arXiv subjects

Marcel Młyńczak

Publications and source records attributed to Marcel Młyńczak.

5 recordsLinked to original sources

arXiv2022

Causality in cardiorespiratory signals in pediatric cardiac patients

Four different Granger causality-based methods - one linear and three nonlinear (Granger Causality, Kernel Granger Causality, large-scale Nonlinear Granger Causality, and Neural Network Granger Causality) were used for assessment and causal-based quantification of the respiratory sinus arrythmia (RSA) in the group of pediatric cardiac patients, based on the single-lead ECG and impedance pneumography signals (the latter as the tidal volume curve equivalent). Each method was able to detect the dependency (in terms of causal inference) between respiratory and cardiac signals. The correlations between quantified RSA and the demographic parameters were also studied, but the results differ for each method.

q-bio.QM↗

arXiv2020

Comparing sleep studies in terms of the Apnea-Hypopnea Index using dedicated Shiny web application

The Apnea-Hypopnea Index (AHI) is one of the most-used parameters from the sleep study that allows assessing both the severity of obstructive sleep apnea and the reliability of new devices and methods. However, in many cases, it is compared with a reference only via a correlation coefficient, or this value is at least the most emphasized. In this paper, we discuss the limitations of such an approach and list several alternative quantitative and qualitative techniques, along with their interpretations. We propose the assessment of clinical significance along with the statistical one. Qualitative analysis can be used for this purpose, or we suggest using the ranking function which enables consideration of various AHI values with different weights. It can be reliable for both adult-related and pediatric sleep studies. The dedicated Shiny web application, written in R, was developed to enable quick analysis for both physicians and statisticians.

stat.AP↗

arXiv2020

Temporal orders and causal vector for physiological data analysis

In addition to the global parameter- and time-series-based approaches, physiological analyses should constitute a local temporal one, particularly when analyzing data within protocol segments. Hence, we introduce the R package implementing the estimation of temporal orders with a causal vector (CV). It may use linear modeling or time series distance. The algorithm was tested on cardiorespiratory data comprising tidal volume and tachogram curves, obtained from elite athletes (supine and standing, in static conditions) and a control group (different rates and depths of breathing, while supine). We checked the relation between CV and body position or breathing style. The rate of breathing had a greater impact on the CV than does the depth. The tachogram curve preceded the tidal volume relatively more when breathing was slower. Clinical relevance - The method can assess (1) relationships between two signals, having one of the two time-shifted in a given range, (2) curves of most optimal inter-signal shift, and (c) their stability across time; also for other physiological studies.

stat.AP↗

arXiv2018

Discovery of causal paths in cardiorespiratory parameters: a time-independent approach in elite athletes

Training of elite athletes requires regular physiological and medical monitoring to plan the schedule, intensity and volume of training, and subsequent recovery. In sports medicine, ECG-based analyses are well established. However, they rarely consider the correspondence of respiratory and cardiac activity. Given such mutual influence, we hypothesize that athlete monitoring might be developed with causal inference and that detailed, time-related techniques should be preceded by a more general, time-independent approach that considers the whole group of participants and parameters describing whole signals. The aim of this study was to discover general causal paths among cardiac and respiratory variables in elite athletes in two body positions (supine and standing), at rest. ECG and impedance pneumography signals were obtained from 100 elite athletes. The mean HR, the RMSSD, its natural logarithm, the mean respiratory rate, the breathing activity coefficients, and the resulting breathing regularity were estimated. Several causal discovery frameworks were applied: generalized correlations, CAM, FGES, GFCI, and two Bayesian network learning algorithms: Hill-Climbing and Tabu. The main, still mild, rules best supported by data are: for supine - tidal volume causes heart activity variation, which causes HR, which causes respiratory timing; and for standing - normalized respiratory activity variation causes average heart activity. The presented approach allows data-driven and time-independent analysis of elite athletes as a particular population, without considering prior knowledge. However, the results seem to be consistent with the medical background. Causality inference is an interesting mathematical approach to the analysis of biological responses, which are complex. One can use it to profile athletes and plan appropriate training.

stat.AP↗

arXiv2018

Data-driven causal path discovery without prior knowledge - a benchmark study

Causal discovery broadens the inference possibilities, as correlation does not inform about the relationship direction. The common approaches were proposed for cases in which prior knowledge is desired, when the impact of a treatment/intervention variable is discovered or to analyze time-related dependencies. In some practical applications, more universal techniques are needed and have already been presented. Therefore, the aim of the study was to assess the accuracies in determining causal paths in a dataset without considering the ground truth and the contextual information. This benchmark was performed on the database with cause-effect pairs, using a framework consisting of generalized correlations (GC), kernel regression gradients (GR) and absolute residuals criteria (AR), along with causal additive modeling (CAM). The best overall accuracy, 80%, was achieved for the (majority voting) combination of GC, AR, and CAM, however, the most similar sensitivity and specificity values were obtained for AR. Bootstrap simulation established the probability of correct causal path determination (which pairs should remain indeterminate). The mean accuracy was then improved to 83% for the selected subset of pairs. The described approach can be used for preliminary dependence assessment, as an initial step for commonly used causality assessment frameworks or for comparison with prior assumptions.

stat.AP↗