Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data

Abstract Background Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition – factors that influence of pain perceptions. Methods We select 52 adult subjects for the study divided into three groups: control group (15), subjects with sporadic migraine (19) and subjects with chronic migraine and medication overuse (18). These subjects underwent magnetic resonance with diffusion tensor to see white matter pathway integrity of the regions of interest involved in pain and emotion. The tests also gather data about pathology. The DTI images and test results were then introduced into feature selection algorithms (Gradient Tree Boosting, L1-based, Random Forest and Univariate) to reduce features of the first dataset and classification algorithms (SVM (Support Vector Machine), Boosting (Adaboost) and Naive Bayes) to perform a classification of migraine group. Moreover we implement a committee method to improve the classification accuracy based on feature selection algorithms. Results When classifying the migraine group, the greatest improvements in accuracy were made using the proposed committee-based feature selection method. Using this approach, the accuracy of classification into three types improved from 67 to 93% when using the Naive Bayes classifier, from 90 to 95% with the support vector machine classifier, 93 to 94% in boosting. The features that were determined to be most useful for classification included are related with the pain, analgesics and left uncinate brain (connected with the pain and emotions). Conclusions The proposed feature selection committee method improved the performance of migraine diagnosis classifiers compared to individual feature selection methods, producing a robust system that achieved over 90% accuracy in all classifiers. The results suggest that the proposed methods can be used to support specialists in the classification of migraines in patients undergoing magnetic resonance imaging

Influenza-associated seizures in healthy adults: Report of 3 cases

To describe seizures occurring in 3 healthy adults with influenza infection. Seizures associated to influenza infection are rare in adults without encephalitis. Clinical observations of 3 healthy adult patients with influenza A and B infection and seizures. We present here 3 healthy adult patients with seizures related to influenza A or B infection without evidence encephalitis, encephalopathy or any other cause for seizures. Prognosis was excellent. Seizures can occur in healthy adults with influenza infection without evidence of encephalitis, a possibility to be borne in mind to avoid potentially harmful therapeutic and diagnostic procedures

Frequency and Characterization of Movement Disorders in Anti-IgLON5 Disease

Background and Objectives Anti-IgLON5 disease is a recently described neurologic disease that shares features of autoimmunity and neurodegeneration. Abnormal movements appear to be frequent and important but have not been characterized and are underreported. We describe the frequency and types of movement disorders in a series of consecutive patients with this disease. Methods In this retrospective, observational study, the presence and phenomenology of movement disorders were assessed with a standardized clinical questionnaire. Available videos were centrally reviewed by 3 experts in movement disorders. Results Seventy-two patients were included. In 41 (57%), the main reason for initial consultation was difficulty walking along with one or several concurrent movement disorders. At the time of anti-IgLON5 diagnosis, 63 (87%) patients had at least 1 movement disorder with a median of 3 per patient. The most frequent abnormal movements were gait and balance disturbances (52 patients [72%]), chorea (24 [33%]), bradykinesia (20 [28%]), dystonia (19 [26%]), abnormal body postures or rigidity (18 [25%]), and tremor (15 [21%]). Other hyperkinetic movements (myoclonus, akathisia, myorhythmia, myokymia, or abdominal dyskinesias) occurred in 26 (36%) patients. The craniofacial region was one of the most frequently affected by multiple concurrent movement disorders (23 patients [32%]) including dystonia (13), myorhythmia (6), chorea (4), or myokymia (4). Considering any body region, the most frequent combination of multiple movement disorders consisted of gait instability or ataxia associated with craniofacial dyskinesias or generalized chorea observed in 31 (43%) patients. In addition to abnormal movements, 87% of patients had sleep alterations, 74% bulbar dysfunction, and 53% cognitive impairment. Fifty-five (76%) patients were treated with immunotherapy, resulting in important and sustained improvement of the movement disorders in only 7 (13%) cases. Discussion Movement disorders are a frequent and leading cause of initial neurologic consultation in patients with anti-IgLON5 disease. Although multiple types of abnormal movements can occur, the most prevalent are disorders of gait, generalized chorea, and dystonia and other dyskinesias that frequently affect craniofacial muscles. Overall, anti-IgLON5 disease should be considered in patients with multiple movement disorders, particularly if they occur in association with sleep alterations, bulbar dysfunction, or cognitive impairment