Quality Assessment of Ambulatory Electrocardiogram Signals by Noise Detection using Optimal Binary Classification

In order to improve the diagnostic capability in Ambulatory Electrocardiogram signal and to reduce the noise signal impacts, there is a need for more robust models in place. In terms of improvising to the existing solutions, this article explores a novel binary classifier that learns from the features optimized by fusion of diversity assessment measures, which performs Quality Assessment of Ambulatory Electrocardiogram Signals (QAAES) by Noise Detection. The performance of the proposed model QAAES has been scaled by comparing it with contemporary models. Concerning performance analysis, the 10-fold cross-validation has been carried on a benchmark dataset. The results obtained from experiments carried on proposed and other contemporary models for cross-validation metrics have been compared to signify the sensitivity, specificity, and noise detection accuracy

Ovarian torsion in infertility treatment

Ovarian torsion, a rare problem in women can lead to serious consequences if not diagnosed and treated at the earliest. It is one of the complications that needs to be included in the differential diagnosis of any girl with abdominal pain or a pelvic or abdominal mass as the symptoms and signs are nonspecific and it can be related to other clinical causes. A 29-year-old woman who was under ovulation induction was diagnosed with ovarian torsion which was treated by performing laparoscopy. Investigations for ovarian torsion should be carried out for all the patients treated for infertility presenting with abdominal pain to minimize the risk before it is too late

Expert consensus on neurodevelopmental outcomes in pregnancy pharmacovigilance studies

Background: Exposure in utero to certain medications can disrupt processes of fetal development, including brain development, leading to a continuum of neurodevelopmental difficulties. Recognizing the deficiency of neurodevelopmental investigations within pregnancy pharmacovigilance, an international Neurodevelopmental Expert Working Group was convened to achieve consensus regarding the core neurodevelopmental outcomes, optimization of methodological approaches and barriers to conducting pregnancy pharmacovigilance studies with neurodevelopmental outcomes. Methods: A modified Delphi study was undertaken based on stakeholder and expert input. Stakeholders (patient, pharmaceutical, academic and regulatory) were invited to define topics, pertaining to neurodevelopmental investigations in medication-exposed pregnancies. Experts were identified for their experience regarding neurodevelopmental outcomes following medicinal, substances of misuse or environmental exposures in utero. Two questionnaire rounds and a virtual discussion meeting were used to explore expert opinion on the topics identified by the stakeholders. Results: Twenty-five experts, from 13 countries and professionally diverse backgrounds took part in the development of 11 recommendations. The recommendations focus on the importance of neurodevelopment as a core feature of pregnancy pharmacovigilance, the timing of study initiation and a core set of distinct but interrelated neurodevelopmental skills or diagnoses which require investigation. Studies should start in infancy with an extended period of investigation into adolescence, with more frequent sampling during rapid periods of development. Additionally, recommendations are made regarding optimal approach to neurodevelopmental outcome measurement, comparator groups, exposure factors, a core set of confounding and mediating variables, attrition, reporting of results and the required improvements in funding for potential later emerging effects. Different study designs will be required depending on the specific neurodevelopmental outcome type under investigation and whether the medicine in question is newly approved or already in widespread use. Conclusion: An improved focus on neurodevelopmental outcomes is required within pregnancy pharmacovigilance. These expert recommendations should be met across a complementary set of studies which converge to form a comprehensive set of evidence regarding neurodevelopmental outcomes in pregnancy pharmacovigilance

Towards point-of-care diagnosis of Alzheimer’s disease: Multi-analyte based portable chemiresistive platform for simultaneous detection of β-amyloid (1–40) and (1–42) in plasma

Label-free simultaneous detection of Alzheimer's disease (AD) specific biomarkers Aβ40 and Aβ42 peptides on a single platform using polypyrrole nanoparticle-based chemiresistive biosensors is reported here. The proposed interdigitated-microelectrode based inexpensive multisensor-platform can concurrently detect Aβ40 and Aβ42 in spiked-plasma in the range of 10-14 – 10-6 g/mL (with LoDs being 5.71 and 9.09 fg/mL, respectively), enabling the estimation of diagnostically significant Aβ42/Aβ40 ratio. A detailed study has been undertaken here to record the individual sensor responses against spiked-plasma samples with varying amounts and proportions of the two target peptides, towards enabling disease-progression monitoring using the Aβ-ratio. As compared to the existing cost-ineffective brain-imaging techniques such as PET and MRI, and the high-risk CSF based invasive AD biomarkers detecting procedures, the proposed approach offers a viable alternative for affordable point-of-care AD diagnostics, with possible usage in performance evaluation of therapeutic drugs. Towards point-of-care applications, the portable readout used in this work was conjugated with an android-based mobile app for data-acquisition and analysis. © 2021 Elsevier B.V

Visualization for Emotion Detection in Mobile-Based Land Monitoring Using User-Centred Design

Many colleges employ online learning as media learning where each piece of multimedia, be it sound, film, or text, can receive feedback from students. The lecture wants to know about the emotions that students experienced when they accessed the media, such as happiness, disappointment, or unhappiness, and instructors want to know how joyful they felt. This study built a tool cell for online media to use in identifying emotions in column comments. This paper focuses on user experience (UX) design, which is based on the Human-Centred Design method that prioritizes empathy for users and focuses on user demands to meet the discovery of user wants and develop high-fidelity prototypes. The results of the testing show that the average accuracy is 1.68%, the average recall is 1.55, the average precision is 1.45, and the average accuracy for the use of this phone website is 80% for emotion recognition based only on a column of comment in the internet media

Modelling of GPS signal scintillations with polynomial coefficients over the Indian region

167-174Performance of Global Positioning System (GPS) and other satellite based navigation systems can be degraded by the ionospheric scintillations of the signal. As the ionospheric scintillations behaviour varies with geographical location (latitude, longitude) and time, modelling is required for estimating and forecasting the scintillation. In this paper, amplitude and phase scintillations (S4 and σφ) of the GPS signal are modelled with the polynomial coefficients developed using the least square solution (LSS) with merit function. For the coefficients development, GPS satellites data from 17 stations of the GPS Aided Geo Augmented Navigation (GAGAN) system network in the Indian region is considered. It is found that estimated / predicted scintillations due to the developed coefficients are very close to the experimental (observed) data of Hyderabad receiver station (17.44°N, 78.47°E). The maximum deviation in predicted S4 and σφ is 0.03 and 0.3 rad, respectively. With the developed coefficients temporal, latitude and longitudinal variations of S4 and σφ are analysed for Hyderabad station (17.44°N, 78.47°E). </span