[go: up one dir, main page]

Brunese et al., 2020 - Google Patents

Deep learning for heart disease detection through cardiac sounds

Brunese et al., 2020

View PDF
Document ID
1557254187719368027
Author
Brunese L
Martinelli F
Mercaldo F
Santone A
Publication year
Publication venue
Procedia Computer Science

External Links

Snippet

Most of death causes are related to cardiovascular disease. In fact, there are several anomalies afflicting the heart beat, for instance heart murmur or artefact. We propose a method for heart disease detection. By gathering a set of feature obtainable directly from …
Continue reading at www.sciencedirect.com (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
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6228—Selecting the most significant subset of features
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Brunese et al. Deep learning for heart disease detection through cardiac sounds
Rai et al. Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data
Babič et al. Predictive and descriptive analysis for heart disease diagnosis
Kim et al. Assessment of electrocardiogram rhythms by GoogLeNet deep neural network architecture
Sengur An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases
Hossain et al. ECG-Adv-GAN: Detecting ECG adversarial examples with conditional generative adversarial networks
Aly et al. A novel deep learning model to detect COVID-19 based on wavelet features extracted from Mel-scale spectrogram of patients’ cough and breathing sounds
KR102483693B1 (en) Method and apparatus of explainable multi electrocardiogram arrhythmia diagnosis
Verde et al. A neural network approach to classify carotid disorders from heart rate variability analysis
Bora et al. Using machine learning to predict heart disease
Hassan et al. Performance comparison of CNN and LSTM algorithms for arrhythmia classification
Abbas et al. A novel deep learning approach for myocardial infarction detection and multi-label classification
Xu et al. Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning
Lilhore et al. An Attention‐Driven Hybrid Deep Neural Network for Enhanced Heart Disease Classification
Anusha et al. Earlier Prediction on the heart disease based on supervised machine learning techniques
da Silva et al. Analysis of heartbeat time series via machine learning for detection of illnesses
Grandhi et al. Detection and Diagnosis of ECH Signal Wearable System for Sportsperson using Improved Monkey-based Search Support Vector Machine
Sajid et al. AI-CADR: Artificial Intelligence Based Risk Stratification of Coronary Artery Disease using Novel Non-invasive Biomarkers
Rao et al. Computational analysis of machine learning algorithms to predict heart disease
Al-Jamimi Intelligent methods for early prediction of heart disease
Bennasar et al. Minimal patient clinical variables to accurately predict stress echocardiography outcome: validation study using machine learning techniques
Goldbaum Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects
Chandralekha et al. An Extensive Machine Learning Framework Employing Ensemble Classifiers for Heart Disease Detection
Gong et al. Causal Inference for Hypertension Prediction With Wearable Electrocardiogram and Photoplethysmogram Signals: Feasibility Study
Sharma et al. Machine and deep learning approaches for heart disease risk assessment