Reported cases of adverse events and product recalls expose limitations of biomedical signal acquisition devices. Approximately, ninety percent of the 1.210 recalls reported by the US Food and Drug Administration (FDA) between 2006 and 2011 were of class 2 devices such as Electrocardiography (ECG) devices. We show in this paper how manufacturers of biomedical signal acquisition devices can argue effectiveness of these devices using Colored Petri Nets (CPN) models and assurance cases in Goal Structuring Notation (GSN) by means of an ECG case study. We illustrate how CPN models are used to generate effectiveness evidences in order to present them during certification. In this context, we use assurance cases in GSN to present evidences arguing effectiveness of the device. We were able to conclude based on the ECG case study that the use of CPN models of devices can decrease costs and development time once manufacturers reuse them during the development and certification process.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/EMBC.2016.7591235 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Comparative Analysis of Machine Learning Algorithms Used for Translating Aptamer-Antigen Binding Kinetic Profiles to Diagnostic Decisions.
ACS Sens
January 2025
Department of Engineering Physics, McMaster University, 1280 Main Street West, L8S 4L8 Hamilton, Ontario, Canada.
Current approaches for classifying biosensor data in diagnostics rely on fixed decision thresholds based on receiver operating characteristic (ROC) curves, which can be limited in accuracy for complex and variable signals. To address these limitations, we developed a framework that facilitates the application of machine learning (ML) to diagnostic data for the binary classification of clinical samples, when using real-time electrochemical measurements. The framework was applied to a real-time multimeric aptamer assay (RT-MAp) that captures single-frequency (12.
View Article and Find Full Text PDFThe underlying mechanisms of the association of bone health with depression - an experimental study.
Mol Biol Rep
January 2025
Medical Sociology and Psychobiology, Department of Health and Physical Activity, University of Potsdam, 14469, Potsdam, Germany.
Background: Depression constitutes a risk factor for osteoporosis, but underlying molecular and cellular mechanisms are not fully understood. MiRNAs influence gene expression and are carried by extracellular vesicles (EV), affecting cell-cell communication.
Aims: (1) Identify the difference in miRNA expression between depressed patients and healthy controls; (2) Analyze associations of these miRNAs with bone turnover markers; (3) Analyze target genes of differentially regulated miRNAs and predict associated pathways regarding depression and bone metabolism.
Neuroregulation during Bone Formation and Regeneration: Mechanisms and Strategies.
ACS Appl Mater Interfaces
January 2025
National Engineering Research Center for Biomaterials, College of Biomedical Engineering, Sichuan University, Chengdu 610064, China.
The skeleton is highly innervated by numerous nerve fibers. These nerve fibers, in addition to transmitting information within the bone and mediating bone sensations, play a crucial role in regulating bone tissue formation and regeneration. Traditional bone tissue engineering (BTE) often fails to achieve satisfactory outcomes when dealing with large-scale bone defects, which is frequently related to the lack of effective reconstruction of the neurovascular network.
View Article and Find Full Text PDFMitochondrial retrograde signaling (MRS) pathways relay the functional status of mitochondria to elicit homeostatic or adaptive changes in nuclear gene expression. Budding yeast have "intergenomic signaling" pathways that sense the amount of mitochondrial DNA (mtDNA) independently of oxidative phosphorylation (OXPHOS), the primary function of genes encoded by mtDNA. However, MRS pathways that sense the amount of mtDNA in mammalian cells remain poorly understood.
View Article and Find Full Text PDFQuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics.
Anal Chem
January 2025
State Key Laboratory of Cellular Stress Biology, Institute of Artificial Intelligence, School of Life Sciences, Faculty of Medicine and Life Sciences, National Institute for Data Science in Health and Medicine, XMU-HBN skin biomedical research center, Xiamen University, Xiamen, Fujian 361102, China.
In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals.
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!