AIMC Topic: Phonocardiography
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Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals.
Sensors (Basel, Switzerland)
Oct 29, 2024
Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardiogram (ECG) and phonocardiogram (PCG), conveying abundant disease-related information, are prevalent...
External evaluation of a commercial artificial intelligence-augmented digital auscultation platform in valvular heart disease detection using echocardiography as reference standard.
International journal of cardiology
Oct 19, 2024
OBJECTIVE: There are few studies evaluating the accuracy of commercially available AI-powered digital auscultation platforms in detecting valvular heart disease (VHD). Therefore, the utility of these systems for diagnosing clinically significant VHD ...
Enhancing cross-domain robustness in phonocardiogram signal classification using domain-invariant preprocessing and transfer learning.
Computer methods and programs in biomedicine
Oct 19, 2024
BACKGROUND AND OBJECTIVE: Phonocardiogram (PCG) signal analysis is a non-invasive and cost-efficient approach for diagnosing cardiovascular diseases. Existing PCG-based approaches employ signal processing and machine learning (ML) for automatic disea...
Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.
Journal of cardiovascular translational research
Jul 17, 2024
Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this ...
Swine, Miniature
Heart Sounds
Phonocardiography
Heart Failure
Heart Rate
Oximetry
Ventricular Pressure
Swine
Electrocardiography
Disease Models, Animal
Predictive Value of Tests
Signal Processing, Computer-Assisted
Feasibility Studies
Animals
Ventricular Function, Left
Stroke Volume
Machine Learning
Effects of precise cardio sounds on the success rate of phonocardiography.
PloS one
Jul 15, 2024
This work investigates whether inclusion of the low-frequency components of heart sounds can increase the accuracy, sensitivity and specificity of diagnosis of cardiovascular disorders. We standardized the measurement method to minimize changes in si...
Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices.
Sensors (Basel, Switzerland)
Jun 14, 2024
The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) ...
Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms.
IEEE journal of biomedical and health informatics
Apr 4, 2024
One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary...
Utilizing Conversational Artificial Intelligence, Voice, and Phonocardiography Analytics in Heart Failure Care.
Heart failure clinics
Mar 4, 2022
Conversational artificial intelligence involves the ability of computers, voice-enabled devices to interact intelligently with the user through voice. This can be leveraged in heart failure care delivery, benefiting the patients, providers, and payer...
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation.
IEEE journal of biomedical and health informatics
Jun 3, 2021
Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed. Som...
Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network.
Medical & biological engineering & computing
Jul 7, 2020
We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal heart sounds. A hidden Markov model is used to find the position of each cardiac cyc...
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