Sleep Signal Analysis for Early Detection of Alzheimer's Disease and Related Dementia (ADRD)

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 5; pp. 1 - 12
• Main Authors: Khosroazad, Somayeh, Abedi, Ali, Hayes, Marie J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alzheimer Disease - diagnosis; Alzheimer's disease; Artificial Intelligence; Brain stem; Cognitive ability; Cognitive Dysfunction - diagnosis; Dementia; Dementia disorders; Diagnostic systems; Diseases; Electroencephalography; Feature extraction; Humans; Hypoxemia; Kernel estimation; Kernels; Medical research; Mild Cognitive Impairment; Neural Network; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Parameters; R&D; Research & development; Respiratory changes; Signal analysis; Signal processing; Signal processing algorithms; Sleep; Sleep apnea; Sleep disorders; Sleep Movements
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2023.3235391

Objective : Alzheimer's Disease and Related Dementia (ADRD) is growing at alarming rates, putting research and development of diagnostic methods at the forefront of the biomedical research community. Sleep disorder has been proposed as an early sign of Mild Cognitive Impairment (MCI) in Alzheimer's disease. Although several clinical studies have been conducted to assess sleep and association with early MCI, reliable and efficient algorithms to detect MCI in home-based sleep studies are needed in order to address both healthcare costs and patient discomfort in hospital/lab-based sleep studies. Methods : In this paper, an innovative MCI detection method is proposed using an overnight recording of movements associated with sleep combined with advanced signal processing and artificial intelligence. A new diagnostic parameter is introduced which is extracted from the correlation between high frequency, sleep-related movements and respiratory changes during sleep. The newly defined parameter, Time-Lag (TL), is proposed as a distinguishing criterion that indicates movement stimulation of brainstem respiratory regulation that may modulate hypoxemia risk during sleep and serve as an effective parameter for early detection of MCI in ADRD. By implementing Neural Networks (NN) and Kernel algorithms with choosing TL as the principle component in MCI detection, high sensitivity (<inline-formula><tex-math notation="LaTeX">86.75\%</tex-math></inline-formula> for NN and <inline-formula><tex-math notation="LaTeX">65\%</tex-math></inline-formula> for Kernel method), specificity (<inline-formula><tex-math notation="LaTeX">89.25\%</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">100\%</tex-math></inline-formula>), and accuracy (<inline-formula><tex-math notation="LaTeX">88\%</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">82.5\%</tex-math></inline-formula>) have been achieved.