Graduation Year

2017

Document Type

Thesis

Degree

M.S.E.E.

Degree Name

MS in Electrical Engineering (M.S.E.E.)

Degree Granting Department

Electrical Engineering

Major Professor

Richard D. Gitlin, Sc.D.

Committee Member

Nasir Ghani, Ph.D.

Committee Member

Srinivas Katkoori, Ph.D.

Keywords

Atrial Fibrillation, Recurrent Neural Networks, Machine Learning, Base Station, Handover

Abstract

This thesis proposes the use of Machine Learning (ML) to two very distinct, yet compelling, applications – predicting cardiac arrhythmia episodes and predicting base station association in 5G networks comprising of virtual cells. In the first scenario, Support Vector Machines (SVMs) are used to classify features extracted from electrocardiogram (EKG) signals. The second problem requires a different formulation departing from traditional ML classification where the objective is to partition feature space into constituent class regions. Instead, the intention here is to identify temporal patterns in unequal-length sequences. Using Recurrent Neural Networks (RNNs), it is demonstrated that accurate predictions can be made as to the base station most likely to provide connectivity for a mobile device as it moves.

Atrial Fibrillation (AF) is a common cardiac arrhythmia affecting several million people in the United States. It is a condition in which the upper chambers of the heart are unable to contract effectively leading to inhibited blood flow to the ventricles. The stagnation of blood is one of the major risk factors for stroke. The Computers in Cardiology Challenge 2001 was organized to further research into the prediction of episodes of AF. This research revisits the problem with some modifications. Patient-specific classifiers are developed for AF prediction using a different dataset and employing shorter EKG signal epochs. SVM classification yielded an average accuracy of just above 95% in identifying EKG epochs appearing just prior to fibrillatory rhythms.

5G cellular networks were envisaged to provide enhanced data rates for mobile broadband, support low-latency communication, and enable the Internet of Things (IoT). Handovers contribute to latency as mobile devices are switched between base stations due to movements. Given that customers may not be willing to continuously share their exact locations due to privacy concerns and the establishment of a mobile network architecture with dynamically created virtual cells, this research presents a solution for proactive mobility management using RNNs. A RNN is trained to identify patterns in variable-length sequences of Received Signal Strength (RSS) values, where a mobile device is permitted to connect to more than a single base station at a time. A classification accuracy of over 98% was achieved in a simulation model that was set up emulating an urban environment.

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