[go: up one dir, main page]

Sarker et al., 2025 - Google Patents

An Innovative Framework for Heart Sound Classification Integrating Adaptive Fuzzy Rank-Based Ensemble of Transfer Learning Models with Multi-Dimensional …

Sarker et al., 2025

View PDF
Document ID
358207978625277412
Author
Sarker S
Preotee F
Muhammad T
Akhter S
Publication year
Publication venue
bioRxiv

External Links

Snippet

Heart sound identification for diagnosing cardiovascular diseases is a complex challenge due to the intricate, spectro-temporal characteristics of conditions like Aortic Stenosis, Mitral Regurgitation, and multi-valvular disorders. Conventional methods frequently inadequately …
Continue reading at www.biorxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402—Electrocardiography, i.e. ECG
    • A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use
    • G10L25/51—Speech 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
    • G10L25/66—Speech 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/02—Stethoscopes
    • A61B7/04—Electric stethoscopes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines

Similar Documents

Publication Publication Date Title
Chen et al. A robust deep learning framework based on spectrograms for heart sound classification
Fahad et al. Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM
Abbas et al. Artificial intelligence framework for heart disease classification from audio signals
Alqudah et al. Classification of heart sound short records using bispectrum analysis approach images and deep learning
Kay et al. DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds
Nabih-Ali et al. A review of intelligent systems for heart sound signal analysis
Singh et al. Short unsegmented PCG classification based on ensemble classifier
Naseri et al. Computerized quality assessment of phonocardiogram signal measurement-acquisition parameters
Fan et al. Le-LWTNet: A learnable lifting wavelet convolutional neural network for heart sound abnormality detection
Taneja et al. Classifying the heart sound signals using textural‐based features for an efficient decision support system
Arora et al. Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram.
Lilhore et al. An attention‐driven hybrid deep neural network for enhanced heart disease classification
Mustafa et al. Detection of heartbeat sounds arrhythmia using automatic spectral methods and cardiac auscultatory: M. Mustafa et al.
Devi et al. RETRACTED ARTICLE: Classification of unsegmented phonocardiogram signal using scalogram and deep learning
Özcan Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence
Altaf et al. Systematic review for phonocardiography classification based on machine learning
Dai et al. Deep learning fusion framework for automated coronary artery disease detection using raw heart sound signals
Alotaibi et al. Classification of heart sound signals with Whisper model
Hassani et al. Heart sound segmentation based on homomorphic filtering
Netto et al. HBNET: A blended ensemble model for the detection of cardiovascular anomalies using phonocardiogram
Rohr et al. Multiple instance learning framework can facilitate explainability in murmur detection
Taneja et al. Heart sound classification method using gammatonegram and SVM
Wanyana et al. Combining machine learning and Bayesian networks for ECG interpretation and explanation
Daniel et al. IoT-based ensemble method on PCG signal classification to predict heart diseases
Hsieh et al. Development and validation of an integrated residual-recurrent neural network model for automated heart murmur detection in pediatric populations