Improvement of the Accuracy in the Identification of Coronary Artery Disease Combining Heart Sound Features

Li, Haixia; Zhang, Guojun; Shao, Guicheng; Wang, Aizhen; Gu, Yarong; Tian, Zhumei; Zhang, Qiong; Shi, Pengcheng

doi:10.1155/2022/3058835

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”

mentioning

confidence: 99%

Retracted: Improvement of the Accuracy in the Identification of Coronary Artery Disease Combining Heart Sound Features

International

2023

BioMed Research International

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mentioning

confidence: 99%

Retracted: Improvement of the Accuracy in the Identification of Coronary Artery Disease Combining Heart Sound Features

International

2023

BioMed Research International

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“…Li et al [33] combined features from the first heart sound (S1) and the diastole to classify CAD. Features from S1 included estimating the instantaneous frequencies of the mitral valve closure (M1) and the tricuspid valve closure (T1).…”

Section: Classification Of Cad Vs Non-cadmentioning

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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Improved pre-test likelihood estimation of coronary artery disease using phonocardiography

Larsen

Winther

Nissen

et al. 2022

European Heart Journal - Digital Health

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Aims Current early risk stratification of coronary artery disease (CAD) consists of pre-test probability scoring such as the 2019 ESC guidelines on chronic coronary syndromes (ESC2019), which has low specificity and thus rule-out capacity. A newer clinical risk factor model (RF-CL) showed significantly improved rule-out capacity over the ESC2019 model. The aim of the current study was to investigate if the addition of acoustic features to the RF-CL model could improve the rule-out potential of the best performing clinical risk factor models. Methods and results Four studies with heart sound recordings from 2222 patients were pooled and distributed into two datasets: training and test. From a feature bank of 40 acoustic features, a forward-selection technique was used to select three features that were added to the RF-CL model. Using a cutoff of 5% predicted risk of CAD, the developed acoustic-weighted clinical likelihood (A-CL) model showed significantly (p < 0.05) higher specificity of 48.6% than the RF-CL model (specificity of 41.5%) and ESC2019 model (specificity of 6.9%) while having the same sensitivity of 84.9% as the RF-CL model. Area under the curve of the receiver operating characteristic for the three models were 72.5% for ESC2019, 76.7% for RF-CL, and 79.5% for A-CL. Conclusion The proposed A-CL model offers significantly improved rule-out capacity over the ESC2019 model and showed better overall performance than the RF-CL model. The addition of acoustic features to the RF-CL model was shown to significantly improve early risk stratification of symptomatic patients suspected of having stable CAD.

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Improvement of the Accuracy in the Identification of Coronary Artery Disease Combining Heart Sound Features

Cited by 5 publications

References 23 publications

Retracted: Improvement of the Accuracy in the Identification of Coronary Artery Disease Combining Heart Sound Features

Retracted: Improvement of the Accuracy in the Identification of Coronary Artery Disease Combining Heart Sound Features

Risk Estimation of Coronary Artery Disease using Phonocardiography

Improved pre-test likelihood estimation of coronary artery disease using phonocardiography

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