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Overview of Mixed and Augmented Reality in Medicine
Published in Terry M. Peters, Cristian A. Linte, Ziv Yaniv, Jacqueline Williams, Mixed and Augmented Reality in Medicine, 2018
In the neurosurgery domain, Wang et al. (2011) developed an AR approach to combine a real environment with virtual models to plan epilepsy surgery. During such procedures, it is important for the surgeon to correlate preoperative cortical morphology (from preoperative images) with the actual surgical field. This team developed an alternate approach to providing enhanced visualization by fusing a direct (photographic) view of the surgical field with the 3D patient model during image-guided epilepsy surgery. To achieve this goal, they correlated the preoperative plan with the intraoperative surgical scene, first by a manual landmark-based registration and then by an intensity-based perspective 3D-2D registration for camera pose estimation. The 2D photographic image was then texture-mapped onto the standard 3D preoperative model created by an image-guidance platform using the calculated camera pose. This approach was validated clinically as part of a neuro-navigation system, and the efficacy of this alternative to sophisticated AR environments for assisting in epilepsy surgery was demonstrated. Requiring no specialized display equipment, the approach also requires minimal changes to existing systems and workflow, thus making it well suited to the OR environment.
Machine Learning Approaches to Automatic Interpretation of EEGs
Published in Ervin Sejdić, Tiago H. Falk, Signal Processing and Machine Learning for Biomedical Big Data, 2018
Most previous studies have focused on small numbers of patients (typically less than 20) and have not demonstrated robust performance. For example, the Seizure Detection Challenge (https://www.kaggle.com/c/seizure-detection) [76] was based on “prolonged intracranial EEG recorded from four dogs with naturally occurring epilepsy and from 8 patients with medication resistant seizures during evaluation for epilepsy surgery.” Most research focused on EEG classification has concentrated on static classification of the data. An example of this type of approach was used by Bao et al. [68], in which 94% classification accuracy was achieved for epilepsy detection on six normal and six epileptic patients. The signal was segmented into 20-second non-overlapping intervals (4096 samples at 200 Hz), and converted to a feature vector using a collection of heterogeneous features that included power spectral intensity, fractal dimension, and other measures of nonlinearity. A probabilistic neural network (PNN) was applied to each channel, and the individual channel outputs were combined using a voting system.
Other Biomedical Signals
Published in Kayvan Najarian, Robert Splinter, Biomedical Signal and Image Processing, 2016
Kayvan Najarian, Robert Splinter
MEG studies in psychiatric disorders have contributed materially to improved understanding of anomalous brain lateralization in the psychoses, have suggested that P50 abnormalities may reflect altered gamma band activity, and have provided evidence of hemisphere-specific abnormalities of short-term auditory memory function The clinical utility of MEG includes presurgical mapping of sensory cortical areas, localization of epileptiform abnormalities, and localization of areas of brain hypoperfusion in stroke patients In pediatric applications, MEG is used for planning of epilepsy surgery and also provides unlimited possibilities to study the brain functions of healthy and developmentally deviant children.
Electric source imaging for presurgical epilepsy evaluation: current status and future prospects
Published in Expert Review of Medical Devices, 2020
Pierre Mégevand, Margitta Seeck
With dozens of millions of people affected worldwide, epilepsy is one of the most common and disabling neurological disorders [1]. Epilepsy consists in an enduring predisposition to suffer from epileptic seizures, which are defined as the transient occurrence of signs or symptoms due to abnormal excessive or synchronous neuronal activity in the brain [2]. Thus, the documentation of abnormal cerebral neuronal activity by electroencephalography (EEG) plays a key role in the diagnosis and management of epilepsy. Drug-resistant epilepsy, where antiepileptic medications fail to control seizures, affects about a third of patients with epilepsy [3]. In these patients, epilepsy surgery, i.e. the removal or disconnection of the brain region responsible for generating seizures (the epileptogenic zone), represents an important therapeutic option, as it can lead to freedom from seizures or significantly reduce seizure frequency and severity [4,5].