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RR INTERVAL ESTIMATION FROM AN ECG USING A LINEAR DISCRETE KALMAN FILTER

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Date Issued:
2005
Abstract/Description:
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.
Title: RR INTERVAL ESTIMATION FROM AN ECG USING A LINEAR DISCRETE KALMAN FILTER.
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Name(s): Janapala, Arun, Author
WEEKS, ARTHUR, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2005
Publisher: University of Central Florida
Language(s): English
Abstract/Description: 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.
Identifier: CFE0000340 (IID), ucf:46279 (fedora)
Note(s): 2005-05-01
M.S.E.E.
Engineering and Computer Science, Department of Electrical and Computer Engineering
Masters
This record was generated from author submitted information.
Subject(s): KALMAN FILTER
ECG
RR INTERVAL ESTIMATION
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0000340
Restrictions on Access: campus 2010-01-31
Host Institution: UCF

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