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Pregled bibliografske jedinice broj: 952912

Optimizing the detection of characteristic waves in ECG based on processing methods combinations


Friganović, Krešimir; Kukolja, Davor; Jović, Alan; Cifrek, Mario; Krstačić, Goran
Optimizing the detection of characteristic waves in ECG based on processing methods combinations // IEEE access, 6 (2018), 50609-50626 doi:10.1109/ACCESS.2018.2869943 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 952912 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Optimizing the detection of characteristic waves in ECG based on processing methods combinations

Autori
Friganović, Krešimir ; Kukolja, Davor ; Jović, Alan ; Cifrek, Mario ; Krstačić, Goran

Izvornik
IEEE access (2169-3536) 6 (2018); 50609-50626

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
ECG, characteristic waves, automatic detection algorithms, clustering, expert system, biomedical signal analysis

Sažetak
Accurate detection of characteristic electrocardiogram (ECG) waves is necessary for ECG analysis and interpretation. In this paper, we distinguish four processing steps of detection algorithms: noise and artefacts reduction, transformations, fiducial marks selection of wave candidates, and decision rule. Processing steps combinations from several detection algorithms are used to find QRS, P, and T wave peaks. Additionally, we consider the search window parameter modification based on waveform templates extracted by heart cycles clustering. The methods are extensively evaluated on two public ECG databases containing QRS, P, and T wave peaks annotations. We found that the combination of morphological mathematical filtering with Elgendi's algorithm works best for QRS detection on MIT-BIH Arrhythmia Database (detection error rate (DER = 0.48%, Lead I). The combination of modified Martinez’s PT and wavelet transform (WT) methods gave the best results for P wave peaks detection on both databases, when both leads are considered (MIT- BIH Arrhythmia Database: DER = 32.13%, Lead I, DER = 42.52%, Lead II ; QT Database: DER = 21.23%, Lead I, DER = 26.80%, Lead II). Waveform templates in combination with Martinez's WT obtained the best results for T wave peaks detection on QT Database (DER = 25.15%, Lead II). Our work demonstrates that combining some of the best proposed methods in literature leads to improvements over the original methods for ECG waves detection, while maintaining satisfactory computation times.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Kliničke medicinske znanosti



POVEZANOST RADA


Projekti:
HRZZ-UIP-2014-09-6889 - Programski sustav za paralelnu analizu više heterogenih nizova vremenskih podataka s primjenom u biomedicini (MULTISAB) (Jović, Alan, HRZZ - 2014-09) ( POIROT)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Ericsson Nikola Tesla d.d.,
Medicinski fakultet, Osijek,
Sveučilište Libertas

Citiraj ovu publikaciju

Friganović, Krešimir; Kukolja, Davor; Jović, Alan; Cifrek, Mario; Krstačić, Goran
Optimizing the detection of characteristic waves in ECG based on processing methods combinations // IEEE access, 6 (2018), 50609-50626 doi:10.1109/ACCESS.2018.2869943 (međunarodna recenzija, članak, znanstveni)
Friganović, K., Kukolja, D., Jović, A., Cifrek, M. & Krstačić, G. (2018) Optimizing the detection of characteristic waves in ECG based on processing methods combinations. IEEE access, 6, 50609-50626 doi:10.1109/ACCESS.2018.2869943.
@article{article, year = {2018}, pages = {50609-50626}, DOI = {10.1109/ACCESS.2018.2869943}, keywords = {ECG, characteristic waves, automatic detection algorithms, clustering, expert system, biomedical signal analysis}, journal = {IEEE access}, doi = {10.1109/ACCESS.2018.2869943}, volume = {6}, issn = {2169-3536}, title = {Optimizing the detection of characteristic waves in ECG based on processing methods combinations}, keyword = {ECG, characteristic waves, automatic detection algorithms, clustering, expert system, biomedical signal analysis} }
@article{article, year = {2018}, pages = {50609-50626}, DOI = {10.1109/ACCESS.2018.2869943}, keywords = {ECG, characteristic waves, automatic detection algorithms, clustering, expert system, biomedical signal analysis}, journal = {IEEE access}, doi = {10.1109/ACCESS.2018.2869943}, volume = {6}, issn = {2169-3536}, title = {Optimizing the detection of characteristic waves in ECG based on processing methods combinations}, keyword = {ECG, characteristic waves, automatic detection algorithms, clustering, expert system, biomedical signal analysis} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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