Temporal Order-based First-Take-All Hashing for Fast Attention-Deficit-Hyperactive-Disorder Detectio
Hao Hu, University of Central Florida; Joey Velez-Ginorio, University of Central Florida; Guojun Qi*, University of Central Florida
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 bench-marks demonstrate the FTA can outperform the state-of-the-art baselines using only neural activity time series with-out any phenotypic information.
Filed under: Time Series and Stream Mining | Mining Rich Data Types | Big Data