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  1. Lai CQ, Ibrahim H, Abd Hamid AI, Abdullah MZ, Azman A, Abdullah JM
    Comput Intell Neurosci, 2020;2020:8923906.
    PMID: 32256555 DOI: 10.1155/2020/8923906
    Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.
    Matched MeSH terms: Brain Concussion/diagnosis*
  2. Hamzah N, Narayanan V, Ramli N, Mustapha NA, Mohammad Tahir NA, Tan LK, et al.
    BMJ Open, 2019 09 18;9(9):e028711.
    PMID: 31537559 DOI: 10.1136/bmjopen-2018-028711
    OBJECTIVES: To measure the clinical, structural and functional changes of an individualised structured cognitive rehabilitation in mild traumatic brain injury (mTBI) population.

    SETTING: A single centre study, Malaysia.

    PARTICIPANTS: Adults aged between 18 and 60 years with mTBI as a result of road traffic accident, with no previous history of head trauma, minimum of 9 years education and abnormal cognition at 3 months will be included. The exclusion criteria include pre-existing chronic illness or neurological/psychiatric condition, long-term medication that affects cognitive/psychological status, clinical evidence of substance intoxication at the time of injury and major polytrauma. Based on multiple estimated calculations, the minimum intended sample size is 50 participants (Cohen's d effect size=0.35; alpha level of 0.05; 85% power to detect statistical significance; 40% attrition rate).

    INTERVENTIONS: Intervention group will receive individualised structured cognitive rehabilitation. Control group will receive the best patient-centred care for attention disorders. Therapy frequency for both groups will be 1 hour per week for 12 weeks.

    OUTCOME MEASURES: Primary: Neuropsychological Assessment Battery-Screening Module (S-NAB) scores. Secondary: Diffusion Tensor Imaging (DTI) parameters and Goal Attainment Scaling score (GAS).

    RESULTS: Results will include descriptive statistics of population demographics, CogniPlus cognitive program and metacognitive strategies. The effect of intervention will be the effect size of S-NAB scores and mean GAS T scores. DTI parameters will be compared between groups via repeated measure analysis. Correlation analysis of outcome measures will be calculated using Pearson's correlation coefficient.

    CONCLUSION: This is a complex clinical intervention with multiple outcome measures to provide a comprehensive evidence-based treatment model.

    ETHICS AND DISSEMINATION: The study protocol was approved by the Medical Research Ethics Committee UMMC (MREC ID NO: 2016928-4293). The findings of the trial will be disseminated through peer-reviewed journals and scientific conferences.

    TRIAL REGISTRATION NUMBER: NCT03237676.

    Matched MeSH terms: Brain Concussion/diagnosis
  3. Veeramuthu V, Narayanan V, Ramli N, Hernowo A, Waran V, Bondi MW, et al.
    World Neurosurg, 2017 Jan;97:416-423.
    PMID: 27751922 DOI: 10.1016/j.wneu.2016.10.041
    OBJECTIVE: To compare the extent of persistent neuropsychological impairment in patients with complicated mild traumatic brain injury (mTBI) and those with uncomplicated mTBI.

    METHODS: Sixty-one patients with mTBI (Glasgow Coma Scale score 13-15) were recruited prospectively, categorized according to baseline computed tomography findings, and subjected to neuropsychological assessment at initial admission (n = 61) as well as at a 6-month follow-up (n = 30). The paired t test, Cohen's d effect size calculation, and repeated-measures analysis of variance were used to establish the differences between the 2 groups in terms of neuropsychological performance.

    RESULTS: A trend toward poorer neuropsychological performance among the patients with complicated mTBI was observed during admission; however, performance in this group improved over time. In contrast, the uncomplicated mTBI group showed slower recovery, especially in tasks of memory, visuospatial processing, and executive functions, at follow-up.

    CONCLUSIONS: Our findings suggest that despite the broad umbrella designation of mTBI, the current classification schemes of injury severity for mild neurotrauma should be revisited. They also raise questions about the clinical relevance of both traumatic focal lesions and the absence of visible traumatic lesions on brain imaging studies in patients with milder forms of head trauma.

    Matched MeSH terms: Brain Concussion/diagnosis
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