Results 11 to 20 of about 4,260,860 (287)

Deep attention-based neural networks for explainable heart sound classification [PDF]

Machine Learning with Applications, 2022
Cardiovascular diseases are the leading cause of death and severely threaten human health in daily life. There have been dramatically increasing demands from both the clinical practice and the smart home application for monitoring the heart status of ...
Zhao Ren, Kun Qian, Fengquan Dong, Zhenyu Dai, Wolfgang Nejdl, Yoshiharu Yamamoto, Björn W. Schuller   +6 more
doaj   +4 more sources

Incremental Learning Frameworks for Dynamic Heart Sound Classification

IEEE Access
Heart sound classification plays a vital role in the early detection of cardiovascular diseases. Accurate and timely classification of these sounds can provide critical insights for patient care. However, deep learning models trained incrementally on new
Gauri Santosh Pisharady, Kuppam Shireesha, Peddamallu Sai Gowthami, X. Anita   +3 more
doaj   +4 more sources

Attention-Fusion-Based Two-Stream Vision Transformer for Heart Sound Classification [PDF]

Bioengineering
Vision Transformers (ViTs), inspired by their success in natural language processing, have recently gained attention for heart sound classification (HSC).
Kalpeshkumar Ranipa, Wei-Ping Zhu, M. N. S. Swamy   +2 more
doaj   +2 more sources

Heart Sound Classification for Early Detection of Cardiovascular Diseases Using XGBoost and Engineered Acoustic Features [PDF]

Sensors
Heart sound-based detection of cardiovascular diseases is a critical task in clinical diagnostics, where early and accurate identification can significantly improve patient outcomes.
P. P. Satya Karthikeya, P. Rohith, B. Karthikeya, M. Karthik Reddy, Akhil V M, Andrea Tigrini, Agnese Sbrollini, Laura Burattini   +7 more
doaj   +2 more sources

Are Artificial Intelligence Models Listening Like Cardiologists? Bridging the Gap Between Artificial Intelligence and Clinical Reasoning in Heart-Sound Classification Using Explainable Artificial Intelligence [PDF]

Bioengineering
In recent years, deep learning has shown promise in automating heart-sound classification. Although this approach is fast, non-invasive, and cost-effective, its diagnostic accuracy still mainly depends on the clinician’s expertise, making it particularly
Sami Alrabie, Ahmed Barnawi
doaj   +2 more sources

Heart sound classification using Gaussian mixture model. [PDF]

Porto Biomed J, 2018
Abstract Background: This article represents a new method of classifying the heart sound status using the loudness features from the heart sound. Materials and methods: The method includes the following 3 main steps. First, the heart sound, which is usually found noisy, is
Shervegar MV, Bhat GV.
europepmc   +4 more sources

Advanced heart disease classification based on multi-channel heart sound coupling features.

PLoS ONE
Conventional heart sound classification methods often rely on single-channel, one-dimensional feature extraction, which inadequately captures pathological relationships across different auscultation zones, thereby limiting the accuracy of heart disease ...
Yu Fang, Dongbo Liu, Zijian Guo, Hongxia Leng, Xing Liu, Xiaochen Wu   +5 more
doaj   +2 more sources

Classifying Heart-Sound Signals Based on CNN Trained on MelSpectrum and Log-MelSpectrum Features

Bioengineering, 2023
The intelligent classification of heart-sound signals can assist clinicians in the rapid diagnosis of cardiovascular diseases. Mel-frequency cepstral coefficients (MelSpectrums) and log Mel-frequency cepstral coefficients (Log-MelSpectrums) based on a ...
Wei Chen, Zixuan Zhou, Junze Bao, Chengniu Wang, Hanqing Chen, Chen Xu, Gangcai Xie, Hongmin Shen, Huiqun Wu   +8 more
doaj   +1 more source

Deep Wavelets for Heart Sound Classification [PDF]

2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2019
Cardiovascular diseases have a high morbidity, and remain the leading cause of mortality. In the past two decades, developing an intelligent auscultation system has attracted tremendous efforts from the field of signal processing and machine learning. We propose a novel framework based on wavelet representations and deep recurrent neural networks for ...
Kun Qian 0003, Zhao Ren, Fengquan Dong, Wen-Hsing Lai, Björn W. Schuller, Yoshiharu Yamamoto   +5 more
openaire   +2 more sources

A wearable nanoscale heart sound sensor based on P(VDF-TrFE)/ZnO/GR and its application in cardiac disease detection

Beilstein Journal of Nanotechnology, 2023
This paper describes a method for preparing flexible composite piezoelectric nanofilms of P(VDF-TrFE)/ZnO/graphene using a high-voltage electrospinning method. Composition and β-phase content of the piezoelectric composite films were analyzed using X-ray
Yi Luo, Jian Liu, Jiachang Zhang, Yu Xiao, Ying Wu, Zhidong Zhao   +5 more
doaj   +1 more source