Ensemble methods with outliers for phonocardiogram classification

Homsi, Masun Nabhan; Warrick, Philip

doi:10.1088/1361-6579/aa7982

Cited by 53 publications

(20 citation statements)

References 15 publications

(31 reference statements)

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“…Therefore, the shortage in performance can be attributed to the quality of HS recordings, likely suggesting that classes B and E contained impractical HS signals, as claimed by references [10,16,25,26]. The potential solutions for this issue could be either to increase the number of input features to ANFIS classifier as in references [19,21,28,37], or to segment S1 and S2 portions from the five seconds HS recordings as in references [17,18,20,28].…”

Section: Discussionmentioning

confidence: 99%

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Assessment of Dual Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

Al-Naami

Fraihat

Al-Nabulsi

et al. 2021

Preprint

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Here we propose a novel de-noising method to improve the outcome of heart sound (HS)-based heart condition identification. We applied Dual Tree Complex Wavelet Transform (DTCWT) in collaboration with Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The method consisted of three steps. First, preprocess to eliminate 50 Hz noise. Second, application of DTCWT to de-noise and reconstruct time-domain HS signal. Third, evaluation of ANFIS on total 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was a 11% increase in SNR after DTCWT, representing a significant improvement in de-noising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc in comparison to other attempts on the same dataset. Therefore, DTCWT is a successful technique in de-noising information such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.

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Section: Discussionmentioning

confidence: 99%

“…The Physio-Net Challenge 2016 dataset, as a single data source, has allowed quantitative comparisons in between different signal processing techniques and associated algorithm [4,[15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Dual Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

Al-Naami

Fraihat

Al-Nabulsi

et al. 2021

Preprint

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“…The experiments in [26,28,[32][33][34][35][36][37]40] were performed with the Physionet database [20]. In [32], features were extracted in time, frequency, wavelet and statistics, obtaining a total of 29 features. In the classification stage, a nested set of ensemble algorithms consisting of random forest (RF), LogitBoost (LB) and cost-sensitive classification (CSS) were used, obtaining an overall accuracy of a 80.1%, specificity of 80.6% and sensibility of 79.6%.…”

Section: Authormentioning

confidence: 99%

Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

2020

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A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

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“…The auscultation of the heart remains an essential diagnostic tool for determining the well-being of the cardiovascular system [12]. As a result, diagnostic techniques have been developed that reasonably minimize the need for non-invasive detection of cardiac disease [13]. The development of prediction models that can determine whether or not a patient has a disease is one of these methods.…”

Section: Introductionmentioning

confidence: 99%

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

Baikuvekov¹,

Tolep²,

Sultan³

et al. 2023

IJACSA

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According to estimations made by World Health Organization, heart disease is the largest cause of mortality throughout the globe, and it is safe to assume that diagnosing heart diseases in their earliest stages is very essential. Diagnosis of cardiovascular disease may be carried out by detection of interference in cardiac signals, one of which is called phonocardiography, and it can be accomplished in a number of various ways. Using phonocardiogram (PCG) inputs and deep learning, the researchers aim to develop a classification system for different types of heart illness. The slicing and normalization of the signal served as the first step in the study's signal preprocessing, which was subsequently followed by a wavelet based transformation method that employs mother wavelet analytic morlet. The results of the decomposition are first shown with the use of a scalogram, afterwards, they are utilized as input for the deep CNN. In this investigation, the analyzed PCG signals were separated into categories, denoting normal and pathological heart sounds. The entire utilized data was divided into two categories as training and test data as 80% to 20%. The developed model demonstrates the degree of clinical diagnosis, sensitivity, specificity and AUC-ROC value.As a result, it has been determined that the proposed method was superior to the mother wavelet as well as other classifier approaches. Consequently, we were able to acquire an electronic stethoscope that has a diagnostic accuracy of more than 90% when it comes to identifying cardiac problems. To be more specific, the proposed deep CNN model has an accuracy of 93.25% in identifying aberrant heart sounds and 93.50% in identifying regular heartbeats. In addition, given the fact that an examination may be completed in only 15 seconds, speed is the primary advantage offered by the suggested stethoscope.

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Ensemble methods with outliers for phonocardiogram classification

Abstract: The approach of our proposed method helped reduce overfitting and improved classification performance, achieving an overall score on the hidden test set of 80.1% (79.6% sensitivity and 80.6% specificity).

Cited by 53 publications

References 15 publications

Assessment of Dual Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

Assessment of Dual Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

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