Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network

Knowledge-Based Systems

Volumn 132, Issue , 2017, Pages 62-71

  Acharya, U Rajendra ^a,b,c   Fujita, Hamido ^d   Lih, Oh Shu ^a   Adam, Muhammad ^a   Tan, Jen Hong ^a   Chua, Chua Kuang ^a  

^a DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^b SINGAPORE UNIVERSITY OF SOCIAL SCIENCES   (Singapore)

^c UNIVERSITY OF MALAYA   (Malaysia)

^d IWATE PREFECTURAL UNIVERSITY   (Japan)

Author keywords

CAD; CNN; ECG; Feature; Heart; Testing; Training

Indexed keywords

BLOOD VESSELS; COMPUTER AIDED DESIGN; COMPUTER AIDED DIAGNOSIS; CONVOLUTION; DECISION MAKING; DIAGNOSIS; ELECTROCARDIOGRAPHY; HEART; NEURAL NETWORKS; PERSONNEL TRAINING; TESTING;

AUTOMATED DETECTION; AUTOMATED DIAGNOSIS SYSTEM; CARDIAC HEALTH; CONVOLUTIONAL NEURAL NETWORK; CORONARY ARTERIES; CORONARY ARTERY DISEASE; FEATURE; MYOCARDIAL INFARCTION;

DISEASES;

EID: 85020802670     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2017.06.003     Document Type: Article

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