Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 650267

Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal


Čić, Maja; Šoda, Joško; Bonković, Mirjana
Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal // Computers in biology and medicine, 43 (2013), 12; 2110-2117 doi:10.1016/j.compbiomed.2013.10.002 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 650267 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal

Autori
Čić, Maja ; Šoda, Joško ; Bonković, Mirjana

Izvornik
Computers in biology and medicine (0010-4825) 43 (2013), 12; 2110-2117

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

Ključne riječi
EEG quantification; sleep classification; Empirical Mode Decomposition (EMD); Intrinsic Mode Function (IMF); Generalized Zero Crossing (GZC); Support Vector Machine (SVM)

Sažetak
This study presents a novel approach for electroencephalogram (EEG) signal quantification in which the empirical mode decomposition method, a time-frequency method designated for nonlinear and non-stationary signals, decomposes the EEG signal into intrinsic mode functions (IMF) with corresponding frequency ranges that characterize the appropriate oscillatory modes embedded in the brain neural activity acquired using EEG. To calculate the instantaneous frequency of IMFs, an algorithm was developed using the Generalized Zero Crossing method. From the resulting frequencies, two different novel features were generated: the median instantaneous frequencies and the number of instantaneous frequency changes during a 30 s segment for seven IMFs. The sleep stage classification for the daytime sleep of 20 healthy babies was determined using the Support Vector Machine classification algorithm. The results were evaluated using the cross-validation method to achieve an approximately 90% accuracy and with new examinee data to achieve 80% average accuracy of classification. The obtained results were higher than the human experts’ agreement and were statistically significant, which positioned the method, based on the proposed features, as an efficient procedure for automatic sleep stage classification. The uniqueness of this study arises from newly proposed features of the time-frequency domain, which bind characteristics of the sleep signals to the oscillation modes of brain activity, reflecting the physical characteristics of sleep, and thus have the potential to highlight the congruency of twin pairs with potential implications for the genetic determination of sleep.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekti:
023-0982886-1612 - Napredni modeli procjene Sunčevog zračenja i primjena u FN pretvorbi energije
023-0232005-2003 - AgISEco - Agentski orijentirani inteligentni sustavi nadzora i zaštite okoliša (Stipaničev, Darko, MZOS ) ( POIROT)

Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split,
Pomorski fakultet, Split

Profili:

Avatar Url Mirjana Bonković (autor)

Avatar Url Maja Čić (autor)

Avatar Url Joško Šoda (autor)

Citiraj ovu publikaciju

Čić, Maja; Šoda, Joško; Bonković, Mirjana
Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal // Computers in biology and medicine, 43 (2013), 12; 2110-2117 doi:10.1016/j.compbiomed.2013.10.002 (međunarodna recenzija, članak, znanstveni)
Čić, M., Šoda, J. & Bonković, M. (2013) Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal. Computers in biology and medicine, 43 (12), 2110-2117 doi:10.1016/j.compbiomed.2013.10.002.
@article{article, year = {2013}, pages = {2110-2117}, DOI = {10.1016/j.compbiomed.2013.10.002}, keywords = {EEG quantification, sleep classification, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF), Generalized Zero Crossing (GZC), Support Vector Machine (SVM)}, journal = {Computers in biology and medicine}, doi = {10.1016/j.compbiomed.2013.10.002}, volume = {43}, number = {12}, issn = {0010-4825}, title = {Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal}, keyword = {EEG quantification, sleep classification, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF), Generalized Zero Crossing (GZC), Support Vector Machine (SVM)} }
@article{article, year = {2013}, pages = {2110-2117}, DOI = {10.1016/j.compbiomed.2013.10.002}, keywords = {EEG quantification, sleep classification, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF), Generalized Zero Crossing (GZC), Support Vector Machine (SVM)}, journal = {Computers in biology and medicine}, doi = {10.1016/j.compbiomed.2013.10.002}, volume = {43}, number = {12}, issn = {0010-4825}, title = {Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal}, keyword = {EEG quantification, sleep classification, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF), Generalized Zero Crossing (GZC), Support Vector Machine (SVM)} }

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
  • MEDLINE


Citati





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font