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Titolo:
AN APPROACH TO INTELLIGENT ISCHEMIA MONITORING
Autore:
BOSNJAK A; BEVILACQUA G; PASSARIELLO G; MORA F; SANSO B; CARRAULT G;
Indirizzi:
UNIV CARABOBO,ESCUELA MED,CTR INVEST MED & BIOTECNOL,PROCESAMIENTO IMAGENES GRP VALENCIA SPAIN UNIV SIMON BOLIVAR,BIOINGN & BIOFIS APLICADA GRP CARACAS 1080A VENEZUELA UNIV SIMON BOLIVAR,CTR ESTADIST & SOFTWARE MATEMAT CARACAS 1080A VENEZUELA UNIV RENNES 1,TRAITEMENT SIGNAL LAB F-35042 RENNES FRANCE
Titolo Testata:
Medical & biological engineering & computing
fascicolo: 6, volume: 33, anno: 1995,
pagine: 749 - 756
SICI:
0140-0118(1995)33:6<749:AATIIM>2.0.ZU;2-0
Fonte:
ISI
Lingua:
ENG
Soggetto:
TREND-DETECTION; KALMAN FILTER; SYSTEMS; TIME;
Keywords:
ALARMING; ECG ANALYSIS; INTELLIGENT INSTRUMENTATION; ISCHEMIA DETECTION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
24
Recensione:
Indirizzi per estratti:
Citazione:
A. Bosnjak et al., "AN APPROACH TO INTELLIGENT ISCHEMIA MONITORING", Medical & biological engineering & computing, 33(6), 1995, pp. 749-756

Abstract

The paper describes an approach to intelligent ischaemia event detection based on ECG ST-T segment analysis. ST-T trends are processed by means of a Bayesian forecasting approach using the multistate Kalman filter. A complete procedure, intended for use in CCU/ICU monitoring areas, is proposed, in order to give the clinician an intelligent monitoring tool. The approach serves to describe trends and their changes in a symbolic way. A novel aspect is its ability to observe certain features of ST-T elevation/depression not detected by other means, and to reject artefacts and erroneous events. A sensivity of 89.58% and a predictivity of 84.31% are obtained on selected records of the European ST-T database. Using a restriction on event amplitude, the predictivity is raised to 95.55%. An ischaemia sensitivity index of 1.2 was determined. The method has been shown to be a robust and practical trend analysis tool, and seems to be appropriate for numeric/symbolic transformations in next-generation intelligent monitoring systems.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 12/07/20 alle ore 13:11:00