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Evaluating Heartbeat Segmentation Methods for the Analysis of Electrocardiogram Data

Abstract Number:

3119 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Tyler Cook (1), Katie Ma (1), Emily Hendryx Lyons (2)

Institutions:

(1) University of Central Oklahoma, N/A, (2) University of Central Oklahoma, United States

Co-Author(s):

Katie Ma  
University of Central Oklahoma
Emily Hendryx Lyons  
University of Central Oklahoma

First Author:

Tyler Cook  
University of Central Oklahoma

Presenting Author:

Tyler Cook  
University of Central Oklahoma

Abstract Text:

Electrocardiogram (ECG) data can provide physicians with valuable clinical information related to a patient's heart health. The analysis of ECG data poses many practical challenges, and the analyst must make several decisions related to data preparation. One important consideration is how to segment raw ECG data – which can include hours of ECG recordings containing thousands of individual heartbeats – into analyzable pieces. Many options exist, but popular methods include looking at time series representations of multiple beats or performing an individual beat-by-beat analysis. Furthermore, there are additional researcher degrees of freedom associated with each approach such as determining the length of the time series or how individual beats should be segmented. This work investigates the performance of several techniques used to process ECG data into individual heartbeats when used to classify arrhythmias. Data is taken from the MIT-BIH Arrythmia Database and used to train several different types of arrythmia classifiers. The results are then compared to explore the possibility of developing a general recommendation for an ECG individual beat segmentation procedure.

Keywords:

Electrocardiogram|Classification| | | |

Sponsors:

Section on Statistical Learning and Data Science

Tracks:

Machine Learning

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