Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree

Karar, Mohamed Esmail; El-Khafif, S.; El-Brawany, Mohamed A.

doi:10.1007/s10916-017-0704-9

Cited by 53 publications

(25 citation statements)

References 23 publications

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“…We excluded 74 studies and the remaining 16 studies were included in the review. Out of the 16 included studies; meta-analysis was conducted for seven studies while nine studies were narratively synthesized (refer to Figure 1 for the study flow diagram) ( DeGroff et al, 2001 ; Yang et al, 2002 ; Bhatikar et al, 2005 ; Higuchi et al, 2006 ; De Vos and Blanckenberg, 2007 ; Ye et al, 2011 ; Gharehbaghi et al, 2015 ; Zhang and Pohl, 2015 ; Gavrovska et al, 2016 ; Kotb et al, 2016 ; Sepehri et al, 2016 ; Karar et al, 2017 ; Pereira et al, 2017 ; Meza et al, 2018 ; Diller et al, 2019a ; Bahado-Singh et al, 2020 ). The characteristics of included studies ( n = 16) have been outlined in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…The main contributor to the unclear risk was the unavailability of information regarding theblinding status in these studies. Majority of the studies (n 10, 62%) had low risk of reporting bias (DeGroff et al, 2001;Yang et al, 2002;De Vos and Blanckenberg, 2007;Zhang and Pohl, 2015;Gavrovska et al, 2016;Sepehri et al, 2016;Karar et al, 2017;Pereira et al, 2017;Meza et al, 2018;Diller et al, 2019a) while five reported unclear risk (31%) (Bhatikar et al, 2005;Higuchi et al, 2006;Gharehbaghi et al, 2015;Kotb et al, 2016;Bahado-Singh et al, 2020) and one reported high risk (7%) (Ye et al, 2011). The reference standard that was used mainly included expert opinion along with gold standard imaging modalities such as echocardiography, thus reducing the likelihood of bias.…”

Section: Methodological Quality Of Included Studiesmentioning

confidence: 99%

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Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis

Hoodbhoy

Jiwani

Sattar

et al. 2021

Front. Artif. Intell.

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Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD.Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to generate the hierarchical Summary ROC (HSROC) curve.Results: We included 16 studies (1217 participants) that used ML algorithm to diagnose CHD. Neural networks were used in seven studies with overall sensitivity of 90.9% (95% CI 85.2–94.5%) and specificity was 92.7% (95% CI 86.4–96.2%). Other ML models included ensemble methods, deep learning and clustering techniques but did not have sufficient number of studies for a meta-analysis. Majority (n=11, 69%) of studies had a high risk of patient selection bias, unclear bias on index test (n=9, 56%) and flow and timing (n=12, 75%) while low risk of bias was reported for the reference standard (n=10, 62%).Conclusion: ML models such as neural networks have the potential to diagnose CHD accurately without the need for trained personnel. The heterogeneity of the diagnostic modalities used to train these models and the heterogeneity of the CHD diagnoses included between the studies is a major limitation.

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Section: Resultsmentioning

confidence: 99%

Section: Methodological Quality Of Included Studiesmentioning

confidence: 99%

Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis

Hoodbhoy

Jiwani

Sattar

et al. 2021

Front. Artif. Intell.

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“…A computer-aided system can capture important information that may be overlooked by the subjective interpretation of the physicians. Conventional methods focus on feature extraction and classification process, and commonly used features are extracted from time [5], frequency [6], nonlinear [7]- [9], and time-frequency [10]- [15] domains. When useful features are obtained, the next step is classification.…”

Section: Introductionmentioning

confidence: 99%

“…When useful features are obtained, the next step is classification. Numerous classifiers have been employed thus far, for example, k-nearest neighbours [11], Gaussian mixture model [12], artificial neural network [16], [17], support vector machine (SVM) [18], [19], and decision tree [8], [9]. In recent years, deep learning techniques based on the convolutional neural network (CNN) have become very popular.…”

Section: Introductionmentioning

confidence: 99%

Dual-Input Neural Network Integrating Feature Extraction and Deep Learning for Coronary Artery Disease Detection Using Electrocardiogram and Phonocardiogram

et al. 2019

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Electrocardiogram (ECG) and phonocardiogram (PCG) signals reflect the electrical and mechanical activities of the heart, respectively. Although studies have documented that some abnormalities in ECG and PCG signals are associated with coronary artery disease (CAD), only few researches have combined the two signals for automatic CAD detection. This paper aims to differentiate between CAD and non-CAD groups using simultaneously collected ECG and PCG signals. To entirely exploit the underlying information in these signals, a novel dual-input neural network that integrates the feature extraction and deep learning methods is developed. First, the ECG and PCG features are extracted from multiple domains, and the information gain ratio is used to select important features. On the other hand, the ECG signal and the decomposed PCG signal (at four scales) are concatenated as a five-channel signal. Then, the selected features and the five-channel signal are fed into the proposed network composed of a fully connected model and a deep learning model. The results show that the classification performance of either feature extraction or deep learning is insufficient when using only ECG or PCG signal, and combining the two signals improves the performance. Further, when using the proposed network, the best result is obtained with accuracy, sensitivity, specificity, and G-mean of 95.62%, 98.48%, 89.17%, and 93.69%, respectively. Comparisons with existing studies demonstrate that the proposed network can effectively capture the combined information of ECG and PCG signals for the recognition of CAD.

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“…The experiments were carried out using 26 normal and 19 abnormal recordings and they reported an average accuracy of 99.0% when using 11-fold cross-validation with grid-based dimensionality reduction. Karar et al 13 segmented the heart sound using the time domain properties of the heart sound signal. Then, the segmented cycle was preprocessed with the discrete wavelet transform and then largest Lyapunov exponents were calculated to generate the dynamical features of heart sound time series.…”

Section: Introductionmentioning

confidence: 99%

Heart sound classification using Gaussian mixture model

Shervegar¹,

Bhat²

2018

Porto Biomedical Journal

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Background: This article represents a new method of classifying the heart sound status using the loudness features from the heart sound. Materials and methods: The method includes the following 3 main steps. First, the heart sound, which is usually found noisy, is heavily filtered by a 6th-order Chebyshev-I filter. The heart sound is then segmented using the event synchronous method to separate the sounds into the first heart sound, the systole and the second heart sound, the diastole. In the second step, the heart sound features namely maximum loudness index and minimum loudness index are obtained from the spectrogram of the sound by taking the row means. As a third step, the heart sound is classified using the Gaussian mixture model approach to categorize the sounds. Results: This method has been tested on a very large database of heart sounds consisting of over 3000 heart sounds recordings with a success rate of 97.77%. Conclusion: Only 2 features are used in this method namely, minimum loudness index and maximum loudness index. Classification of sounds using these 2 features yields high accuracy even under noisy conditions and is comparable to any state-of-the-art technique.

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Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree

Cited by 53 publications

References 23 publications

Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis

Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis

Dual-Input Neural Network Integrating Feature Extraction and Deep Learning for Coronary Artery Disease Detection Using Electrocardiogram and Phonocardiogram

Heart sound classification using Gaussian mixture model

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