Authors:
G. de Lannoy
1
;
A. de Decker
2
and
M. Verleysen
2
Affiliations:
1
Machine Learning Group, Université catholique de Louvain; Université catholique de Louvain, Belgium
;
2
Machine Learning Group, Université catholique de Louvain, Belgium
Keyword(s):
Continuous wavelet transform, automatic ECG annotation, R spike detection, supervised learning.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Soft Computing
;
Wavelet Transform
Abstract:
One of the most important tasks in automatic annotation of the ECG is the detection of the R spike. The wavelet transform is a widely used tool for R spike detection. The time-frequency decomposition is indeed a powerful tool to analyze non-stationary signals. Still, current methods use consecutive wavelet scales in an a priori restricted range and may therefore lack adaptivity. This paper introduces a supervised learning algorithm which learns the optimal scales for each dataset using the annotations provided by physicians on a small training set. For each record, this method allows a specific set of non consecutive scales to be selected, based on the record characteristics. The selected scales are then used on the original long-term ECG signal recording and a hard thresholding rule is applied on the derivative of the wavelet coefficients to label the R spikes. This algorithm has been tested on the MIT-BIH arrhythmia database and obtains an average sensitivity rate of 99.7% and aver
age positive predictivity rate of 99.7%.
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