The Classification of Electrooculogram (EOG) Through the Application of Linear Discriminant Analysis (LDA) of Selected Time-Domain Signals

An explosion of healthcare data has occurred in recent years due to the widespread availability of sophisticated physiological signal monitoring devices and the development of telemetry and cognitive communication systems. Additionally, the accessibility of medical data for the establishment of applications in big data has rapidly increased due to affordable and efficient storage and power techniques. With the current state of technology, healthcare professionals are unable to effectively handle and understand large, rapidly changing, and complex data; this is where big data applications come in. Making medical services more cost- effective and sustainable is a driving force behind the creation of such systems. In this article, we present a discussion of the present condition of big data applications that make use of physiological signals or derived metrics to aid in medical decision making in the home and in the hospital. Specifically, we examine critical care systems designed for continuous healthcare management and address the obstacles that must be surmounted before such systems may be used in real-world practice. Big data technologies might revolutionize future hospital administration if these problems are solved.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

334 Leonard St

Brooklyn, NY 11211

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

The Classification of Electrooculogram (EOG) Through the Application of Linear Discriminant Analysis (LDA) of Selected Time-Domain Signals

Cited by 2 publications

References 18 publications

Unsupervised Transfer Learning Approach With Adaptive Reweighting and Resampling Strategy for Inter-Subject EOG-Based Gaze Angle Estimation

Unsupervised Transfer Learning Approach With Adaptive Reweighting and Resampling Strategy for Inter-Subject EOG-Based Gaze Angle Estimation

A Survey on Big Data Application for Modality and Physiological Signal Analysis

Contact Info

Product

Resources

About