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(Stroke. 1999;30:807-810.)
© 1999 American Heart Association, Inc.

Original Contributions

Automatic Embolus Detection by a Neural Network

Vendel Kemény, MD; Dirk W. Droste, MD; Stefan Hermes; Darius G. Nabavi, MD; Gernot Schulte-Altedorneburg, MD; Mario Siebler, MD E. Bernd Ringelstein, MD

From the Department of Neurology (V.K., D.W.D., S.H, D.G.N., G.S.-A., E.B.R.), University of Münster; and the Department of Neurology (M.S.), Heinrich-Heine-University, Düsseldorf, Germany.

Correspondence to Vendel Kemény, MD, Department of Neurology, University of Münster, Albert-Schweitzer-Str. 33, D-48129 Münster, Germany. E-mail kemeny{at}uze.net

Background and Purpose—Embolus detection using transcranial Doppler ultrasound is a useful method for the identification of active embolic sources in cerebrovascular 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 trained neural 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 or both posterior cerebral arteries. In the normal subjects, we produced 1342 additional artifacts to use the latter as false-positives. Detection of microembolic signals (MES) was done offline from digital audiotapes (1) by an experienced 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 and verification of MES by an experienced investigator is still mandatory.

Key Words: cerebral embolism • image processing, computer-assisted • ultrasonography, Doppler

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