C-BER - Indexed Articles in Journals

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Browsing C-BER - Indexed Articles in Journals by Author "Achilles,F"
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    Deep convolutional neural networks for automatic identification of epileptic seizures in infrared and depth images
    ( 2015) Achilles,F ; Belagiannis,V ; Tombari,F ; Loesch,A ; João Paulo Cunha ; Navab,N ; Noachtar,S
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    EP 114. Uncovering epileptic seizures – A feasibility study for the semiological analysis of hidden patient motion during epileptic seizures
    ( 2016) Achilles,F ; Hugo Miguel Choupina ; Loesch,A ; João Paulo Cunha ; Remi,J ; Vollmar,C ; Tombari,F ; Navab,N ; Noachtar,S
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    NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification
    ( 2016) João Paulo Cunha ; Hugo Miguel Choupina ; Rocha,AP ; Fernandes,JM ; Achilles,F ; Loesch,AM ; Vollmar,C ; Hartl,E ; Noachtar,S
    Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure -induced body movements - called seizure semiology. Thus, synchronous EEG and (2D) video recording systems (known as Video-EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists' subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient's body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m(3) vs. 0.14 m(3), p< 0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p< 0.01) and presented higher jerking levels (mean = 345 ms(-3) vs 172 ms(-3), p< 0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body motion trajectories when compared to a 2D frame by frame tracking procedure. We conclude that this new approach provides a more comfortable (both for patients and clinical professionals), simpler, faster and lower-cost procedure than previous approaches, therefore providing a reliable tool to quantitatively analyze MOI patterns of epileptic seizures in the routine of EMUs around the world. We hope this study encourages other EMUs to adopt similar approaches so that more quantitative information is used to improve epilepsy diagnosis.