arXiv subjects

Maarten de Vos

Publications and source records attributed to Maarten de Vos.

4 recordsLinked to original sources

arXiv2022

Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones

Personalized longitudinal disease assessment is central to quickly diagnosing, appropriately managing, and optimally adapting the therapeutic strategy of multiple sclerosis (MS). It is also important for identifying the idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using sensor data that may contain missing values. First, we collect digital measurements related to gait and balance, and upper extremity functions using sensor-based assessments administered on a smartphone. Next, we treat missing data via imputation. We then discover potential markers of MS by employing a generalized estimation equation. Subsequently, parameters learned from multiple training datasets are ensembled to form a simple, unified longitudinal predictive model to forecast MS over time in previously unseen people with MS. To mitigate potential underestimation for individuals with severe disease scores, the final model incorporates additional subject-specific fine-tuning using data from the first day. The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time.

stat.ME↗

arXiv2022

Thou Shalt Not Reject the P-value

Since its debut in the 18th century, the P-value has been an important part of hypothesis testing-based scientific discoveries. As the statistical engine accelerates, questions are beginning to be raised, asking to what extent scientific discoveries based on P-values are reliable and reproducible, and the voice calling for adjusting the significance level or banning the P-value has been increasingly heard. Inspired by these questions and discussions, here we enquire into the useful roles and misuses of the P-value in scientific studies. For common misuses and misinterpretations, we provide modest recommendations for practitioners. Additionally, we compare statistical significance with clinical relevance. In parallel, we review the Bayesian alternatives for seeking evidence. Finally, we discuss the promises and risks of using meta-analysis to pool P-values from multiple studies to aggregate evidence. Taken together, the P-value underpins a useful probabilistic decision-making system and provides evidence at a continuous scale. But its interpretation must be contextual, considering the scientific question, experimental design (including the model specification, sample size, and significance level), statistical power, effect size, and reproducibility.

stat.ME↗

arXiv2021

Light-weight sleep monitoring: electrode distance matters more than placement for automatic scoring

Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring. Many different combinations of wet and dry electrodes, ear-centered, forehead-mounted or headband-inspired designs have been proposed, alongside an ever growing variety of machine learning algorithms for automatic sleep scoring. In this paper, we compare 13 different, realistic sensor setups derived from the same data set and analysed with the same pipeline. We find that all setups which include both a lateral and an EOG derivation show similar, state-of-the-art performance, with average Cohen's kappa values of at least 0.80. This indicates that electrode distance, rather than position, is important for accurate sleep scoring. Finally, based on the results presented, we argue that with the current competitive performance of automated staging approaches, there is an urgent need for establishing an improved benchmark beyond current single human rater scoring.

q-bio.QM↗

arXiv2018

Personalizing deep learning models for automatic sleep staging

Despite continued advancement in machine learning algorithms and increasing availability of large data sets, there is still no universally acceptable solution for automatic sleep staging of human sleep recordings. One reason is that a skilled neurophysiologist scoring brain recordings of a sleeping person implicitly adapts his/her staging to the individual characteristics present in the brain recordings. Trying to incorporate this adaptation step in an automatic scoring algorithm, we introduce in this paper a method for personalizing a general sleep scoring model. Starting from a general convolutional neural network architecture, we allow the model to learn individual characteristics of the first night of sleep in order to quantify sleep stages of the second night. While the original neural network allows to sleep stage on a public database with a state of the art accuracy, personalizing the model further increases performance (on the order of two percentage points on average, but more for difficult subjects). This improvement is particularly present in subjects where the original algorithm did not perform well (typically subjects with accuracy less than $80\%$). Looking deeper, we find that optimal classification can be achieved when broad knowledge of sleep staging in general (at least 20 separate nights) is combined with subject-specific knowledge. We hypothesize that this method will be very valuable for improving scoring of lower quality sleep recordings, such as those from wearable devices.

q-bio.NC↗