AIMC Topic: Phonocardiography

Clear Filters Showing 1 to 10 of 41 articles

TF-crossnet: a cross-modal attention fusion network for cardiovascular disease classification using pcg and ecg signals.

Biomedical physics & engineering express
Electrocardiogram (ECG) and phonocardiogram (PCG) have emerged as crucial non-invasive and portable diagnostic modalities for early cardiovascular disease (CVD) screening. Despite the individual merits of these signal modalities in CVD detection, sig...

Explainable attention-based deep learning for classification and interpretation of heart murmurs using phonocardiograms.

Scientific reports
Cardiovascular diseases (CVDs) remain a leading global health challenge, necessitating diagnostic solutions that combine high accuracy with clinical interpretability and reproducibility. Traditional auscultation methods rely extensively on clinician ...

Machine learning-based CAD detection using integrated ECG and PCG parameter features.

Biomedical physics & engineering express
The combined analysis of electrocardiogram (ECG) and phonocardiogram signals(PCG) has demonstrated significant potential in the non-invasive detection of coronary artery disease (CAD). The efficacy of combining cardiac pathological parameters such as...

Deep Learning for Cardiac Overload Estimation - Predicting B-Type Natriuretic Peptide (BNP) Levels From Heart Sounds and Electrocardiogram.

Circulation journal : official journal of the Japanese Circulation Society
BACKGROUND: B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-pro-BNP) are key biomarkers used for heart failure (HF) management. Although traditional auscultation lacks objective evaluation, the SSS01-series phonocardiogram enables rapid r...

Deep learning-based evaluation of the severity of mitral regurgitation in canine myxomatous mitral valve disease patients using digital stethoscope recordings.

BMC veterinary research
BACKGROUND: Myxomatous mitral valve disease (MMVD) represents the most prevalent cardiac disorder in dogs, frequently resulting in mitral regurgitation (MR) and congestive heart failure. Although echocardiography is the gold standard for diagnosis, i...

Deep learning models for segmenting phonocardiogram signals: a comparative study.

PloS one
Cardiac auscultation requires the mechanical vibrations occurring on the body's surface, which carries a range of sound frequencies. These sounds are generated by the movement and pulsation of different cardiac structures as they facilitate blood cir...

Multiscale analysis of heart sound signals in the wavelet domain for heart murmur detection.

Scientific reports
A heart murmur is an atypical sound produced by blood flow through the heart. It can indicate a serious heart condition, so detecting heart murmurs is critical for identifying and managing cardiovascular diseases. However, current methods for identif...

Abnormal heart sound recognition using SVM and LSTM models in real-time mode.

Scientific reports
Cardiovascular diseases are non-communicable diseases that are considered the leading cause of death worldwide accounting for 17.9 million fatalities. Auscultation of heart sounds is the most common and valuable way of diagnosing heart diseases. Norm...

Extraction of fetal heartbeat locations in abdominal phonocardiograms using deep attention transformer.

Computers in biology and medicine
Assessing fetal health traditionally involves techniques like echocardiography, which require skilled professionals and specialized equipment, making them unsuitable for low-resource settings. An emerging alternative is Phonocardiography (PCG), which...

Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks.

Scientific reports
Detection and classification of cardiovascular diseases are crucial for early diagnosis and prediction of heart-related conditions. Existing methods rely on either electrocardiogram or phonocardiogram signals, resulting in higher false positive rates...