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Biomedical Engineering and Bioengineering Commons™
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Full-Text Articles in Biomedical Engineering and Bioengineering
Understanding The Surface Fouling Mechanism Of Ultrananocrystalline Diamond Microelectrodes Using Microfluidics For Neurochemical Detection, An-Yi Chang
Doctoral Dissertations
Electrochemical methods are widely used for chronic neurochemical sensing, but thus far, the organic solution redox reactions fouled the electrodes' surface. It caused the reduction of sensitivity and the electrodes' lifetime.
Here, we present the boron-doped nanocrystalline diamond microelectrodes (BDUNCD) as the next generation electrode material for neurochemical sensor development. To aid in long-term chronic monitoring of neurochemicals, they have a wide window of electrochemical potential, extremely low background current, and excellent chemical inertness. The main research goal is to reduce the rate of electrode fouling due to the reaction by-products, and significantly extend their useful lifetime.
We systematically characterize …
Wireless Monitoring Of Driver's Pulse Rate And Temperature Using Hand Gloves Approach, Rohith Reddy Narala
Wireless Monitoring Of Driver's Pulse Rate And Temperature Using Hand Gloves Approach, Rohith Reddy Narala
Graduate Theses and Dissertations
There is growing concern about dangers correlated with driving, for people with known cardiovascular diseases. However, the association between having a chronic cardiovascular disease and being involved in a motor vehicle crash remains controversial. This study aims to monitor people with known medical emergencies or other medical conditions while driving [1]. It also helps the co-passengers to be cautious while the person is driving with an abnormal health condition. Designed it to be convenient and also can be easily adaptable by the end user.
The proposed project focuses on a wearable sensor glove that equipped with a pulse rate sensor, …
Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan
Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan
Doctoral Dissertations
Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined …