Using Manifold Learning for ECG Processing

Please enable javascript in your browser.

Lashgari, Elnaz | 2013

451 Viewed

Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 45703 (05)
University: Sharif University of Technology
Department: Electrical Engineering
Advisor(s): Jahed, Mehran; Hossein Khalaj, Babak
Abstract:
The human heart is a complex system that contains many clues about its function in its electrocardiogram (ECG) signal. Due to the high mortality rate of heart diseases, detection and recognition of ECG arrhythmias is essential. The most difficult problem faced by ECG analysis is the vast variations among morphologies of ECG signals. In this study, we propose an approach for y detection of abnormal beats and data visualization with respect to ECG morphologies by using manifold learning. In order to do so, a nonlinear dimensionality reduction method based on the Laplacian Eigenmaps is used to reduce the high dimensions of the ECG signals, followed by the application of Bayesian and FLDA method to classify the ECG data
Keywords:
Electrocardiogram ; Dimensionality Reduction ; Manifold-Based Learning

No TOC