Differentiating Between Migraine and Post-traumatic Headache Using a Machine Learning Classifier

Neurology ◽  
2021 ◽  
Vol 98 (1 Supplement 1) ◽  
pp. S5.2-S6
Author(s):  
Gina Dumkrieger ◽  
Catherine Daniela Chong ◽  
Katherine Ross ◽  
Visar Berisha ◽  
Todd J. Schwedt
Keyword(s):  
Functional Connectivity ◽  
Classification Accuracy ◽  
Diffusion Tensor ◽  
Structural Data ◽  
Tension Type Headache ◽  
Mri Imaging ◽  
Chi Square ◽  
Average Classification Accuracy ◽  
History Of ◽  
Input Variables

ObjectiveThe objective was to develop classification models differentiating persistent PTH (PPTH) and migraine using clinical data and MRI-based measures of brain structure and functional connectivity.BackgroundPTH and migraine commonly have similar phenotypes. Furthermore, migraine is a risk factor for developing PTH, sometimes making it difficult to differentiate PTH from exacerbation of migraine symptoms.Design/MethodsThirty-four individuals with migraine without history of TBI and 48 individuals with mild TBI attributed to PPTH but without history of migraine or prior frequent tension type headache were included. Subjects completed questionnaires assessing headache characteristics, mood, sensory hypersensitivities and cognitive function and underwent MRI imaging during the same day. Clinical features and structural brain measures from T1-weighted imaging, diffusion tensor imaging and functional resting-state measures were included as potential variables. A classifier using ridge logistic regression of principal components (PC) was fit. Since PCs can hinder identification of significant variables in a model, a second regression model was fit directly to the data. In the non-PC based model, input variables were selected based on lowest t-test or chi-square p-value by modality. Average accuracy was calculated using leave-one-out cross validation. The importance of variables to the classifier were examined.ResultsThe PC-based classifier achieved an average classification accuracy of 85%. The non-PC based classifier achieved an average classification accuracy of 74.4%. Both classifiers were more accurate at classifying migraine subjects than PPTH. The PC-based model incorrectly classified 9/48 (18.8%) PPTH subjects compared to 3/34 (8.8%) migraine patients, whereas the non-PC classifier incorrectly classed 16/48 (33.3%) vs 5/34 (14.7%) of migraine subjects. Important variables in the non-PC model included static and dynamic functional connectivity values, several questions from the Beck Depression Inventory, and worsening symptoms and headaches with mental activity.ConclusionsMultivariate models including clinical characteristics, functional connectivity, and brain structural data accurately classify and differentiate PPTH vs migraine.

HUBUNGAN TUNGAU DEBU RUMAH, PAPARAN ASAP ROKOK DAN RIWAYAT PENYAKIT KELUARGA TERHADAP KEJADIAN ASMA BRONKIAL PADA ANAK USIA 5 – 10 TAHUN DI WILAYAH KERJA PUSKESMAS LEMPAKE KOTA SAMARINDA TAHUN 2016

2018 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Sri Evi New Yearsi Pangadongan ◽  
Agustina Rahyu ◽  
Selvy Pasulu
Keyword(s):  
Cigarette Smoke ◽  
Bronchial Asthma ◽  
Sampling Technique ◽  
Total Sample ◽  
Control Group ◽  
Case Group ◽  
Chi Square ◽  
History Of ◽  
Weather Changes ◽  
And Gender

Bronchial Asthma generally starts from childhood which is condition where respiration channel experiences constriction because of hyperactivity with some specific stimulation which cause inflammation. Some risk factors are smoking exposure of cigarette smoke, weather changes, mite on house dirt, pet and history of family sickness. The purpose of this research is to know Relation of mite on house dirt, exposure of cigarette smoke  and history of family sickness with bronchial asthma incident to child 5 – 10  years old on working area of Puskesmas Lempake Samarinda City in 2016. Method which used was analytic survey with Case Control approaching. The total sample was 36 children which consisted of 18 case group and 18 control group with matching by using age and gender which submitted with Purposive Sampling technique. Data Analysis used Chi Square with wrong degree α = 0,005. The result showed that there was relation of mite of house dirt (p = 0,006), history of family sickness (p = 0,001) and no relation with exposure of cigarette smoke (p = 0,370) with bronchial asthma incident to child 5 – 10 years old on working area of Puskesmas Lempake Samarinda City in 2016.

scholarly journals Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 231
Author(s):  
Weiheng Jiang ◽  
Xiaogang Wu ◽  
Yimou Wang ◽  
Bolin Chen ◽  
Wenjiang Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Classification Accuracy ◽  
Communication Systems ◽  
Mimo Systems ◽  
Decision Fusion ◽  
Frequency Characteristics ◽  
Modulation Classification ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Average Classification Accuracy

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

scholarly journals White matter integrity and functional connectivity in adolescents with a parental history of substance use disorder

2021 ◽  
Vol 1 (3) ◽  
pp. 100037
Author(s):  
Xiaofu He ◽  
Diana V. Rodriguez-Moreno ◽  
Yael M. Cycowicz ◽  
Keely Cheslack-Postava ◽  
Huilan Tang ◽  
...  
Keyword(s):  
Substance Use ◽  
White Matter ◽  
Functional Connectivity ◽  
Substance Use Disorder ◽  
White Matter Integrity ◽  
Parental History ◽  
History Of

scholarly journals Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module

2021 ◽  
Vol 11 (10) ◽  
pp. 4614
Author(s):  
Xiaofei Chao ◽  
Xiao Hu ◽  
Jingze Feng ◽  
Zhao Zhang ◽  
Meili Wang ◽  
...  
Keyword(s):  
Disease Control ◽  
Classification Accuracy ◽  
Mobile Terminal ◽  
Disease Classification ◽  
Memory Usage ◽  
Compression Scheme ◽  
Accurate Identification ◽  
Average Classification Accuracy ◽  
Network Compression ◽  
Control And Management

The fast and accurate identification of apple leaf diseases is beneficial for disease control and management of apple orchards. An improved network for apple leaf disease classification and a lightweight model for mobile terminal usage was designed in this paper. First, we proposed SE-DEEP block to fuse the Squeeze-and-Excitation (SE) module with the Xception network to get the SE_Xception network, where the SE module is inserted between the depth-wise convolution and point-wise convolution of the depth-wise separable convolution layer. Therefore, the feature channels from the lower layers could be directly weighted, which made the model more sensitive to the principal features of the classification task. Second, we designed a lightweight network, named SE_miniXception, by reducing the depth and width of SE_Xception. Experimental results show that the average classification accuracy of SE_Xception is 99.40%, which is 1.99% higher than Xception. The average classification accuracy of SE_miniXception is 97.01%, which is 1.60% and 1.22% higher than MobileNetV1 and ShuffleNet, respectively, while its number of parameters is less than those of MobileNet and ShuffleNet. The minimized network decreases the memory usage and FLOPs, and accelerates the recognition speed from 15 to 7 milliseconds per image. Our proposed SE-DEEP block provides a choice for improving network accuracy and our network compression scheme provides ideas to lightweight existing networks.

scholarly journals Structural brain signature of cognitive decline in Parkinson’s disease: DTI-based evidence from the LANDSCAPE study

2019 ◽  
Vol 12 ◽  
pp. 175628641984344 ◽  
Author(s):  
Martin Gorges ◽  
Hans-Peter Müller ◽  
Inga Liepelt-Scarfone ◽  
Alexander Storch ◽  
Richard Dodel ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Corpus Callosum ◽  
Cognitive Decline ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Structural Data ◽  
The Body ◽  
Normal Cognitive Function

Background: The nonmotor symptom spectrum of Parkinson’s disease (PD) includes progressive cognitive decline mainly in late stages of the disease. The aim of this study was to map the patterns of altered structural connectivity of patients with PD with different cognitive profiles ranging from cognitively unimpaired to PD-associated dementia. Methods: Diffusion tensor imaging and neuropsychological data from the observational multicentre LANDSCAPE study were analyzed. A total of 134 patients with PD with normal cognitive function (56 PD-N), mild cognitive impairment (67 PD-MCI), and dementia (11 PD-D) as well as 72 healthy controls were subjected to whole-brain-based fractional anisotropy mapping and covariance analysis with cognitive performance measures. Results: Structural data indicated subtle changes in the corpus callosum and thalamic radiation in PD-N, whereas severe white matter impairment was observed in both PD-MCI and PD-D patients including anterior and inferior fronto-occipital, uncinate, insular cortices, superior longitudinal fasciculi, corona radiata, and the body of the corpus callosum. These regional alterations were demonstrated for PD-MCI and were more pronounced in PD-D. The pattern of involved regions was significantly correlated with the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total score. Conclusions: The findings in PD-N suggest impaired cross-hemispherical white matter connectivity that can apparently be compensated for. More pronounced involvement of the corpus callosum as demonstrated for PD-MCI together with affection of fronto-parieto-temporal structural connectivity seems to lead to gradual disruption of cognition-related cortico-cortical networks and to be associated with the onset of overt cognitive deficits. The increase of regional white matter damage appears to be associated with the development of PD-associated dementia.

scholarly journals Microbial Colonization and their Relation with Potential Cofactors in Patients with Denture Stomatitis

2012 ◽  
Vol 13 (4) ◽  
pp. 456-459 ◽  
Author(s):  
Bhushan Arun Jawale ◽  
Rahul Redasani ◽  
Lalit Chaudhari ◽  
Narendra Chopde ◽  
Amol Pharande ◽  
...  
Keyword(s):  
Mucous Membrane ◽  
Microbial Colonization ◽  
Microbiological Analysis ◽  
Chi Square ◽  
Carbohydrate Ingestion ◽  
Denture Stomatitis ◽  
Dietary History ◽  
Salivary Ph ◽  
Chi Square Test ◽  
History Of

ABSTRACT Aim To determine microbial prevalence in the mucous membrane and prosthesis of denture wearers with and without denture stomatitis and to study its relationship with potential clinical cofactors. Materials and methods Saliva was collected from 200 patients (100 female and 100 male) wearing dental prosthesis for measurement of pH. Oral samples of the mucous membrane and of dentures were taken with sterile swab for microbiological analysis. Medical and dietary history of the subjects was recorded. Chi-square test and analysis of varience were used for statistical analysis. Results Diabetes, hypertension and high carbohydrate ingestion was observed in majority of patients. The pH average in saliva was of 5.1. The presence of C. albicans, S. aureus and S. mutans in the mucous membrane was of 55.4, 56.4 and 63.6% respectively. C. albicans was isolated in 69.7% from the prosthesis, whereas S. aureus and S. mutans were isolated in 45.3%. The isolation of C.albicans was more frequent in patients with deture stomatitis. Conclusion C. albicans, S. aureus and S. mutans frequently colonize the oral mucous of denture wearers. This is more frequently observed in patients with denture stomatitis. Clinical significance Denture stomatitis is associated to Candida albicans, different bacteria and other cofactors, such as salivary pH, carbohydrate ingestion, systemic illnesses and medication. How to cite this article Chopde N, Jawale B, Pharande A, Chaudhari L, Hiremath V, Redasani R. Microbial Colonization and their Relation with Potential Cofactors in Patients with Denture Stomatitis. J Contemp Dent Pract 2012;13(4):456-459.

History of Headache Research in Denmark

Cephalalgia ◽  
2001 ◽  
Vol 21 (7) ◽  
pp. 748-752 ◽  
Author(s):  
P Tfelt-Hansen
Keyword(s):  
Migraine With Aura ◽  
Tension Type Headache ◽  
Regional Cerebral Blood ◽  
Danish Population ◽  
Genetic Studies ◽  
History Of ◽  
Headache Clinic ◽  
Type Headache ◽  
Acute Headache ◽  
First Time

Headache research in Denmark started with the description in 1949 by Dalsgaard-Nielsen of the percutaneous nitroglycerin test. In 1976 Jes Olesen started The Copenhagen Acute Headache Clinic and from that time modern headache research began in Denmark. Specific changes in regional cerebral blood flow during attacks of migraine with aura, spreading oligaemia, were described for the first time in 1980. The first headache classification with operational diagnostic criteria was published in 1988 and used in a Danish population study from 1989. The lifetime prevalence of migraine was 8% in men and 25% in women. An intravenous nitroglycerin test was introduced in 1989 and has been developed as an experimental headache model. In 1993 it was suggested by Jes Olesen et al. that NO supersensitivity could be a possible molecular mechanism of migraine pain. Recent genetic studies have supported the distinction between migraine with aura and migraine without aura. From the middle of the 1980s the pathophysiology of tension-type headache has been investigated and recent results indicate central sensitization in patients with chronic tension-type headache.

scholarly journals Older Adults’ Adoption of Mobility Devices: Interplay of Predisposing Factors and Precipitating Health Events

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 484-485
Author(s):  
Farhanaz Sharmin ◽  
Laura Sands
Keyword(s):  
Older Adults ◽  
Logistic Regression ◽  
Chronic Conditions ◽  
High School Education ◽  
Living Alone ◽  
Mobility Limitations ◽  
Chi Square ◽  
Health Events ◽  
Mobility Devices ◽  
History Of

Abstract Existing mobility limitations and chronic conditions increase likelihood of adopting mobility-related devices such as canes and walkers. Prior research has not considered how recent acute events such as falls and hospitalizations contribute to the adoption of mobility devices. We studied 4,592 older adults who responded to the 2015 and 2016 National Health and Aging Trends Study surveys, and classified adoption of mobility devices as: (i) Never users (did not use mobility devices either year) and (ii) New users (started using mobility devices in 2016). We determined through chi-square tests, that predisposing characteristics from 2015 that were significantly associated with being a New User in 2016 were: being female, aged 80+, minority race, having a high-school education or lower, living alone, being obese, and having a history of dementia, arthritis, stroke, mobility difficulties, falls, and hospitalization (all P’s<0.05). We used logistic regression to determine the contribution of recent precipitating events on the adoption of mobility devices among older adults after controlling for 2015 characteristics that were significantly associated with being a New user. Precipitating events were significantly associated with being a New user of mobility equipment. Specifically, older adults who, between the 2015 and 2016 interviews, experienced a fall (OR=1.7; 95% CI=1.1-2.9), hospitalization (OR=3.7; 95% CI=2.3-5.9) or increase in mobility difficulties (OR=3.7; 95% CI=2.3-5.9) were more likely to be New users. Study findings reveal the importance precipitating events on the adoption of mobility devices, signaling the importance of assessing for need for mobility devices after these events.

Abstract TP207: Sex Differences in Cardiovascular Risk Profiles of Patients with Diabetes in the Greater Cincinnati/ Northern Kentucky Stroke Study

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Tracy E Madsen ◽  
Jane C Khoury ◽  
Kathleen Alwell ◽  
Charles J Moomaw ◽  
Stacie L Demel ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Stroke Risk ◽  
Negative Impact ◽  
Age Difference ◽  
Risk Profiles ◽  
Chi Square ◽  
Adult Age ◽  
Stroke Study ◽  
Patients With Diabetes ◽  
History Of

Introduction: Diabetes mellitus (DM) carries a greater stroke risk for females than males, possibly because of a difference in cardiovascular risk profiles between females and males with DM. Our aim was to compare the sex-specific risk factor profiles for patients with DM with those without DM among patients with acute ischemic stroke (AIS) in the Greater Cincinnati/ Northern Kentucky Stroke Study (GCNKSS). Methods: The GCNKSS ascertained cases of AIS in 2005 and 2010 among adult (age ≥20 years) residents of a biracial population of 1.3 million. Past and current stroke risk factors, obtained via chart review, were compared between those with and without DM using chi-square to examine bivariate differences and multiple logistic regression to examine sex-specific profiles. P < 0.05 was considered statistically significant. Results: There were 3515 patients with incident AIS; 1919 (55%) were female, 697 (20%) were black, and 1146 (33%) had DM. A lower proportion of females with DM were over 65 years old compared with those without DM. The proportion of males >65 with DM was not significantly different from that of males without DM. Among both females and males with DM, significantly more were Black, obese, and had histories of hypertension, high cholesterol, CAD and myocardial infarction compared to those without DM. In sex-specific adjusted analyses, women with DM were significantly less likely to be over 65 and more likely to have CAD than women without DM, whereas age and CAD were not significant factors in differentiating the profiles of men with and without DM. Conclusions: The result that females had their strokes at a younger age if they had a history of DM, and that no such age difference existed in males, suggests that DM is more severe and has a greater negative impact on females than males. As opposed to males, females with DM were also more likely to have CAD compared to those without DM, consistent with a possible sex difference in the association between DM and vascular disease.

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