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- All Subjects: Electrical Engineering
- Status: Published
Description
Demand for biosensor research applications is growing steadily. According to a new report by Frost & Sullivan, the biosensor market is expected to reach $14.42 billion by 2016. Clinical diagnostic applications continue to be the largest market for biosensors, and this demand is likely to continue through 2016 and beyond. Biosensor technology for use in clinical diagnostics, however, requires translational research that moves bench science and theoretical knowledge toward marketable products. Despite the high volume of academic research to date, only a handful of biomedical devices have become viable commercial applications. Academic research must increase its focus on practical uses for biosensors. This dissertation is an example of this increased focus, and discusses work to advance microfluidic-based protein biosensor technologies for practical use in clinical diagnostics. Four areas of work are discussed: The first involved work to develop reusable/reconfigurable biosensors that are useful in applications like biochemical science and analytical chemistry that require detailed sensor calibration. This work resulted in a prototype sensor and an in-situ electrochemical surface regeneration technique that can be used to produce microfluidic-based reusable biosensors. The second area of work looked at non-specific adsorption (NSA) of biomolecules, which is a persistent challenge in conventional microfluidic biosensors. The results of this work produced design methods that reduce the NSA. The third area of work involved a novel microfluidic sensing platform that was designed to detect target biomarkers using competitive protein adsorption. This technique uses physical adsorption of proteins to a surface rather than complex and time-consuming immobilization procedures. This method enabled us to selectively detect a thyroid cancer biomarker, thyroglobulin, in a controlled-proteins cocktail and a cardiovascular biomarker, fibrinogen, in undiluted human serum. The fourth area of work involved expanding the technique to produce a unique protein identification method; Pattern-recognition. A sample mixture of proteins generates a distinctive composite pattern upon interaction with a sensing platform consisting of multiple surfaces whereby each surface consists of a distinct type of protein pre-adsorbed on the surface. The utility of the "pattern-recognition" sensing mechanism was then verified via recognition of a particular biomarker, C-reactive protein, in the cocktail sample mixture.
ContributorsChoi, Seokheun (Author) / Chae, Junseok (Thesis advisor) / Tao, Nongjian (Committee member) / Yu, Hongyu (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2011
Description
A wireless hybrid device for detecting volatile organic compounds (VOCs) has been developed. The device combines a highly selective and sensitive tuning-fork based detector with a pre-concentrator and a separation column. The selectivity and sensitivity of the tuning-fork based detector is optimized for discrimination and quantification of benzene, toluene, ethylbenzene, and xylenes (BTEX) via a homemade molecular imprinted polymer, and a specific detection and control circuit. The device is a wireless, portable, battery-powered, and cell-phone operated device. The device has been calibrated and validated in the laboratory and using selected ion flow tube mass spectrometry (SFIT-MS). The capability and robustness are also demonstrated in some field tests. It provides rapid and reliable detection of BTEX in real samples, including challenging high concentrations of interferents, and it is suitable for occupational, environmental health and epidemiological applications.
ContributorsChen, Zheng (Author) / Tao, Nongjian (Thesis advisor) / Chae, Junseok (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advances in miniaturized sensors and wireless technologies have enabled mobile health systems for efficient healthcare. A mobile health system assists the physician to monitor the patient's progress remotely and provide quick feedbacks and suggestions in case of emergencies, which reduces the cost of healthcare without the expense of hospitalization. This work involves development of an innovative mobile health system with adaptive biofeedback mechanism and demonstrates the importance of biofeedback in accurate measurements of physiological parameters to facilitate the diagnosis in mobile health systems. Resting Metabolic Rate (RMR) assessment, a key aspect in the treatment of diet related health problems is considered as a model to demonstrate the importance of adaptive biofeedback in mobile health. A breathing biofeedback mechanism has been implemented with digital signal processing techniques for real-time visual and musical guidance to accurately measure the RMR. The effects of adaptive biofeedback with musical and visual guidance were assessed on 22 healthy subjects (12 men, 10 women). Eight RMR measurements were taken for each subject on different days under same conditions. It was observed the subjects unconsciously followed breathing biofeedback, yielding consistent and accurate measurements for the diagnosis. The coefficient of variation of the measured metabolic parameters decreased significantly (p < 0.05) for 20 subjects out of 22 subjects.
ContributorsKrishnan, Ranganath (Author) / Tao, Nongjian (Thesis advisor) / Forzani, Erica (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alzheimer's disease (AD) and Alzheimer's Related Dementias (ADRD) is projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating exercises, can reduce the risk of MCI. Early detection of MCI is challenging due to subtle and often unnoticed cognitive decline, traditionally monitored through infrequent clinical tests. As part of this research, the Smart Driving System was proposed, a novel, unobtrusive, and economical technology to detect early stages of neurodegenerative diseases. This system, leveraging a multi-modal biosensing array (MMS) and AI algorithms, assesses daily driving behavior, offering insights into a driver's cognitive function. The ultimate goal is to develop the Smart Driving Device and App, integrating it into vehicles, and validating its effectiveness in detecting MCI through comprehensive pilot studies. The Smart Driving System represents a breakthrough in AD/ADRD management, promising significant improvements in early detection and offering a scalable, cost-effective solution for monitoring cognitive health in real-world settings.
ContributorsSerhan, Peter (Author) / Forzani, Erica (Thesis advisor) / Wu, Teresa (Committee member) / Hihath, Joshua (Committee member) / Arizona State University (Publisher)
Created2024
Description
Wearable technology has brought in a rapid shift in the areas of healthcare and lifestyle management. The recent development and usage of wearable devices like smart watches has created significant impact in areas like fitness management, exercise tracking, sleep quality assessment and early diagnosis of diseases like asthma, sleep apnea etc. This thesis is dedicated to the development of wearable systems and algorithms to fulfill unmet needs in the area of cardiorespiratory monitoring.
First, a pneumotach based flow sensing technique has been developed and integrated into a face mask for respiratory profile tracking. Algorithms have been developed to convert the pressure profile into respiratory flow rate profile. Gyroscope-based correction is used to remove motion artifacts that arise from daily activities. By using Principal Component Analysis, the follow-up work established a unique respiratory signature for each subject based on the flow profile and lung parameters computed using the wearable mask system.
Next, wristwatch devices to track transcutaneous gases like oxygen (TcO2) and carbon dioxide (TcCO2), and oximetry (SpO2) have been developed. Two chemical sensing approaches have been explored. In the first approach, miniaturized low-cost commercial sensors have been integrated into the wristwatch for transcutaneous gas sensing. In the second approach, CMOS camera-based colorimetric sensors are integrated into the wristwatch, where a part of camera frame is used for photoplethysmography while the remaining part tracks the optical signal from colorimetric sensors.
Finally, the wireless connectivity using Bluetooth Low Energy (BLE) in wearable systems has been explored and a data transmission protocol between wearables and host for reliable transfer has been developed. To improve the transmission reliability, the host is designed to use queue-based re-request routine to notify the wearable device of the missing packets that should be re-transmitted. This approach avoids the issue of host dependent packet losses and ensures that all the necessary information is received.
The works in this thesis have provided technical solutions to address challenges in wearable technologies, ranging from chemical sensing, flow sensing, data analysis, to wireless data transmission. These works have demonstrated transformation of traditional bench-top medical equipment into non-invasive, unobtrusive, ergonomic & stand-alone healthcare devices.
First, a pneumotach based flow sensing technique has been developed and integrated into a face mask for respiratory profile tracking. Algorithms have been developed to convert the pressure profile into respiratory flow rate profile. Gyroscope-based correction is used to remove motion artifacts that arise from daily activities. By using Principal Component Analysis, the follow-up work established a unique respiratory signature for each subject based on the flow profile and lung parameters computed using the wearable mask system.
Next, wristwatch devices to track transcutaneous gases like oxygen (TcO2) and carbon dioxide (TcCO2), and oximetry (SpO2) have been developed. Two chemical sensing approaches have been explored. In the first approach, miniaturized low-cost commercial sensors have been integrated into the wristwatch for transcutaneous gas sensing. In the second approach, CMOS camera-based colorimetric sensors are integrated into the wristwatch, where a part of camera frame is used for photoplethysmography while the remaining part tracks the optical signal from colorimetric sensors.
Finally, the wireless connectivity using Bluetooth Low Energy (BLE) in wearable systems has been explored and a data transmission protocol between wearables and host for reliable transfer has been developed. To improve the transmission reliability, the host is designed to use queue-based re-request routine to notify the wearable device of the missing packets that should be re-transmitted. This approach avoids the issue of host dependent packet losses and ensures that all the necessary information is received.
The works in this thesis have provided technical solutions to address challenges in wearable technologies, ranging from chemical sensing, flow sensing, data analysis, to wireless data transmission. These works have demonstrated transformation of traditional bench-top medical equipment into non-invasive, unobtrusive, ergonomic & stand-alone healthcare devices.
ContributorsTipparaju, Vishal Varun (Author) / Xian, Xiaojun (Thesis advisor) / Forzani, Erica (Thesis advisor) / Blain Christen, Jennifer (Committee member) / Angadi, Siddhartha (Committee member) / Arizona State University (Publisher)
Created2020
Description
Contaminated aerosols and micro droplets are easily generated by infected hosts through sneezing, coughing, speaking and breathing1-3 and harm humans’ health and the global economy. While most of the efforts are usually targeted towards protecting individuals from getting infected,4 eliminating transmissions from infection sources is also important to prevent disease transmission. Supportive therapies for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) pneumonia such as oxygen supplementation, nebulizers and non-invasive mechanical ventilation all carry an increased risk for viral transmission via aerosol to healthcare workers.5-9 In this work, I study the efficacy of five methods for self-containing aerosols emitted from infected subjects undergoing nebulization therapies with a diverse spectrum on Non-Invasive Positive Pressure Ventilator (NIPPV) with oxygen delivery therapies. The work includes five study cases: Case I: Use of a Full-Face Mask with biofilter in bilevel positive airway pressure device (BiPAP) therapy, Case II: Use of surgical mask in High Flow Nasal Cannula (HFNC) therapy, Case III: Use of a modified silicone disposable mask in a HFNC therapy, Case IV: Use of a modified silicone disposable mask with a regular nebulizer and normal breathing, Case V: Use of a mitigation box with biofilter in a BiPAP. We demonstrate that while cases I, III and IV showed efficacies of 98-100%; cases II and V, which are the most commonly used, resulted with significantly lower efficacies of 10-24% to mitigate the dispersion of nebulization aerosols. Therefore, implementing cases I, III and IV in health care facilities may help battle the contaminations and infections via aerosol transmission during a pandemic.
ContributorsShyamala Pandian, Adithya (Author) / Forzani, Erica (Thesis advisor) / Patel, Bhavesh (Committee member) / Xian, Xiaojun (Committee member) / Arizona State University (Publisher)
Created2021