Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy

Zhang, Huan; Wang, Xinpei; Liu, Changchun; Li, Yuanyang; Liu, Yuanyuan; Jiao, Yu; Liu, Tongtong; Dong, Huiwen; Wang, Jikuo

doi:10.3390/e23070823

Cited by 8 publications

(3 citation statements)

References 57 publications

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“…A limitation of this study is that only PCG will be used, without simultaneously recorded ECG. In many studies, ECG was used as reference for PCG cycle segmentation 38. However, in recent years, some studies have shown that segmentation may not be necessary when deep learning is used for cardiac sound classification.…”

Section: Discussionmentioning

confidence: 99%

“…In many studies, ECG was used as reference for PCG cycle segmentation. 38 However, in recent years, some studies have shown that segmentation may not be necessary when deep learning is used for cardiac sound classification. By using Shapley values and occlusion maps, Dissanayake et al 17 showed that deep learning methods can automatically emphasise the components on PCG data that make significant contribution to the classification.…”

Section: Discussionmentioning

confidence: 99%

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Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study

Zhang,

Cheng,

et al. 2024

BMJ Open

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IntroductionMitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for large-scale MR screening. Cardiac auscultation with electronic stethoscope and artificial intelligence (AI) can be a fast and economical modality for assessing MR severity. Our objectives are (1) to establish a deep neural network (DNN)-based cardiac auscultation method for assessing the severity of MR; and (2) to quantitatively measure the performance of the developed AI-based MR assessment method by virtual clinical trial.Methods and analysisIn a cross-sectional design, phonocardiogram will be recorded at the mitral valve auscultation area of outpatients. The enrolled patients will be checked by echocardiography to confirm the diagnosis of MR or no MR. Echocardiographic parameters will be used as gold standard to assess the severity of MR, classified into four levels: none, mild, moderate and severe. The study consists of two stages. First, an MR-related cardiac sound database will be created on which a DNN-based MR severity classifier will be trained. The automatic MR severity classifier will be integrated with the Smartho-D2 electronic stethoscope. Second, the performance of the developed smart device will be assessed in an independent clinical validation data set. Sensitivity, specificity, precision, accuracy and F1 score of the developed smart MR assessment device will be evaluated. Agreement on the performance of the smart device between cardiologist users and patient users will be inspected. The interpretability of the developed model will also be studied with statistical comparisons of occlusion map-guided variables among the four severity groups.Ethics and disseminationThe study protocol was approved by the Medical Ethics Committee of Huzhou Central Hospital, China (registration number: 202302009-01). Informed consent is required from all participants. Dissemination will be through conference presentations and peer-reviewed journals.Trial registration numberChiCTR2300069496.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study

Zhang,

Cheng,

et al. 2024

BMJ Open

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“…More recently (2021), Zhang et al [60] classified patients into varying degrees of diameter reduction by combining ECG and PCG signals and calculating a number of entropy measures which were combined in a support vector machine (SVM). Also in 2021, Khan et al [61] along with Iqtidar et al [62] classified patients into one of four categories: normal, single-, dual-, or triple-vessel disease, whereas Mushtaq et al [63] only classified into diseased groups.…”

Section: Classification Of Cad Severitymentioning

confidence: 99%

Risk Estimation of Coronary Artery Disease using Phonocardiography

Larsen¹

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General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research. -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal - 5 ENGLISH SUMMARYCoronary artery disease (CAD) is the leading cause of death world-wide and continues to be a major health care expense also in developed countries. It is often of significant concern when patients show symptoms of the disease, and this might be one of the reasons that only 6-12% of suspected CAD patients are diagnosed with obstructive-CAD, even after general practitioners and cardiologists have evaluated the patients. This means that a large portion of patients go through expensive and sometimes invasive testing that might be avoided through more careful pre-test risk estimation. This thesis consists of four studies, which in concert investigate the possibility to perform early risk estimation of suspected CAD patients and to safely rule-out a portion of these before proceeding to more expensive and invasive testing. The studies analyze heart sound recordings from a collection of studies performed by Acarix that in total contains more than 2500 patients. Features from heart sound analysis are used to augment an existing clinical risk model for the rule-out of healthy patients suspected of CAD.Study I established a relationship between heart sounds and clinical parameters age, sex, and BMI. Study II documented the development of a whitening filter designed to emphasize high-frequency differences in heart sounds from CAD patients. Using knowledge and methods obtained in the previous two studies, Study III investigated the spectral differences between CAD and non-CAD patients. Finally, in Study IV, acoustic features were extracted from the heart sound, and joined to an existing highperforming clinical risk model. The addition of acoustic features to the clinical risk models significantly increased the specificity from 41.5% to 48.6% while keeping the sensitivity the same 84.9%.The heart sound of patients with CAD carry information beyond what is contained in the parameters of modern clinical risk estimation models.

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Non-destructive detection of CAD stenosis severity using ECG-PCG coupling analysis

Dong,

Wang,

et al. 2023

Biomedical Signal Processing and Control

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Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy

Cited by 8 publications

References 57 publications

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study

Risk Estimation of Coronary Artery Disease using Phonocardiography

Non-destructive detection of CAD stenosis severity using ECG-PCG coupling analysis

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