Current Search:   pvc (x)

View All Items

  • CSV Spreadsheet
(1 - 2 of 2)
DETECTION OF THE R-WAVE IN ECG SIGNALS
Title
DETECTION OF THE R-WAVE IN ECG SIGNALS.
Creator
Valluri, Sasanka, Weeks, Arthur, University of Central Florida
Abstract / Description
This thesis aims at providing a new approach for detecting R-waves in the ECG signal and generating the corresponding R-wave impulses with the delay between the original R-waves and the R-wave impulses being lesser than 100 ms. The algorithm was implemented in Matlab and tested with good results against 90 different ECG recordings from the MIT-BIH database. The Discrete Wavelet Transform (DWT) forms the heart of the algorithm providing a multi-resolution analysis of the ECG signal. The...
Show moreThis thesis aims at providing a new approach for detecting R-waves in the ECG signal and generating the corresponding R-wave impulses with the delay between the original R-waves and the R-wave impulses being lesser than 100 ms. The algorithm was implemented in Matlab and tested with good results against 90 different ECG recordings from the MIT-BIH database. The Discrete Wavelet Transform (DWT) forms the heart of the algorithm providing a multi-resolution analysis of the ECG signal. The wavelet transform decomposes the ECG signal into frequency scales where the ECG characteristic waveforms are indicated by zero crossings. The adaptive threshold algorithms discussed in this thesis search for valid zero crossings which characterize the R-waves and also remove the Preventricular Contractions (PVC's). The adaptive threshold algorithms allow the decision thresholds to adjust for signal quality changes and eliminate the need for manual adjustments when changing from patient to patient. The delay between the R-waves in the original ECG signal and the R-wave impulses obtained from the algorithm was found to be less than 100 ms.
Show less
Date Issued
2005
Identifier
CFE0000498, ucf:46369
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000498
The Identification and Segmentation of Astrocytoma Prior to Critical Mass, by means of a Volumetric/Subregion Regression Analysis of Normal and Neoplastic Brain Tissue
Title
The Identification and Segmentation of Astrocytoma Prior to Critical Mass, by means of a Volumetric/Subregion Regression Analysis of Normal and Neoplastic Brain Tissue.
Creator
Higgins, Lyn, Hughes, Charles, Morrow, Patricia Bockelman, Bagci, Ulas, Lisle, Curtis, University of Central Florida
Abstract / Description
As the underlying cause of Glioblastoma Multiforme (GBM) is presently unclear, this research implements a new approach to identifying and segmenting plausible instances of GBM prior to critical mass. Grade-IV Astrocytoma, or GBM, is an aggressive and malignant cancer arising from star-shaped glial cells, or astrocytes, where the astrocytes, functionally, assist in the support and protection of neurons within the central nervous system and spinal cord. Subsequently, our motivation for...
Show moreAs the underlying cause of Glioblastoma Multiforme (GBM) is presently unclear, this research implements a new approach to identifying and segmenting plausible instances of GBM prior to critical mass. Grade-IV Astrocytoma, or GBM, is an aggressive and malignant cancer arising from star-shaped glial cells, or astrocytes, where the astrocytes, functionally, assist in the support and protection of neurons within the central nervous system and spinal cord. Subsequently, our motivation for researching the ability to recognize GBM is that the underlying cause of the mutation is presently unclear, leading to the operative that GBM is only detectable through a combination of MRI and CT brain scans, cooperatively, along with a resection biopsy. Since astrocytoma only becomes evident at critical mass, when the cellular structure of the neoplasm becomes visible within the image, this research seeks to achieve earlier identification and segmentation of the neoplasm by evaluating the malignant area via a volumetric voxel approach to removing noise artifacts and analyzing voxel differentials. In order to investigate neoplasm continuity, a differential approach has been implemented utilizing a multi-polynomial/multi-domain regression algorithm, thus, ultimately, providing a graphical and mathematical analysis of the differentials within critical mass and non-critical mass images. Given these augmentations to MRI and CT image rectifications, we theorize that our approach will improve on astrocytoma recognition and segmentation, along with achieving greater accuracy in diagnostic evaluations of the malignant area.
Show less
Date Issued
2018
Identifier
CFE0007336, ucf:52111
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007336