, Biomedical Signal Processing and Control, Volume 65, March 2021
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain that ultimately results in the death of neurons and dementia. The prevalence of the disease in the world is increasing rapidly. In recent years, many studies have been done to automatically detect this disease from brain signals. Method: In this paper, the Hjorth parameters are used along with other common features to improve the AD detection accuracy from EEG signals in early stages.
, Medical Image Analysis, Volume 67, January 2021
The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power.
, Biomedical Signal Processing and Control, Volume 62, September 2020
Objective imaging-based biomarker discovery for psychiatric conditions is critical for accurate diagnosis and treatment. Using a machine learning framework, this work investigated the utility of brain's functional network topology (complex network features) extracted from functional magnetic resonance imaging (fMRI) functional connectivity (FC) as viable biomarker of autism spectrum disorder (ASD). To this end, we utilized resting-state fMRI data from the publicly available ABIDE dataset consisting of 432 ASD patients and 556 matched healthy controls.
, Biomedical Signal Processing and Control, Volume 57, March 2020
Clinical assessment of speech abnormalities in Cerebellar Ataxia (CA) is subjective and prone to intra- and inter-clinician inconsistencies. This paper presents an automated objective method based on a single syllable repetition task to detect and quantify speech-timing anomalies in ataxic speech. Such a technique is non-invasive, reliable, fast, cost-effective and can be used in the comfort of home without any professional assistance.