Temporal Order-Based First-Take-All Hashing For Fast Attention-Deficit-Hyperactive-Disorder Detection

Keywords

ADHD detection; First-take-all; Time series hashing

Abstract

Attention Deficit Hyperactive Disorder (ADHD) is one of the most common childhood disorders and can continue through adolescence and adulthood. Although the root cause of the problem still remains unknown, recent advancements in brain imaging technology reveal there exists differences between neural activities of Typically Developing Children (TDC) and ADHD subjects. Inspired by this, we propose a novel First-Take-All (FTA) hashing framework to investigate the problem of fast ADHD subjects detection through the fMRI time-series of neuron activities. By hashing time courses from regions of interests (ROIs) in the brain into fixed-size hash codes, FTA can compactly encode the temporal order differences between the neural activity patterns that are key to distinguish TDC and ADHD subjects. Such patterns can be directly learned via minimizing the training loss incurred by the generated FTA codes. By conducting similarity search on the resultant FTA codes, data-driven ADHD detection can be achieved in an efficient fashion. The experiments' results on real-world ADHD detection benchmarks demonstrate the FTA can outperform the state-of the-art baselines using only neural activity time series without any phenotypic information.

Publication Date

8-13-2016

Publication Title

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volume

13-17-August-2016

Number of Pages

905-914

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2939672.2939774

Socpus ID

84985004227 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84985004227

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