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Sun S, Song W, Tong Y, Li X, Zhao M, Deng Q, Liu G, Liu Z, Liu C. A novel methodology for evaluation of S 2 wide split via estimated parameters. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107777. [PMID: 37714021 DOI: 10.1016/j.cmpb.2023.107777] [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: 09/11/2021] [Revised: 07/25/2023] [Accepted: 08/22/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Aimed at the shortcomings of using time interval ( [Formula: see text] ) between the sounds produced by the aortic valve closure (A2) and the pulmonary valve closure (P2) to detect the wide splitting of the second heart sound (S2), which are the [Formula: see text] easily influenced by the heartbeat and not easily distinguished from the fixed splitting of S2 without considering the entire respiratory phase, and from the third heart sound (S3), this study proposes a novel methodology to detect the wide splitting of S2 using an estimated split coefficient of S2 ( [Formula: see text] ) combined with an adaptive number (NAda) of S2. METHODOLOGY The methodology is orderly summarized as follows: Stage 1 describes the segmentation-based S2 automatic location and extraction. A Gaussian mixture model (GMM)-based regression model for S2 is proposed to estimate the positions of A2 and P2, then an overlapping rate (OLR)-based [Formula: see text] and the [Formula: see text] are estimated, and finally, a NAda-S2 is automatically determined to calculate the statistics of [Formula: see text] and [Formula: see text] . In stage 3, based on the combination of estimated features, the detection of wide splitting of S2 is determined. RESULTS The performance is evaluated using a total of 3350-period heart sounds from 72 patients, with an overall accuracy of 100%, F1=1 and a Cohen's kappa value (κ) of 1. DISCUSSION The significant contributions are highlighted: A novel GMM-based efficient methodology is proposed for estimating the characteristics of A2 and P2. A novel OLR-based [Formula: see text] is defined to replace the current state-of-the-art criterion for evaluating the split degree of S2. Considering respiration phases combined with CR are proposed for the high-precision diagnosis of S2 wide split.
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Affiliation(s)
- Shuping Sun
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China.
| | - Wei Song
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Yaonan Tong
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China.
| | - Xu Li
- School of Economic, Bohai University, Liaoning Jinzhou 121013, China
| | - Man Zhao
- School of Economic, Bohai University, Liaoning Jinzhou 121013, China
| | - Qi Deng
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Guangyu Liu
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Zhi Liu
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Chao Liu
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
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Phonocardiogram transfer learning-based CatBoost model for diastolic dysfunction identification using multiple domain-specific deep feature fusion. Comput Biol Med 2023; 156:106707. [PMID: 36871337 DOI: 10.1016/j.compbiomed.2023.106707] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 02/11/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023]
Abstract
Left ventricular diastolic dyfunction detection is particularly important in cardiac function screening. This paper proposed a phonocardiogram (PCG) transfer learning-based CatBoost model to detect diastolic dysfunction noninvasively. The Short-Time Fourier Transform (STFT), Mel Frequency Cepstral Coefficients (MFCCs), S-transform and gammatonegram were utilized to perform four different representations of spectrograms for learning the representative patterns of PCG signals in two-dimensional image modality. Then, four pre-trained convolutional neural networks (CNNs) such as VGG16, Xception, ResNet50 and InceptionResNetv2 were employed to extract multiple domain-specific deep features from PCG spectrograms using transfer learning, respectively. Further, principal component analysis and linear discriminant analysis (LDA) were applied to different feature subsets, respectively, and then these different selected features are fused and fed into CatBoost for classification and performance comparison. Finally, three typical machine learning classifiers such as multilayer perceptron, support vector machine and random forest were employed to compared with CatBoost. The hyperparameter optimization of the investigated models was determined through grid search. The visualized result of the global feature importance showed that deep features extracted from gammatonegram by ResNet50 contributed most to classification. Overall, the proposed multiple domain-specific feature fusion based CatBoost model with LDA achieved the best performance with an area under the curve of 0.911, accuracy of 0.882, sensitivity of 0.821, specificity of 0.927, F1-score of 0.892 on the testing set. The PCG transfer learning-based model developed in this study could aid in diastolic dysfunction detection and could contribute to non-invasive evaluation of diastolic function.
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Albiges T, Sabeur Z, Arbab-Zavar B. Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases. SENSORS (BASEL, SWITZERLAND) 2023; 23:1439. [PMID: 36772480 PMCID: PMC9921371 DOI: 10.3390/s23031439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome. The requirements for achieving scalable deployments of data-driven methods using artificial intelligence for meeting such a challenge in modern COPD healthcare have become of paramount and critical importance. In this study, we have established the experimental foundations for acquiring and indeed generating biomedical observation data, for good performance signal analysis and machine learning that will lead us to the intelligent diagnosis and monitoring of COPD conditions for individual patients. Further, we investigated on the multi-resolution analysis and compression of lung audio signals, while we performed their machine classification under two distinct experiments. These respectively refer to conditions involving (1) "Healthy" or "COPD" and (2) "Healthy", "COPD", or "Pneumonia" classes. Signal reconstruction with the extracted features for machine learning and testing was also performed for securing the integrity of the original audio recordings. These showed high levels of accuracy together with the performances of the selected machine learning-based classifiers using diverse metrics. Our study shows promising levels of accuracy in classifying Healthy and COPD and also Healthy, COPD, and Pneumonia conditions. Further work in this study will be imminently extended to new experiments using multi-modal sensing hardware and data fusion techniques for the development of the next generation diagnosis systems for COPD healthcare of the future.
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Affiliation(s)
| | - Zoheir Sabeur
- Department of Computing and Informatics, Bournemouth University, Bournemouth BH12 5BB, UK
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Zheng Y, Guo X, Wang Y, Qin J, Lv F. A multi-scale and multi-domain heart sound feature-based machine learning model for ACC/AHA heart failure stage classification. Physiol Meas 2022; 43. [PMID: 35512699 DOI: 10.1088/1361-6579/ac6d40] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/05/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Heart sounds can reflect detrimental changes in cardiac mechanical activity that are common pathological characteristics of chronic heart failure (CHF). The ACC/AHA heart failure (HF) stage classification is essential for clinical decision-making and the management of CHF. Herein, a machine learning model that makes use of multi-scale and multi-domain heart sound features was proposed to provide an objective aid for ACC/AHA HF stage classification. APPROACH A dataset containing phonocardiogram (PCG) signals from 275 subjects was obtained from two medical institutions and used in this study. Complementary ensemble empirical mode decomposition and tunable-Q wavelet transform were used to construct self-adaptive sub-sequences and multi-level sub-band signals for PCG signals. Time-domain, frequency-domain and nonlinear feature extraction were then applied to the original PCG signal, heart sound sub-sequences and sub-band signals to construct multi-scale and multi-domain heart sound features. The features selected via the least absolute shrinkage and selection operator were fed into a machine learning classifier for ACC/AHA HF stage classification. Finally, mainstream machine learning classifiers, including least-squares support vector machine (LS-SVM), deep belief network (DBN) and random forest (RF), were compared to determine the optimal model. MAIN RESULTS The results showed that the LS-SVM, which utilized a combination of multi-scale and multi-domain features, achieved better classification performance than the DBN and RF using multi-scale or multi-domain features alone or together, with average sensitivity, specificity, and accuracy of 0.821, 0.955 and 0.820 on the testing set, respectively. SIGNIFICANCE PCG signal analysis provides efficient measurement information regarding CHF severity and is a promising noninvasive method for ACC/AHA HF stage classification.
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Affiliation(s)
- Yineng Zheng
- Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, P.R.China, Chongqing, Chongqing, 400016, CHINA
| | - Xingming Guo
- Bioengineering College, Chongqing University, Chongqing 400044, Chongqing, 400044, CHINA
| | - Yingying Wang
- Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, P.R.China, Chongqing, Chongqing, 400016, CHINA
| | - Jian Qin
- Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, P.R.China, Chongqing, Chongqing, 400016, CHINA
| | - Fajin Lv
- Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, P.R.China, Chongqing, 400016, CHINA
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Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010952. [PMID: 34682696 PMCID: PMC8535944 DOI: 10.3390/ijerph182010952] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 09/04/2021] [Accepted: 09/29/2021] [Indexed: 12/01/2022]
Abstract
Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.
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YANG LIJUN, LI SHUANG, ZHANG ZHI, YANG XIAOHUI. CLASSIFICATION OF PHONOCARDIOGRAM SIGNALS BASED ON ENVELOPE OPTIMIZATION MODEL AND SUPPORT VECTOR MACHINE. J MECH MED BIOL 2020. [DOI: 10.1142/s0219519419500623] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The prevention and diagnosis of cardiovascular diseases have become one of the primary problems in the medical community since the mortality of this kind of diseases accounts for 31% of global deaths in 2016. Heart sound, which is an important physiological signal of human body, mainly comes from the pulsing of cardiac structures and blood turbulence. The analysis of heart sounds plays an irreplaceable role in early diagnosis of heart disease since they contain a large amount of pathological information about each part of human heart. Heart sounds can be detected and recorded by Phonocardiogram (PCG). As a noninvasive method to detect and diagnose heart disease, PCG signals have been paid more and more attention by researchers. In this paper, a novel envelope extraction model is proposed and used to estimate the cardiac cycle of each PCG signal. We present a strategy combining empirical mode decomposition (EMD) technique and the proposed envelope model to extract the time-domain features. After applying EMD process to each PCG signal, the second intrinsic mode function is chosen for further analysis. Based on the proposed envelope model, the cardiac cycles of PCG signals can be estimated and then the time-domain features can be extracted. Combining with the frequency-domain features and wavelet-domain features, the feature vectors are obtained. Finally, the support vector machine (SVM) classifier is used to detect the normal and abnormal PCG signals. Two public datasets are used to test our framework in this paper. And classification accuracies of more than [Formula: see text] on both datasets show the effectiveness of the proposed model.
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Affiliation(s)
- LIJUN YANG
- School of Mathematics and Statistics, Henan University, Kaifeng 475004, P. R. China
| | - SHUANG LI
- School of Mathematics and Statistics, Henan University, Kaifeng 475004, P. R. China
| | - ZHI ZHANG
- Department of Computer Science, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - XIAOHUI YANG
- School of Mathematics and Statistics, Henan University, Kaifeng 475004, P. R. China
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A Review of Computer-Aided Heart Sound Detection Techniques. BIOMED RESEARCH INTERNATIONAL 2020; 2020:5846191. [PMID: 32420352 PMCID: PMC7201685 DOI: 10.1155/2020/5846191] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/03/2019] [Accepted: 07/29/2019] [Indexed: 01/08/2023]
Abstract
Cardiovascular diseases have become one of the most prevalent threats to human health throughout the world. As a noninvasive assistant diagnostic tool, the heart sound detection techniques play an important role in the prediction of cardiovascular diseases. In this paper, the latest development of the computer-aided heart sound detection techniques over the last five years has been reviewed. There are mainly the following aspects: the theories of heart sounds and the relationship between heart sounds and cardiovascular diseases; the key technologies used in the processing and analysis of heart sound signals, including denoising, segmentation, feature extraction and classification; with emphasis, the applications of deep learning algorithm in heart sound processing. In the end, some areas for future research in computer-aided heart sound detection techniques are explored, hoping to provide reference to the prediction of cardiovascular diseases.
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Mubarak QUA, Akram MU, Shaukat A, Hussain F, Khawaja SG, Butt WH. Analysis of PCG signals using quality assessment and homomorphic filters for localization and classification of heart sounds. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 164:143-157. [PMID: 30195422 DOI: 10.1016/j.cmpb.2018.07.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 06/26/2018] [Accepted: 07/16/2018] [Indexed: 05/21/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate localization of heart beats in phonocardiogram (PCG) signal is very crucial for correct segmentation and classification of heart sounds into S1 and S2. This task becomes challenging due to inclusion of noise in acquisition process owing to number of different factors. In this paper we propose a system for heart sound localization and classification into S1 and S2. The proposed system introduces the concept of quality assessment before localization, feature extraction and classification of heart sounds. METHODS The signal quality is assessed by predefined criteria based upon number of peaks and zero crossing of PCG signal. Once quality assessment is performed, then heart beats within PCG signal are localized, which is done by envelope extraction using homomorphic envelogram and finding prominent peaks. In order to classify localized peaks into S1 and S2, temporal and time-frequency based statistical features have been used. Support Vector Machine using radial basis function kernel is used for classification of heart beats into S1 and S2 based upon extracted features. The performance of the proposed system is evaluated using Accuracy, Sensitivity, Specificity, F-measure and Total Error. The dataset provided by PASCAL classifying heart sound challenge is used for testing. RESULTS Performance of system is significantly improved by quality assessment. Results shows that proposed Localization algorithm achieves accuracy up to 97% and generates smallest total average error among top 3 challenge participants. The classification algorithm achieves accuracy up to 91%. CONCLUSION The system provides firm foundation for the detection of normal and abnormal heart sounds for cardiovascular disease detection.
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Affiliation(s)
- Qurat-Ul-Ain Mubarak
- Department of Computer & Software Engineering, College of Electrical & Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan.
| | - Muhammad Usman Akram
- Department of Computer & Software Engineering, College of Electrical & Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Arslan Shaukat
- Department of Computer & Software Engineering, College of Electrical & Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Farhan Hussain
- Department of Computer & Software Engineering, College of Electrical & Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Sajid Gul Khawaja
- Department of Computer & Software Engineering, College of Electrical & Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Wasi Haider Butt
- Department of Computer & Software Engineering, College of Electrical & Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan
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