Classification of sleep quality and aging as a function of brain complexity: a multiband non-linear EEG analysis

• Author(s): Penalba-Sánchez, Lucía ; Silva, Gabriel ; Crook-Rumsey, Mark ; Sumich, Alexander ; Rodrigues, Pedro Miguel ; Oliveira-Silva, Patrícia ; Cifre, Ignacio
• Date: 2024
• Persistent ID: http://hdl.handle.net/10400.14/45240
• Origin: Veritati - Repositório Institucional da Universidade Católica Portuguesa
• Subject(s): Brain complexity; Classification; EEG; Healthy aging; Machine learning; Non-linear multiband analysis; PSQI; Sleep quality
• Document Type: Journal article
• Language: English
• Contributor(s): Veritati

Description

Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.