Classification of coronary artery diseased and normal subjects using multi-channel phonocardiogram signal

Samanta, Pranab; Pathak, Akanksha; Mandana, Kayapanda; Saha, Goutam

doi:10.1016/j.bbe.2019.02.003

Cited by 44 publications

(14 citation statements)

References 28 publications

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“… Refs. Method Input data type Detection task Performance % 31 Time–frequency analysis of PCG signal using chirplet transform PCG Valve disease diagnosis Accuracy 98.33 32 Recurrent neural network with long short-term memory CCTA Calcified plaque detection Accuracy 90.3 Sensitivity 92.1 Specificity 88.9 33 CNN ECG Diagnosis of different cardiovascular diseases Accuracy 95 34 Optimal time–frequency concentrated biorthogonal wavelet-based features ECG CAD diagnosis Accuracy 98.53 35 Binomial rendition of the bivariate mixed-effects regression model CCTA, ECG CAD diagnosis Sensitivity 99 Specificity 88 36 Discrete wavelet transform, multivariate multi-scale entropy, ECG CAD diagnosis Accuracy 98.67 37 Sequential minimal optimization, Naive Bayes, and ensemble algorithm ECG CAD diagnosis Accuracy 88.5 38 Computing complex ventricular excitation index Magneto-cardiography CAD diagnosis Sensitivity 91 Specificity 84 39 Extracted time- and frequency-domain feature...…”

Section: Resultsmentioning

confidence: 99%

RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance

Khozeimeh

Sharifrazi

Izadi

et al. 2022

Sci Rep

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Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and can serve as a gatekeeper to downstream invasive testing. Machine learning methods are increasingly applied for automated interpretation of imaging and other clinical results for medical diagnosis. In this study, we proposed a novel CAD detection method based on CMR images by utilizing the feature extraction ability of deep neural networks and combining the features with the aid of a random forest for the very first time. It is necessary to convert image data to numeric features so that they can be used in the nodes of the decision trees. To this end, the predictions of multiple stand-alone convolutional neural networks (CNNs) were considered as input features for the decision trees. The capability of CNNs in representing image data renders our method a generic classification approach applicable to any image dataset. We named our method RF-CNN-F, which stands for Random Forest with CNN Features. We conducted experiments on a large CMR dataset that we have collected and made publicly accessible. Our method achieved excellent accuracy (99.18%) using Adam optimizer compared to a stand-alone CNN trained using fivefold cross validation (93.92%) tested on the same dataset.

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Section: Resultsmentioning

confidence: 99%

RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance

Khozeimeh

Sharifrazi

Izadi

et al. 2022

Sci Rep

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“…Furthermore, this segmentation approach necessitates expertise and specialized equipment. 13 On the other hand, non-ECG methods such as envelope detection (Hilbert transform, Homomorphic filtering, and Shannon energy) or segmentation based on K-Mean statistical models, HMM (Hidden Markov Model), and HSMM (Hidden semi-Markov Model) were proposed. However, due to the fluctuations of behavioral characteristics of cardiac signals in different people, especially at different ages, it is impossible to use a fixed method to find cardiac cycles and systole-diastole locations.…”

Section: Basic Concepts and Literature Reviewmentioning

confidence: 99%

Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model

Sabouri

Ghadimi

Kiani-Sarkaleh

et al. 2022

Proc Inst Mech Eng H

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Incidence and exacerbation of some of the cardiovascular diseases in the presence of the coronavirus will lead to an increase in the mortality rate among patients. Therefore, early diagnosis of such diseases is critical, especially during the COVID-19 pandemic (mild COVID-19 infection). Thus, for diagnosing the heart diseases related to the COVID-19, an automatic, non-invasive, and inexpensive method based on the heart sound processing approach is proposed. In the present study, a set of features related to the nature of heart signals is defined and extracted. The investigated features included morphological and statistical features in the heart sound frequencies. By extracting and selecting a set of effective features related to the mentioned diseases, and avoiding to use different segmentation and filtering techniques, dependence on a limited dataset and specific sampling procedures has been eliminated. Different classifiers with various kernels are applied for diagnosis in data unbalanced and balanced conditions. The results showed 93.15% accuracy and 93.72% F1-score using 60 effective features in data balanced conditions. The identification system using the extracted features from Azad dataset is able to achieve the desired results in a generalized dataset. In this way, in the shortest possible sampling time, the present system provided an effective and generalizable method and a practical model for diagnosing important cardiovascular diseases in the presence of coronavirus in the COVID-19 pandemic.

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“…The diagnostic useful frequency ranges of ECG and PCG are usually accepted as 0.05-75Hz [24] and 25-250Hz [25], respectively. Therefore, all raw signals were first bandpass filtered with second-order Butterworth filters with the passband of 0.05-75 Hz for ECG and 25-250 Hz for PCG followed by band-stop filtering to remove the power interference (50 Hz) [26], [27].…”

Section: B Construction Of Cardiac Electromechanical Time-seriesmentioning

confidence: 99%

Variability of Cardiac Electromechanical Delay With Application to the Noninvasive Detection of Coronary Artery Disease

Wang

Liu

et al. 2019

IEEE Access

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Heart rate variability (HRV), systolic period variability (SPV), and diastolic period variability (DPV) have shown potential for assessing cardiac function. It is unknown whether the time delay between the myocardial electrical and mechanical activities (i.e., electromechanical delay, EMD) also possesses variability, and if it does, whether this EMD variability (EMDV) could render additional value for cardiac function assessment. In this paper, we extracted the beat-to-beat EMD from 5-min simultaneously recorded electrocardiogram and phonocardiogram signals in 30 patients with coronary artery disease (CAD) and 30 healthy control subjects, and studied its variability using the same methods as applied for HRV including time-domain measures [mean and standard deviation (SD)], frequency-domain measures [normalized lowand high-frequency (LFn, HFn) and LF/HF)], and nonlinear measures [sample entropy (SampEn), permutation entropy (PE), and dynamical patterns]. In addition, we examined whether the addition of EMDV could offer improved performance for distinguishing between the two groups compared to using the HRV, SPV, and DPV features. Support vector machine with 10-fold cross-validation was used for classification. Results showed increased SD of SPV, increased mean, SD and decreased SampEn of EMDV in CAD patients. Besides, the dynamical pattern analysis showed that CAD patients had significantly increased fluctuated patterns and decreased monotonous patterns in EMDV. In particular, the addition of EMDV indices dramatically increased the classification accuracy from 0.729 based on HRV, SPV, and DPV features to 0.958. Our results suggest promising of the EMDV analysis that could potentially be helpful for detecting CAD noninvasively. INDEX TERMS Electromechanical delay (EMD), heart rate variability (HRV), systolic period variability (SPV), diastolic period variability (DPV), dynamical patterns, noninvasive detection, coronary artery disease (CAD). I. INTRODUCTION Electrocardiogram (ECG) and phonocardiogram (PCG) measurements are two commonly-used non-invasive and non-intrusive methods for diagnosing cardiac diseases. ECG reflects the cardiac electrical activity while PCG records heart sounds produced by myocardial mechanical The associate editor coordinating the review of this manuscript and approving it for publication was György Eigner. activities (i.e., contraction and relaxation). One cardiac cycle is composed of two mechanical intervals, namely, systolic period (SP) and diastolic period (DP). Previous studies have found reduced mean cardiac cycle (i.e., mean RR intervals in ECG) in diabetes [1] and patients with chronic congestive heart failure [2]. Besides, researchers also found that abnormal SP and DP could indicate mechanical abnormalities of ventricular function [3]. Prolonged SP and shortened DP were found in children with heart failure [4], [5], and the shortening

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Classification of coronary artery diseased and normal subjects using multi-channel phonocardiogram signal

Cited by 44 publications

References 28 publications

RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance

RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance

Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model

Variability of Cardiac Electromechanical Delay With Application to the Noninvasive Detection of Coronary Artery Disease

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