Biomedical and Health Sciences
This research introduces the Open Seizure Database and Toolkit as a novel, publicly accessible resource designed to advance non-electroencephalogram seizure detection research. This paper highlights the scarcity of resources in the non-electroencephalogram domain and establishes the Open Seizure Database as the first openly accessible database containing multimodal sensor data from 49 participants in real-world, in-home environments.
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<p>Ten individuals in good health were enlisted to execute 16 distinct movements involving the wrist and fingers in real-time. Before commencing the experimental procedure, explicit consent was obtained from each participant. Participants were informed that they had the option to withdraw from the study at any point during the experimental session. The experimental protocol adhered to the principles outlined in the Declaration of Helsinki and received approval from the local ethics committee at the National University of Sciences and Technology, Islamabad, Pakistan.
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We submitted the paper Automatic Control System for Precise Intravenous Therapy using Computer Vision-Based on Deep Learning and provide experimental data related to this.
The purpose of this paper is to improve the accuracy of intravenous (IV) infusion using artificial intelligence and feedback loops to prevent errors in the process of administering IV Therapy, and we applied the system we developed to achieve the goal.
Therefore, we applied the system sequentially while creating a prototype and provide the data obtained through experiments.
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Set of samples resulting from an evaluative, population-based cross-sectional study, approved by the Research Ethics Committee of Federal University of Alfenas, protocol: 415856. These samples pertain to 1,027 rural workers from the southern region of Minas Gerais, Brazil, where comprehensive data on socioeconomic factors, history of pesticide intoxication and hospitalization, and usage of personal protective equipment were collected. Blood samples were obtained to measure biomarkers of pesticide exposure as well as indicators of renal and hepatic sequelae.
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This dataset includes data from 43 hospitalized patients with type 2 diabetes mellitus. Dexcom G5 and Dexcom G6 mobile continuous glucose measurement (CGM) devices were used to measure continuous blood glucose levels. The data collection period is from April 2019 to January 2022. We selected 43 patients whose records with a more extended recording period of more than seven days. Data was collected for 7 to 10 days at 5-minute intervals. This data can be used for glucose level prediction or hypoglycemia occurrence prediction for patient with type 2 diabetes mellitus.
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The demand for artificial intelligence (AI) in healthcare is rapidly increasing. However, significant challenges arise from data scarcity and privacy concerns, particularly in medical imaging. While existing generative models have achieved success in image synthesis and image-to-image translation tasks, there remains a gap in the generation of 3D semantic medical images. To address this gap, we introduce Med-DDPM, a diffusion model specifically designed for semantic 3D medical image synthesis, effectively tackling data scarcity and privacy issues.
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The accurate placement of a needle into the spinal column is critical for spinal anesthesia, spinal taps, and other spinal procedures. Currently, the insertion of the needle is guided by visual and palpation feedback, which can be limited in accuracy and reliability. This study presents a novel approach to providing tactile feedback during needle insertion into the spinal column. This study aims to investigate the effectiveness of providing feedback during the insertion of a needle into the epidural column.
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We present a sEMG signal database corresponding to the Indian population named “ElectroMyography Analysis of Human Activities - DataBase -2 (EMAHA-DB2).” This data set consists of two different weight training activities which involve isotonic and isometric contractions. Weight training activities are effective for improving muscle strength, overall health, and regaining limb functionality for people undergoing rehabilitation post stroke-related episodes. The EMG signals acquired during weight training can be used for muscle recruitment analysis.
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This article provides an introduction to the field of datasets, including their types, characteristics, and applications. Datasets refer to collections of data that have been organized for specific purposes. They can come in various forms, including structured data, unstructured data, and semi-structured data. Each type of dataset has its own unique characteristics and uses. For example, structured data typically includes datasets that have been organized into tables and rows, such as spreadsheets or databases, while unstructured data typically includes text, images, and videos.
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