An LSTM Framework for the Effective Screening of Dementia for Deployment on Edge Devices

Abstract

AbstractDementia is a series of neurodegenerative disorders that affect 1 in 4 people over the age of 80 and can greatly reduce the quality of life of those afflicted. Alzheimer’s disease (AD) is the most common variation, accounting for roughly 60% of cases. The current financial cost of these diseases is an estimated $1.3 trillion per year. While treatments are available to help patients maintain their mental function and slow disease progression, many of those with AD are asymptomatic in the early stages, resulting in late diagnosis. The addition of the routine testing needed for an effective level of early diagnosis would put a costly burden on both patients and healthcare systems. This research proposes a novel framework for the modelling of dementia, designed for deployment in edge hardware. This work extracts a wide variety of thoroughly researched Electroencephalogram (EEG) features, and through extensive feature selection, model testing, tuning, and edge optimization, we propose two novel Long Short-Term Memory (LSTM) neural networks. The first, uses 4 EEG sensors and can classify AD and Frontotemporal Dementia from cognitively normal (CN) subjects. The second, requires 3 EEG sensors and can classify AD from CN subjects. This is achieved with optimisation that reduces the model size by 83×, latency by 3.7×, and performs with an accuracy of 98%. Comparative analysis with existing research shows this performance exceeds current less portable techniques. The deployment of this model in edge hardware could aid in routine testing, providing earlier diagnosis of dementia, reducing the strain on healthcare systems, and increasing the quality of life for those afflicted with the disease.