Literature DB >> 28268350

Classification of ADHD and non-ADHD using AR models.

Juan L Lopez Marcano, Martha Ann Bell, A A Louis Beex.   

Abstract

Accurate and confident diagnosis of ADHD is highly dependent on subjective observations. Several quantitative methods have been proposed, posing it as a two-class classification problem (ADHD and non-ADHD). However, the results have not made it past the research stage yet, as misclassification rates must be close to 0%. This study aims to discriminate ADHD and non-ADHD subjects using autoregressive models, with a high level of accuracy (85-95%). In addition, a confidence metric is proposed, expressing with how much confidence the classification of ADHD and non-ADHD subjects is made.

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Year:  2016        PMID: 28268350     DOI: 10.1109/EMBC.2016.7590715

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  2 in total

1.  Classification of ADHD and Non-ADHD Subjects Using a Universal Background Model.

Authors:  Juan Lopez Marcano; Martha Ann Bell; A A Louis Beex
Journal:  Biomed Signal Process Control       Date:  2017-08-30       Impact factor: 3.880

Review 2.  A Systematic Review on Feature Extraction in Electroencephalography-Based Diagnostics and Therapy in Attention Deficit Hyperactivity Disorder.

Authors:  Pasquale Arpaia; Attilio Covino; Loredana Cristaldi; Mirco Frosolone; Ludovica Gargiulo; Francesca Mancino; Federico Mantile; Nicola Moccaldi
Journal:  Sensors (Basel)       Date:  2022-06-29       Impact factor: 3.847

  2 in total

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