Name
Abstract | ||
|---|---|---|
ECG Feature Extraction plays a significant role in diagnosing most of the
cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T
waves. This feature extraction scheme determines the amplitudes and intervals
in the ECG signal for subsequent analysis. The amplitudes and intervals value
of P-QRS-T segment determines the functioning of heart of every human.
Recently, numerous research and techniques have been developed for analyzing
the ECG signal. The proposed schemes were mostly based on Fuzzy Logic Methods,
Artificial Neural Networks (ANN), Genetic Algorithm (GA), Support Vector
Machines (SVM), and other Signal Analysis techniques. All these techniques and
algorithms have their advantages and limitations. This proposed paper discusses
various techniques and transformations proposed earlier in literature for
extracting feature from an ECG signal. In addition this paper also provides a
comparative study of various methods proposed by researchers in extracting the
feature from ECG signal. |
Year | Venue | Keywords |
|---|---|---|
2010 | Clinical Orthopaedics and Related Research | artificial neural network,feature extraction,support vector machine,evolutionary computing,fuzzy logic,artificial intelligent,signal analysis,genetic algorithm |
Field | DocType | Volume |
Data mining,Signal processing,ECG feature,Pattern recognition,Computer science,Fuzzy logic,Support vector machine,Feature extraction,Artificial intelligence,Artificial neural network,Machine learning,Genetic algorithm | Journal | abs/1005.0 |
Citations | PageRank | References |
21 | 1.59 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Karpagachelvi | 1 | 37 | 2.63 |
| M. Arthanari | 2 | 37 | 2.63 |
| M. Sivakumar | 3 | 22 | 2.96 |