A Framework Classification of Heart Sound Signals in PhysioNet Challenge 2016 Using High Order Statistics and Adaptive Neuro-Fuzzy Inference System

Al-Naami, Bassam; Fraihat, Hossam; Gharaibeh, Nasr Y.; Al-Hinnawi, Abdel-Razzak

doi:10.1109/access.2020.3043290

Cited by 22 publications

(24 citation statements)

References 35 publications

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“…Khrisnan et al and Liu et al suggested that a data record of at least 5 s is required for reliable detection of heart abnormalities. Therefore, the duration was set to 5 s, similar to that in previous studies [12,15]. Compared with the aforementioned studies that used the same duration length to detect abnormalities in heart sound signals, the proposed model obtained superior performance accuracy as shown in Table 2.…”

Section: Discussionmentioning

confidence: 95%

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An Optimal Approach for Heart Sound Classification Using Artificial Neural Network

Fuadah

Lim

2021

Preprint

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Heart sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician’s skill and judgement. Several studies have shown promising results in the automatic detection of cardiovascular disorders based on heart sound signals. However, the accuracy performance needs to be improved as automated heart sound classification aids in the early detection and prevention of the dangerous effects of cardiovascular problems. In this study, an optimal heart sound classification method based on machine learning technologies for cardiovascular disease prediction is performed. It consists of three steps: pre-processing that sets the 5 s duration of the Physionet Challenge 2016 datasets, feature extraction using mel-frequency cepstrum coefficients (MFCC), and classification using an artificial neural network (ANN) with one hidden layer that provides low parameter consumption. Ten-fold cross-validation was used to evaluate the performance of the proposed method. The best model obtained 94% accuracy and 93% AUC score, which were assessed using 1626 test datasets. Taken together, the results show that the proposed method obtained excellent classification results and provided low parameter consumption, thereby reducing computational time to facilitate a real-time implementation.

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Section: Discussionmentioning

confidence: 95%

“…Al-Naami et al reported the highest accuracy at 89% using high-order spectral analysis and adaptive neuro-fuzzy inference system (ANFIS) [12]. However, they only used 1837 heart sound recordings from folder 'a', 'b', and 'e' in Physionet Challenge 2016 datasets.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Approach for Heart Sound Classification Using Artificial Neural Network

Fuadah

Lim

2021

Preprint

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“…Various CV diseases can be detected from the heart sounds, mostly during S1 and S2 but the accuracy of such diagnosis is largely depending on experience and expertise of a cardiologist [2,3]. However, PCG is a complicated non-stationary signal in nonlinear low frequency, which can be easily interfered by surrounding signal sources [3,4].…”

Section: Introductionmentioning

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%

“…A number of different techniques and algorithm have been tested using Physio-Net Challenge 2016 data set, including AdaBoost Classifier, Gram Polynomials and probabilistic Neural Networks, and Convolutional Neural Network (CNN), tensor technique, Ensemble feature-based Feed-Forward Neural Networks (FNN), Drop Connected Neural Networks (DCNN), LogitBoost, Random Forest, and Cost-Sensitive Classifier [4,15,16,[26][27][28]. The outcome indicated a range of classification accuracies from 75% to 92% from the dataset.…”

Section: Introductionmentioning

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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Classification of unsegmented phonocardiogram signal using scalogram and deep learning

et al. 2023

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A Framework Classification of Heart Sound Signals in PhysioNet Challenge 2016 Using High Order Statistics and Adaptive Neuro-Fuzzy Inference System

Cited by 22 publications

References 35 publications

An Optimal Approach for Heart Sound Classification Using Artificial Neural Network

An Optimal Approach for Heart Sound Classification Using Artificial Neural Network

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

Classification of unsegmented phonocardiogram signal using scalogram and deep learning

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