scholarly journals One-Channel Simple Frontal Electroencephalography Detection of Tinnitus and Related Distress

2021 ◽  
Author(s):  
Sho Kanzaki ◽  
Yasue MITSUKURA ◽  
Masaru MIMURA
Keyword(s):  
Support Vector Machine ◽  
Medical History ◽  
Objective Test ◽  
Chronic Tinnitus ◽  
Support Vector ◽  
Healthy Controls ◽  
Analysis Technique ◽  
Recorded Data

Abstract Background: No simple objective test is available so far for diagnosing tinnitus. Thus, diagnosis is typically based on the patients’ medical history. Herein, we propose the usefulness of a simple one-channel electroencephalography (EEG) with a newly developed analysis technique to objectively detect tinnitus.Methods: We developed a portable EEG device to measure frontal Fp1 activities. The recorded data of 31 patients with chronic tinnitus and 29 healthy controls were analyzed with a support vector machine.Results: We identified tinnitus by analyzing the frequency obtained by frontal Fp EEG. We discovered that 9- and 13-Hz changes were critical for identifying tinnitus.Conclusions: One-channel Fp1 measurement reliably detected tinnitus (sensitivity, 72%; specificity, 96%). EEG measurement may also be related with tinnitus-related distress in patients. Further EEG studies are warranted to determine more accurately the pathophysiology of tinnitus.

Sleep–Wake Transition in Narcolepsy and Healthy Controls Using a Support Vector Machine

2014 ◽  
Vol 31 (5) ◽  
pp. 397-401 ◽  
Author(s):  
Julie B. Jensen ◽  
Helge B. D. Sorensen ◽  
Jacob Kempfner ◽  
Gertrud L. Sørensen ◽  
Stine Knudsen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Healthy Controls ◽  
Wake Transition

scholarly journals Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study

2011 ◽  
Vol 42 (5) ◽  
pp. 1037-1047 ◽  
Author(s):  
J. Mourao-Miranda ◽  
A. A. T. S. Reinders ◽  
V. Rocha-Rego ◽  
J. Lappin ◽  
J. Rondina ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Psychotic Episode ◽  
Svm Classifier ◽  
Healthy Controls ◽  
Imaging Data ◽  
Healthy Individuals ◽  
First Psychotic Episode ◽  
Episodic Course ◽  
Individualized Prediction

BackgroundTo date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.MethodOne hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls.ResultsAt baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035).ConclusionsWe provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data.

scholarly journals Heart Disease Risk Predictor using Support Vector Machine

2019 ◽  
Vol 8 (2) ◽  
pp. 4629-4636
Keyword(s):  
Support Vector Machine ◽  
Heart Disease ◽  
Medical History ◽  
Disease Risk ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Mining Technique ◽  
Disease Risk Factor ◽  
Risk Predictor ◽  
Heart Disease Risk

Nearly 17.5 million deaths occur due to cardiovascular diseases throughout the world. If we could create such a mechanism or system that could tell people about their heart condition based on their medical history and warn them of any risk than it could be of huge help. In our work, we will use machine learning algorithms to forecast the heart disease risk factor for a person depending upon some attributes in their medical history. The data mining technique Naive Bayes, Decision tree, Support Vector Machine, and Logistic Regression is analyzed on the Heart disease database. The accuracy of different algorithms is measured and then the algorithms are compared.

scholarly journals Machine Learning Approaches: From Theory to Application in Schizophrenia

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Elisa Veronese ◽  
Umberto Castellani ◽  
Denis Peruzzo ◽  
Marcella Bellani ◽  
Paolo Brambilla
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Clinical Practice ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Healthy Controls ◽  
Learning Approaches ◽  
Neuroimaging Data ◽  
Comparison Of The Results

In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.

scholarly journals Broad Learning Enhanced 1H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yan Li ◽  
Zuhao Ge ◽  
Zhiyan Zhang ◽  
Zhiwei Shen ◽  
Yukai Wang ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Early Diagnosis ◽  
Lupus Erythematosus ◽  
Learning System ◽  
Support Vector ◽  
Healthy Controls ◽  
Systemic Lupus ◽  
1H Mrs

In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (1H-MRS) to diagnose neuropsychiatric systemic lupus erythematosus (NPSLE) with the assistance of a support vector machine broad learning system (BL-SVM). We retrospectively analysed 23 confirmed patients and 16 healthy controls, who underwent a 3.0 T magnetic resonance imaging (MRI) sequence with multivoxel 1H-MRS in our hospitals. One hundred and seventeen metabolic features were extracted from the multivoxel 1H-MRS image. Thirty-three metabolic features selected by the Mann-Whitney U test were considered to have a statistically significant difference ( p < 0.05 ). However, the best accuracy achieved by conventional statistical methods using these 33 metabolic features was only 77%. We turned to develop a support vector machine broad learning system (BL-SVM) to quantitatively analyse the metabolic features from 1H-MRS. Although not all the individual features manifested statistics significantly, the BL-SVM could still learn to distinguish the NPSLE from the healthy controls. The area under the receiver operating characteristic curve (AUC), the sensitivity, and the specificity of our BL-SVM in predicting NPSLE were 95%, 95.8%, and 93%, respectively, by 3-fold cross-validation. We consequently conclude that the proposed system effectively and efficiently working on limited and noisy samples may brighten a noinvasive in vivo instrument for early diagnosis of NPSLE.

scholarly journals Human action recognition using dimensionality reduction and support vector machine

2019 ◽  
pp. 48-53 ◽  
Author(s):  
L V Shiripova ◽  
E V Myasnikov
Keyword(s):  
Support Vector Machine ◽  
Dimensionality Reduction ◽  
Experimental Studies ◽  
Principal Component ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Human Actions ◽  
Analysis Technique

The paper is devoted to the problem of recognizing human actions in videos recorded in the optical range of wavelengths. An approach proposed in this paper consists in the detection of a moving person on a video sequence with the subsequent size normalization, generation of subsequences and dimensionality reduction using the principal component analysis technique. The classification of human actions is carried out using a support vector machine classifier. Experimental studies performed on the Weizmann dataset allowed us to determine the best values of the method parameters. The results showed that with a small number of action classes, high classification accuracy can be achieved.

scholarly journals Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

2020 ◽  
Vol 10 (8) ◽  
pp. 562
Author(s):  
Yingying Guo ◽  
Jianfeng Qiu ◽  
Weizhao Lu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Structural Changes ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Healthy Controls

Structural changes in the hippocampus and amygdala have been demonstrated in schizophrenia patients. However, whether morphological information from these subcortical regions could be used by machine learning algorithms for schizophrenia classification were unknown. The aim of this study was to use volume of the amygdaloid and hippocampal subregions for schizophrenia classification. The dataset consisted of 57 patients with schizophrenia and 69 healthy controls. The volume of 26 hippocampal and 20 amygdaloid subregions were extracted from T1 structural MRI images. Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid subregions in the classification of schizophrenia. The proposed SBE-SVM model achieved a classification accuracy of 81.75% on 57 patients and 69 healthy controls, with a sensitivity of 84.21% and a specificity of 81.16%. AUC was 0.8241 (p < 0.001 tested with 1000-times permutation). The results demonstrated evidence of hippocampal and amygdaloid structural changes in schizophrenia patients, and also suggested that morphological features from the amygdaloid and hippocampal subregions could be used by machine learning algorithms for the classification of schizophrenia.

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