals can be used to detect coronary artery disease (CAD). But accuracy based on single-channel heart sound signal is not satisfactory. This paper proposed a method based on multi-domain feature fusion of multi-channel heart sound signals, in which entropy features and cross entropy features are also included. A total of 36 subjects enrolled in the data collection, including 21 CAD patients and 15 non-CAD subjects. For each subject, five-channel heart sound signals were recorded synchronously for 5 min. After data segmentation and quality evaluation, 553 samples were left in the CAD group and 438 samples in the non-CAD group. The time-domain, frequency-domain, entropy, and cross entropy features were extracted. After feature selection, the optimal feature set was fed into the support vector machine for classification. The results showed that from single-channel to multi-channel, the classification accuracy has increased from 78.75% to 86.70%. After adding entropy features and cross entropy features, the classification accuracy continued to increase to 90.92%. The study indicated that the method based on multi-domain feature fusion of multi-channel heart sound signals could provide more information for CAD detection, and entropy features and cross entropy features played an important role in it.
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Mehrabbeik M, Rashidi S, Fallah A, Rafiei Khoshnood E. Phonocardiography-based mitral valve prolapse detection with using fractional fourier transform. Biomed Phys Eng Express 2020; 7. [PMID: 35090147 DOI: 10.1088/2057-1976/abcaab] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 11/16/2020] [Indexed: 11/11/2022]
Abstract
Mitral Valve Prolapse (MVP) is a common condition among people, which is often benign and does not need any serious treatment. However, this doesn't mean that MVP can't cause any problems. In malignant conditions, MVP can cause mitral failure and also heart failure. Early diagnosis of MVP is significantly important to control and reduce its complications. Since the phonocardiogram signal provides useful information about heart valves function, it can be used for MVP detection. To detect MVP, the signal was denoised and segmented into heart cycles and constant three-second pieces in the first and second approaches, respectively. Next, based on the Fractional Fourier Transform (FrFT), the desired features were extracted. Then, the extracted features were windowed by a Moving Logarithmic Median Window (MLMW) and optimum features were selected using Mahalanobis, Bhattacharyya, Canberra, and Minkowski distance criteria. Finally, using the selected features, classification was performed by using the K-Nearest Neighbor (KNN) and the Suppor Vector Machine (SVM) classifiers to find out whether a segment is prolapsed. The best results of the experiment on the collected database contain 15 prolapsed and 6 non-prolapsed subjects using the A-test method show 96.25 ± 2.43 accuracy, 98.5 ± 3.37 sensitivity, 94.0 ± 5.16 specificity, 96.0 ± 3.44 precision, 92.5 ± 4.86 kappa, and 96.6 ± 2.34 f-score with the SVM classifier.
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Affiliation(s)
- Mahtab Mehrabbeik
- Faculty of Biomedical Engineering, Amirkabir University, Tehran, Iran
| | - Saeid Rashidi
- Faculty of Medical Sciences & Technologies, Science & Research Branch, Islamic Azad University, Tehran, Iran
| | - Ali Fallah
- Faculty of Biomedical Engineering, Amirkabir University, Tehran, Iran
| | - Elaheh Rafiei Khoshnood
- Shahid Sadoughi University of Medical Sciences and Health Services, Medical School, Yazd, Iran
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Tiam Kapen P, Youssoufa M, Kouam Kouam SU, Foutse M, Tchamda AR, Tchuen G. Phonocardiogram: A robust algorithm for generating synthetic signals and comparison with real life ones. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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