Diagnostic Performance of an Automated Blood Pressure Monitor With an Irregular Heartbeat Algorithm Designed to Detect Atrial Fibrillation

Background: Early detection of atrial fibrillation (AF) remains an unsolved challenge and because the greatest risk factor for AF is hypertension, blood pressure (BP) monitors with AF detectors have been developed. We evaluated the clinical performance of an irregular heartbeat (IHB) algorithm built into an A&D automated BP monitor for AF diagnosis. Methods and Results: Each of the 239 enrolled patients underwent BP measurement 3 times using the A&D UM-212 with the IHB algorithm. Real-time 3-lead ECG was recorded using automated ECG analysis software. Independent of the ECG analysis software results, 2 cardiologists interpreted the ECG and made the final diagnosis. Of the 239 patients, 135 were in sinus rhythm, 31 had AF, and 73 had non-AF arrhythmias. The respective sensitivity, specificity, and accuracy of the IHB algorithm for AF diagnosis were 98.9%, 91.2%, and 92.2% for the per-measurement evaluation, and 96.8%, 95.7%, and 95.8% for the per-patient evaluation (3/3 positive measurements). The respective sensitivity, specificity, and accuracy of the ECG analysis software for AF diagnosis were 91.4%, 97.9%, and 97.1% for the per-measurement evaluation, and 77.4%, 99.5%, and 96.7% for the per-patient evaluation (3/3 positive measurements). Conclusions: The IHB algorithm built into an A&D automated BP monitor had high diagnostic performance for AF in general cardiology patients, especially when multiple measurements were obtained.

The Basics of Visualizing, Analyzing, and Reporting Preclinical PET/CT Imaging Data

Positron emission tomography (PET) has transformed medical imaging, and while first developed and applied to the human setting, it has found widespread application at the preclinical level over the past two decades. Its strength is that it offers noninvasive 3D tomographic imaging in a quantitative manner at very high sensitivity. Paired with the right molecular probes, invaluable insights into physiology and pathophysiology have been accessible and therapeutic development has been enhanced through preclinical PET imaging. PET imaging is now often routinely combined with either computed tomography (CT) or magnetic resonance imaging (MRI) to provide additional anatomical context. All these developments were accompanied by the provision of ever more complex and powerful analysis software enabling users to visualize and quantify signals from PET imaging data. Aside from experimental complexities, there are also various pitfalls in PET image data analysis, which can negatively impact on reporting and reproducibility.Here, we provide a protocol intended to guide the inexperienced user through PET/CT data analysis. We describe the general principles and workflows required for PET/CT image data visualization and quantitative analysis using various software packages popular in the field. Moreover, we present recommendations for reporting of preclinical PET/CT data including examples of good and poor practice.

Data evaluation for surface-sensitive label-free methods to obtain real-time kinetic and structural information of thin films: A practical review with related software packages

Interfacial layers are important in a wide range of applications in biomedicine, biosensing, analytical chemistry and the maritime industries. Given the growing number of applications, analysis of such layers and understanding their behavior is becoming crucial. Label-free surface sensitive methods are excellent for monitoring the formation kinetics, structure and its evolution of thin layers, even at the nanoscale. In this paper, we review existing and commercially available label-free techniques and demonstrate how the experimentally obtained data can be utilized to extract kinetic and structural information during and after formation, and any subsequent adsorption/desorption processes. We outline techniques, some traditional and some novel, based on the principles of optical and mechanical transduction. Our special focus is the current possibilities of combining label-free methods, which is a powerful approach to extend the range of detected and deduced parameters. We summarize the most important theoretical considerations for obtaining reliable information from measurements taking place in liquid environments and, hence, with layers in a hydrated state. A thorough treamtmaent of the various kinetic and structural quantities obtained from evaluation of the raw label-free data are provided. Such quantities include layer thickness, refractive index, optical anisotropy (and molecular orientation derived therefrom), degree of hydration, viscoelasticity, as well as association and dissociation rate constants and occupied area of subsequently adsorbed species. To demonstrate the effect of variations in model conditions on the observed data, simulations of kinetic curves at various model settings are also included. Based on our own extensive experience with optical waveguide lightmode spectroscopy (OWLS) and the quartz crystal microbalance (QCM), we have developed dedicated software packages for data analysis, which are made available to the scientific community alongside this paper.

PySTACHIO: Python Single-molecule TrAcking stoiCHiometry Intensity and simulatiOn, a flexible, extensible, beginner-friendly and optimized program for analysis of single-molecule microscopy data

As camera pixel arrays have grown larger and faster, and optical microscopy techniques ever more refined, there has been an explosion in the quantity of data acquired during routine light microscopy. At the single-molecule level, analysis involves multiple steps and can rapidly become computationally expensive, in some cases intractable on office workstations. Complex bespoke software can present high activation barriers to entry for new users. Here, we redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with GUI and command-line implementations to facilitate use on local machines and remote clusters, by beginners and advanced users alike. We demonstrate that its performance is on par with previous MATLAB implementations but runs an order of magnitude faster. We tested it against challenge data and demonstrate its performance is comparable to state-of-the-art analysis platforms. We show the code can extract fluorescence intensity values for single reporter dye molecules and, using these, estimate molecular stoichiometries and cellular copy numbers of fluorescently-labeled biomolecules. It can evaluate 2D diffusion coefficients for the characteristically short single-particle tracking data. To facilitate benchmarking we include data simulation routines to compare different analysis programs. Finally, we show that it works with 2-color data and enables colocalization analysis based on overlap integration, to infer interactions between differently labelled biomolecules. By making this freely available we aim to make complex light microscopy single-molecule analysis more democratized.

Quantification of 3D microstructural parameters of trabecular bone is affected by the analysis software

Over the last decades, the use of high-resolution imaging systems to assess bone microstructural parameters has grown immensely. Yet, no standard defining the quantification of these parameters exists. It has been reported that different voxel size and/or segmentation techniques lead to different results. However, the effect of the evaluation software has not been investigated so far. Therefore, the aim of this study was to compare the bone microstructural parameters obtained with two commonly used commercial software packages, namely IPL (Scanco, Switzerland) and CTan (Bruker, Belgium). We hypothesized that even when starting from the same segmented scans, different software packages will report different results. Nineteen trapezia and nineteen distal radii were scanned at two resolutions (20 μm voxel size with microCT and HR-pQCT 60 μm). The scans were segmented using the scanners' default protocol. The segmented images were analyzed twice, once with IPL and once with CTan, to quantify bone volume fraction (BV/TV), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N) and specific bone surface (BS/BV). Only small differences between IPL and CTan were found for BV/TV. For Tb.Th, Tb.Sp and BS/BV high correlations (R² ≥ 0.99) were observed between the two software packages, but important relative offsets were observed. For microCT scans, the offsets were relative constant, e.g., around 15% for Tb.Th. However, for the HR-pQCT scans the mean relative offsets ranged over the different bone samples (e.g., for Tb.Th from 14.5% to 19.8%). For Tb.N, poor correlations (0.43 ≤ R² ≤ 0.81) for all tested cases were observed. We conclude that trabecular bone microstructural parameters obtained with IPL and CTan cannot be directly compared except for BV/TV. For Tb.Th, Tb.Sp and BS/BV, correction factors can be determined, but these depend on both the image voxel size and specific anatomic location. The two software packages did not produce consistent data on Tb.N. The development of a universal standard seems desirable.

cfNOMe - A single assay for comprehensive epigenetic analyses of cell-free DNA

Cell-free DNA (cfDNA) analysis has become essential in cancer diagnostics and prenatal testing. We present cfNOMe, a two-in-one method of measuring cfDNA cytosine methylation and nucleosome occupancy in a single assay using non-disruptive enzymatic cytosine conversion and a custom bioinformatic pipeline. We show that enzymatic cytosine conversion better preserves cfDNA fragmentation information than does bisulfite conversion. Whereas previously separate experiments were required to study either epigenetic marking, cfNOMe delivers reliable results for both, enabling more comprehensive and inexpensive epigenetic cfDNA profiling. cfNOMe has the potential to advance biomarker discovery and diagnostic usage in diseases with systemic perturbations of cfDNA composition.

RR-APET - Heart rate variability analysis software

BACKGROUND AND OBJECTIVES

Heart rate variability (HRV) has increasingly been linked to medical phenomena and several HRV metrics have been found to be good indicators of patient health. This has enabled generalised treatment plans to be developed in order to respond to subtle personal differences that are reflected in HRV metrics. There are several established HRV analysis platforms and methods available within the literature; some of which provide command line operation across databases but do not offer extensive graphical user interface (GUI) and editing functionality, while others offer extensive ECG editing but are not feasible over large datasets without considerable manual effort. The aim of this work is to provide a comprehensive open-source package, in a well known and multi-platform language, that offers considerable graphical signal editing features, flexibility within the algorithms used for R-peak detection and HRV quantification, and includes graphical functionality for batch processing. Thereby, providing a platform suited to either physician or researcher.

METHODS

RR-APET's software was developed in the Python language and is modular in format, providing a range of different modules for established R-peak detection algorithms, as well as an embedded template for alternate algorithms. These modules also include several easily adjustable features, allowing the user to optimise any of the algorithms for different ECG signals or databases. Additionally, the software's user-friendly GUI platform can be operated by both researchers or medical professionals to accomplish different tasks, such as: the in-depth visual analysis of a single ECG, or the analysis multiple signals in a single iteration using batch processing. RR-APET also supports several popular data formats, including text, HDF5, Matlab, and Waveform Database (WFDB) files.

RESULTS

The RR-APET platform presents multiple metrics that quantify the heart rate variability features of an R-to-R interval series, including time-domain, frequency-domain, and nonlinear metrics. When known R-peak annotations are available, positive predictability, sensitivity, detection error rate, and accuracy measures are also provided to assess the validity of the implemented R-peak detection algorithm. RR-APET scored an overall usability rating of 4.16 out of a possible 5, when released on a trial basis for user evaluation.

CONCLUSIONS

With its unique ability to both create and operate on large databases, this software provides a strong platform from which to conduct further research in the field of HRV analytics and its correlation to patient healthcare outcomes. This software is available free of charge at https://gitlab.com/MegMcC/rr-apet-hrv-analysis-software and can be operated as an executable file within Windows, Mac and Linux systems.

R-DECO: an open-source Matlab based graphical user interface for the detection and correction of R-peaks

Many of the existing electrocardiogram (ECG) toolboxes focus on the derivation of heart rate variability features from RR-intervals. By doing so, they assume correct detection of the QRS-complexes. However, it is highly likely that not all detections are correct. Therefore, it is recommended to visualize the actual R-peak positions in the ECG signal and allow manual adaptations. In this paper we present R-DECO, an easy-to-use graphical user interface (GUI) for the detection and correction of R-peaks. Within R-DECO, the R-peaks are detected by using a detection algorithm which uses an envelope-based procedure. This procedure flattens the ECG and enhances the QRS-complexes. The algorithm obtained an overall sensitivity of 99.60% and positive predictive value of 99.69% on the MIT/BIH arrhythmia database. Additionally, R-DECO includes support for several input data formats for ECG signals, three basic filters, the possibility to load other R-peak locations and intuitive methods to correct ectopic, wrong, or missed heartbeats. All functionalities can be accessed via the GUI and the analysis results can be exported as Matlab or Excel files. The software is publicly available. Through its easy-to-use GUI, R-DECO allows both clinicians and researchers to use all functionalities, without previous knowledge.

IOCBIO Sparks detection and analysis software

Analysis of calcium sparks in cardiomyocytes can provide valuable information about functional changes of calcium handling in health and disease. As a part of the calcium sparks analysis, sparks detection and characterization is necessary. Here, we describe a new open-source platform for automatic calcium sparks detection from line scan confocal images. The developed software is tailored for detecting only calcium sparks, allowing us to design a graphical user interface specifically for this task. The software enables detecting sparks automatically as well as adding, removing, or adjusting regions of interest marking each spark. The results of the analysis are stored in an SQL database, allowing simple integration with statistical tools. We have analyzed the performance of the algorithm using a large set of synthetic images with varying spark sizes and noise levels and also compared the analysis results with results obtained by software established in the field. The use of our software is illustrated by an analysis of the effect of isoprenaline (ISO) on spark frequency, amplitude, and spatial and temporal characteristics. For that, cardiomyocytes from C57BL/6 mice were used. We demonstrated an increase in spark frequency, tendency of having larger spark amplitudes, sparks with a longer duration, and occurrence of multiple sparks from the same site in the presence of ISO. We also show that the duration and the width of sparks with the same amplitude were similar in the absence and presence of ISO. The software was released as an open source repository and is available for free use and collaborative development.

AFFINImeter: A software to analyze molecular recognition processes from experimental data

The comprehension of molecular recognition phenomena demands the understanding of the energetic and kinetic processes involved. General equations valid for the thermodynamic analysis of any observable that is assessed as a function of the concentration of the involved compounds are described, together with their implementation in the AFFINImeter software. Here, a maximum of three different molecular species that can interact with each other to form an enormous variety of supramolecular complexes are considered. The corrections currently employed to take into account the effects of dilution, volume displacement, concentration errors and those due to external factors, especially in the case of ITC measurements, are included. The methods used to fit the model parameters to the experimental data, and to generate the uncertainties are described in detail. A simulation tool and the so called kinITC analysis to get kinetic information from calorimetric experiments are also presented. An example of how to take advantage of the AFFINImeter software for the global multi-temperature analysis of a system exhibiting cooperative 1:2 interactions is presented and the results are compared with data previously published. Some useful recommendations for the analysis of experiments aimed at studying molecular interactions are provided.

EGG DWPack: System for Multi-Channel Electrogastrographic Signals Recording and Analysis

Electrogastrography (EGG) is a non-invasive examination method for investigating the myolectrical activity of a stomach. Nowadays, abdominal surface electrogastrography is the one of methods of stomach examination that is used for diagnosing patients with chronic intractable nausea, vomiting and gastroparesis. The electrogastrographic signals are recorded by using cutaneous electrodes placed on the stomach surface. EGG DWPack system is a highly developed and easy to use software package for four channel electrogastrography recording and analysis. The part of the software for analysis is a MATLAB based software and requires the specific ASCII format of the EGG data. The analyzed EGG signals could be conditioned with the wide range of sampling frequency and various resolutions of analog to digital conversion. Additionally, if the EGG data fulfills certain conditions associated with sampling frequency, the software can be used to study the basic parameters of heart rate variability (HRV) simultaneously with the EGG parameters. The software includes different digital filters for the EGG signal extraction and tools for artifacts exclusion. The software computes the majority of EGG parameters which are commonly used in a clinical practice. The EGG analysis can be made for several adjustable analysis settings and various methods, and it can optimize the analysis methods for different preferences or requirements. The analysis result can be saved in a MAT-file, and exported to MS Excel and ASCII files. Validation of the software was performed using synthetic and real EGG signals. This paper contains, as an example of use, an analysis of four synthetic, and fourteen human 4-channel EGG data recording with water, yogurt and a solid meal stimulation. The mean values of the dominant frequency for fast, and postprandial stage were found to be 2.96±0.21 cpm (cycle per minute), and 3.05±0.33 cpm, respectively. The values established in the validation process are consistent with typical human physiological values. In addition, the results were compared to outcomes from commercial system. The results of validation have proved that EGG DWPack software produces reliable outcomes. The software is available for free of charge for Windows operating system for the all possible non commercial use.

A gamma-ray spectrometry analysis software environment

At the JRC-Geel's RadioNuclide Metrology sector, a Monte Carlo code based on EGSnrc, and a general purpose calculation sheet implemented in Microsoft Excel^®, have been developed to make the quantitative gamma-ray spectrometry analysis of samples simpler and more robust. The further aim is that the software can be used by non-experts in gamma-ray spectrometry e.g. external researchers using JRC-Geel's facilities through the EUFRAT transnational access scheme. This paper presents the developed Monte Carlo software and the functionality included in the calculation sheet.

Extracting cluster distributions from mass spectra: IsotopeFit

The availability of high resolution mass spectrometry in the study of atomic and molecular clusters opens up challenges for the interpretation of the data. In complex systems each resolved mass peak may contain contributions from multiple species because of the isotope structure of constituent elements and because a multitude of different types of clusters with different compositions are present. A computational procedure which can help to identify a specific cluster from this complex dataset and quantify its relative abundance would be extremely helpful to many who work in this field. Here some new software designed for this purpose, known as IsotopeFit, is described.

Kubios HRV--heart rate variability analysis software

Kubios HRV is an advanced and easy to use software for heart rate variability (HRV) analysis. The software supports several input data formats for electrocardiogram (ECG) data and beat-to-beat RR interval data. It includes an adaptive QRS detection algorithm and tools for artifact correction, trend removal and analysis sample selection. The software computes all the commonly used time-domain and frequency-domain HRV parameters and several nonlinear parameters. There are several adjustable analysis settings through which the analysis methods can be optimized for different data. The ECG derived respiratory frequency is also computed, which is important for reliable interpretation of the analysis results. The analysis results can be saved as an ASCII text file (easy to import into MS Excel or SPSS), Matlab MAT-file, or as a PDF report. The software is easy to use through its compact graphical user interface. The software is available free of charge for Windows and Linux operating systems at http://kubios.uef.fi.