☆ 4.7 Article

Heart sound classification based on bispectrum features and Vision Transformer mode

ALEXANDRIA ENGINEERING JOURNAL (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Biotechnology & Applied Microbiology

An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning

Yunendah Nur Fuadah et al.

Summary: Heart-sound auscultation is a widely used method for cardiovascular disorder detection, but it relies on the skill and judgment of physicians. This study proposes an optimal heart-sound classification method based on machine learning techniques for predicting cardiovascular diseases. The method achieved high accuracy performance of 95.78% and 76.31% on PhysioNet Challenge 2016 and 2022 datasets, respectively, demonstrating its potential as an additional tool for diagnosing heart sound abnormalities.

BIOENGINEERING-BASEL (2023)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

A novel heart sound segmentation algorithm via multi-feature input and neural network with attention mechanism

Yang Guo et al.

Summary: In this paper, a novel method called C-LSTM-A was proposed for heart sound segmentation, which incorporated the 0.5-order smooth Shannon entropy envelope, instantaneous phase waveform, and third intrinsic mode function of PCG signal to reduce the difficulty of neural network learning features. Experimental results showed that this method had advantages for both normal and pathological PCG signals, and the segmented components were beneficial for subsequent heart sound classification study.

BIOMEDICAL PHYSICS & ENGINEERING EXPRESS (2023)

添加到收藏夹

Article Biophysics

A learnable front-end based efficient channel attention network for heart sound classification

Aolei Liu et al.

Summary: This study proposes a learnable front-end approach for heart sound classification, which enhances accuracy by optimizing waveform-to-spectrogram transformation and utilizing a convolutional recurrent neural network with feature selection. Experimental results on a public dataset demonstrate its effectiveness in heart sound classification research and applications.

PHYSIOLOGICAL MEASUREMENT (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Cardiac anomaly detection considering an additive noise and convolutional distortion model of heart sound recordings

Farhat Binte Azam et al.

Summary: This paper aims to develop a method for detecting cardiac abnormalities in cardiac auscultation sounds when both additive noise and sensor degradation are present. Experimental validation on open-access multiclass heart sound dataset and 2016 Physionet/CinC Challenge dataset demonstrates the proposed method's ability to perform well in the presence of noise.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2022)

添加到收藏夹

Article Multidisciplinary Sciences

A lightweight hybrid deep learning system for cardiac valvular disease classification

Yazan Al-Issa et al.

Summary: In this study, a cardiac diagnostic system combining CNN and LSTM components was developed to identify five heart valvular conditions using phonocardiogram signals. The results showed excellent performance in all evaluation metrics. In the future, this system can be extended to incorporate both PCG and ECG signals in a multimodal structure.

SCIENTIFIC REPORTS (2022)

添加到收藏夹

Article Chemistry, Multidisciplinary

Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning

Yi He et al.

Summary: The study utilized CNN for segmentation and classification of heart sound signals, achieving high accuracy and performance through data preprocessing and the application of deep learning networks.

APPLIED SCIENCES-BASEL (2021)

添加到收藏夹

Article Chemistry, Multidisciplinary

Methods for Improving Deep Learning-Based Cardiac Auscultation Accuracy: Data Augmentation and Data Generalization

Yoojin Jeong et al.

Summary: The study focuses on improving the performance of cardiac auscultation through data augmentation and generalization, showing an overall improvement in sensitivity, specificity, and F1-score.

APPLIED SCIENCES-BASEL (2021)

添加到收藏夹

Review Medicine, General & Internal

Progress of clinical evaluation for vascular aging in humans

Yumin Qiu et al.

Summary: The review discusses the advancements in clinical evaluation of vascular aging in humans, including functional evaluation, structural assessment, and cellular molecular markers. It emphasizes the significance of detecting vascular aging and calls for greater attention to evaluation for improving the quality of life in the elderly population.

JOURNAL OF TRANSLATIONAL INTERNAL MEDICINE (2021)

添加到收藏夹

Article Medicine, General & Internal

Age related differences in acute coronary syndrome: An observation at a central hospital in Vietnam

Dinh The Anh et al.

Summary: This study compared the characteristics of young Vietnamese patients with acute coronary syndrome (ACS) to their older counterparts. Young ACS patients tended to have more severe angina, lower systolic pressure, higher prevalence of unstable angina and STEMI, and lower prevalence of NSTEMI. They also had higher rates of single-vessel CAD and normal/nonobstructive CAD. Gensini, GRACE, and TIMI scores were lower in young patients, and smoking was identified as a risk factor for obstructive CAD in this group.

JOURNAL OF TRANSLATIONAL INTERNAL MEDICINE (2021)

添加到收藏夹

Article Biophysics

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Faiq Ahmad Khan et al.

PHYSIOLOGICAL MEASUREMENT (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

Classification of Heart Sounds Using Convolutional Neural Network

Fan Li et al.

APPLIED SCIENCES-BASEL (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network

Yongchao Chen et al.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Heart sound classification based on improved MFCC features and convolutional recurrent neural networks

Muqing Deng et al.

NEURAL NETWORKS (2020)

添加到收藏夹

Article Computer Science, Information Systems

A Framework Classification of Heart Sound Signals in PhysioNet Challenge 2016 Using High Order Statistics and Adaptive Neuro-Fuzzy Inference System

Bassam Al-Naami et al.

IEEE ACCESS (2020)

添加到收藏夹

Article Education, Scientific Disciplines

Cardiac auscultation skills among junior doctors: effects of sound simulation lesson

Yasuharu Tokuda et al.

INTERNATIONAL JOURNAL OF MEDICAL EDUCATION (2020)

添加到收藏夹

Article Engineering, Biomedical

Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network

Palani Thanaraj Krishnan et al.

PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE (2020)

添加到收藏夹

Article Health Care Sciences & Services

Classifying Heart Sounds Using Images of Motifs, MFCC and Temporal Features

Diogo Marcelo Nogueira et al.

JOURNAL OF MEDICAL SYSTEMS (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption

Bin Xiao et al.

NEUROCOMPUTING (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

ECG CLASSIFICATION USING HIGHER ORDER SPECTRAL ESTIMATION AND DEEP LEARNING TECHNIQUES

H. Alquran et al.

NEURAL NETWORK WORLD (2019)

添加到收藏夹

Article Engineering, Biomedical

Heart sound classification based on scaled spectrogram and partial least squares regression

Wenjie Zhang et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2017)

添加到收藏夹

Article Multidisciplinary Sciences

Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation

Necmettin Sezgin

SCIENTIFIC WORLD JOURNAL (2013)

添加到收藏夹

Article Medicine, General & Internal

Association between phonocardiographic third and fourth heart sounds and objective measures of left ventricular function

GM Marcus et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2005)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.