Catalogo Articoli (Spogli Riviste)

OPAC HELP

Titolo:
Automatic embolus detection by a neural network
Autore:
Kemeny, V; Droste, DW; Hermes, S; Nabavi, DG; Schulte-Altedorneburg, G; Siebler, M; Ringelstein, EB;
Indirizzi:
Univ Munster, Dept Neurol, D-48129 Munster, Germany Univ Munster MunsterGermany D-48129 pt Neurol, D-48129 Munster, Germany Univ Dusseldorf, Dept Neurol, D-4000 Dusseldorf, Germany Univ Dusseldorf Dusseldorf Germany D-4000 ol, D-4000 Dusseldorf, Germany
Titolo Testata:
STROKE
fascicolo: 4, volume: 30, anno: 1999,
pagine: 807 - 810
SICI:
0039-2499(199904)30:4<807:AEDBAN>2.0.ZU;2-4
Fonte:
ISI
Lingua:
ENG
Soggetto:
TRANSCRANIAL DOPPLER ULTRASOUND; PROSTHETIC CARDIAC VALVES; MIDDLE CEREBRAL-ARTERY; CAROTID ENDARTERECTOMY; INTERCENTER AGREEMENT; SIGNALS; IDENTIFICATION; MICROEMBOLI; STENOSIS;
Keywords:
cerebral embolism; image processing, computer-assisted ultrasonography, Doppler;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Life Sciences
Citazioni:
20
Recensione:
Indirizzi per estratti:
Indirizzo: Kemeny, V Univnyunster, Dept Neurol, Albert Schweitzer Str 33, D-48129 Munster, Germa Univ Munster Albert Schweitzer Str 33 Munster Germany D-48129 a
Citazione:
V. Kemeny et al., "Automatic embolus detection by a neural network", STROKE, 30(4), 1999, pp. 807-810

Abstract

Background and Purpose-Embolus detection using transcranial Doppler ultrasound is a useful method for the identification of active embolic sources incerebrovascular diseases. Automated embolus detection systems have been developed to reduce the time of evaluation in long-term recordings and to provide more "objective" criteria. The purpose of this study was to evaluate the critical conditions of automated embolus detection by means of a trainedneural network (EMBotec V5.1 One, STAC GmbH, Germany). Methods-In 11 normal volunteers and in 11 patients with arterial or cardiac embolic sources, we performed simultaneous recordings from both middle orboth posterior cerebral arteries. In the normal subjects, we produced 1342additional artifacts to use the latter as false-positives. Detection of microembolic signals (MES) was done offline from digital audiotapes (1) by anexperienced blinded investigator used as a reference and (2) by a trained 3-layer-feed-forward neural network. Results-From the 1342 provoked artifacts the neural network labeled 216 events as microemboli, yielding an artifact rejection of 85%. In microembolus-positive patients the neural network detected 282 events as emboli, among these 122 signals originating from artifacts; 58 "real" events were not detected. This result revealed a sensitivity of 73.4% and a positive predictive value of 56.7. The spectral power of the detected artifact signals was 16.5+/-5 dB above background signal. MES from patients with artificial heart valves had a spectral power of 6.4+/-2.1 dB; however, in patients with other sources of emboli, MES had an averaged energy reflection of 2.7+/-0.9 dB. Conclusions-The neural network is a promising tool for automated embolus detection, the formal algorithm for signal identification is unknown. However, extreme signal qualities, eg, strong artifacts, lead to misdiagnosis. Similar to other automated embolus detection systems, good signal quality andverification of MES by an experienced investigator is still mandatory.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 29/03/20 alle ore 14:42:09