TY - GEN
T1 - ECGLens
T2 - 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018
AU - Xu, Ke
AU - Guo, Shunan
AU - Cao, Nan
AU - Gotz, David
AU - Xu, Aiwen
AU - Qu, Huamin
AU - Yao, Zhenjie
AU - Chen, Yixin
N1 - Publisher Copyright:
Copyright © 2017 ACM.
PY - 2018/4/20
Y1 - 2018/4/20
N2 - The Electrocardiogram (ECG) is commonly used to detect arrhythmias. Traditionally, a single ECG observation is used for diagnosis, making it difficult to detect irregular arrhythmias. Recent technology developments, however, have made it cost-effective to collect large amounts of raw ECG data over time. This promises to improve diagnosis accuracy, but the large data volume presents new challenges for cardiologists. This paper introduces ECGLens, an interactive system for arrhythmia detection and analysis using large-scale ECG data. Our system integrates an automatic heartbeat classification algorithm based on convolutional neural network, an outlier detection algorithm, and a set of rich interaction techniques. We also introduce A-glyph, a novel glyph designed to improve the readability and comparison of ECG signals. We report results from a comprehensive user study showing that A-glyph improves the efficiency in arrhythmia detection, and demonstrate the effectiveness of ECGLens in arrhythmia detection through two expert interviews.
AB - The Electrocardiogram (ECG) is commonly used to detect arrhythmias. Traditionally, a single ECG observation is used for diagnosis, making it difficult to detect irregular arrhythmias. Recent technology developments, however, have made it cost-effective to collect large amounts of raw ECG data over time. This promises to improve diagnosis accuracy, but the large data volume presents new challenges for cardiologists. This paper introduces ECGLens, an interactive system for arrhythmia detection and analysis using large-scale ECG data. Our system integrates an automatic heartbeat classification algorithm based on convolutional neural network, an outlier detection algorithm, and a set of rich interaction techniques. We also introduce A-glyph, a novel glyph designed to improve the readability and comparison of ECG signals. We report results from a comprehensive user study showing that A-glyph improves the efficiency in arrhythmia detection, and demonstrate the effectiveness of ECGLens in arrhythmia detection through two expert interviews.
KW - Artifact or system
KW - Health - clinical
KW - Interaction design
KW - Visual design
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85046951736&partnerID=8YFLogxK
U2 - 10.1145/3173574.3174237
DO - 10.1145/3173574.3174237
M3 - Conference contribution
AN - SCOPUS:85046951736
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 21 April 2018 through 26 April 2018
ER -