A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community

della Monica, Ciro; Ravindran, Kiran K. G.; Atzori, Giuseppe; Lambert, Damion J.; Rodriguez, Thalia; Mahvash-Mohammadi, Sara; Bartsch, Ullrich; Skeldon, Anne C.; Wells, Kevin; Hampshire, Adam; Nilforooshan, Ramin; Hassanin, Hana; ,; Revell, Victoria L.; Dijk, Derk-Jan

doi:10.3390/clockssleep6010010

Cited by 4 publications

References 75 publications

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P-tau217 and other blood biomarkers of dementia: variation with time of day

della Monica,

Revell,

Atzori

et al. 2024

Transl Psychiatry

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Plasma biomarkers of dementia, including phosphorylated tau (p-tau217), offer promise as tools for diagnosis, stratification for clinical trials, monitoring disease progression, and assessing the success of interventions in those living with Alzheimer’s disease. However, currently, it is unknown whether these dementia biomarker levels vary with the time of day, which could have implications for their clinical value. In two protocols, we studied 38 participants (70.8 ± 7.6 years; mean ± SD) in a 27-h laboratory protocol with either two samples taken 12 h apart or 3-hourly blood sampling for 24 h in the presence of a sleep–wake cycle. The study population comprised people living with mild Alzheimer’s disease (PLWA, n = 8), partners/caregivers of PLWA (n = 6) and cognitively intact older adults (n = 24). Single-molecule array technology was used to measure phosphorylated tau (p-tau217) (ALZpath), amyloid-beta 40 (Aβ40), amyloid-beta 42 (Aβ42), glial fibrillary acidic protein, and neurofilament light (NfL) (Neuro 4-Plex E). Analysis with a linear mixed model (SAS, PROC MIXED) revealed a significant effect of time of day for p-tau217, Aβ40, Aβ42, and NfL, and a significant effect of participant group for p-tau217. For p-tau217, the lowest levels were observed in the morning upon waking and the highest values in the afternoon/early evening. The magnitude of the diurnal variation for p-tau217 was similar to the reported increase in p-tau217 over one year in amyloid-β-positive mild cognitively impaired people. Currently, the factors driving this diurnal variation are unknown and could be related to sleep, circadian mechanisms, activity, posture, or meals. Overall, this work implies that the time of day of sample collection may be relevant in the implementation and interpretation of plasma biomarkers in dementia research and care.

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P-tau217 and other blood biomarkers of dementia: variation with time of day

della Monica,

Revell,

Atzori

et al. 2024

Transl Psychiatry

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From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People

Hammour,

Davies,

Atzori

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. Methods and procedures: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work [1]. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches [2,3,4,5]. Results: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p <0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. Conclusion: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. Clinical impact: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.

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Contactless longitudinal monitoring of ageing and dementia-related sleep trajectories in the home

Soreq,

Kolanko,

Ravindran

et al. 2024

Preprint

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Disturbed sleep is common in ageing and dementia, but objectively quantifying it over time is challenging. We validated a contactless under-mattress pressure sensor and developed a data analysis method to assess sleep patterns in the home over long periods. Data from 13,588 individuals (3.7 million nights) from the general population were compared to a dementia cohort of 93 patients (>40,000 nights). Dementia was associated with heterogeneous sleep disturbances primarily characterised by advanced and delayed sleep timing, longer time in bed, and more bed exits. Explainable machine learning was used to derive the Dementia Research Institute Sleep Index (DRI-SI), a digital biomarker quantifying sleep disturbances and their evolution. The DRI-SI can detect the effects of acute clinical events and dementia progression at the individual level. This approach bridges a gap in dementia care by providing a feasible method for monitoring health events, disease progression and dementia risk.

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A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community

Cited by 4 publications

References 75 publications

P-tau217 and other blood biomarkers of dementia: variation with time of day

P-tau217 and other blood biomarkers of dementia: variation with time of day

From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People

Contactless longitudinal monitoring of ageing and dementia-related sleep trajectories in the home

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