Keywords

Kalman Filter, ECG, RR Interval Estimation

Abstract

An electrocardiogram (ECG) is used to monitor the activity of the heart. The human heart beats seventy times on an average per minute. The rate at which a human heart beats can exhibit a periodic variation. This is known as heart rate variability (HRV). Heart rate variability is an important measurement that can predict the survival after a heart attack. Studies have shown that reduced HRV predicts sudden death in patients with Myocardial Infarction (MI). The time interval between each beat is called an RR interval, where the heart rate is given by the reciprocal of the RR interval expressed in beats per minute. For a deeper insight into the dynamics underlying the beat to beat RR variations and for understanding the overall variance in HRV, an accurate method of estimating the RR interval must be obtained. Before an HRV computation can be obtained the quality of the RR interval data obtained must be good and reliable. Most QRS detection algorithms can easily miss a QRS pulse producing unreliable RR interval values. Therefore it is necessary to estimate the RR interval in the presence of missing QRS beats. The approach in this thesis is to apply KALMAN estimation algorithm to the RR interval data calculated from the ECG. The goal is to improve the RR interval values obtained from missed beats of ECG data.

Notes

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Graduation Date

2005

Semester

Spring

Advisor

Weeks, Arthur

Degree

Master of Science in Electrical Engineering (M.S.E.E.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Electrical Engineering

Format

application/pdf

Identifier

CFE0000340

URL

http://purl.fcla.edu/fcla/etd/CFE0000340

Language

English

Release Date

January 2010

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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