[go: up one dir, main page]

El Badlaoui et al., 2020 - Google Patents

Novel PCG analysis method for discriminating between abnormal and normal heart sounds

El Badlaoui et al., 2020

View PDF
Document ID
11973785065089485842
Author
El Badlaoui O
Benba A
Hammouch A
Publication year
Publication venue
Irbm

External Links

Snippet

A novel approach for separation among normal and heart murmurs sounds based on Phonocardiogram (PCG) analysis is introduced in this paper. The purpose of this work is to find the appropriate algorithm able to detect heart failures. Different features have been …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
    • G10L25/48Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use
    • G10L25/51Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination
    • G10L25/66Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00Speaker identification or verification
    • G10L17/26Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
    • G10L25/03Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 characterised by the type of extracted parameters

Similar Documents

Publication Publication Date Title
El Badlaoui et al. Novel PCG analysis method for discriminating between abnormal and normal heart sounds
EP3885974B1 (en) Methods and systems for identifying presence of abnormal heart sounds of a subject
Deng et al. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks
Er Heart sounds classification using convolutional neural network with 1D-local binary pattern and 1D-local ternary pattern features
Hamidi et al. Classification of heart sound signal using curve fitting and fractal dimension
Cheng et al. Automated sleep apnea detection in snoring signal using long short-term memory neural networks
Fahad et al. Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM
Sengupta et al. Lung sound classification using cepstral-based statistical features
Abbas et al. Artificial intelligence framework for heart disease classification from audio signals
Karar et al. Automated diagnosis of heart sounds using rule-based classification tree
Zheng et al. A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification
Delgado-Trejos et al. Digital auscultation analysis for heart murmur detection
Abduh et al. Classification of heart sounds using fractional Fourier transform based mel-frequency spectral coefficients and stacked autoencoder deep neural network
Singh et al. Short unsegmented PCG classification based on ensemble classifier
Das et al. Heart valve diseases detection based on feature-fusion and hierarchical LSTM network
Zeng et al. Automatic detection of heart valve disorders using Teager–Kaiser energy operator, rational-dilation wavelet transform and convolutional neural networks with PCG signals
Banerjee et al. A semi-supervised approach for identifying abnormal heart sounds using variational autoencoder
Taneja et al. Classifying the heart sound signals using textural‐based features for an efficient decision support system
Azam et al. Heart sound classification considering additive noise and convolutional distortion
Aji et al. CNN-LSTM for heartbeat sound classification
Mustafa et al. Detection of heartbeat sounds arrhythmia using automatic spectral methods and cardiac auscultatory: M. Mustafa et al.
Lilhore et al. An attention‐driven hybrid deep neural network for enhanced heart disease classification
Ajitkumar Singh et al. An improved unsegmented phonocardiogram classification using nonlinear time scattering features
Nehary et al. A deep convolutional neural network classification of heart sounds using fractional fourier transform
Al-Shannaq et al. Abnormal heart sound recognition using SVM and LSTM models in real-time mode