Magnetocardiogram Topography with Automatic Artifact Correction using Principal Component Analysis and Artificial Neural Network
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Magnetocardiogram (MCG) topography is a useful diagnostic technique that employs multi-channel magnetocardiograms. Measurement of artifact-free MCG signals is essenctial to obtain MCG topography or map for a diagnosis of human heart. Principal component analysis (PCA) combined with an artificial neural network (ANN) is proposed to remove a pulse-type artifact in the MCG signals. The algorithm is composed of a PCA module which decomposes the obtained signal into its principal components, followed by an ANN module for the classification of the components automatically. In the experiments with volunteer subjects, 97% of the decisions that were made by the ANN were identical to those by the human experts. Using the proposed technique, the MCG topography was successfully obtained without the artifact.
Abstract
Magnetocardiogram (MCG) topography is a useful diagnostic technique that employs multi-channel magnetocardiograms. Measurement of artifact-free MCG signals is essenctial to obtain MCG topography or map for a diagnosis of human heart. Principal component analysis (PCA) combined with an artificial neural network (ANN) is proposed to remove a pulse-type artifact in the MCG signals. The algorithm is composed of a PCA module which decomposes the obtained signal into its principal components, followed by an ANN module for the classification of the components automatically. In the experiments with volunteer subjects, 97% of the decisions that were made by the ANN were identical to those by the human experts. Using the proposed technique, the MCG topography was successfully obtained without the artifact.
참고문헌 (8)
- "Prinicples and Applications of SQUIDs", 77, pp.1208~1223 (1989)
- "SQUID Magnetometers for Biomagnetism and Nondestructive Testing: Important Questions and Initial Answers", 5, pp.74~119 (1995)
- "Magnetism in Medicine" (1998), Wiley-VCH
- "Principal Component Elimination Method for the Improvement of S", N in Evoked Neuromagnetic Field Measurements, 1999, pp.46, 951~958
- "Automatic Artifact Correction Using Principal Component Analysis and Neural Net- work in Magnetocardiography Signal", pp.961~962 (2005)
- "Magnetic Noise Reduction in MCG Using Spatial Filters", 24, pp.287~292 (2003)
- "Automatic Artifact Removal Using a Neural Network in MCG Signal", pp.163~164 (2004)
- "EEG and Artifact Classifi- cation using Neural Network", pp.306~ (1996)