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Embedded platform based heart murmur classification using deep learning approach. Int J Health Sci (Qassim) 2022. [DOI: 10.53730/ijhs.v6ns2.6082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Ubiquitous Perturbations in cardiac auscultation properties, cardiovascular diseases (CVDs) are widely recognized. In the auscultation procedure, the appearance of pathological cardiac murmurs is linked to heart disorders. A noble automated detection system using 1-D Convolutional Neural Network (CNN) for the detection of pathological heart murmurs is proposed in this study, which removes the difficult task of extracting and selecting features. It directly acts on the phonocardiogram (PCG) signals. The fundamental purpose of this research is to develop a classification model for consistent recognition of cardiac murmurs when the data-set is imbalanced. In view of this, the proposed study for the imbalanced data-set incorporates the Adaptive Synthetic (ADASYN) approach to generate synthetic data for the minority class. The outcome analysis illustrates the positive result in the identification of heart murmurs on both balanced and imbalanced data-sets. Therefore, the developed deep learning model will learn better from the minority class and classify heart murmurs accurately.
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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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Yoo TK, Ryu IH, Lee G, Kim Y, Kim JK, Lee IS, Kim JS, Rim TH. Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. NPJ Digit Med 2019; 2:59. [PMID: 31304405 PMCID: PMC6586803 DOI: 10.1038/s41746-019-0135-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 05/30/2019] [Indexed: 12/26/2022] Open
Abstract
Recently, it has become more important to screen candidates that undergo corneal refractive surgery to prevent complications. Until now, there is still no definitive screening method to confront the possibility of a misdiagnosis. We evaluate the possibilities of machine learning as a clinical decision support to determine the suitability to corneal refractive surgery. A machine learning architecture was built with the aim of identifying candidates combining the large multi-instrument data from patients and clinical decisions of highly experienced experts. Five heterogeneous algorithms were used to predict candidates for surgery. Subsequently, an ensemble classifier was developed to improve the performance. Training (10,561 subjects) and internal validation (2640 subjects) were conducted using subjects who had visited between 2016 and 2017. External validation (5279 subjects) was performed using subjects who had visited in 2018. The best model, i.e., the ensemble classifier, had a high prediction performance with the area under the receiver operating characteristic curves of 0.983 (95% CI, 0.977-0.987) and 0.972 (95% CI, 0.967-0.976) when tested in the internal and external validation set, respectively. The machine learning models were statistically superior to classic methods including the percentage of tissue ablated and the Randleman ectatic score. Our model was able to correctly reclassify a patient with postoperative ectasia as an ectasia-risk group. Machine learning algorithms using a wide range of preoperative information achieved a comparable performance to screen candidates for corneal refractive surgery. An automated machine learning analysis of preoperative data can provide a safe and reliable clinical decision for refractive surgery.
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Affiliation(s)
- Tae Keun Yoo
- B&VIIt Eye Center, Seoul, South Korea.,2Institute of Vision Research, Department of Ophthalmology, Yonsei University College of Medicine, Seoul, South Korea
| | | | | | | | | | | | | | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Duke-NUS Medical School, Singapore, Singapore.,5Department of Ophthalmology, Yonsei University College of Medicine, Graduate School, Seoul, Korea
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Fahad HM, Ghani Khan MU, Saba T, Rehman A, Iqbal S. Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM. Microsc Res Tech 2018; 81:449-457. [DOI: 10.1002/jemt.22998] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 12/18/2017] [Accepted: 01/14/2018] [Indexed: 12/19/2022]
Affiliation(s)
- H. M. Fahad
- Department of Computer Science and Engineering University of Engineering & Technology; Lahore Pakistan
| | - M. Usman Ghani Khan
- Department of Computer Science and Engineering University of Engineering & Technology; Lahore Pakistan
| | - Tanzila Saba
- College of Computer and Information Sciences Prince Sultan University Riyadh; 11586 Saudi Arabia
| | - Amjad Rehman
- College of Computer and Information Systems Al Yamamah University Riyadh; 11512 Saudi Arabia
| | - Sajid Iqbal
- Department of Computer Science and Engineering University of Engineering & Technology; Lahore Pakistan
- Department of Computer Science Bahauddin Zakariya University Multan Pakistan
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Kang S, Doroshow R, McConnaughey J, Shekhar R. Automated Identification of Innocent Still's Murmur in Children. IEEE Trans Biomed Eng 2016; 64:1326-1334. [PMID: 27576242 DOI: 10.1109/tbme.2016.2603787] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Still's murmur is the most common innocent heart murmur in children. It is also the most commonly misdiagnosed murmur, resulting in a high number of unnecessary referrals to pediatric cardiologist. The purpose of this study was to develop a computer algorithm for automated identification of Still's murmur that may help reduce unnecessary referrals. METHODS We first developed an accurate segmentation algorithm to locate the first and the second heart sounds. Once these sounds were identified, we extracted signal features specific to Still's murmur. Subsequently, machine learning-based classifiers, artificial neural network and support vector machine, were used to identify Still's murmur. RESULTS We evaluated our classifiers using the jackknife method using 87 Still's murmurs and 170 non-Still's murmurs. Our algorithm identified Still's murmur accurately with 84-93% sensitivity and 91-99% specificity. CONCLUSION We have achieved accurate automated identification of Still's murmur while minimizing false positives. The performance of our algorithm is comparable to the rate of murmur identification by auscultation by pediatric cardiologists. SIGNIFICANCE To our knowledge, our solution is the first murmur classifier that focuses singularly on Still's murmur. Following further refinement and testing, the presented algorithm could reduce the number of children with Still's murmur referred unnecessarily to pediatric cardiologists.
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Gavrovska A, Zajić G, Bogdanović V, Reljin I, Reljin B. Paediatric heart sound signal analysis towards classification using multifractal spectra. Physiol Meas 2016; 37:1556-72. [PMID: 27510224 DOI: 10.1088/0967-3334/37/9/1556] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Healthy versus unhealthy heart sound computer-aided classification tools are very popular for supporting clinical decisions. In this paper a new method is proposed for the classification of heart sound recordings from a statistical standpoint without detection and localization of fundamental heart sounds (S1, S2). This study analyzes the possibility of detecting healthy heart sound signal from a large set of measurements, corresponding to different pathologies, such as aortic regurgitation, mitral regurgitation, aortic stenosis and ventricular septal defects. The proposed method employs singularity spectra analysis and long-term dependency of irregular structures. Healthy signals are firstly separated from the rest of the recordings. In the second step, the signals with a click syndrome, used here as a reference, are detected in the unhealthy group. Innocent murmurs have not been considered in this paper. Each auscultatory recording is classified into one of the following classes: healthy; click syndrome; and other heart dysfunctions. The results of the proposed method provided high recall and precision values for each of the three classes. Since the presence of additive noise may affect the classification, we also analyzed the possibility of classifying signals in such circumstances. The method was tested, verified and showed high accuracy.
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Affiliation(s)
- Ana Gavrovska
- School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia
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Nunes D, Leal A, Couceiro R, Henriques J, Mendes L, Carvalho P, Teixeira C. A low-complex multi-channel methodology for noise detection in phonocardiogram signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:5936-9. [PMID: 26737643 DOI: 10.1109/embc.2015.7319743] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The phonocardiography (PCG) is an important technique for the diagnosis of several heart conditions. However, the PCG signal is highly prone to noise, which can be an obstacle for the detection and interpretation of physiological heart sounds. Thus, the detection and elimination of noise present in PCG signals is crucial for the accurate analysis of heart sounds, especially in p-health environments. Noise can be introduced by various internal factors (e.g., respiration and laughing) and by external conditions (e.g., phone ringing or door closing). To mention also that the noise frequency components are typically overlapped with the PCG spectrum, increasing the complexity of the analysis. The purpose of the present work consists in the detection of noisy periods willfully introduced during the performance of three different sets of tasks. The developed method returns the classification of the signal content, in a window-by-window analysis and can be divided in two distinct phases. The first step consists in the search for a noise free window using a feature obtained from the PCG time-domain. In the second step, the noise free window is compared with the remaining signal. The classification between clean and contaminated PCG is performed using two features from the frequency domain. The algorithm was able to discriminate clean from contamined PCG sections with an average sensitivity and specificity of 95.59% and 92.68%, respectively.
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A novel method for discrimination between innocent and pathological heart murmurs. Med Eng Phys 2015; 37:674-82. [DOI: 10.1016/j.medengphy.2015.04.013] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Revised: 11/18/2014] [Accepted: 04/25/2015] [Indexed: 11/21/2022]
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Jiang Z, Tao T, Wang H. New approach on analysis of pathologic cardiac murmurs based on WPD energy distribution. JOURNAL OF HEALTHCARE ENGINEERING 2014; 5:375-91. [PMID: 25516123 DOI: 10.1260/2040-2295.5.4.375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
In this paper, an approach on analysis of the pathologic cardiac murmurs for congenital heart defects was proposed based on the wavelet packet (WP) technique. Considering the difference of the energy intensity distributions for the innocent and pathologic murmurs in frequency domain, the WP decomposition was introduced and the WP energies at each frequency band were calculated and compared. Based on the analysis of a large amount of clinic heart sound data, the murmurs energy distributions were divided into five frequency bands, and the relative evaluation indexes for cardiac murmurs (ICM) were proposed for analysis of the pathologic murmurs. Finally, the threshold values between the innocent and pathologic cardiac murmurs were determined based on the statistical results of the normal heart sounds. The analysis results validate the proposed evaluation indexes and the corresponding thresholds.
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Affiliation(s)
- Zhongwei Jiang
- Graduate School of Science and Engineering, Yamaguchi University, Yamaguchi, Japan
| | - Ting Tao
- Graduate School of Science and Engineering, Yamaguchi University, Yamaguchi, Japan
| | - Haibin Wang
- School of Electrical and Information Engineering, Xihua University, Chengdu, China
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Safara F, Doraisamy S, Azman A, Jantan A, Abdullah Ramaiah AR. Multi-level basis selection of wavelet packet decomposition tree for heart sound classification. Comput Biol Med 2013; 43:1407-14. [DOI: 10.1016/j.compbiomed.2013.06.016] [Citation(s) in RCA: 100] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2013] [Revised: 06/25/2013] [Accepted: 06/27/2013] [Indexed: 11/26/2022]
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Marascio G, Modesti PA. Current trends and perspectives for automated screening of cardiac murmurs. HEART ASIA 2013; 5:213-8. [PMID: 27326133 PMCID: PMC4832733 DOI: 10.1136/heartasia-2013-010392] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 08/22/2013] [Indexed: 01/19/2023]
Abstract
Although in high income countries rheumatic heart disease is now rare, it remains a major burden in low and middle income countries. In these world areas, physicians and expert sonographers are rare, and screening campaigns are usually performed by nomadic caregivers who can only recognise patients in an advanced phase of heart failure with high economic and social costs. Therefore, great interest exists regarding the possibility of developing a simple, low-cost procedure for screening valvular heart disease. With the development of computer science, the cardiac sound signal can be analysed in an automatic way. More precisely, a panel of features characterising the acoustic signal are extracted and sent to a decision-making software able to provide the final diagnosis. Although no system is currently available in the market, the rapid evolution of these technologies recently led to the activation of clinical trials. The aim of this note is to review the state of advancement of this technology (trends in feature selection and automatic diagnostic strategies), data available regarding performance of the technology in the clinical setting and finally what obstacles still need to be overcome before automated systems can be clinically/commercially viable.
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Affiliation(s)
- Giuseppe Marascio
- Department of Clinical and Experimental Medicine, Clinica Medica Generale e Cardiologia, University of Florence, Florence, Italy
- Centre for Civil Protection and Risk Studies, University of Florence (CESPRO), Florence, Italy
| | - Pietro Amedeo Modesti
- Department of Clinical and Experimental Medicine, Clinica Medica Generale e Cardiologia, University of Florence, Florence, Italy
- Centre for Civil Protection and Risk Studies, University of Florence (CESPRO), Florence, Italy
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Wavelet packet entropy for heart murmurs classification. Adv Bioinformatics 2012; 2012:327269. [PMID: 23227043 PMCID: PMC3512213 DOI: 10.1155/2012/327269] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2012] [Revised: 10/05/2012] [Accepted: 10/24/2012] [Indexed: 11/17/2022] Open
Abstract
Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.
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