Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning

Rasheed, Waqas; Bhatti, Mehwish Saba; Hamid, Nor Hisham; Tang, Tong Boon; Idris, Zamzuri

doi:10.1109/primeasia.2017.8280355

Cited by 1 publication

(1 citation statement)

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“…A method based on the positive and unseen learning algorithm was implemented by Rasheed et al for the identification of mild traumatic brain injury (mTBI), which was considered principally similar with diagnosing MCI using default mode network analysis in [48]. In detail, the classification was performed by devising default coherence limits between all pairs of MEG sensors for positive (control) group (7 subjects), and the assessment of severity (15 subjects) was carried out by using the positive and unseen learning method (single class model).…”

Section: Other Neuromarkersmentioning

confidence: 99%

M/EEG-Based Bio-Markers to Predict the MCI and Alzheimer's Disease: A Review From the ML Perspective

Yang

Sánchez-Bornot

Wong‐Lin

et al. 2019

IEEE Trans. Biomed. Eng.

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This work reviews the state-of-the-art neuromarkers development for the prognosis of Alzheimer's diseases (AD) and mild cognitive impairment (MCI). The first part of this study is devoted to reviewing the recently emerged machine learning (ML) algorithms based on electroencephalography (EEG) and magnetoencephalography (MEG) modalities. In particular, the methods are categorized by different types of neuromarkers. The second part of the review is dedicated to a series of investigations that further highlight the differences between these two modalities. Firstly, several source reconstruction methods are reviewed and their source-level performances explored, followed by an objective comparison between EEG and MEG from multiple perspectives. Finally, a number of the most recent reports on classification of MCI/AD during resting state using EEG/MEG are documented to show the up-to-date performance for this wellrecognized data collecting scenario. It is noticed that the MEG modality may be particularly effective in distinguishing between subjects with MCI and healthy controls, a high classification accuracy of more than 98% was reported recently; whereas the EEG seems to be performing well in classifying AD and healthy subjects, which also reached around 98% of the accuracy. A number of influential factors have also been raised and suggested for careful considerations while evaluating the ML-based diagnosis systems in the real-world scenarios.

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Section: Other Neuromarkersmentioning

confidence: 99%