AIMC Topic: Heart Sounds

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PCG Classification Using Multidomain Features and SVM Classifier.

BioMed research international Jul 9, 2018
This paper proposes a method using multidomain features and support vector machine (SVM) for classifying normal and abnormal heart sound recordings. The database was provided by the PhysioNet/CinC Challenge 2016. A total of 515 features are extracted...
Databases, Factual Sensitivity and Specificity Entropy Heart Sounds Support Vector Machine Humans Algorithms
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A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection.

Computers in biology and medicine Jun 25, 2018
This study concerns the task of automatic structural heart abnormality risk detection from digital phonocardiogram (PCG) signals aiming at pediatric heart disease screening applications. Recently, various systems based on convolutional neural network...
Heart Defects, Congenital Heart Sounds Databases, Factual Humans Signal Processing, Computer-Assisted Neural Networks, Computer
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Heart Sound Segmentation-An Event Detection Approach Using Deep Recurrent Neural Networks.

IEEE transactions on bio-medical engineering Jun 1, 2018
OBJECTIVE: In this paper, we accurately detect the state-sequence first heart sound (S1)-systole-second heart sound (S2)-diastole, i.e., the positions of S1 and S2, in heart sound recordings. We propose an event detection approach without explicitly ...
Algorithms Signal Processing, Computer-Assisted Humans Neural Networks, Computer Diastole Heart Sounds Phonocardiography Sound Spectrography Systole
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DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds.

Physiological measurement Jul 31, 2017
OBJECTIVE: Automatic heart sound analysis has the potential to improve the diagnosis of valvular heart diseases in the primary care phase, as well as in countries where there is neither the expertise nor the equipment to perform echocardiograms. An a...
Wavelet Analysis Heart Sounds Signal Processing, Computer-Assisted Markov Chains Neural Networks, Computer Time Factors
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Recognition of normal-abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients.

Physiological measurement Jul 31, 2017
UNLABELLED: Intensive care unit patients are heavily monitored, and several clinically-relevant parameters are routinely extracted from high resolution signals.
Humans Neural Networks, Computer Heart Sounds Phonocardiography Signal Processing, Computer-Assisted
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Combining sparse coding and time-domain features for heart sound classification.

Physiological measurement Jul 31, 2017
OBJECTIVE: This paper builds upon work submitted as part of the 2016 PhysioNet/CinC Challenge, which used sparse coding as a feature extraction tool on audio PCG data for heart sound classification.
Phonocardiography Support Vector Machine Humans Signal Processing, Computer-Assisted Databases, Factual Heart Sounds
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Heart sounds analysis using probability assessment.

Physiological measurement Jul 31, 2017
OBJECTIVE: This paper describes a method for automated discrimination of heart sounds recordings according to the Physionet Challenge 2016. The goal was to decide if the recording refers to normal or abnormal heart sounds or if it is not possible to ...
Probability Heart Sounds Artificial Intelligence Algorithms Signal Processing, Computer-Assisted Software
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A robust method for online heart sound localization in respiratory sound based on temporal fuzzy c-means.

Medical & biological engineering & computing Oct 19, 2014
This work presents a detailed framework to detect the location of heart sound within the respiratory sound based on temporal fuzzy c-means (TFCM) algorithm. In the proposed method, respiratory sound is first divided into frames and for each frame, th...
Area Under Curve Adult Online Systems Female Adolescent Child Young Adult Male Time Factors Fuzzy Logic Humans Middle Aged Sound Localization Algorithms Respiratory Sounds Heart Sounds
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Stratification of Heart Sounds Morphology Through Unsupervised Learning.

Studies in health technology and informatics Aug 22, 2024
The use of heart sounds for the assessment of the hemodynamic condition of the heart in telemonitoring applications is object of wide research at date. Many different approaches have been tried out for the analysis of the first (S1) and second (S2) h...
Signal Processing, Computer-Assisted Humans Unsupervised Machine Learning Heart Sounds Phonocardiography
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Explainable Multimodal Deep Learning for Heart Sounds and Electrocardiogram Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
We introduce a Gradient-weighted Class Activation Mapping (Grad-CAM) methodology to assess the performance of five distinct models for binary classification (normal/abnormal) of synchronized heart sounds and electrocardiograms. The applied models com...
Electrocardiography Signal Processing, Computer-Assisted Heart Sounds Deep Learning Neural Networks, Computer ROC Curve Algorithms Humans
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