Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection

Physiological Measurement
Philip Warrick, Masun Nabhan Homsi

Abstract

Atrial fibrillation is a common type of heart rhythm abnormality caused by a problem with the heart's electrical system. Early detection of this disease has important implications for stroke prevention and management. Our objective is to construct an intelligent tool that assists cardiologists in identifying automatically cardiac arrhythmias and noise in electrocardiogram (ECG) recordings. Our base deep classifier combined a convolutional neural network (CNNs) and a sequence of long short-term memory units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and the final prediction was selected for classification. Ten standalone models that used our base classifier architecture were first cross-validated separately on 90% of the PhysioNet/CinC Challenge 2017 dataset and then tested on 10%. An ensemble classifier selected the label of the best average probability from the ten sub-models to improve prediction quality. Our original result submitted to the challenge gave a mean F1-measure of 80%. The new proposed method improved the test score to 82%, which was tied for the third-highest score in the follow-up phase of the challenge. Withou...Continue Reading

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References

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Citations

Jun 4, 2019·Physiological Measurement·Philip A WarrickMasun Nabhan Homsi
Jul 14, 2020·Computers in Biology and Medicine·Shenda HongJimeng Sun
Jun 1, 2021·Frontiers in Physiology·Rahimeh RouhiFabien Lauer
Nov 30, 2019·Computers in Biology and Medicine·Wenjuan CaiJianwen Luo

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