Pregled bibliografske jedinice broj: 1271167
Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time–Frequency Distributions with Application in EEG Seizure Signals Analysis
Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time–Frequency Distributions with Application in EEG Seizure Signals Analysis // Sensors, 23 (2023), 10; 4680, 29 doi:10.3390/s23104680 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1271167 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Method for Automatic Estimation of Instantaneous
Frequency and Group Delay in Time–Frequency
Distributions with Application in EEG Seizure
Signals Analysis
Autori
Jurdana, Vedran ; Vrankić, Miroslav ; Lopac, Nikola ; Madhale Jadav, Guruprasad
Izvornik
Sensors (1424-8220) 23
(2023), 10;
4680, 29
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
time–frequency distributions ; Rényi entropy ; instantaneous frequency ; group delay ; EEG
Sažetak
Instantaneous frequency (IF) is commonly used in the analysis of electroencephalogram (EEG) signals to detect oscillatory-type seizures. However, IF cannot be used to analyze seizures that appear as spikes. In this paper, we present a novel method for the automatic estimation of IF and group delay (GD) in order to detect seizures with both spike and oscillatory characteristics. Unlike previous methods that use IF alone, the proposed method utilizes information obtained from localized Rényi entropies (LREs) to generate a binary map that automatically identifies regions requiring a different estimation strategy. The method combines IF estimation algorithms for multicomponent signals with time and frequency support information to improve signal ridge estimation in the time–frequency distribution (TFD). Our experimental results indicate the superiority of the proposed combined IF and GD estimation approach over the IF estimation alone, without requiring any prior knowledge about the input signal. The LRE-based mean squared error and mean absolute error metrics showed improvements of up to 95.70% and 86.79%, respectively, for synthetic signals and up to 46.45% and 36.61% for real-life EEG seizure signals.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Projekti:
--uniri-mladi-tehnic-22-16 - Analiza i klasifikacija nestacionarnih signala korištenjem naprednih metoda dubokoga učenja (Lopac, Nikola) ( CroRIS)
VLASTITA-SREDSTVA-uniri-tehnic-17 - Računalom potpomognuta digitalna analiza i klasifikacija signala (UNIRI-TEHNIC-18-17) (Lerga, Jonatan, VLASTITA-SREDSTVA - UNIRI2018) ( CroRIS)
Ustanove:
Tehnički fakultet, Rijeka,
Pomorski fakultet, Rijeka
Citiraj ovu publikaciju:
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
Uključenost u ostale bibliografske baze podataka::
- INSPEC