Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis

Author:

Penalba-Sánchez Lucía1234ORCID,Silva Gabriel5,Crook-Rumsey Mark67,Sumich Alexander3ORCID,Rodrigues Pedro Miguel5ORCID,Oliveira-Silva Patrícia2ORCID,Cifre Ignacio1ORCID

Affiliation:

1. Facultat de Psicología, Ciències de l’Educació i de l’Esport (FPCEE), Blanquerna, Universitat Ramon Llull, 08022 Barcelona, Spain

2. Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculty of Education and Psychology, Universidade Católica Portuguesa, 4169-005 Porto, Portugal

3. Department of Psychology, Nottingham Trent University (NTU), Nottingham NG1 4FQ, UK

4. Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke-University Magdeburg (OVGU), 39120 Magdeburg, Germany

5. Centro de Biotecnologia e Química Fina (CBQF)—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal

6. UK Dementia Research Institute (UK DRI), Centre for Care Research and Technology, Imperial College London, London W1T 7NF, UK

7. UK Dementia Research Institute (UK DRI), Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RX, UK

Abstract

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.

Funder

European Regional Development Fund

Publisher

MDPI AG

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