scholarly journals Real-Time Detection of State Transitions in Stochastic Signals from Biological Systems

ACS Omega ◽  
2021 ◽  
Author(s):  
Max H. Bergkamp ◽  
Leo J. van IJzendoorn ◽  
Menno W.J. Prins
Keyword(s):  
Real Time ◽  
Biological Systems ◽  
State Transitions ◽  
Stochastic Signals ◽  
Real Time Detection

A fluorogenic probe based on chelation–hydrolysis-enhancement mechanism for visualizing Zn2+ in Parkinson's disease models

2019 ◽  
Vol 7 (14) ◽  
pp. 2252-2260 ◽  
Author(s):  
Gaobin Zhang ◽  
Yanfei Zhao ◽  
Bo Peng ◽  
Zheng Li ◽  
Chenchen Xu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Real Time ◽  
Biological Systems ◽  
Disease Models ◽  
Enhancement Mechanism ◽  
Fluorogenic Probe ◽  
Real Time Detection ◽  
Role Of Zn

Developing efficient methods for real-time detection of Zn2+ level in biological systems is highly relevant to improve our understanding of the role of Zn2+ in the progression of Parkinson's disease (PD).

A novel porphyrin-based near-infrared fluorescent probe for hypochlorite detection and its application in vitro and in vivo

The Analyst ◽  
2018 ◽  
Vol 143 (11) ◽  
pp. 2641-2647 ◽  
Author(s):  
Xiaoyi Wang ◽  
Jie Min ◽  
Weijie Wang ◽  
Yan Wang ◽  
Gui Yin ◽  
...  
Keyword(s):  
Real Time ◽  
Fluorescent Probe ◽  
Near Infrared ◽  
Biological Systems ◽  
Real Time Detection ◽  
Selective Sensor

A porphyrin-based highly selective sensor for hypochlorite was applied in real-time detection of exogenous and endogenous ClO− in biological systems.

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown
Keyword(s):  
Real Time ◽  
Steering Wheel ◽  
Real Time Detection

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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