Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation
<p/> <p>This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five...
| Hoofdauteurs: | , |
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| Formaat: | Artikel |
| Taal: | English |
| Gepubliceerd in: |
SpringerOpen
2007-01-01
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| Reeks: | EURASIP Journal on Advances in Signal Processing |
| Online toegang: | http://asp.eurasipjournals.com/content/2007/056215 |
| _version_ | 1831559417110200320 |
|---|---|
| author | Andreão Rodrigo Varejão Boudy Jérôme |
| author_facet | Andreão Rodrigo Varejão Boudy Jérôme |
| author_sort | Andreão Rodrigo Varejão |
| collection | DOAJ |
| description | <p/> <p>This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC) detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.</p> |
| first_indexed | 2024-12-17T05:28:42Z |
| format | Article |
| id | doaj.art-d4cc5d51b2fe41dba78c5d65feaa079d |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-17T05:28:42Z |
| publishDate | 2007-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-d4cc5d51b2fe41dba78c5d65feaa079d2022-12-21T22:01:47ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802007-01-0120071056215Combining Wavelet Transform and Hidden Markov Models for ECG SegmentationAndreão Rodrigo VarejãoBoudy Jérôme<p/> <p>This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC) detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.</p>http://asp.eurasipjournals.com/content/2007/056215 |
| spellingShingle | Andreão Rodrigo Varejão Boudy Jérôme Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation EURASIP Journal on Advances in Signal Processing |
| title | Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation |
| title_full | Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation |
| title_fullStr | Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation |
| title_full_unstemmed | Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation |
| title_short | Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation |
| title_sort | combining wavelet transform and hidden markov models for ecg segmentation |
| url | http://asp.eurasipjournals.com/content/2007/056215 |
| work_keys_str_mv | AT andre227orodrigovarej227o combiningwavelettransformandhiddenmarkovmodelsforecgsegmentation AT boudyj233r244me combiningwavelettransformandhiddenmarkovmodelsforecgsegmentation |