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Background: Huntington's disease (HD) is a rare, genetically determined braindisorder that limits the life of the patient, although early prognosis of HDcan substantially improve the patient's quality of life. Current HD prognosismethods include using a variety of complex biomarkers such as clinical andimaging factors, however these methods have many shortfalls, such as theirresource demand and failure to distinguish symptomatic and asymptomaticpatients. Quantitative biomedical signaling has been used for diagnosis ofother neurological disorders such as schizophrenia and has potential forexposing abnormalities in HD patients. Methodology: In this project, we used apremade, certified dataset collected at a clinic with 27 HD positive patients,36 controls, and 6 unknowns with electroencephalography, electrocardiography,and functional near-infrared spectroscopy data. We first preprocessed the dataand extracted a variety of features from both the transformed and raw signals,after which we applied a plethora of shallow machine learning techniques.Results: We found the highest accuracy was achieved by a scaled-out ExtremelyRandomized Trees algorithm, with area under the curve of the receiver operatorcharacteristic of 0.963 and accuracy of 91.353%. The subsequent featureanalysis showed that 60.865% of the features had p<0.05, with the features fromthe raw signal being most significant. Conclusion: The results indicate thepromise of neural and cardiac signals for marking abnormalities in HD, as wellas evaluating the progression of the disease in patients.

Automated Huntington's Disease Prognosis via Biomedical Signals and Shallow Machine Learning