Persistent homology as a new method of the assessment of heart rate variability

Topological Data Analysis in Cardiovascular Signals: An Overview

Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

The impact of war on the development and progression of arterial hypertension and cardiovascular disease: protocol of a prospective study among Ukrainian female refugees

Background

Growing evidence supports the impact of psychological factors such as traumatic experiences and Post Traumatic Stress Disorder (PTSD) on the incidence of arterial hypertension (HTN) and cardiovascular diseases (CVD). The war in Ukraine is exposing million inhabitants to traumatic experiences and severe stress. Part of Ukrainians (mostly women and children) left the country to escape war. We report the protocol of a prospective study aiming at the assessment of the impact of war-induced stress on HTN and CVD in women Ukrainian refugees who moved to Poland.

Methods and design

The study will be conducted in 3 stages. Stage 1 will assess the prevalence of HTN and PTSD among Ukrainian refugees and will estimate the impact of war-related trauma exposure on these parameters. Data on office blood pressure (BP) will be compared to data already collected in STEPS data 2019 and May Measurement Month 2021 in Ukraine, matched for age and sex. Stage 2 will involve subjects diagnosed with HTN and/or PTSD referred for management and follow-up of these conditions. Psychologic targeted therapies will be offered to subjects with confirmed PTSD, with a periodical reassessment of the severity of PTSD-associated symptoms and of its impact on HTN and cardiovascular health. Clinical history and characteristics will be compared among three groups: subjects with HTN and PTSD, with HTN without PTSD, with PTSD but without HTN. Stage 3 will involve a subgroup among those screened in Stage 1, with the objective of investigating the biological mechanisms underlying the relation between HTN and trauma exposure, identifying early signs of subclinical target organ damage in subjects with HTN with/without PTSD.

Discussion

This study will test the hypothesis that trauma exposure and psychological stress contribute to BP elevation and progression of CVD in this population. It will provide new evidence on the effect of an integrated management, including psychological therapy, on BP and cardiovascular risk. Such approach may be further tested and extrapolated to other populations exposed to war and chronic violence, migrants and refugees around the world.

Research Study Registration

number 2022/45/P/NZ5/02812.

Flicker Noise in Resistive Gas Sensors-Measurement Setups and Applications for Enhanced Gas Sensing

We discuss the implementation challenges of gas sensing systems based on low-frequency noise measurements on chemoresistive sensors. Resistance fluctuations in various gas sensing materials, in a frequency range typically up to a few kHz, can enhance gas sensing by considering its intensity and the slope of power spectral density. The issues of low-frequency noise measurements in resistive gas sensors, specifically in two-dimensional materials exhibiting gas-sensing properties, are considered. We present measurement setups and noise-processing methods for gas detection. The chemoresistive sensors show various DC resistances requiring different flicker noise measurement approaches. Separate noise measurement setups are used for resistances up to a few hundred kΩ and for resistances with much higher values. Noise measurements in highly resistive materials (e.g., MoS2, WS2, and ZrS3) are prone to external interferences but can be modulated using temperature or light irradiation for enhanced sensing. Therefore, such materials are of considerable interest for gas sensing.

Geometric characterization of the persistence of 1D maps

We characterize critical points of 1-dimensional maps paired in persistent homology geometrically and this way get elementary proofs of theorems about the symmetry of persistence diagrams and the variation of such maps. In particular, we identify branching points and endpoints of networks as the sole source of asymmetry and relate the cycle basis in persistent homology with a version of the stable marriage problem. Our analysis provides the foundations of fast algorithms for maintaining a collection of sorted lists together with its persistence diagram.

Dynamic ECG signal quality evaluation based on persistent homology and GoogLeNet method

Cardiovascular disease is a serious health problem. Continuous Electrocardiograph (ECG) monitoring plays a vital role in the early detection of cardiovascular disease. As the Internet of Things technology continues to mature, wearable ECG signal monitors have been widely used. However, dynamic ECG signals are extremely susceptible to contamination. Therefore, it is necessary to evaluate the quality of wearable dynamic ECG signals. The topological data analysis method (TDA) with persistent homology, which can effectively capture the topological information of high-dimensional data space, has been widely studied. In this study, a brand-new quality assessment method of wearable dynamic ECG signals was proposed based on the TDA with persistent homology method. The point cloud of an ECG signal was constructed, and then the complex sequence was generated and displayed as a persistent barcode. Finally, GoogLeNet based on the transfer learning model with a 10-fold cross-validation method was used to train the classification model. A total of 12-leads ECGs Dataset and single-lead ECGs Dataset, established based on the 2011 PhysioNet/CinC challenge dataset, were both used to verify the performance of this method. In the study, 773 "acceptable" and 225 "unacceptable" signals were used as 12-leads ECGs Dataset. We relabeled 12,000 ECG signals in the challenge dataset, and treated them as single-lead ECGs Dataset after empty lead detection and balance datasets. Compared with the traditional ECG signal quality assessment method mainly based on waveform characteristics and time-frequency characteristics, the performance of the quality assessment method proposed. In this study, the classification performance of the proposed method are fairly great, mAcc = 98.04%, F1 = 98.40%, Se = 97.15%, Sp = 98.93% for 12-leads ECGs Dataset and mAcc = 98.55%, F1 = 98.62%, Se = 98.37%, Sp = 98.85% for single-lead ECGs Dataset.

ECG Classification Based on Wasserstein Scalar Curvature

Electrocardiograms (ECG) analysis is one of the most important ways to diagnose heart disease. This paper proposes an efficient ECG classification method based on Wasserstein scalar curvature to comprehend the connection between heart disease and the mathematical characteristics of ECG. The newly proposed method converts an ECG into a point cloud on the family of Gaussian distribution, where the pathological characteristics of ECG will be extracted by the Wasserstein geometric structure of the statistical manifold. Technically, this paper defines the histogram dispersion of Wasserstein scalar curvature, which can accurately describe the divergence between different heart diseases. By combining medical experience with mathematical ideas from geometry and data science, this paper provides a feasible algorithm for the new method, and the theoretical analysis of the algorithm is carried out. Digital experiments on the classical database with large samples show the new algorithm's accuracy and efficiency when dealing with the classification of heart disease.