Smartwatch Versus Routine Tremor Documentation: Descriptive Comparison.

We addressed the limitations of subjective clinical tremor assessment by comparing routine neurological evaluation with a Tremor Occurrence Score derived from smartwatch sensor data, among 142 participants with Parkinson disease and 77 healthy controls. Our findings highlight the potential of smartwatches for automated tremor detection as a valuable addition to conventional assessments, applicable in both clinical and home settings.

Machine Learning in the Parkinson's disease smartwatch (PADS) dataset.

The utilisation of smart devices, such as smartwatches and smartphones, in the field of movement disorders research has gained significant attention. However, the absence of a comprehensive dataset with movement data and clinical annotations, encompassing a wide range of movement disorders including Parkinson's disease (PD) and its differential diagnoses (DD), presents a significant gap. The availability of such a dataset is crucial for the development of reliable machine learning (ML) models on smart devices, enabling the detection of diseases and monitoring of treatment efficacy in a home-based setting. We conducted a three-year cross-sectional study at a large tertiary care hospital. A multi-modal smartphone app integrated electronic questionnaires and smartwatch measures during an interactive assessment designed by neurologists to provoke subtle changes in movement pathologies. We captured over 5000 clinical assessment steps from 504 participants, including PD, DD, and healthy controls (HC). After age-matching, an integrative ML approach combining classical signal processing and advanced deep learning techniques was implemented and cross-validated. The models achieved an average balanced accuracy of 91.16% in the classification PD vs. HC, while PD vs. DD scored 72.42%. The numbers suggest promising performance while distinguishing similar disorders remains challenging. The extensive annotations, including details on demographics, medical history, symptoms, and movement steps, provide a comprehensive database to ML techniques and encourage further investigations into phenotypical biomarkers related to movement disorders.

Classification of Parkinson's Disease from Voice - Analysis of Data Selection Bias.

A growing number of studies have been researching biomarkers of Parkinson's disease (PD) using mobile technology. Many have shown high accuracy in PD classification using machine learning (ML) and voice records from the mPower study, a large database of PD patients and healthy controls. Since the dataset has unbalanced class, gender and age distribution, it is important to consider appropriate sampling when assessing classification scores. We analyse biases, such as identity confounding and implicit learning of non-disease-specific characteristics and present a sampling strategy to highlight and prevent these problems.

DeepTSE: A Time-Sensitive Deep Embedding of ICU Data for Patient Modeling and Missing Data Imputation.

Missing data is a common problem in the intensive care unit as a variety of factors contribute to incomplete data collection in this clinical setting. This missing data has a significant impact on the accuracy and validity of statistical analyses and prognostic models. Several imputation methods can be used to estimate the missing values based on the available data. Although simple imputations with mean or median generate reasonable results in terms of mean absolute error, they do not account for the currentness of the data. Furthermore, heterogeneous time span of data records adds to this complexity, especially in high-frequency intensive care unit datasets. Therefore, we present DeepTSE, a deep model that is able to cope with both, missing data and heterogeneous time spans. We achieved promising results on the MIMIC-IV dataset that can compete with and even outperform established imputation methods.