Inclusion of ECG and EEG analysis in neural network models

Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136) - Tập 2 - Trang 1621-1624 vol.2
M.E. Cohen1,2, D.L. Hudson2
1University of California, San Francisco, USA
2California State University, Fresno, USA

Tóm tắt

Evaluation of biomedical signals is important in the diagnosis of numerous diseases, chiefly in cardiology through the use of electrocardiograms, and to a more limited extent, in neurology through the use of electroencephalograms. While automated techniques exist for both ECG and EEG analysis, it is likely that additional information can be extracted from these signals through the use of new methods. A chaotic method for analysis of signal analysis variability is presented here that identifies the degree of variability in the signal over time. A second focus is to develop higher order decision models that can incorporate these results with other clinical parameters to represent a more comprehensive view of the disease state, using a neural network model.

Từ khóa

#Electrocardiography #Electroencephalography #Neural networks #Brain modeling #Signal analysis #Biological neural networks #Cardiac disease #Cardiovascular diseases #Cardiology #Nervous system

Tài liệu tham khảo

10.1142/S0218488594000353

10.1002/047134608X.W2469

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Thông tin
Thông tin xuất bản

Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136)

Tập 2

1621-1624 vol.2

Thông tin tác giả