Defibrillation is the most effective method of treating ventricular fibrillation(VF), this paper introduces wearable automatic external defibrillators based on embedded system which includes EGG measurements, bioelectrical impedance measurement, discharge defibrillation module, which can automatic identify VF signal, biphasic exponential waveform defibrillation discharge. After verified by animal tests, the device can realize EGG acquisition and automatic identification. After identifying the ventricular fibrillation signal, it can automatic defibrillate to abort ventricular fibrillation and to realize the cardiac electrical cardioversion.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images.
Sci Rep
January 2025
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China.
Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. Our study aimed to exploit this correlation to improve the performance of both of the IDH mutation status identification and epilepsy diagnosis models in patients with glioma II-IV.
View Article and Find Full Text PDFVAE-Surv: A novel approach for genetic-based clustering and prognosis prediction in myelodysplastic syndromes.
Comput Methods Programs Biomed
January 2025
Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126, Torino, Italy.
Background And Objectives: Several computational pipelines for biomedical data have been proposed to stratify patients and to predict their prognosis through survival analysis. However, these analyses are usually performed independently, without integrating the information derived from each of them. Clustering of survival data is an underexplored problem, and current approaches are limited for biomedical applications, whose data are usually heterogeneous and multimodal, with poor scalability for high-dimensionality.
View Article and Find Full Text PDFArtificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study.
BMC Cancer
January 2025
Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China.
Background: Accurate detection of hepatocellular carcinoma (HCC) in multiphasic contrast CT is essential for effective treatment and surgical planning. However, the variety of CT images, the misdiagnosis and missed diagnosis, and the inconsistent diagnosis among different radiologists pose challenges to accurate detection which demands sufficient clinical experience and can be time-consuming and labor-intensive.
Purpose: To evaluate the detection performance of an artificial intelligence (AI)-assisted platform for HCC by the external validation dataset.
Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis.
Sensors (Basel)
January 2025
Institute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, Spain.
Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF).
View Article and Find Full Text PDFExternal delay and dispersion correction of automatically sampled arterial blood with dual flow rates.
Biomed Phys Eng Express
January 2025
Brain Health Imaging Centre, Centre for Addiction and Mental Health, B68-250 College St, Toronto, Ontario, M5T 1R8, CANADA.
Objective: Arterial sampling for PET imaging often involves continuously measuring the radiotracer activity concentration in blood using an automatic blood sampling system (ABSS). We proposed and validated an external delay and dispersion correction procedure needed when a change in flow rate occurs during data acquisition. We also measured the external dispersion constant of [11C]CURB, [18F]FDG, [18F]FEPPA, and [18F]SynVesT-1.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!