AIMC Topic: Phonocardiography

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

Physical and engineering sciences in medicine Feb 11, 2020
Given the patient to doctor ratio of 50,000:1 in low income and middle-income countries, there is a need for automated heart sound classification system that can screen the Phonocardiogram (PCG) records in real-time. This paper proposes deep neural n...
Humans Neural Networks, Computer Area Under Curve Heart Sounds Phonocardiography ROC Curve Models, Theoretical
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Towards Domain Invariant Heart Sound Abnormality Detection Using Learnable Filterbanks.

IEEE journal of biomedical and health informatics Jan 31, 2020
OBJECTIVE: Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases. While machine learning based systems can aid in automatically screening patients, the robustness of these syste...
Phonocardiography Heart Diseases Heart Sounds Neural Networks, Computer Algorithms Databases, Factual Humans Sensitivity and Specificity Signal Processing, Computer-Assisted
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Heart Sound Segmentation Using Bidirectional LSTMs With Attention.

IEEE journal of biomedical and health informatics Oct 25, 2019
OBJECTIVE: This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection o...
Female Animals Male Humans Neural Networks, Computer Heart Sounds Deep Learning Phonocardiography Signal Processing, Computer-Assisted
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A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography.

Sensors (Basel, Switzerland) Feb 24, 2019
Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiograph...
Signal Processing, Computer-Assisted Wavelet Analysis Phonocardiography Neural Networks, Computer Algorithms
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Phonocardiogram classification using deep neural networks and weighted probability comparisons.

Journal of medical engineering & technology Feb 18, 2019
Cardiac auscultation is one of the most conventional approaches for the initial assessment of heart disease, however the technique is highly user-dependent and with low repeatability. Several computational approaches based on the analysis of the phon...
Heart Sounds Phonocardiography Algorithms Signal Processing, Computer-Assisted Probability Deep Learning
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Deep Convolutional Neural Networks for Heart Sound Segmentation.

IEEE journal of biomedical and health informatics Jan 21, 2019
This paper studies the use of deep convolutional neural networks to segment heart sounds into their main components. The proposed methods are based on the adoption of a deep convolutional neural network architecture, which is inspired by similar appr...
Databases, Factual Signal Processing, Computer-Assisted Heart Sounds Phonocardiography Markov Chains Humans Neural Networks, Computer Algorithms
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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 ...
Neural Networks, Computer Phonocardiography Algorithms Humans Systole Signal Processing, Computer-Assisted Diastole Sound Spectrography Heart Sounds
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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.
Phonocardiography Neural Networks, Computer Signal Processing, Computer-Assisted Heart Sounds Humans
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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.
Heart Sounds Phonocardiography Databases, Factual Support Vector Machine Signal Processing, Computer-Assisted Humans
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A novel method for discrimination between innocent and pathological heart murmurs.

Medical engineering & physics May 21, 2015
This paper presents a novel method for discrimination between innocent and pathological murmurs using the growing time support vector machine (GTSVM). The proposed method is tailored for characterizing innocent murmurs (IM) by putting more emphasis o...
Aortic Valve Stenosis Wavelet Analysis Heart Murmurs Middle Aged Child Humans Aged Phonocardiography Infant Support Vector Machine Databases, Factual Adolescent Aged, 80 and over Severity of Illness Index Child, Preschool Sensitivity and Specificity
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