DocumentCode :
2369066
Title :
Assessment of resampling methodologies of electrocardiogram signals for feature extraction, statistical and neural networks applications
Author :
Srikanth, T. ; Napper, SA ; Gu, H.
Author_Institution :
Dept. of Biomed. Eng., Louisiana Tech. Univ., Ruston, LA, USA
fYear :
1998
fDate :
13-16 Sep 1998
Firstpage :
537
Lastpage :
540
Abstract :
Developing new applications in statistics and neural networks related to ECG pattern recognition problems require large, well-tested databases. A procedure needs to be developed to normalize the sampling frequencies of ECG data from various sources to a single sampling frequency. The present work provides a methodology to compare the effectiveness of different resampling techniques. The comparison is based on the least-distortion of the original signal in time and frequency domains. Results indicate the numerical analysis methods to be superior to signal processing methods in terms of simplicity, speed and accuracy, for the cases of both high and low sampling rates. This procedure helps to speed up the data collection process for large studies
Keywords :
electrocardiography; feature extraction; medical signal processing; neural nets; statistical analysis; ECG pattern recognition problems; accuracy; data collection process speeding up; electrocardiogram signals; electrodiagnostics; high sampling rate; large well-tested databases; low sampling rate; numerical analysis; resampling methodologies; simplicity; speed; Databases; Electrocardiography; Frequency domain analysis; Neural networks; Numerical analysis; Pattern recognition; Sampling methods; Signal processing; Signal sampling; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computers in Cardiology 1998
Conference_Location :
Cleveland, OH
ISSN :
0276-6547
Print_ISBN :
0-7803-5200-9
Type :
conf
DOI :
10.1109/CIC.1998.731921
Filename :
731921
Link To Document :
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