On Thursday 22/a0/2025 at 3 pm, Eleni Panagiotopoulou, a graduate student in the program “Data Science and Information Technologies, Biomedical Data Science – Bioinformatics” will present their MSc thesis titled:

Actigraphy Feature Representations for Mild Cognitive Impairment Classification: Comparing Summary, Circadian and Wavelet Features across Sensitivity–Specificity Operating Points
Mild cognitive impairment (MCI) is characterized by cognitive deficits that exceed those expected with normal aging without substantially compromising independence in everyday activities. Actigraphy provides a non-invasive means of monitoring rest-activity patterns and may offer additional information for identifying cognitive impairment. This thesis investigated how different actigraphy-derived feature representations distinguish participants with MCI from those with normal cognition.
We compared three feature families: summary statistics, combined cosinor and non-parametric rest-activity descriptors, and discrete wavelet transform (DWT) features. An integrated software pipeline supported signal preprocessing, feature extraction, feature selection, and evaluation of multiple classical machine-learning algorithms within a repeated nested cross-validation framework. We compared configurations that met predefined performance criteria using recall-specificity Pareto fronts and parsimony-based selection. Additional analyses examined age-only models, matched signal lengths, time-of-day windows, model complexity, and hyperparameter tuning.
The feature families supported different recall-specificity trade-offs. Summary statistics achieved the highest median recall, reaching 0.818 at a median specificity of 0.700. Combined cosinor and non-parametric descriptors achieved recall of 0.800 and specificity of 0.750, while DWT features achieved the highest specificity (0.850) with recall of 0.727. Age-only models provided a competitive reference, but incorporating actigraphy features improved the attainable trade-offs. Compact actigraphy representations for wavelet feature extraction retained high specificity, although with lower recall than the leading full-series configuration. Simpler models and configurations without hyperparameter tuning also achieved competitive performance.
These findings indicate that the preferred actigraphy representation depends on the sensitivity-specificity balance sought, rather than a single feature family being universally superior. The thesis contributes an extended analysis pipeline and a systematic comparison of complementary activity representations for MCI classification. The results encourage further investigation of actigraphy as a source of information for clinical decision support, while the limited cohort and exploratory configuration-selection process necessitate independent validation before clinical application.

Examination Committee:

Prof. Em. Elias S. Manolakos (advisor), Dept. of Informatics and Telecommunications, NKUA
Dr. Nikolaos Scarmeas, Professor of Neurology, Department of Medicine, NKUA
Prof. Dimitrios J. Stravopodis, Assoc. Professor of Biology, Department of Biology, NKUA
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