Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel method to perform the classification of electrocardiogram (ECG) beats on mobile phones. The Discrete Wavelet Transform and Higher Order Statistics are used to extract a set of eleven features from each ECG beat, and a Multilayer Perceptron is used to classify it between six types of beats. Our experiments show that a mobile phone can classify the ECG beats with an overall accuracy of 99.83%, and the sensitivity rates are greater than 99.48% for all beat. Running in a mobile phone phone, it needs only 27ms to classify each heart beat, what makes this method a reliable choice to perform computer-aided diagnosis on remote and critical situations. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CBMS.2011.5999107 | Computer-Based Medical Systems |
Keywords | Field | DocType |
critical situation,overall accuracy,eleven feature,novel method,computer-aided diagnosis,higher order statistics,multilayer perceptron,mobile phone,discrete wavelet transform,mobile phone phone,automatic classification,mobile computing,statistics,accuracy,feature extraction,sensitivity | Mobile computing,Computer science,Higher-order statistics,Feature extraction,Speech recognition,Phone,Multilayer perceptron,Discrete wavelet transform,Beat (music),Mobile phone | Conference |
ISSN | ISBN | Citations |
1063-7125 | 978-1-4577-1189-3 | 1 |
PageRank | References | Authors |
0.39 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fernando Arena Varella | 1 | 1 | 0.39 |
| Guilherme Lazzarotto de Lima | 2 | 1 | 0.39 |
| Cirano Iochpe | 3 | 180 | 29.10 |
| Valter Roesler | 4 | 36 | 11.96 |