Name
Title | ||
|---|---|---|
Preclustering of Electrocardiographic Signals Using Left-to-Right Hidden Markov Models |
Abstract | ||
|---|---|---|
Holter signals are ambulatory long-term electrocardiographic (ECG) registers used to detect heart diseases which are difficult to find in normal ECGs. These signals normally include several channels and its duration is up to 48 hours. The principal problem for the cardiologists consists of the manual inspection of the whole holter ECG to find all those beats whose morphology differ from the normal synus rhythm. The later analisys of these arrhythmia beats yields a diagnostic from the pacient's heart condition. The Hidden Markov Models (HMM) can be used in ECG diagnosis avoiding the manual inspection. In this paper we improve the performance of the HMM clustering method introducing a preclustering stage in order to diminish the number of elements to be finally processed and reducing the global computational cost. An experimental comparative study is carried out, utilizing records form the MIT-BIH Arrhythmia database. Finally some results are presented in order to validate the procedure. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-27868-9_103 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
comparative study,hidden markov model | Pattern recognition,Markov model,Computer science,Algorithm,Artificial intelligence,Cluster analysis,Electrocardiography,Hidden Markov model,Distributed computing,Statistical analysis | Conference |
Volume | ISSN | Citations |
3138 | 0302-9743 | 1 |
PageRank | References | Authors |
0.48 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pau Micó | 1 | 13 | 3.04 |
| D. Cuesta-Frau | 2 | 149 | 23.78 |
| Daniel Novák | 3 | 26 | 9.29 |