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Titolo:
A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms
Autore:
Papaloukas, C; Fotiadis, DI; Liavas, AP; Likas, A; Michalis, LK;
Indirizzi:
Univ Ioannina, Dept Comp Sci, GR-45110 Ioannina, Greece Univ Ioannina Ioannina Greece GR-45110 mp Sci, GR-45110 Ioannina, Greece Univ Ioannina, Sch Med, Dept Med Phys, GR-45110 Ioannina, Greece Univ Ioannina Ioannina Greece GR-45110 d Phys, GR-45110 Ioannina, Greece Univ Ioannina, Sch Med, Dept Cardiol, GR-45110 Ioannina, Greece Univ Ioannina Ioannina Greece GR-45110 ardiol, GR-45110 Ioannina, Greece
Titolo Testata:
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
fascicolo: 1, volume: 39, anno: 2001,
pagine: 105 - 112
SICI:
0140-0118(200101)39:1<105:AKTFAD>2.0.ZU;2-5
Fonte:
ISI
Lingua:
ENG
Soggetto:
ARTIFICIAL NEURAL-NETWORK; ST-SEGMENT ANALYSIS; MYOCARDIAL-INFARCTION; ECG ANALYSIS; ISCHEMIA; RECOVERY; SYSTEM;
Keywords:
ischaemic episodes detection; knowledge-based method; ECG noise handling;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Engineering, Computing & Technology
Citazioni:
34
Recensione:
Indirizzi per estratti:
Indirizzo: Fotiadis, DI Univ Ioannina, Dept Comp Sci, GR-45110 Ioannina, Greece Univ Ioannina Ioannina Greece GR-45110 110 Ioannina, Greece
Citazione:
C. Papaloukas et al., "A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms", MED BIO E C, 39(1), 2001, pp. 105-112

Abstract

A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics).

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Documento generato il 10/07/20 alle ore 00:20:11