Name
Title | ||
|---|---|---|
Electrocardiogram Pattern Recognition by Means of MLP Network and PCA: A Case Study on Equal Amount of Input Signal Types |
Abstract | ||
|---|---|---|
At the present scenario, one of the main causes ofdeath in developed and in emerging countries is thecardiovascular related diseases. Most of these deathscould be avoided if there was a pre-monitoring and a pre-diagnosticof these cardiac arrhythmia and myocardialisquemy by using an electrocardiogram (ECG) tool.In this scenario, this work proposes a system to helpthe doctor to detect cardiac arrhythmia. As reference, ituses the Normal, Fusion and PVC signals of the MITdatabase. Then, we extract the principal characteristics ofthe signal by means of the Principal Component Analysis(PCA) technique. One key-point in this work is the inputsignals extraction, which are captured in the sameamount. So, the number of segments for each signal is thesame. After signal preprocessing, they are applied to anArtificial Neural Network Multilayer Perceptron (ANNMLP). The MLP with 5 neurons was verified to have thebest accuracy. Based on this idea (the use of the sameinformation amount for all input signal types), weachieved better results in comparison with other works inthe field. This consideration is very important due to thefact that the ANN could be more sensible to the signal typewith major predominance.Keywords: Electrocardiogram (ECG); Artificial NeuralNetwork (ANN); Pattern Recognition; PrincipalComponent Analysis (PCA). |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/SBRN.2002.1181474 | SBRN |
Keywords | Field | DocType |
present scenario,signal preprocessing,principal component analysis,case study,pvc signal,ofthe signal,signal typewith major predominance,mlp network,cardiac arrhythmia,input signal type,equal amount,input signal types,electrocardiogram pattern recognition,principalcomponent analysis,artificial neuralnetwork,patient monitoring,cardiology,pattern recognition,multilayer perceptron,computer aided software engineering,backpropagation,artificial neural networks,heart,artificial neural network | Pattern recognition,Remote patient monitoring,Computer science,Speech recognition,Multilayer perceptron,Preprocessor,Artificial intelligence,Backpropagation,Artificial neural network,Principal component analysis,Machine learning,Signal extraction | Conference |
ISBN | Citations | PageRank |
0-7695-1709-9 | 5 | 0.89 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Vargas | 1 | 171 | 30.44 |
| Maria Cristina Felippetto de Castro | 2 | 11 | 2.90 |
| Marcello Macarthy | 3 | 5 | 0.89 |
| Djones Lettnin | 4 | 39 | 7.68 |