Alz-Sense+: An Auto Time-synchronized Multi-class Algorithm for Dementia Detection

Abstract

Dementia, a cognitive disease that affects more than 50 million people, causes some degree of disability in remembering simple things and following basic instructions with unusual delays. Researchers proposed different pre-clinical methods with mediocre performance leaving the door open for further improvement. One of the most successful pre-clinical tests, SLUMS (Saint Louis University Mental Status), incorpo-rates verbal responses in the form of standardized questionnaires. It involves expert judgment to label patients such as dementia, MCI (Mild Cognitive Impairment), or healthy based on an overall score. However, a nonverbal stress response is also taken into account in the Alz-Sense algorithm, which has a few underlying false assumptions, i) uniformity of answering duration, ii) equity of questions stress level, and iii) unfair stress penalty while discarding healthy patient detection. Moreover, the stress data of the corresponding question is manually synchronized using the examiner's hand-shaken data of the wearable device. As a goal to improve the original Alz-Sense algorithm, Alz-Sense+ is proposed to handle these three assumptions by incorporating the windowing process, statistical and visual approach. Be-sides, it also automated the synchronization between questions and corresponding sensor data by estimating time slots while proposing an optimal ordering of questions that mitigates the unintended consequences. Alz-Sense+ achieved 81.39%, 80.76%, and 82.35 % accuracy, sensitivity, and specificity, respectively, which is 7.39%, 0.01 %, and 15.75% improvement over the original Alz-Sense algorithm. In a nutshell, the new Alz-Sense+ algorithm outperformed the existing algorithm by addressing a few underlying assumptions while eliminating a few limitations of the original algorithm.