scholarly journals Platform for Analysis and Labeling of Medical Time Series

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7302
Author(s):  
Andrejs Fedjajevs ◽  
Willemijn Groenendaal ◽  
Carlos Agell ◽  
Evelien Hermeling
Keyword(s):  
Time Series ◽  
Software Tool ◽  
Motion Artifacts ◽  
Signal Quality ◽  
Wide Range ◽  
Characteristic Points ◽  
Automated Processing ◽  
User Friendly ◽  
Medical Signals ◽  
Electrocardiogram Ecg

Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations—(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.

scholarly journals RhythmicAlly: Your R and Shiny–Based Open-Source Ally for the Analysis of Biological Rhythms

2019 ◽  
Vol 34 (5) ◽  
pp. 551-561 ◽  
Author(s):  
Lakshman Abhilash ◽  
Vasu Sheeba
Keyword(s):  
Time Series ◽  
Open Source ◽  
Time Series Data ◽  
Biological Rhythms ◽  
Series Data ◽  
Biological Time ◽  
Source Program ◽  
Wide Range ◽  
Infradian Rhythms ◽  
User Friendly

Research on circadian rhythms often requires researchers to estimate period, robustness/power, and phase of the rhythm. These are important to estimate, owing to the fact that they act as readouts of different features of the underlying clock. The commonly used tools, to this end, suffer from being very expensive, having very limited interactivity, being very cumbersome to use, or a combination of these. As a step toward remedying the inaccessibility to users who may not be able to afford them and to ease the analysis of biological time-series data, we have written RhythmicAlly, an open-source program using R and Shiny that has the following advantages: (1) it is free, (2) it allows subjective marking of phases on actograms, (3) it provides high interactivity with graphs, (4) it allows visualization and storing of data for a batch of individuals simultaneously, and (5) it does what other free programs do but with fewer mouse clicks, thereby being more efficient and user-friendly. Moreover, our program can be used for a wide range of ultradian, circadian, and infradian rhythms from a variety of organisms, some examples of which are described here. The first version of RhythmicAlly is available on Github, and we aim to maintain the program with subsequent versions having updated methods of visualizing and analyzing time-series data.

scholarly journals Meteo Browser South Tyrol: A Shiny App to download the meteorological time series from the Open Data Catalogue of the Province of Bolzano/Bozen - Italy

2019 ◽  
Vol 5 ◽  
Author(s):  
Giulio Genova ◽  
Mattia Rossi ◽  
Georg Niedrist ◽  
Stefano Della Chiesa
Keyword(s):  
Time Series ◽  
Open Data ◽  
R Package ◽  
Private Companies ◽  
Web Based ◽  
South Tyrol ◽  
Wide Range ◽  
Shiny App ◽  
On Line ◽  
User Friendly

Meteo Browser South Tyrol is a user-friendly web-based application that helps to visualize and download the hydro-meteorological time series freely available in South Tyrol, Italy. It is designed for a wide range of users, from common citizens to students as well as researchers, private companies and the public administration. Meteo Browser South Tyrol is a Shiny App inside an R package and can be used on a local machine or accessed on-line. Drop down menus allow the user to select hydro-meteorological station and measurements. A simple map shows where the monitoring stations are, the latest measurements available, and lets the user subset the selected stations geographically by drawing a polygon.

scholarly journals Evaluation of patient electrocardiogram datasets using signal quality indexing

2019 ◽  
Vol 8 (2) ◽  
pp. 519-526
Author(s):  
Nazrul Anuar Nayan ◽  
Hafifah Ab Hamid
Keyword(s):  
Vital Signs ◽  
Extraction Process ◽  
Motion Artifacts ◽  
Accurate Information ◽  
Signal Quality ◽  
Emergency Rooms ◽  
Ecg Signals ◽  
Detection Quality ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved.

A Design Exploration of Intelligent Wearable Companion of Smartphone for Fitness and Healthcare

2019 ◽  
Vol 33 (08) ◽  
pp. 1959023
Author(s):  
Wen Qi
Keyword(s):  
Wearable Devices ◽  
Motion Artifacts ◽  
Signal Quality ◽  
Ecg Signals ◽  
Analog Front End ◽  
Fitness Industry ◽  
Different Types ◽  
Electrocardiogram Ecg ◽  
Product Forms ◽  
Future Topics

Wearable devices are increasingly gaining more attentions in healthcare and fitness industry due to their potentials to measure valuable physiological signals on the move. There are many researchers who have proposed different types of designs that embed biosensors into miniature wearable devices. In this paper, we present a wearable companion that monitors the cardiac activities of a wearer with smartphone. The device makes use of a single, integrated biosensor that is designed with a unique analog front-end circuitry and a dedicated signal processing pipeline. In order to meet the requirements of possible but different user scenarios, three types of product forms are presented. The experimental results show that electrocardiogram (ECG) signals collected are valid and consistent through the systems. Future topics include adding extra algorithms to remove motion artifacts in order to achieve better signal quality in various settings and include wireless communication through 4G.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

scholarly journals Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series

2021 ◽  
Vol 13 (2) ◽  
pp. 205
Author(s):  
Philipp Hochreuther ◽  
Niklas Neckel ◽  
Nathalie Reimann ◽  
Angelika Humbert ◽  
Matthias Braun
Keyword(s):  
Time Series ◽  
Observation Interval ◽  
Lake Area ◽  
Climate Data ◽  
Melt Pond ◽  
Automated Processing ◽  
Average Observation ◽  
Average Size ◽  
Lake Size ◽  
Sentinel 2

The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km2 area at the 79°N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m2. Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km2 and a maximum individual lake size of 30 km2. With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks.

scholarly journals GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses

2019 ◽  
Vol 12 (11) ◽  
pp. 4661-4679 ◽  
Author(s):  
Bin Cao ◽  
Xiaojing Quan ◽  
Nicholas Brown ◽  
Emilie Stewart-Jones ◽  
Stephan Gruber
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Ground Surface ◽  
Reanalysis Data ◽  
Meteorological Variables ◽  
Ensemble Simulations ◽  
Driving Time ◽  
Wide Range ◽  
Global Coverage ◽  
Uncertain Input

Abstract. Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses – currently ERA5, ERA-Interim, JRA-55 and MERRA-2 – to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for 10 terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulations and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data.

scholarly journals Biodiversity of Secondary Metabolites Compounds Isolated from Phylum Actinobacteria and Its Therapeutic Applications

Molecules ◽  
2021 ◽  
Vol 26 (15) ◽  
pp. 4504
Author(s):  
Muhanna Al-shaibani ◽  
Radin Maya Saphira Radin Mohamed ◽  
Nik Sidik ◽  
Hesham Enshasy ◽  
Adel Al-Gheethi ◽  
...  
Keyword(s):  
Secondary Metabolites ◽  
Bioactive Compounds ◽  
Extreme Environments ◽  
Software Tool ◽  
Gene Clusters ◽  
Well Being ◽  
Bioactive Substances ◽  
Diverse Range ◽  
Wide Range ◽  
Potential Applications

The current review aims to summarise the biodiversity and biosynthesis of novel secondary metabolites compounds, of the phylum Actinobacteria and the diverse range of secondary metabolites produced that vary depending on its ecological environments they inhabit. Actinobacteria creates a wide range of bioactive substances that can be of great value to public health and the pharmaceutical industry. The literature analysis process for this review was conducted using the VOSviewer software tool to visualise the bibliometric networks of the most relevant databases from the Scopus database in the period between 2010 and 22 March 2021. Screening and exploring the available literature relating to the extreme environments and ecosystems that Actinobacteria inhabit aims to identify new strains of this major microorganism class, producing unique novel bioactive compounds. The knowledge gained from these studies is intended to encourage scientists in the natural product discovery field to identify and characterise novel strains containing various bioactive gene clusters with potential clinical applications. It is evident that Actinobacteria adapted to survive in extreme environments represent an important source of a wide range of bioactive compounds. Actinobacteria have a large number of secondary metabolite biosynthetic gene clusters. They can synthesise thousands of subordinate metabolites with different biological actions such as anti-bacterial, anti-parasitic, anti-fungal, anti-virus, anti-cancer and growth-promoting compounds. These are highly significant economically due to their potential applications in the food, nutrition and health industries and thus support our communities’ well-being.

scholarly journals Motion correction for routine X-ray lung CT imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Doil Kim ◽  
Jiyoung Choi ◽  
Duhgoon Lee ◽  
Hyesun Kim ◽  
Jiyoung Jung ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Motion Correction ◽  
Ct Imaging ◽  
Motion Artifacts ◽  
Ct Scans ◽  
Breath Hold ◽  
X Ray ◽  
Wide Range ◽  
Thorax Ct ◽  
Lung Ct

AbstractA novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.

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