Use of an artificial neural network to analyse an ECG with QS complex in V1-2 leads.
A feed-forward neural network with back-propagation algorithm is used to distinguish anterior wall myocardial infarction (AI) and non-infarction based on analysis of computerised electrocardiograms. Data used in the study are from 132 patients diagnosed as having AI by automated electrocardiograph analysis. Their ECGs show an abnormal Q-wave (or QS complex) or small R progression in leads V1 and V2. However, 66 of them are diagnosed as old AI from the history, physical examination, echocardiogram and other laboratory data, whereas the other 66 are not. The network is trained with the data from half of the AI and non-infarction patients; respectively. The diagnostic accuracy rate is then tested with the remaining 66 patients (33 infarction, 33 non-infarction) who have not been exposed to the network. The neural network correctly identifies 90.2% of the patients with AI and 93.3% of the patients without infarction. The neural network is capable of diagnosing anterior wall myocardial infarction better than a computer electrocardiograph.
Ouyang
N
N
Department of Medical Information & Medical Records, Nagoya University Hospital, Japan.
Ikeda
M
M
Yamauchi
K
K
D003663:Decision Trees; D003937:Diagnosis, Differential; D004562:Electrocardiography; D006801:Humans; D009203:Myocardial Infarction / Q000175:diagnosis*; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted