Studies have indicated that cognitive, behavioral, sensory and motor changes may precede clinical manifestations of AD by several years ( 7). Although there is currently no treatment for AD, early diagnosis ( 5) is essential so that the patients, their family and caretakers are better prepared, by making the necessary financial and legal decisions and testing different lifestyle interventions and medication in the MCI or another prodromal stage, to potentially delay or even prevent AD development ( 6). People diagnosed with MCI have to be constantly tested over time to ensure that they have not transitioned from mild to severe dementia ( 4). Mild Cognitive Impairment (MCI) ( 1, 2) affects 20% of people over 65 years old worldwide, causing cognitive decline, beyond normal aging, especially in the areas of memory and executive functions, while increasing the probability for the manifestation of the neurodegenerative Alzheimer's disease (AD) ( 3). The current findings indicate the potentiality of new digital biomarkers to capture early stages of cognitive decline, providing a highly specific remote screening tool in-the-wild. The acquired results have shown: (a) keystroke features with a k-NN classifier achieved an Area Under Curve (AUC) of 0.78, (b) NLP features with a Logistic regression classifier achieved an AUC of 0.76 (95% CI: 0.65–0.85 SP/SE: 0.80/0.71), and (c) an ensemble model with the fusion of keystroke and NLP features resulted in AUC of 0.75 (95% CI:0.63–0.86 SP/SE 0.90/0.60). A cascaded-classifier scheme was realized under three different feature combinations and validated via a Leave-One-Subject-Out cross-validation scheme. The proposed approach was tested on two demographically matched groups of 11 MCI patients and 12 HC, having undergone the same neuropsychological tests, producing 4,930 typing sessions and 78 short texts, within 6 months, for analysis. Analytically, three indices of FMI (rigidity, bradykinesia and alternate finger tapping) and nine NLP features, related with lexical richness, grammatical, syntactical complexity, and word deficits, formed the feature space. In particular, keystroke dynamics derived from touchscreen typing activities, using Convolutional Neural Networks, along with linguistic features of SWS through Natural Language Processing (NLP), were used to distinguish amongst MCI patients and healthy controls (HC). In this vein, the diagnostic ability of digital biomarkers, drawn from Fine Motor Impairment (FMI)- and Spontaneous Written Speech (SWS)-related data analysis, are examined here. From an alternative perspective, smartphone interaction behavioral data, unobtrusively acquired in a non-clinical setting, can assist the screening and monitoring of MCI and its symptoms' progression. Mild cognitive impairment (MCI), an identified prodromal stage of Alzheimer's Disease (AD), often evades detection in the early stages of the condition, when existing diagnostic methods are employed in the clinical setting. 3Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
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