Name
Abstract | ||
|---|---|---|
In this paper we introduce a coarse-to-fine arrhythmia classification technique that can be used for efficient processing of large Electrocardiogram (ECG) records. This technique reduces time-complexity of arrhythmia classification by reducing size of the beats as well as by quantizing the number of beats using Multi-Section Vector Quantization (MSVQ) without compromising on the accuracy of the classification. The proposed solution is tested on MIT-BIH arrhythmia database. This work achieves a highest computational speed-up factor of 2.2:1 in comparison with standard arrhythmia classification technique with marginal loss (<;1%) in classification accuracy. The clinical application of this technique enhances physician's throughput by factor of 2x while processing large ECG records from Holter system. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6943873 | EMBC |
Keywords | Field | DocType |
medical information systems,electrocardiography,large database classification,medical disorders,diseases,arrhythmia classification accuracy,real-time arrhythmia classification,holter system,large electrocardiogram record processing,mit-bih arrhythmia database,clinical application,computational speed-up factor,medical signal processing,beat size reduction,vector quantisation,coarse-to-fine arrhythmia classification,computational complexity,msvq method,time-complexity reduction,multisection vector quantization,signal classification,standard arrhythmia classification technique,physician throughput,classification,large ecg record processing,time series,real-time systems,beat number quantization | Computer science,Speech recognition | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandipan Chakroborty | 1 | 31 | 3.34 |
| Meru A. Patil | 2 | 0 | 1.01 |