Open Access. Powered by Scholars. Published by Universities.®
Biomedical Engineering and Bioengineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Allergy (1)
- Anaphylaxis (1)
- Auto-injector (1)
- Bicuspid valve (1)
- Cancer (1)
-
- Capsule pump (1)
- Cytotoxic assay (1)
- Drug delivery (1)
- Epileptic; EEG Signals; Classifier-Driven; Feature Reduction; Random Forest; Seizure Detection; Classification; (1)
- High throughput (1)
- Internet of Things (1)
- Internet-of-things (1)
- Microfluidics (1)
- Micropump (1)
- Reconfigurable (1)
- Remote (1)
- Shape memory alloy (1)
- Smartwatch (1)
- Wearable (1)
- Wireless (1)
Articles 1 - 2 of 2
Full-Text Articles in Biomedical Engineering and Bioengineering
Shape Memory Alloy Capsule Micropump For Drug Delivery Applications, Youssef Mohamed Kotb
Shape Memory Alloy Capsule Micropump For Drug Delivery Applications, Youssef Mohamed Kotb
Theses and Dissertations
Implantable drug delivery devices have many benefits over traditional drug administration techniques and have attracted a lot of attention in recent years. By delivering the medication directly to the tissue, they enable the use of larger localized concentrations, enhancing the efficacy of the treatment. Passive-release drug delivery systems, one of the various ways to provide medication, are great inventions. However, they cannot dispense the medication on demand since they are nonprogrammable. Therefore, active actuators are more advantageous in delivery applications. Smart material actuators, however, have greatly increased in popularity for manufacturing wearable and implantable micropumps due to their high energy …
An Epileptic Seizure Detection Method From Eeg Signals Based On A Classifier-Driven Feature Reduction Technique, Raymond N. Kamel
An Epileptic Seizure Detection Method From Eeg Signals Based On A Classifier-Driven Feature Reduction Technique, Raymond N. Kamel
Theses and Dissertations
Epileptic seizure detection can improve the quality of life of epileptic patients, allow for more accurate medication, and minimize the risk of sudden unexpected death in epilepsy (SUDEP). This thesis work aims to develop a robust and stable algorithm for epileptic seizure detection through the classification of EEG signals. To achieve this aim, a methodology is proposed to develop a classifier that can differentiate between the healthy (normal), interictal, and ictal states of EEG signals, while maximizing the classification accuracy and minimizing the computational redundancy. The main pillar upon which this methodology is designed is using a problem-specific classifier-driven feature …