Current methods for pattern analysis in time series mainly rely on statistical features or probabilistic learning and inference methods to identify patterns and trends in the data. Such methods do not generalize well when applied to multivariate, multi-source, state-varying, and noisy time-series data. To address these issues, we propose a highly generalizable method that uses information theory-based features to identify and learn from patterns in multivariate time-series data. To demonstrate the proposed approach, we analyze pattern changes in human activity data. For applications with stochastic state transitions, features are developed based on Shannon's entropy of Markov chains, entropy rates of Markov chains, entropy production of Markov chains, and von Neumann entropy of Markov chains. For applications where state modeling is not applicable, we utilize five entropy variants, including approximate entropy, increment entropy, dispersion entropy, phase entropy, and slope entropy. The results show the proposed information theory-based features improve the recall rate, F1 score, and accuracy on average by up to 23.01% compared with the baseline models and a simpler model structure, with an average reduction of 18.75 times in the number of model parameters.

No abstract

Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. Analysing agitation episodes will also help identify modifiable factors such as ambient temperature and sleep as possible components causing agitation in an individual. This preliminary study presents a supervised learning model to anal- * The authors are part of the

Background A frequent cause for hospitalisation in people living with dementia (PLWD) is urinary tract infections (UTIs) (Dufour et al., 2015). Early detection aids in avoiding unplanned hospitalisations and machine learning and connected sensors enable development of risk analysis models based on in‐home monitoring data. This work makes use of data from the ongoing Minder study at the UK Dementia Research Institute, including in‐door movement, appliance use, physiology, sleep, and environmental information. Previous work (Li et al., 2020) shows complex models can achieve high accuracy when detecting UTIs, but interpretability and generalisability remain an open issue. This work evaluates how clinically interpretable features and simple models perform. Method The engineered features reflect visible symptoms of UTIs in older adults and are daily aggregated and pre‐processed information from PLWD’s homes containing passive sensors. 14 activity, sleep and physiological features were engineered relating to bathroom visits, entropy rate, physiological readings, and sleep behaviour. Raw data consisted of frequencies of the activity sensor firings. Their use in predicting cases of UTIs was compared by evaluating multilayer perceptron models on labelled data (643 negatives and 311 positives, from 39 PLWD), using 5‐fold cross‐validation (80%/20% train/test split), with 5 repeats. Prior to training and testing, the data was z‐normalised using unlabelled data. When evaluating, recall is most essential because reducing false positive rates is crucial in our setting. Result The highest performing model achieved the best accuracy, recall, precision and F1 score (85.6%, 69.9%, 81.5%, 74.8%) on combinations of engineered and raw features; though not significantly higher than with raw features alone (83.2%, 63.5%, 80.5%, 70.8%). Full results can be seen in the attached figure. Conclusion Our engineered features do not significantly improve results but add interpretability and generalisability to predictions. The models' hidden layers have a high capacity for learning patterns in the raw data, however, may not be clinically interpretable and we hypothesise that engineered features improve generalisation. Engineered features are homogeneous across the cohort and new data, and so create a way of representing the data which is not single device dependent. To verify this, we will continue our data collection and will conduct further experiments.