Real-time detection of fatigue effect on active muscle hemodynamics using diffuse correlation spectroscopy

2020 ◽  
Author(s):  
Kanichiro Nozaki ◽  
Mikie Nakabayashi ◽  
Masashi Ichinose ◽  
Yumie Ono
Keyword(s):  
Real Time ◽  
Correlation Spectroscopy ◽  
Active Muscle ◽  
Diffuse Correlation Spectroscopy ◽  
Fatigue Effect ◽  
Real Time Detection

scholarly journals Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy

2020 ◽  
Author(s):  
Chien-Sing Poon ◽  
Feixiao Long ◽  
Ulas Sunar
Keyword(s):  
Blood Flow ◽  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Correlation Spectroscopy ◽  
Flow Quantification ◽  
Tissue Blood Flow ◽  
Diffuse Correlation Spectroscopy ◽  
Flow Changes ◽  
Deep Learning Model

ABSTRACTDiffuse correlation spectroscopy (DCS) is increasingly used in the optical imaging field to assess blood flow in humans due to its non-invasive, real-time characteristics and its ability to provide label-free, bedside monitoring of blood flow changes. Previous DCS studies have utilized a traditional curve fitting of the analytical or Monte Carlo models to extract the blood flow changes, which are computationally demanding and less accurate when the signal to noise ratio decreases. Here, we present a deep learning model that eliminates this bottleneck by solving the inverse problem more than 2300% faster, with equivalent or improved accuracy compared to the nonlinear fitting with an analytical method. The proposed deep learning inverse model will enable real-time and accurate tissue blood flow quantification with the DCS technique.

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.

Sign in / Sign up

Export Citation Format

Share Document