Development and assessment of machine learning based heart disease detection using imbalanced heart sound signal

Rath, Adyasha; Mishra, Debahuti; Panda, Ganapati; Pal, Madhumita

doi:10.1016/j.bspc.2022.103730

Cited by 18 publications

(10 citation statements)

References 46 publications

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“…We also performed experiments to show the importance of the proposed CVT-Trans system compared to state-of-the-art approaches, such as Cheng-FRED [ 19 ], Rath-RF-MFO-XGB [ 20 ], Li-PCA-TSVM [ 21 ], Khan-ANN-LSTM [ 22 ], Saputra-NN-PSO [ 24 ], and Arsalan-RF [ 25 ], in terms of SE, SP, F1-score, RL, PR, and ACC measures. The standard hyper-parameters were defined, as presented in the corresponding studies.…”

Section: Resultsmentioning

confidence: 99%

“…When one or more of the four heart valves are damaged or flawed, it is called heart valve disease (HVD). The pulmonary, aortic, mitral, and tricuspid valves are the four valves of the human heart [ 20 ]. The heart’s mechanical activity and function are good, and blood backflow is prevented when the heart valves open and seal correctly.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, time–frequency (TF) domain feature extraction is growing in popularity. The TF-based features can offer more thorough information about the PCG signal and better feature extraction performance results, even though they require greater computing complexity than features based just on time or frequency [ 20 ]. Wavelet transformation, discrete and packet wavelet transform (DPWT), Hilbert transform (HT), empirical wavelet transform (EWT), variational mode decomposition (VMD), and adjustable Q-wavelet transform are some of the popular TF feature extraction techniques for PCG signals (TQWT).…”

Section: Literature Reviewmentioning

confidence: 99%

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Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

2022

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The major cause of death worldwide is due to cardiovascular disorders (CVDs). For a proper diagnosis of CVD disease, an inexpensive solution based on phonocardiogram (PCG) signals is proposed. (1) Background: Currently, a few deep learning (DL)-based CVD systems have been developed to recognize different stages of CVD. However, the accuracy of these systems is not up-to-the-mark, and the methods require high computational power and huge training datasets. (2) Methods: To address these issues, we developed a novel attention-based technique (CVT-Trans) on a convolutional vision transformer to recognize and categorize PCG signals into five classes. The continuous wavelet transform-based spectrogram (CWTS) strategy was used to extract representative features from PCG data. Following that, a new CVT-Trans architecture was created to categorize the CWTS signals into five groups. (3) Results: The dataset derived from our investigation indicated that the CVT-Trans system had an overall average accuracy ACC of 100%, SE of 99.00%, SP of 99.5%, and F1-score of 98%, based on 10-fold cross validation. (4) Conclusions: The CVD-Trans technique outperformed many state-of-the-art methods. The robustness of the constructed model was confirmed by 10-fold cross-validation. Cardiologists can use this CVT-Trans system to help patients with the diagnosis of heart valve problems.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

2022

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“…A fundamental issue in data is class imbalance. Recent research works have been predominant in working on the class imbalance problem (Rath et al 2022). Though sampling techniques such as synthetic minority oversampling technique (SMOTE) (Chawla et al 2018), ADASYN (), borderline-SMOTE (Hui et al 2005), safelevel SMOTE (Bunkhumpornpat et al 2009), and Rose (Lunardon et al 2014) are prevalent, the model developed for diagnosis should be immune to the problem of overfitting.…”

Section: Literature Surveymentioning

confidence: 99%

Diagnosis of heart disease using oversampling methods and decision tree classifier in cardiology

Albert¹,

Murugan

Sripriya

2022

Res. Biomed. Eng.

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Purpose Heart disease is one of the most prevalent and critical diseases that endangers the lives of human beings. In addition to clinical diagnosis, machine learning and deep learning-based approaches are vital in the diagnosis of heart disease. Method This paper proposes a balanced and optimized machine-learning algorithm for heart disease detection. This technique combines oversampling techniques, attribute pruning, CART decision tree classifier, and rule pruning through hyper-parameter tuning to identify the presence of heart disease. It further identifies the key attributes that contribute to the occurrence of heart malfunctioning. Results Experimental results show that SMOTE sampled dataset exhibits effective performance when implemented using a balanced and optimized machine learning algorithm, with an improvement of 11%, 75%, 62%, and 71% in accuracy, precision, recall, and f1 scores when compared with the dataset that was not subjected to sampling. The algorithm works effectively when the imbalance ratio is high for a dataset. Conclusion The algorithm can be used to predict the presence of heart disease even in highly imbalanced datasets and identify critical features in the malfunctioning of the heart.

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“…In another study using CinC2016 dataset [9], 88.7% accuracy was obtained by using Random Forest (RF), Extreme Gradient Boosting (XGB), k nearest neighbor (kNN) and their ensemble form. In addition, Rath et al [9] investigated the optimal k value of the kNN method for PCG classification and determined it as 50.…”

Section: Introductionmentioning

confidence: 99%

Spektral Özellikler ve MFCC Tabanlı Özellikleri Kullanan Klasik Makine Öğrenmesi Metotlarıyla PCG Parça Sınıflandırması

GÜNDÜZ

Talu

2022

EJOSAT

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Cardiovascular diseases are some of the most common diseases today. Congenital abnormalities, diseases caused by impaired heart rhythm, vascular occlusion, post-operation arrhythmias, heart attacks and irregularities in heart valves are some of the various cardiovascular diseases. Early recognition of them is very important for obtaining positive results in treatment. For this purpose, it is tried to diagnose and detect cardiovascular diseases by listening to the sounds coming from the heart. During the rhythmic work of the heart, the contraction and relaxation of the heart chambers and the filling and discharge of blood from the heart into the veins create the sounds that are identified with the heart. Among the characteristic sounds of the heart, there can be some sounds similar to rustling which are indicators of pathological conditions. These unexpected sounds, similar to rustling, are called heart murmurs. Phonocardiograph device is used to record these mechanical sounds via microphone. Heart sounds recordings captured by a phonocardiograph device are called phonocardiograms (PCGs). Expert physicians try to detect the heart murmurs by listening to the heart sounds and examining PCGs. Ambient noise, the squeak of the microphone, and the patient's breathing sounds are the factors that make this task more difficult and challenging. Computer-aided systems supported with machine learning, signal processing and artificial intelligence algorithms offer solutions to help physicians in this regard. In this study, detection of heart murmur from PCG frames was examined. PCG frames of equal length, obtained by fragmenting the PCG recordings into 1-second-long frames, were classified by widely used machine learning methods namely C4.5 decision tree, Naive Bayes, Support Vector Machines and k-nearest neighbor. To train those classifiers we used spectral features of PCG signals, averages of MFCC values and some refined features obtained from a deep learning model which was inputted MFCC values. At the end of this manuscript the accuracies of those machine learning methods were compared.

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Development and assessment of machine learning based heart disease detection using imbalanced heart sound signal

Cited by 18 publications

References 46 publications

Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

Diagnosis of heart disease using oversampling methods and decision tree classifier in cardiology

Spektral Özellikler ve MFCC Tabanlı Özellikleri Kullanan Klasik Makine Öğrenmesi Metotlarıyla PCG Parça Sınıflandırması

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