scholarly journals Performance Comparison of Parameter Estimation Techniques for the Quantitation of Local Cerebral Blood Flow by Dynamic Positron Computed Tomography

1985 ◽  
Vol 5 (2) ◽  
pp. 224-234 ◽  
Author(s):  
Robert A. Koeppe ◽  
James E. Holden ◽  
W. Raymond Ip
Keyword(s):  
Performance Comparison ◽  
Measured Data ◽  
Simultaneous Estimation ◽  
Estimation Techniques ◽  
Computed Tomographic ◽  
Advantages And Disadvantages ◽  
Functional Images ◽  
Model Predictions ◽  
Integration Techniques ◽  
Parameter Estimation Techniques

Local CBF (LCBF) can be quantitated from positron computed tomographic (PCT) data and physiologically based mathematical models by several general methods. Those using a dynamic sequence of PCT scans allow the simultaneous estimation of both LCBF and p, the indicator's tissue–blood partition coefficient. This article presents a comparison of three rapid estimation techniques for use with inert diffusible radioindicators and serial PCT, each of which is based on the original Kety model. One method, developed in our laboratory, involves minimizing the mean squared discrepancy between measured data and model predictions, whereas the other two methods, recently reported in the literature, are weighted integration techniques that involve multiplying the measured data by time-dependent weighting functions. Simulation studies of noise propagation and other sources of error were performed under a variety of simulated conditions. Functional images of LCBF and p were calculated using each method for both phantom and human subject data. Errors can differ by as much as a factor of 2–3 between methods, with each having its own unique advantages and disadvantages.

scholarly journals A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

2021 ◽  
Vol 11 (5) ◽  
pp. 668
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Isselmou Abd El Kader ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Performance Comparison ◽  
Eeg Signal ◽  
Detection Algorithms ◽  
Advantages And Disadvantages ◽  
Monitoring And Diagnosis

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

scholarly journals Minimizing Vibratory Strain Measurement Error

1998 ◽  
Author(s):  
Mark D. Sensmeier ◽  
Kurt L. Nichol
Keyword(s):  
Measurement Error ◽  
Objective Function ◽  
Strain Gage ◽  
Strain Measurement ◽  
Measured Data ◽  
Dynamic Strain ◽  
Model Predictions ◽  
Modes Of Vibration ◽  
Instrumentation Error ◽  
Time Basis

Correlation between dynamic strain gage measurements and modal analysis results can be adversely affected by gage misplacement and gage misorientation. An optimization algorithm has been developed which allows the modeled strain gage locations and orientations to be varied within specified tolerances. An objective function is defined based on the least squares sum of the differences between experimental and model results. The Kuhn-Tucker conditions are then applied to find the gage locations and orientations which minimize this objective function. The procedure is applied on a one-time basis considering all measured modes of vibration simultaneously. This procedure minimizes instrumentation error which then allows the analyst to modify the model to more accurately represent other factors, including boundary conditions. Flat plate vibratory data was used to demonstrate a significant improvement in correlation between measured data and model predictions.

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