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Introduction of an embolus detection system based on analysis of the transcranial Doppler audio-signal

R. W. M. KEUNEN*{, R. HOOGENBOEZEM{, R. WIJNANDS{, A. C. M. VAN DEN HENGEL{and R. G. A. ACKERSTAFFx{Department of Neurology and Clinical Neurophysiology, Haga Teaching Hospitals (location Leyenburg),
Leyweg 275 2545 CH, The Hague, The Netherlands
{Department of Electro Engineering of the Technical High School of The Hague (HHS, sector techniek,
afdeling electro-techniek), The Netherlands
xDepartment of Neurology and Clinical Neurophysiology, Antonius Hospital Nieuwegein/Utrecht,
The Netherlands

A new embolus detection system (EDS) is presented, built with the intention of detecting ongoing cerebral embolization in patients at risk of transient ischaemic attacks or stroke.It is based on the analysis of the audio-Doppler signal of a transcranial Doppler machine.The algorithm of the EDS estimates the intensity, duration and zero-crossing dynamics of the audio signal. The EDS has a multi-layer neural network which classifies events into micro-emboli signals (MES) or artefacts. The decision-making component of the software has been validated against human experts. Data from patients in the postoperative phase of carotid surgery were used for the validation process. The results showed agreement in MES and artefact classification of493%. Apart from a monitoring display, the monitoring system includes a verification unit that allows the user to listen and to look at all data of individual MES and artefacts. Moreover, the system allows the user to record, store and re-calculate all data files. Data are stored using European Data Format, which allows data transportation over the Internet. The EDS may have a potential in stroke risk stratification, evaluating the effect of novel antithrombotic therapies, and in peri-operative and remote monitoring of carotid endarterectomy.

提出了一種新的栓塞檢測系統(tǒng)(EDS),其目的是對經(jīng)顱多普勒機(jī)器的音頻多普勒信號進(jìn)行分析,以檢測處于短暫性腦缺血發(fā)作或中風(fēng)風(fēng)險的患者正在進(jìn)行的腦栓塞。 EDS算法估計音頻信號的強(qiáng)度,持續(xù)時間和過零動態(tài)。 EDS具有多層神經(jīng)網(wǎng)絡(luò),可將事件分類為微栓塞信號(MES)或偽影。該軟件的決策組件已針對人類專家進(jìn)行了驗證。頸動脈手術(shù)術(shù)后階段的患者數(shù)據(jù)用于驗證過程。結(jié)果表明,在MES和偽造物分類上的一致性為493%。除了監(jiān)控顯示之外,監(jiān)控系統(tǒng)還包括一個驗證單元,該驗證單元使用戶能夠收聽并查看各個MES和人工制品的所有數(shù)據(jù)。此外,該系統(tǒng)允許用戶記錄,存儲和重新計算所有數(shù)據(jù)文件。數(shù)據(jù)使用歐洲數(shù)據(jù)格式存儲,從而可以通過Internet進(jìn)行數(shù)據(jù)傳輸。 EDS可能在卒中風(fēng)險分層,評估新型抗血栓形成療法的效果以及圍手術(shù)期和遠(yuǎn)程監(jiān)測頸動脈內(nèi)膜切除術(shù)方面具有潛力。(譯文來自GOOGLE)

Embolus Detection System.pdf

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