Automated Classification of Normal and Abnormal Heart Sounds using Support Vector Machines

Bouril, Anthony; Aleinikava, Darya; Guillem, Maria S; Mirsky, Grace M.

doi:10.22489/cinc.2016.158-329

Cited by 12 publications

(16 citation statements)

References 12 publications

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“…The organizers of the PhysioNet/CinC Challenge 2016 set up a large collection of recordings from various research groups in the world. In the conference, many methods were proposed for this discrimination purpose, like deep learning methods [ 17 – 19 ], tensor based methods [ 20 ], support vector machine based methods [ 21 , 22 ], and others [ 23 – 27 ]. Generally, time and/or frequency domain features were used in these papers.…”

Section: Introductionmentioning

confidence: 99%

PCG Classification Using Multidomain Features and SVM Classifier

Tang

Dai

Jiang

et al. 2018

BioMed Research International

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This paper proposes a method using multidomain features and support vector machine (SVM) for classifying normal and abnormal heart sound recordings. The database was provided by the PhysioNet/CinC Challenge 2016. A total of 515 features are extracted from nine feature domains, i.e., time interval, frequency spectrum of states, state amplitude, energy, frequency spectrum of records, cepstrum, cyclostationarity, high-order statistics, and entropy. Correlation analysis is conducted to quantify the feature discrimination abilities, and the results show that “frequency spectrum of state”, “energy”, and “entropy” are top domains to contribute effective features. A SVM with radial basis kernel function was trained for signal quality estimation and classification. The SVM classifier is independently trained and tested by many groups of top features. It shows the average of sensitivity, specificity, and overall score are high up to 0.88, 0.87, and 0.88, respectively, when top 400 features are used. This score is competitive to the best previous scores. The classifier has very good performance with even small number of top features for training and it has stable output regardless of randomly selected features for training. These simulations demonstrate that the proposed features and SVM classifier are jointly powerful for classifying heart sound recordings.

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Section: Introductionmentioning

confidence: 99%

PCG Classification Using Multidomain Features and SVM Classifier

Tang

Dai

Jiang

et al. 2018

BioMed Research International

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“…The Challenge 2016 data set consists of a, b, c, d, e, and f five data sets, which were collected from more than 1,000 subjects of different ages, different genders, and different physical conditions, totaling 3,240 heart sounds. In order to reduce the training deviation caused by the imbalance of normal and abnormal dataset distribution, and to avoid the inconvenience caused by manual data labeling, reference [24] proposed that the dataset can be balanced and reduced by means of average extraction. The formation of the experimental data set is given in Tab.…”

Section: Fig 1 (ⅱ) Experimental Datamentioning

confidence: 99%

“…Candidate classifiers for S1 and S2 classification include K-nearest neighbor (KNN) classifier [40], random forest (RF) classifier [41], support vector machine (SVM) classifier [24], deep neural network (DNN) classifier [7], and so on. Based on the research of reference [7], we further study the classifier based on deep neural network.…”

Section: Classifier For S1 and S2mentioning

confidence: 99%

“…Support vector machine is a supervised learning model that is used in pattern recognition, classification and regression analysis. The basic principle of SVM is to convert the original sequence into higher dimensions and classify the data in the higher dimensions [17].Compared to neural networks, SVM can avoid the problems caused by excessive sample dimensions, because the decision function is determined by the support vector machine [24].The decision function [17] of the SVM classifier can be expressed as equation (49).…”

Section: ) Svm Classifiermentioning

confidence: 99%

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Heart Sound Segmentation Based on Personalized Gaussian Mixture Model and Convolutional Neural Network

Chun

Zhou

Ying

et al. 2020

Preprint

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Background: Heart sound segmentation is a long-standing problem in heart analysis, and it is mainly caused by noise interference and diversification of heart sounds. Faced with the challenging of heart sound segmentation, a more applicable segmentation model was studied. Methods: In this process, the optimal modified Log-spectral amplitude and wavelet were used to suppress the noise in the heart sound, and used the duration-dependent hidden Markov model based on personalized Gaussian mixture model (PGMM-DHMM) to segment the fundamental heart sound (FHS) and the non-fundamental heart sound (non-FHS). Then used the optimized Mel frequency cepstral coefficient (MFCC) to realize the classification of S1 and S2 heart sound frames through the Convolutional neural network (CNN) classifier, which can avoid the errors caused by the ambiguity of the time domain features. Results: PGMM-DHMM can segment FHS more effectively, and the accuracy is 94.3%. The CNN classifier obtained the best results in the S1 and S2 classifications, the accuracy is 90.92%, the precision of S1 is 90.76%, the recall is 91.05%, the F-measure is 90.9%, and the precision of S2 is 91.07%, the recall is 90.79%, the F-measure is 90.93%. The final segmentation accuracy is 92.92%. In addition, the experimental results further indicate that CNN has more robust performance when classifying abnormal S1 and abnormal S2. Conclusions: The PGMM-DHMM model can better segment FHS and Non-FHS. The optimization of MFCC improves the classification effect of S1 and S2, and the improvement effect by the CNN classifier is significant, especially for abnormal heart sounds. The proposed algorithm is better than other algorithms at this stage.

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“…In the work of Bouril, A. et al [27], 3000 phonocardiograms from 9 locations of the body of both adults and children were taken to identify normal and abnormal heart sounds using SVM. Here, 74 features of time and frequency domain were considered.…”

Section: Same As Neural Network Approaches the Hidden Markovmentioning

confidence: 99%

Classification of Voice Content in the Context of Public Radio Broadcasting

Karunarathna

Jayaratne

Gunawardana

2020

Int J on Adv. in ICT for Emerging Countries

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With the rapid development of mass media technology, content classification of radio broadcasting has emerged as a major research area facilitating the automation of radio broadcasting monitoring process. This research focuses on the voice dominant content classification of radio broadcasting by employing a multi-class Support Vector Machine (SVM) in order to automate monitoring of radio broadcasting in Sri Lanka. This study investigates the performance of "One Vs. One" and "One Vs. All" methods known to be two conventional ways to build a multi-class SVM. These two multi-class SVM models are trained to classify five voice dominant classes as news, conversations, and advertisements without jingles, radio drama and religious programs.One of the substantial measures in creating such a classification is selection of the optimal feature sets. For that purpose, time domain features, frequency domain features, cepstral features, and chroma features are manually analyzed for each binary SVM classifier independently. Two multi-class SVM models are trained based on the selected features and the "One Vs. One" model was able to better classify the recordings with an 85% accuracy compared to 83% accuracy achieved by "One Vs. All" model. Further, the results revealed the importance of careful feature selection in order to achieve higher classification accuracies.

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Automated Classification of Normal and Abnormal Heart Sounds using Support Vector Machines

Abstract: (Sensitivity = 0.733, Specificity = 0.8398).

Cited by 12 publications

References 12 publications

PCG Classification Using Multidomain Features and SVM Classifier

PCG Classification Using Multidomain Features and SVM Classifier

Heart Sound Segmentation Based on Personalized Gaussian Mixture Model and Convolutional Neural Network

Classification of Voice Content in the Context of Public Radio Broadcasting

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