A comprehensive dataset of pattern electroretinograms for ocular electrophysiology research.

The Pattern Electroretinogram (PERG) is an essential tool in ophthalmic electrophysiology, providing an objective assessment of the central retinal function. It quantifies the activity of cells in the macula and the ganglion cells of the retina, assisting in the differentiation of macular and optic nerve conditions. In this study, we present the IOBA-PERG dataset, an extensive collection of 1354 transient PERG responses accessible on the PhysioNet repository.

ECGMiner: A flexible software for accurately digitizing ECG.

Background And Objective: The electrocardiogram (ECG) is the most important non-invasive method for elucidating information about heart and cardiovascular disease diagnosis. Typically, the ECG system manufacturing companies provide ECG images, but store the numerical data in a proprietary format that is not interpretable and is not therefore useful for automatic diagnosis. There have been many efforts to digitize paper-based ECGs.

CIRCUST: A novel methodology for temporal order reconstruction of molecular rhythms; validation and application towards a daily rhythm gene expression atlas in humans.

The circadian system drives near-24-h oscillations in behaviors and biological processes. The underlying core molecular clock regulates the expression of other genes, and it has been shown that the expression of more than 50 percent of genes in mammals displays 24-h rhythmic patterns, with the specific genes that cycle varying from one tissue to another. Determining rhythmic gene expression patterns in human tissues sampled as single timepoints has several challenges, including the reconstruction of temporal order of highly noisy data.

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis.

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal.

Compelling new electrocardiographic markers for automatic diagnosis.

Background And Objective: The automatic diagnosis of heart diseases from the electrocardiogram (ECG) signal is crucial in clinical decision-making. However, the use of computer-based decision rules in clinical practice is still deficient, mainly due to their complexity and a lack of medical interpretation. The objetive of this research is to address these issues by providing valuable diagnostic rules that can be easily implemented in clinical practice.

Real-time imaging of asthmatic epithelial cells identifies migratory deficiencies under type-2 conditions.

Background: The epithelium is increasingly recognized as a pathologic contributor to asthma and its phenotypes. Although delayed wound closure by asthmatic epithelial cells is consistently observed, underlying mechanisms remain poorly understood, partly due to difficulties in studying dynamic physiologic processes involving polarized multilayered cell systems. Although type-2 immunity has been suggested to play a role, the mechanisms by which repair is diminished are unclear.

The hidden waves in the ECG uncovered revealing a sound automated interpretation method.

A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental P, Q, R, S and T waves plus an error term to account for artifacts in the data which provides a meaningful, physical interpretation of the heart's electric system. The morphology of each wave is concisely described using four parameters that allow all the different patterns in heartbeats to be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals.

Frequency Modulated Möbius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences.

Motivated by applications in physical and biological sciences, we developed a Frequency Modulated Möbius (FMM) model to describe rhythmic patterns in oscillatory systems. Unlike standard symmetric sinusoidal models, FMM is a flexible parametric model that allows deformations to sinusoidal shape to accommodate commonly seen asymmetries in applications. FMM model parameters are easy to estimate and the model is easy to interpret complex rhythmic data.

Order restricted inference in chronobiology.

This paper is motivated by applications in oscillatory systems where researchers are typically interested in discovering components of those systems that display rhythmic temporal patterns. The contributions of this paper are twofold. First, a methodology is developed based on a circular signal plus error model that is defined using order restrictions.

Microarray Data Normalization and Robust Detection of Rhythmic Features.

Data derived from microarray technologies are generally subject to various sources of noise and accordingly the raw data are pre-processed before formally analysed. Data normalization is a key pre-processing step when dealing with microarray experiments, such as circadian gene-expressions, since it removes systematic variations across arrays. A wide variety of normalization methods are available in the literature.

Order restricted inference for oscillatory systems for detecting rhythmic signals.

Motivation: Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data.