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

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

INTRODUCTION

Mitral 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 ANALYSIS

In 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 DISSEMINATION

The 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 NUMBER

ChiCTR2300069496.

A new approach to phonocardiogram severity analysis

Phonocardiogram signal (PCG) has been the subject of several signal processing studies, where researchers applied various analysis techniques and extracted numerous features for different purposes, like cardiac pathologies identification, healthy/pathologic case discrimination, and severity assessment. When talking about cardiac severity, many think directly about the intensity or energy of the signal as the most reliable parameter. However, cardiac severity is not always reflected by the intensity or energy of the signal but includes other variables as well. In this paper, we will discuss the probability of having a Discrete Wavelet Transform (DWT) parameter that discriminates, identifies, and assesses the pathological cardiac severity levels, a parameter that takes into consideration other variables and elements for the severity study. For this purpose, we studied six PCGs signals that contain reduced murmurs (clicks) and eight murmur signals with four different cardiac severity levels. We extracted the Entropy of Approximation Coefficients (EAC) from the Discrete Wavelet Transform (DWT) sub-bands as the feature to study in this novel approach. The Energetic Ratio (ER) served as a reference parameter to evaluate the EAC evolution, due to its proven efficiency in cardiac severity tracking. While the DWT-EAC algorithm results revealed that the EAC provides better results for the paper purposes, the One versus All Support Vector Machine (OVA-SVM) classifier affirmed the efficiency of the Entropy of Approximation Coefficients (EAC) for cardiac severity assessment and proved the accuracy of this novel approach.

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

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 (risk factor-weighted clinical likelihood, 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 data sets: 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 ESC 2019 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 was 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.