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Class-specific discriminant time-frequency analysis using novel jointly learnt non-negative matrix factorization
Title
Class-specific discriminant time-frequency analysis using novel jointly learnt non-negative matrix factorization.
Creator
Ghoraani, Behnaz
Date Issued
2016-12-07
PURL
http://purl.flvc.org/fau/fd/FAUIR000143
Format
Citation
DEEP LEARNING REGRESSION MODELS FOR LIMITED BIOMEDICAL TIME-SERIES DATA
Title
DEEP LEARNING REGRESSION MODELS FOR LIMITED BIOMEDICAL TIME-SERIES DATA.
Creator
Hssayeni, Murtadha D., Behnaz Ghoraani, Behnaz, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Time-series data in biomedical applications are gaining an increased interest to detect and predict underlying diseases and estimate their severity, such as Parkinson’s disease (PD) and cardiovascular diseases. This interest is driven by advances in wearable sensors and deep learning models to a large extent. In the literature, less attention has been paid to regression models for continuous outcomes in these applications, especially when dealing with limited data. Training deep learning...
Show moreTime-series data in biomedical applications are gaining an increased interest to detect and predict underlying diseases and estimate their severity, such as Parkinson’s disease (PD) and cardiovascular diseases. This interest is driven by advances in wearable sensors and deep learning models to a large extent. In the literature, less attention has been paid to regression models for continuous outcomes in these applications, especially when dealing with limited data. Training deep learning models on raw limited data results in overfitted models, which is the main technical challenge we address in this dissertation. An example of limited and\or imbalanced time-series data is PD’s motion signals that are needed for the continuous severity estimation of Parkinson’s disease (PD). The significance of this continuous estimation is providing a tool for longitudinal monitoring of daily motor and non-motor fluctuations and managing PD medications. The dissertation objective is to train generalizable deep learning models for biomedical regression problems when dealing with limited training time-series data. The goal is designing, developing, and validating an automatic assessment system based on wearable sensors that can measure the severity of PD complications in the home-living environment while patients with PD perform their activities of daily living (ADL). We first propose using a combination of domain-specific feature engineering, transfer learning, and an ensemble of multiple modalities. Second, we utilize generative adversarial networks (GAN) and propose a new formulation of conditional GAN (cGAN) as a generative model for regression to handle an imbalanced training dataset. Next, we propose a dual-channel auxiliary regressor GAN (AR-GAN) trained using Wasserstein-MSE-correlation loss. The proposed AR-GAN is used as a data augmentation method in regression problems.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00013992
Subject Headings
Deep learning (Machine learning), Regression analysis--Mathematical models, Biomedical engineering
Format
Document (PDF)
DEVELOPMENT OF AN ALGORITHM TO GUIDE A MULTI-POLE DIAGNOSTIC CATHETER FOR IDENTIFYING THE LOCATION OF ATRIAL FIBRILLATION SOURCES
Title
DEVELOPMENT OF AN ALGORITHM TO GUIDE A MULTI-POLE DIAGNOSTIC CATHETER FOR IDENTIFYING THE LOCATION OF ATRIAL FIBRILLATION SOURCES.
Creator
Ganesan, Prasanth, Ghoraani, Behnaz, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Atrial Fibrillation (AF) is a debilitating heart rhythm disorder affecting over 2.7 million people in the US and over 30 million people worldwide annually. It has a high correlation with causing a stroke and several other risk factors, resulting in increased mortality and morbidity rate. Currently, the non-pharmocological therapy followed to control AF is catheter ablation, in which the tissue surrounding the pulmonary veins (PVs) is cauterized (called the PV isolation - PVI procedure) aims...
Show moreAtrial Fibrillation (AF) is a debilitating heart rhythm disorder affecting over 2.7 million people in the US and over 30 million people worldwide annually. It has a high correlation with causing a stroke and several other risk factors, resulting in increased mortality and morbidity rate. Currently, the non-pharmocological therapy followed to control AF is catheter ablation, in which the tissue surrounding the pulmonary veins (PVs) is cauterized (called the PV isolation - PVI procedure) aims to block the ectopic triggers originating from the PVs from entering the atrium. However, the success rate of PVI with or without other anatomy-based lesions is only 50%-60%. A major reason for the suboptimal success rate is the failure to eliminate patientspecific non-PV sources present in the left atrium (LA), namely reentry source (a.k.a. rotor source) and focal source (a.k.a. point source). It has been shown from several animal and human studies that locating and ablating these sources significantly improves the long-term success rate of the ablation procedure. However, current technologies to locate these sources posses limitations with resolution, additional/special hardware requirements, etc. In this dissertation, the goal is to develop an efficient algorithm to locate AF reentry and focal sources using electrograms recorded from a conventionally used high-resolution multi-pole diagnostic catheter.
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Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FA00013310
Subject Headings
Atrial Fibrillation--diagnosis, Algorithm, Catheter ablation
Format
Document (PDF)