Biomedical Commons^™

Design And Development Of Biomimetic Hydrogel Interfaces For Enhanced Bioelectrical Signal Acquisition, Daniela Nikoloska

UNLV Theses, Dissertations, Professional Papers, and Capstones

Every second, human skin processes over one million sensory signals while maintaining properties such as electrical conductivity, mechanical adaptability, and regenerative capability that surpass all synthetic materials. Contemporary bioelectronic devices prove inadequate when contacting skin surfaces due to poor adhesion and electrical contact issues that prevent effective sensing. Despite advancements in wearable and bioelectronic technologies, current devices face major drawbacks when interfacing with human skin, particularly in maintaining firm adhesion, conformability, and low-noise electrical signal acquisition.

This research focuses on the development of a biomimetic hydrogel-based interface for bioelectronic sensing. Specifically, a hybrid hydrogel system composed of polydopamine (PDA)- doped …

Rapid Prototyping Of Low-Cost Sensor Systems Towards A Platform For Upper Limb Posture Estimation, Russell Rathbun

Electrical Engineering and Computer Science Undergraduate Honors Theses

Physical therapy requires patients to perform repeated actions to achieve meaningful results in rehabilitation. This thesis explores production methods and various sensor systems by utilizing rapid prototyping, inertial measurement units (IMUs), and capacitive sensor arrays (CSAs). CSAs can be made from a wide ar- ray of materials and techniques including 3d printing and laser ablation–to rapidly create CSAs that can be custom fit to enable proximity, force, and touch detection. IMU and CSA systems individually are able to track upper limb movements, ges- tures, and positions. This combination of sensors enables accurate upper limb pos- ture estimation of patients. This …

Unmixing In Very High Spatial Resolution Hyperspectral Images, Ana C. Chavez Lopez

Open Access Theses & Dissertations

Hyperspectral Imaging (HSI) captures hundreds of contiguous narrow wavelength bands across the optical region of the electromagnetic spectrum collecting the spectral signature of materials in the field of view of the sensor enabling detailed analysis of each pixel's spectral signature. Satellite or airborne remote sensing systems often capture imagery with low to moderate spatial resolution (LMSR). At these resolutions, the measured spectral signature is a mixture of the signatures of the materials within a single pixel. This mixing of spectral information makes analysis and material identification difficult. Hyperspectral unmixing is an analysis technique that decomposes a pixel's spectrum into constituent …

Capacity, Allocation And Update Dynamics Of Human Memory Systems, Shaoying Wang

Electronic Theses and Dissertations

Information is encoded and stored in three types of memory: sensory memory (SM), short-term memory (STM), and long-term memory (LTM). SM has a large capacity but retains information for only a brief period. When information transfers to STM, only a limited amount can be stored. Information in STM can then be transferred to LTM, which has a much larger capacity and longer retention time. STM is often conceptualized as working memory (WM) to highlight its role in active information processing. Due to the limited capacity of STM, it is commonly believed that STM serves as the bottleneck for information processing. …

Abdominal Ultrasound Image Dataset For Organ Classification And Disease Detection, Sifat Zina Karim

Research Data

This is a dataset of Ultrasound (US) images of abdominal organs. US imaging is widely accessible and a very common diagnostic tool, as it is non-invasive and does not involve radiation risk. This dataset was curated solely for research in deep learning, with potential applications in supervised, semi-supervised, and unsupervised learning to support disease detection in resource-constrained settings.

The dataset comprises 5,468 unique images of different abdominal organs, namely: Abdominal Aorta (0), Gallbladder (1), Hepatic Vein (2), Kidneys (3), Liver (4), Ovaries (5), Pancreas (6), Portal Vein (7), Spleen (8), and the Urinary System (9), which includes the Urinary Bladder, …

Non-Invasive Way For Detection Of Neonatal Jaundice Using Gbr, Priti V. Bhagat, Mukesh Raghuwanshi, Ashutosh D. Bagde

Chulalongkorn Medical Journal

Background: Neonatal jaundice is a common condition in newborns due to excess levels of bilirubin. The traditional method for bilirubin testing is invasive, i.e., through blood tests only, which is painful to infants. Therefore, this study uses machine learning algorithms to develop a non-invasive way to detect neonatal jaundice.

Objectives: Design a computer-aided support system to detect neonatal jaundice using machine learning algorithm.

Methods: The gradient-boosting regression model is used to predict the bilirubin level. Gradient Boosting is a robust boosting algorithm that combines several weak learners into strong learners, in which each new model is trained to minimize the …

Predicting The Response Of Tendon-Driven Prosthetic Finger With Hyperelastic Joints, Lucas Gallup, Mohamed Trabia, Brendan O'Toole

Mechanical Engineering Faculty Research

Properly designed prosthetics hands can enhance the quality of life for those suffering from limb loss. Recently, 3D-printed prosthetic hands are becoming common. In these prostheses, fingers flex through the tendons that are activated by motion of the wrist. To provide spring action, thermoplastic polyurethane (TPU) hyperelastic joints are used to connect digits to each other as well as to the wrist. While these designs are common, no model for the relationship between tendon tension and joint flexion is available. This work has developed a quasi-static virtual work-based model to predict the relationship between tendon forces and the flexion of …

Continuous Versus Discrete-Time Sigma-Delta Analog To Digital Converters For Biomedical Applications: A Comparison, Haya H. Binsalim, Batool Alaidroos, Basma Shigdar, Salma Badaam, Aziza I. Hussein

Effat Undergraduate Research Journal

Continuous-time (CT) and discrete-time (DT) sigma-delta (ΔΣ) converters are two commonly used techniques for analog-to-digital conversion. While both methods operate based on the principles of oversampling and noise shaping, they differ in their implementation and performance characteristics. CT ΔΣ converters use analog circuits to sample and process signals continuously, while DT ΔΣ utilizes digital circuits to sample and process signals at discrete intervals. This paper presents a comprehensive comparison between CT and DT ΔΣ converters, highlighting their advantages and limitations. The comparison is made in terms of design complexity, power consumption, signal-to-noise ratio (SNR), and other essential parameters in medical …

Exploring Longitudinal Stability Of Spatiotemporal Sequential Patterns By Means Of Autoencoder Schemes For Eeg Based Personal Identification, Muhammed E. Oztemel

LSU Doctoral Dissertations

Robust personal identification remains a critical and challenging task in the digital era. Electroencephalography (EEG) offers a unique biometric modality that captures individual brain dynamics through complex neural signals. This dissertation proposes autoencoder (AE) based feature extraction and subject identification through these features. EEG recordings are first transformed into topographic maps to represent spatial brain activity. Consecutive topomaps are then concatenated to capture temporal transitions across frames. Convolutional autoencoders (CAEs) are used to learn spatial and temporal patterns, while domain-adaptive AEs are designed to model evoked potential based responses. Additionally, self-attention mechanism is incorporated to enhance feature representation. To analyze …

Functional Biopolymers Applied To Sustainable Technologies In The Environment And Healthcare, Fengjie He

UNLV Theses, Dissertations, Professional Papers, and Capstones

Biodegradable polymeric materials (biopolymers) are naturally derived materials known for their excellent biocompatibility, biodegradability, sustainability, and versatile chemical functionality. They have attracted increasing attention in various applications as alternative to synthetic materials ranging from food packaging to tissue engineering. Meanwhile, with intrinsic advantages, biopolymers have also emerged as promising materials in addressing contemporary challenges in both biomedical and environmental fields. Motivated by the significant potential of biopolymers and the growing need for sustainable materials, my research focuses on the design and engineering of biodegradable polymers with novel modification methods and application directions. In this work, two representative biopolymers are selected: …

Study Of Ai Applications In Biomedical Data Acquisition, Communication, And Analysis: Cest Mri Acceleration And Ecg Transmissions, Adarsha Bhattarai

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation investigates the application of artificial intelligence in biomedical data acquisition, communication, and analysis to advance neurological research and to enable the early detection of cardiovascular conditions. Despite significant advances in imaging and physiological modalities, challenges persist. Imaging modalities, such as the chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) technique are challenged by a prolonged data acquisition time and high operational costs. In addition, physiological modalities such as electrocardiogram (ECG) sensors face constraints in providing uninterrupted signal monitoring which is crucial for the timely detection of premature cardiac abnormalities. The primary goal of this work is to …

Thermal Inkjet Bioprinting Of Human Fibroblasts Into Stem Cell Environment Leads To Stem-Like Gene And Protein Expression And Changes In Hippo Pathway Effectors Yap/Taz, Patricia Ablanedo Morales

Open Access Theses & Dissertations

Thermal inkjet bioprinting (TIB) has emerged as a powerful tool with many potential applications, such as organ regeneration, drug testing, and cell differentiation, among others. Despite the forces and stress that cells are subjected to during the printing process, there is little research that investigates in detail the combined effects of the TIB process and the surrounding environment on cells. Furthermore, a cell's biological environment greatly influences its behavior. Therefore, understanding the effects of bioprinting on cells in a particular environment at a genetic level can provide clues regarding changes in cell characteristics. Bioprinting could potentially be used as a …

Effects Of Environmental Stressors On Human Tissue-On-A-Chip Platforms, Andie Padilla

Open Access Theses & Dissertations

As space exploration begins to extend beyond low earth orbit, it has become increasingly critical to understand the interaction of the extreme environment of space flight with human systems. While it is known that space-travel induces a vast array of complications to cardiac, neural, musculoskeletal, and immune systems, the mechanisms by which these complications occur are poorly understood. Current research to study the effects of microgravity and radiation are limited to ground simulations, which rarely account for the multifactorial stressors experienced during spaceflight, or long duration studies aboard the International Space Station. Similarly, traditional two-dimensional (2D) models lack the ability …

A Deep Learning Approach For Semantic Segmentation And Its Application On Ctc., Samir Farag Harb

Electronic Theses and Dissertations

This dissertation explores the modeling and analysis of medical images, focusing on the intricate task of colon segmentation and subsequent 3D reconstruction, which are critical steps in Computed Tomography Colonography (CTC) systems. The primary objective of this research is to develop precise segmentation approaches to enhance the accuracy of colon identification and reconstruction from abdominal CT scans. Three distinct segmentation approaches are proposed and evaluated: a Markov Random Field (MRF)-based approach, a convolutional neural network (CNN)-based deep learning (DL) approach, and a sequential episodic training with dual contrastive learning Approach (G-SET-DCL) that has a flavor of few-shot learning (FSL). To …

Ac Electric Fields Manipulate And Concentrate Dna Molecules On Electrodes, Akila Wijesinghe, Dharmakeerthi Nawarathna

Graduate Student Government Association Research Conference

Point-of-care (POC) electric field-based biosensors have emerged as a promising tool to detect early cancer biomarkers such as circulating tumor DNA (ctDNA), microRNA (miRNA), and proteins. To be effective in screening in clinical settings, these biosensors must be simple and easy to use. In this study, we have studied the manipulation of short DNA molecules suspended in a sessile drop to achieve this goal. Alternative current (AC) electric fields were used to polarize DNA molecules and produce dielectrophoretic (DEP) force on DNA molecules. DEP force is used to manipulate polarized DNA molecules towards the higher electric field gradients (toward the …

A Parallel Fuzzy Logic Framework For Surgical Skill Evaluation Via Instance Segmentation And Deepsort Tracking, Mohsen M. Mohaidat

Dissertations

Manual evaluation of suturing skills during laparoscopic training is often subjective and labor-intensive, resulting in the lack of scalable and consistent feedback for trainees. This study proposes an automated framework that not only significantly reduces the need for in-person assessment by experts but also ensures scalability, thereby addressing the objectivity and cost-effectiveness limitations. While low-cost laparoscopic box trainers have become increasingly popular for residency training, performance assessment still depends on expert supervision. The proposed system aims to alleviate these limitations.

This study introduces a novel automated framework incorporating an optimized DeepSORT algorithm for classifying, localizing, and tracking surgical tools using …

The Interplay Of Accommodation And Oculomotor Vergence Within Young Adults With Binocularly Normal Vision And Typically-Occurring Convergence Insufficiency, Sebastian Fine

Dissertations

Concerted binocular coordination evoking oculomotor and refractive responses to visual stimuli are essential to daily function. Oculomotor dysfunctions can inhibit binocular responses to visually-near stimuli and have high comorbidities to accommodative dysfunctions. Three visual cues for inward (convergent) and outward (divergent) oculomotor movements, when presented concertedly create natural-viewing conditions: disparity- the binocular difference in light cast onto the fovea due to differing ocular perspectives, blur- the acuity of a visual target which stimulates accommodation, and proximal- the perceived distance of a visual stimuli based on size.

This study aims to quantitatively investigate oculomotor vergence and accommodation performances between individuals with …

Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam

Engineering Faculty Articles and Research

Anemia, characterized by low blood hemoglobin (Hgb) levels, afflicts >2 billion individuals worldwide. Here, we report real-world data generated by a smartphone app that noninvasively screens for anemia using only “fingernail selfies.” App data for anemia screening were obtained from >1.4 million uses across the United States enabling geographic mapping of Hgb levels. Of those, 9,061 users also self-reported complete blood count Hgb levels for comparison, resulting in accuracy and performance that match gold standard laboratory testing and a sensitivity and specificity of 89% and 93%, respectively, when using an anemia cutoff of 12.5 g/dL. Geotagged data enabled construction of …

Fibrin-Polycaprolactone Scaffolds For The Differentiation Of Human Neural Progenitor Cells Into Dopaminergic Neurons, Salma Paulina Ramirez

Open Access Theses & Dissertations

This project aimed to develop a tissue-on-a-chip platform for studying Parkinson's Disease (PD) using dopaminergic (DA) neurons. PD is a neurodegenerative disorder characterized by progressive loss of DA neurons, leading to involuntary movements and other symptoms. Early diagnosis and deeper understanding of PD pathogenesis are crucial for improving disease management and patient outcomes. To model PD in vitro, this research utilized human-induced pluripotent stem cell (hiPSC)-derived neural progenitor cells (NPCs) cultured on electrospun (ES) polycaprolactone (PCL) scaffolds. Given PCL's hydrophobicity, ECM-based biomaterial coatings, including Cell Basement Membrane (CBM) proteins, Matrigel, and Fibrin, were explored to enhance NPC adhesion, differentiation, and …

Optimizing And Training An Svm-Based Breast Cancer Tumor Classifier, Kevin Lopatka

Master's Theses

With advancements in technology, turning to machine learning has become a popular choice for aiding clinicians in the diagnoses of breast cancer malignancies. While the neural networking approach has been vetted thoroughly, this work aims to take advantage of traditional machine learning techniques; mainly support vector machine learning and the optimizing of feature extraction. The discrete-wavelet transform is used in the feature extraction stage of machine learning. Previous works that use this feature extraction technique are analyzed and expanded upon by utilizing a variety of different wavelets as well as other color-spaces with the goal of achieving higher result metrics …

An Automatic Colorectal Polyps Detection Approach For Ct Colonography., Mohamed Yousuf

Electronic Theses and Dissertations

Colon cancer, also known as colorectal cancer, is a significant health concern, with increasing incidence rates, particularly among individuals under 50. This rise has led experts to recommend the introduction of regular screenings at 45 years of age for adults at average risk. Early detection through such screenings can identify precancerous polyps, allowing their removal before they develop into cancer. This proactive approach has the potential to reduce colorectal cancer deaths by up to 60%. In addition, research indicates that people diagnosed before age 50 have better survival rates, which emphasizes the importance of early diagnosis. Therefore, adhering to recommended …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

Deep Learning-Based Multi-Class Classification Of Breast Cancer Ultrasound Images Using Convolutional Neural Networks, Andres E. Dewendt Urdaneta

ATU Scholars Symposium

The National Cancer Institute forecasts 2,001,140 cancer diagnoses in 2024, with approximately 600,000 expected deaths. Breast cancer is projected to be the most prevalent, with about 310,000 cases. Early diagnosis is critical to improving outcomes, and various diagnostic technologies, including imaging, biopsies, and blood tests, play a vital role. Image testing methods include X-rays, ultrasounds, magnetic resonance imaging (MRI), and PET scans. Artificial intelligence (AI) has recently significantly improved cancer detection, improving speed, accuracy, and effectiveness. This research project uses a convolution neural network (CNN) to analyze ultrasound breast images, classifying them as benign, malignant, or normal. Our CNN model …

Removing Eog Artifacts From Eeg Recordings Using Deep Learning, Christian O'Reilly, Scott Huberty

Faculty Publications

The electroencephalogram (EEG) directly measures the electrical activity generated by the brain. Unfortunately, it is often contaminated by various artifacts, notably those caused by eye movements and blinks (EOG artifacts). Such artifacts are usually removed using an independent component analysis (ICA) or other blind source separation techniques. However, it is difficult to assess whether subtracting EOG components estimated through ICA removes some neurogenic activity. It is crucial to address this question to avoid biasing EEG analyses. Toward that objective, we developed a deep learning model for EOG artifact removal that exploits information about eye movements available through eye-tracking (ET). Using …

A Reliable And Efficient Detection Pipeline For Rodent Ultrasonic Vocalizations, Sabah Shahnoor Anis, Devin Mark Kellis, Kris Ford Kaigler, Marlene A. Wilson, Christian O'Reilly

Faculty Publications

Analyzing ultrasonic vocalizations (USVs) is crucial for understanding rodents' affective states and social behaviors, but the manual analysis is time-consuming and prone to errors. Automated USV detection systems have been developed to address these challenges. Yet, these systems often rely on machine learning and fail to generalize effectively to new datasets. To tackle these shortcomings, we introduce ContourUSV, an efficient automated system for detecting USVs from audio recordings. Our pipeline includes spectrogram generation, cleaning, pre-processing, contour detection, post-processing, and evaluation against manual annotations. To ensure robustness and reliability, we compared ContourUSV with three state-of-the-art systems using an existing open-access USV …

Clinical Use Of Sit2stand Ai Application For Kinematic Analysis In Prosthesis Users., Samerial Brown

Posters - 2025

Biomechanical analysis is a tool to evaluate prosthetic and orthotic patient's. These tools offer the clinician capability of understanding the mechanism of injury, gait deviation or prosthesis problem. Video based analysis require expensive hardware, software, and training which sometimes costs $40-100,000.

The recent advent of artificial intelligence (AI) has opened up the possibility of acquiring high speed human motion video analysis using low-cost hardware and open-source machine learning algorithms. Still, free assessments like the Sit2Stand test is a current clinical outcome measure which assesses ability of a patient to stand and sit as fast as possible 5x. The faster the …

Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review, Lauren E. Perkins

Honors College Theses

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified …

Deep Learning Applications For Predictive Modeling In Cancer Therapy And Enzyme Encoding, Mengmeng Liu

LSU Doctoral Dissertations

Predictive modeling has revolutionized computational biology and molecular bioinformatics, enabling significant advancements in cancer therapy and functional enzyme characterization. Despite considerable progress, significant challenges remain in accurately predicting combinational cancer therapies and systematically representing enzyme functions for computational applications. Traditional methods struggle with capturing the complex interactions between drugs and biological networks, as well as representing hierarchical relationships within enzyme classifications. This dissertation addresses these limitations by developing advanced deep learning models tailored to enhance predictive performance in both domains.

First, a data augmentation strategy is introduced to improve anticancer drug synergy prediction by generating pharmacologically relevant drug pairs based …

Electrochemical Detection Of Dopamine Using Screen-Printed Graphene Electrode For Cancer Diagnosis And Therapy, Pritu P. Sarkar, Nazmul Islam

Research Symposium

Background: Dopamine plays a critical role in various essential functions, including motor control, hormone regulation, cognition, learning, and the reward system. In healthy individuals, dopamine levels are extremely low, with concentrations ranging from 0 to 0.25 nM in blood and 0.3 to 3.13 µM in urine. Abnormal levels are linked to disorders like Parkinson’s, schizophrenia, Alzheimer’s, epilepsy, hypertension, and arrhythmia. Abnormal dopamine levels can indicate the presence of certain cancers. Dopamine receptors may be therapeutic targets for treating cancer, especially breast and colon cancer. Thus dopamine can increase the efficacy of anticancer drugs in breast and colon cancer. Detecting dopamine …

Conversational Voice User Interfaces Supporting Individuals With Down Syndrome: A Literature Review, Franceli L. Cibrian, Concepción Valdez, Lauren Min, Vivian Genaro Motti

Engineering Faculty Articles and Research

Conversational Voice User Interfaces (CVUIs) are widely used in commercial applications such as personal assistants. CVUIs are beneficial for most users as they enable interaction through speech and natural language. However, recent studies indicate that underrepresented user groups, such as individuals with speech impairments and specifically those with Down syndrome, face challenges in using voice commands to control CVUIs. The anatomical and physiological differences affecting the voice, speech, fluency, and prosody of users with Down syndrome hinder their experience with CVUIs. This article presents the results of 43 papers related to the use of voice user interfaces supporting individuals with …