Agnieszka Kitlas Golińska

Abstract Poincaré plot is a return map which can help perform graphical analysis of data. We can also fit an ellipse to the plot shape by determining descriptors SD1, SD2 and SD1/SD2 ratio to study the data quantitatively. In this paper we show examples of application of Poincaré plots in analysis of various kinds of biomedical signals: RR intervals, EMG, gait data and EHG.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

Abstract: The analysis of the uterine contraction signals in nonpregnant states gives information about physiological changes during the menstrual cycle. Spontaneous uterine activity was recorded directly by a dual microtip catheter. The device consisted of two ultra‐miniature pressure sensors. One sensor was placed in the fundus, the other in the cervix. It was important to identify time delays between contractions in two topographic locations, which may be of potential diagnostic significance in various pathologies: dysmenorrhea, endometriosis, and fecundity disorders. In this study the following synchronization measures—the cross‐correlation, the semblance, the mutual information—were used to visualize the time delay changes over time. These measures were computed in a moving window with a width corresponding to approximately two or three contractions. As a result, the running synchronization functions were obtained. The running synchronization functions visualize changes in the propagation of the two simultaneously recorded signals. The propagation% parameter assessed from these functions allows for quantitative description of synchronization. Finally, we illustrate the use of running synchronization functions to investigate the effect of treatment with tamoxifen on primary dysmenorrhea.

The aim of this study is to present a coherence function, which can be used to find common frequencies of two signals and to evaluate the similarity of these signals. Another method is to use wavelet coherence function, which not only can find common frequencies of two signals, but also gives information when these frequencies appear. We would like to demonstrate the usefulness of coherence function in biomedical signal processing - in analysis of EEG, ECG, and uterine contraction activity signals. We have chosen four papers using coherence function in EEG analysis, four in ECG analysis and two in uterine contraction activity signals analysis (where we present some of our original work). Thus, these functions can be useful in analyzing two simultaneously recorded biomedical signals and they can provide some diagnostic value.