Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

686. Antibiotic Prescribing For Urinary Tract Infections In Outpatient Settings: A Multicenter Assessment Based On The Five Ds Of Stewardship, Amy Y. Kang, Oliver Tan, Sunwoo Oh, Nikki Derleth, Megan Shieh, Helen Vu, Inna Tagarino, Audrey Harrington, Deborah Kupferwasser, Loren G. Miller

Pharmacy Faculty Articles and Research

Background   Urinary tract infections (UTIs) are a leading cause of outpatient antibiotic use. Prior studies have documented inappropriate prescribing during outpatient UTI visits, but few have assessed intervention opportunities using a structured stewardship framework. We evaluated UTI prescribing practices in a healthcare system using the Five Ds framework: Diagnosis, Drug, Dose, Duration, and De-escalation.   Methods

We conducted a retrospective study across 4 medical centers and > 20 outpatient clinics within the Los Angeles County Department of Health Services, the second largest U.S. safety net healthcare system. To ensure adequate representation across sites while maintaining feasibility for manual chart review, we reviewed …

Learning From Covid-19: Clinical Trials, Health Information Technology, And Patient Mortality, Christos Nicolaides, Avinash Collis, Seth Benzell, Indranil Bardhan

Economics Faculty Articles and Research

During the COVID-19 pandemic, deaths per case in the United States decreased from 7.46% in April 2020 to 1.76% in April 2021. One mechanism that could explain this decline is a learning effect associated with testing of new treatments by hospitals. Hospitals that participated in clinical trials developed better organizational capabilities to diagnose and treat COVID-19 patients. Simultaneously, hospitals used health information technologies (IT) that integrated health information across healthcare providers to facilitate greater learning and sharing of best practices. Using US county-level data on clinical trial participation, use of health IT, and COVID-19 cases and deaths, we show that …

Inflammation And Detection: Rethinking The Biomarker Landscape In Gastric Cancer, Keykavous Parang, Koosha Paydary

Pharmacy Faculty Articles and Research

Gastric carcinoma is a leading cause of cancer-related mortality worldwide, yet reliable noninvasive biomarkers for its early detection remain limited. As research continues to elucidate the inflammatory underpinnings of tumor initiation and progression, it has become increasingly clear that pro-inflammatory cytokines may hold promise as diagnostic adjuncts. Serum cytokines such as interleukin (IL)-1β, IL-6, IL-8, and interferon-gamma have been frequently reported as elevated in gastric cancer patients compared to healthy individuals. These molecules, known for their roles in modulating tumor-promoting inflammation, angiogenesis, and immune evasion, may serve as accessible indicators of disease presence or progression. Several studies have shown that …

Clinical Value Of Chatgpt For Epilepsy Presurgical Decision-Making: Systematic Evaluation Of Seizure Semiology Interpretation, Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R. Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J. Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu

Engineering Faculty Articles and Research

Background: For patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures. Accurate localization of the EZ is crucial and is typically achieved through comprehensive presurgical approaches such as seizure semiology interpretation, electroencephalography (EEG), magnetic resonance imaging (MRI), and intracranial EEG (iEEG). However, interpreting seizure semiology is challenging because it heavily relies on expert knowledge. The semiologies are often inconsistent and incoherent, leading to variability and potential limitations in presurgical evaluation. To overcome these challenges, advanced technologies like large language models (LLMs)—with ChatGPT being a notable example—offer valuable tools for …

A Review Of Racial Differences And Disparities In Ecg, Jianwei Zheng, Chizobam Ani, Islam Abudayyeh, Yunfan Zheng, Cyril Rakovski, Ehsan Yaghmaei, Omolola Ogunyemi

Mathematics, Physics, and Computer Science Faculty Articles and Research

The electrocardiogram (ECG) is a widely used, non-invasive tool for diagnosing a range of cardiovascular conditions, including arrhythmia and heart disease-related structural changes. Despite its critical role in clinical care, racial and ethnic differences in ECG readings are often underexplored or inadequately addressed in research. Variations in key ECG parameters, such as PR interval, QRS duration, QT interval, and T-wave morphology, have been noted across different racial groups. However, the limited research in this area has hindered the development of diagnostic criteria that account for these differences, potentially contributing to healthcare disparities, as ECG interpretation algorithms largely developed from major …

Exosomes And Encapsulated Exomirs In Breast Cancer: Theragnostic Applications And Clinical Implications, Muhammad Imran Sajid, Shafia Bukhari, Hamid Ilyas, Rubina Malik, Minahil Fatima, Rakesh Kumar Tiwari, Surya M. Nauli, Khawaja Husnain Haider

Pharmacy Faculty Books and Book Chapters

Exosomes, released by cells, are small vesicles that have emerged as critical theranostic tools in breast cancer due to their unique ability to transport diverse biomolecules such as proteins, lipids, and RNAs, including microRNAs (ExomiRs). These vesicles are critical in modulating the tumor microenvironment, driving processes like cancer proliferation, invasion, metastasis, and drug resistance. Exosomal microRNAs such as ExomiR-21, ExomiR-1246, and ExomiR-155, with their profound influence on the gene expressions in the recipient cells, are the unsung heroes in tumor progression and immune modulation. This chapter delves into exosome's dual diagnostic and therapeutic potential in breast cancer. It highlights their …

Real-World Impact Of Acupuncture On Analgesics And Healthcare Resource Utilization In Breast Cancer Survivors With Pain, Dina Quan Ng, Sanghoon Lee, Richard T. Lee, Yun Wang, Alexandre Chan

Pharmacy Faculty Articles and Research

Background

This study evaluated the real-world impact of acupuncture on analgesics and healthcare resource utilization among breast cancer survivors.

Methods

From a United States (US) commercial claims database (25% random sample of IQVIA PharMetrics® Plus for Academics), we selected 18–63 years old malignant breast cancer survivors experiencing pain and ≥ 1 year removed from cancer diagnosis. Using the difference-in-difference technique, annualized changes in analgesics [prevalence, rates of short-term (< 30-day supply) and long-term (≥ 30-day supply) prescription fills] and healthcare resource utilization (healthcare costs, hospitalizations, and emergency department visits) were compared between acupuncture-treated and non-treated patients.

Results

Among 495 (3%) acupuncture-treated patients (median age: 55 years, stage 4: 12%, average 2.5 years post cancer diagnosis), most had commercial health insurance (92%) and experiencing musculoskeletal pain (98%). Twenty-seven …

Histopathology Imaging And Clinical Data Including Remission Status In Pediatric Inflammatory Bowel Disease, Chloe Martin-King, Ali Nael, Louis Ehwerhemuepha, Blake Calvo, Quinn Gates, Jamie Janchoi, Elisa Ornelas, Melissa Perez, Andrea Venderby, John Miklavcic, Peter Chang, Aaron Sassoon, Kenneth Grant

Food Science Faculty Articles and Research

The incidence of inflammatory bowel disease (IBD) is increasing annually. Children with IBD often suffer significant morbidity due to physical and emotional effects of the disease and treatment. Corticosteroids, often a component of therapy, carry undesirable side effects with long term use. Steroid-free remission has become a standard for care-quality improvement. Anticipating therapeutic outcomes is difficult, with treatments often leveraged in a trial-and-error fashion. Artificial intelligence (AI) has demonstrated success in medical imaging classification tasks. Predicting patients who will attain remission will help inform treatment decisions. The provided dataset comprises 951 tissue section scans (167 whole-slides) obtained from 18 pediatric …

Ultrasoft Platelet-Like Particles Stop Bleeding In Rodent And Porcine Models Of Trauma, Kimberly Nellenbach, Emily Mihalko, Seema Nandi, Drew W. Koch, Jagathpala Shetty, Leandra Moretti, Jennifer Sollinger, Nina Moiseiwitsch, Ana Sheridan, Sanika Pandit, Maureane Hoffman, Lauren V. Schnabel, L. Andrew Lyon, Thomas H. Barker, Ashley C. Brown

Engineering Faculty Articles and Research

Uncontrolled bleeding after trauma represents a substantial clinical problem. The current standard of care to treat bleeding after trauma is transfusion of blood products including platelets; however, donated platelets have a short shelf life, are in limited supply, and carry immunogenicity and contamination risks. Consequently, there is a critical need to develop hemostatic platelet alternatives. To this end, we developed synthetic platelet-like particles (PLPs), formulated by functionalizing highly deformable microgel particles composed of ultralow cross-linked poly (N-isopropylacrylamide) with fibrin-binding ligands. The fibrin-binding ligand was designed to target to wound sites, and the cross-linking of fibrin polymers was designed …

Harnessing Exosomes As A Platform For Drug Delivery In Breast Cancer: A Systematic Review For In Vivo And In Vitro Studies, Abdulwahab Teflischi Gharavi, Saeed Irian, Azadeh Niknejad, Keykavous Parang, Mona Salimi

Pharmacy Faculty Articles and Research

Breast cancer remains a significant global health concern, emphasizing the critical need for effective treatment strategies, especially targeted therapies. This systematic review summarizes the findings from in vitro and in vivo studies regarding the therapeutic potential of exosomes as drug delivery platforms in the field of breast cancer treatment. A comprehensive search was conducted across bibliographic datasets, including Web of Science, PubMed, and Scopus, using relevant queries from several related published articles and the Medical Subject Headings Database. Then, all morphological, biomechanical, histopathological, and cellular-molecular outcomes were systematically collected. A total of 30 studies were identified based on the Preferred …

Correlation Enhanced Distribution Adaptation For Prediction Of Fall Risk, Ziqi Guo, Teresa Wu, Thurmon Lockhart, Rahul Soangra, Hyunsoo Yoon

Physical Therapy Faculty Articles and Research

With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different …

Classification Of Colorectal Cancer Using Resnet And Efficientnet Models, Abhishek Ranjan, Priyanshu Srivastva, B Prabadevi, R Sivakumar, Rahul Soangra, Shamala K. Subramaniam

Physical Therapy Faculty Articles and Research

Introduction:

Cancer is one of the most prevalent diseases from children to elderly adults. This will be deadly if not detected at an earlier stage of the cancerous cell formation, thereby increasing the mortality rate. One such cancer is colorectal cancer, caused due to abnormal growth in the rectum or colon. Early screening of colorectal cancer helps to identify these abnormal growth and can exterminate them before they turn into cancerous cells.

Aim:

Therefore, this study aims to develop a robust and efficient classification system for colorectal cancer through Convolutional Neural Networks (CNNs) on histological images.

Methods:

Despite challenges in …

Applications Of Causal Inference Methods For The Estimation Of Effects Of Bone Marrow Transplant And Prescription Drugs On Survival Of Aplastic Anemia Patients, Yesha M. Patel

Computational and Data Sciences (PhD) Dissertations

This dissertation provides an in-depth exploration into the treatment effectiveness for aplastic anemia using causal inference methods, structured around three pivotal research papers. Each paper contributes to a nuanced understanding of treatment impacts, specifically focusing on bone marrow transplantation (BMT) and prescription drugs, and the identification of optimal treatment strategies.

The first paper, "Causal Inference Analysis for Assessing the Effect of Bone Marrow Transplantation on the One-Year Survival of Adult and Pediatric Aplastic Anemia Patients," sets the foundation. It examines the short-term effectiveness of BMT in both adult and pediatric patients, providing crucial insights into how this treatment affects survival …

A Portable And Reliable Tool For On-Site Physical Reaction Time (Rt) Measurement, Brent Harper, Michael Shiraishi, Rahul Soangra

Physical Therapy Faculty Articles and Research

The drop-stick test system (DTS) invention has the capability to measure reaction accurately for sideline mild traumatic brain injury (mTBI) assessment. The reaction time (RT) measurements showed moderate to good inter-instrument reliability with an overall ICC of 0.82 (95 % CI 0.78–0.85). RT is a useful biomarker of mTBI or concussion, but existing technologies in controlled laboratory environments are not feasible for assessments in the field. With wearable technologies and wireless connection with smartphones, it is now easier to conduct RT assessments on the field. The purpose was to develop a portable DTS involving wearable inertial sensors translatable from the …

Digital 3d Brain Mri Arterial Territories Atlas, Chin-Fu Liu, Johnny Hsu, Xin Xu, Ganghyun Kim, Shannon M. Sheppard, Erin L. Meier, Michael I. Miller, Argye E. Hillis, Andreia V. Faria

Communication Sciences and Disorders Faculty Articles and Research

The locus and extent of brain damage in the event of vascular insult can be quantitatively established quickly and easily with vascular atlases. Although highly anticipated by clinicians and clinical researchers, no digital MRI arterial atlas is readily available for automated data analyses. We created a digital arterial territory atlas based on lesion distributions in 1,298 patients with acute stroke. The lesions were manually traced in the diffusion-weighted MRIs, binary stroke masks were mapped to a common space, probability maps of lesions were generated and the boundaries for each arterial territory was defined based on the ratio between probabilistic maps. …

Reliability Of Accelerometer-Based Reaction Time Tests, Jacob Hepp, Warner Rhodes, Jordan Walton, Rahul Soangra, Brent Harper

Student Scholar Symposium Abstracts and Posters

Concussions are traumatic brain injuries that affect the function of the brain. One of the primary symptoms of a concussion is a lack of reaction time. The people that are most susceptible to concussions are athletes; Laker’s (2011) study found that 135,000 patients that suffer concussions from playing sports are expected to be hospitalized each year, with football making up 75% of concussions at high school and college levels. Honda et al. (2018) suggested reaction time as an important biomarker of concussion. Laboratory camera-based motion capture data, while reliable, is not a realistic tool to use outside of a laboratory …

A High Precision Machine Learning-Enabled System For Predicting Idiopathic Ventricular Arrhythmia Origins, Jianwei Zheng, Guohua Fu, Daniele Struppa, Islam Abudayyeh, Tahmeed Contractor, Kyle Anderson, Huimin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Background: Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy.

Method: A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, …

Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead

Engineering Faculty Articles and Research

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of …

Mobile Phone Sensors Can Discern Medication-Related Gait Quality Changes In Parkinson's Patients In The Home Environment, Albert Pierce, Niklas König Ignasiak, Wilford K. Eiteman-Pang, Cyril Rakovski, Vincent Berardi

Psychology Faculty Articles and Research

Patients with Parkinson's Disease (PD) experience daytime symptom fluctuations, which result in small amplitude, slow and unstable walking during times when medication attenuates. The ability to identify dysfunctional gait patterns throughout the day from raw mobile phone acceleration and gyroscope signals would allow the development of applications to provide real-time interventions to facilitate walking performance by, for example, providing external rhythmic cues. Patients (n = 20, mean Hoehn and Yahr: 2.25) had their ambulatory data recorded and were directly observed twice during one day: once after medication abstention, (OFF) and once approximately 30 min after intake of their medication …

Does Subthalamic Deep Brain Stimulation Impact Asymmetry And Dyscoordination Of Gait In Parkinson’S Disease?, Deepak K. Ravi, Christian R. Baumann, Elena Bernasconi, Michelle Gwerder, Niklas K. Ignasiak, Mechtild Uhl, Lennart Stieglitz, William R. Taylor, Navrag B. Singh

Physical Therapy Faculty Articles and Research

Background. Subthalamic deep brain stimulation (STN-DBS) is an effective treatment for selected Parkinson’s disease (PD) patients. Gait characteristics are often altered after surgery, but quantitative therapeutic effects are poorly described. Objective. The goal of this study was to systematically investigate modifications in asymmetry and dyscoordination of gait 6 months postoperatively in patients with PD and compare the outcomes with preoperative baseline and to asymptomatic controls without PD. Methods. A convenience sample of thirty-two patients with PD (19 with postural instability and gait disorder (PIGD) type and 13 with tremor dominant disease) and 51 asymptomatic controls participated. Parkinson patients …

Characterization Of The Growth Factor Receptor Network Oncogenes In Lung Cancer, Ashley Duche

Pharmaceutical Sciences (MS) Theses

Lung cancer remains the leading cause of cancer related deaths worldwide, reportedly contributing to 1.8 million of the 10.0 million mortalities documented in the year 2020. Although advancements have been made in therapeutics and diagnostic methods, formulation of effective treatments and development of drug resistance continues to be a challenge. These challenges arise from our lack of understanding of intricate signaling pathways, such as the Growth Factor Receptor Network (GFRN), which contributes to complex lung tumor heterogeneity allowing for drug resistance development. In this study, gene expression signatures of six GFRN oncogenes overexpressed in human mammary epithelial cells (HMECs) were …

Unsupervised Machine Learning For Identifying Challenging Behavior Profiles To Explore Cluster-Based Treatment Efficacy In Children With Autism Spectrum Disorder: Retrospective Data Analysis Study, Julie Gardner-Hoag, Marlena N. Novack, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis Dixon, Erik Linstead

Engineering Faculty Articles and Research

Background: Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking.

Objective: The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups.

Methods: Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed. Participants were clustered based on 8 observed challenging behaviors using k means, and multiple linear regression was performed to test interactions between skill mastery and treatment …

Associations Between Daily Affect And Sleep Vary By Sleep Assessment Type: What Can Ambulatory Eeg Add To The Picture?, Brett Messman, Danica C. Slavish, Jessica R. Dietch, Brooke N. Jenkins, Maia Ten Brink, Daniel J. Taylor

Psychology Faculty Articles and Research

Objective/Background

Disrupted sleep can be a cause and a consequence of affective experiences. However, daily longitudinal studies show sleep assessed via sleep diaries is more consistently associated with positive and negative affect than sleep assessed via actigraphy. The objective of the study was to test whether sleep parameters derived from ambulatory electroencephalography (EEG) in a naturalistic setting were associated with day-to-day changes in affect.

Participants/Method

Eighty adults (mean age = 32.65 years, 63% female) completed 7 days of affect and sleep assessments. We examined bidirectional associations between morning positive affect and negative affect with sleep assessed via diary, actigraphy, and …

Cyclic Peptide [R4w4] In Improving The Ability Of First-Line Antibiotics To Inhibit Mycobacterium Tuberculosis Inside In Vitro Human Granulomas, Joshua Hernandez, David Ashley, Ruoqiong Cao, Rachel Abrahem, Timothy Nguyen, Kimberly To, Aram Yegiazaryan, Ajayi Akinwale David, Rakesh Kumar Tiwari, Vishwanath Venketaraman

Pharmacy Faculty Articles and Research

Tuberculosis (TB) is currently one of the leading causes of global mortality. Medical non-compliance due to the length of the treatment and antibiotic side effects has led to the emergence of multidrug-resistant (MDR) strains of Mycobacterium tuberculosis (M. tb) that are difficult to treat. A current therapeutic strategy attempting to circumvent this issue aims to enhance drug delivery to reduce the duration of the antibiotic regimen or dosage of first-line antibiotics. One such agent that may help is cyclic peptide [R₄W₄], as it has been shown to have antibacterial properties (in combination with tetracycline) …

Data Augmentation For Deep-Learning-Based Electroencephalography, Elnaz Lashgari, Dehua Liang, Uri Maoz

Psychology Faculty Articles and Research

Background

Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.

New method

We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?

Results

DA for DL on EEG begun …

Recombinant Human Proteoglycan-4 Mediates Interleukin-6 Response In Both Human And Mouse Endothelial Cells Induced Into A Sepsis Phenotype, Holly A. Richendrfer, Mitchell M. Levy, Khaled A. Elsaid, Tannin A. Schmidt, Ling Zhang, Ralph Cabezas, Gregory D. Jay

Pharmacy Faculty Articles and Research

Objectives:

Sepsis is a leading cause of death in the United States. Putative targets to prevent systemic inflammatory response syndrome include antagonism of toll-like receptors 2 and 4 and CD44 receptors in vascular endothelial cells. Proteoglycan-4 is a mucinous glycoprotein that interacts with CD44 and toll-like receptor 4 resulting in a blockade of the NOD-like receptor pyrin domain-containing-3 pathway. We hypothesized that endothelial cells induced into a sepsis phenotype would have less interleukin-6 expression after recombinant human proteoglycan 4 treatment in vitro.

Design:

Enzyme-linked immunosorbent assay and reverse transcriptase-quantitative polymerase chain reaction to measure interleukin-6 protein and gene expression.

Setting: …

Nanoparticle-Mediated Drug Delivery For The Treatment Of Cardiovascular Diseases, Rajasekharreddy Pala, Vt Anju, Madhu Dyavaiah, Siddhardha Busi, Surya M. Nauli

Pharmacy Faculty Articles and Research

Cardiovascular diseases (CVDs) are one of the foremost causes of high morbidity and mortality globally. Preventive, diagnostic, and treatment measures available for CVDs are not very useful, which demands promising alternative methods. Nanoscience and nanotechnology open a new window in the area of CVDs with an opportunity to achieve effective treatment, better prognosis, and less adverse effects on non-target tissues. The application of nanoparticles and nanocarriers in the area of cardiology has gathered much attention due to the properties such as passive and active targeting to the cardiac tissues, improved target specificity, and sensitivity. It has reported that more than …

Optimal Multi-Stage Arrhythmia Classification Approach, Jianwei Zhang, Huimin Chu, Daniele Struppa, Jianming Zhang, Sir Magdi Yacoub, Hesham El-Askary, Anthony Chang, Louis Ehwerhemuepha, Islam Abudayyeh, Alexander Barrett, Guohua Fu, Hai Yao, Dongbo Li, Hangyuan Guo, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead …

A 12-Lead Electrocardiogram Database For Arrhythmia Research Covering More Than 10,000 Patients, Jianwei Zhang, Jianming Zhang, Sidy Daniako, Hai Yao, Hangyuan Guo, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

This newly inaugurated research database for 12-lead electrocardiogram signals was created under the auspices of Chapman University and Shaoxing People’s Hospital (Shaoxing Hospital Zhejiang University School of Medicine) and aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. Certain types of arrhythmias, such as atrial fibrillation, have a pronounced negative impact on public health, quality of life, and medical expenditures. As a non-invasive test, long term ECG monitoring is a major and vital diagnostic tool for detecting these conditions. This practice, however, generates large amounts of data, the analysis of which requires considerable …

Image Restoration Using Automatic Damaged Regions Detection And Machine Learning-Based Inpainting Technique, Chloe Martin-King

Computational and Data Sciences (PhD) Dissertations

In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also …