Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics

Eric P. Lehman; Rahul G. Krishnan; Xiaopeng Zhao; Roger G. Mark; Li-wei H. Lehman

Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics

Eric P. Lehman, Rahul G. Krishnan, Xiaopeng Zhao, Roger G. Mark, Li-wei H. Lehman ;

Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR 85:571-586, 2018.

Abstract

The high rate of intensive care unit false arrhythmia alarms can lead to disruption of care and slow response time due to desensitization of clinical staff. We study the use of machine learning models to detect false ventricular tachycardia (v-tach) alarms using ECG waveform recordings. We propose using a Superv sed Denoising Autoencoder (SDAE) to detect false alarms using a low-dimensional representation of ECG dynamics learned by minimizing a combined reconstruct on and classification loss. We evaluate our algorithms on the PhysioNet Challenge 2015 dataset, containing over 500 records (over 300 training and 200 testing) with v-tach alarms. Our results indicate that using the SDAE on Fast Fourier Transformed (FFT) ECG at a beat-by-beat level outperforms several competitive baselines on the task of v-tach false alarm classification. We show that it is important to exploit the underlying known physiological structure using beat-by-beat frequency distribution from multiple cardiac cycles of the ECG waveforms to obtain competitive results and improvement over previous entries from the 2015 PhysioNet Challenge.

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@InProceedings{pmlr-v85-lehman18a, title = {Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics}, author = {Lehman, Eric P. and Krishnan, Rahul G. and Zhao, Xiaopeng and Mark, Roger G. and Lehman, Li-wei H.}, booktitle = {Proceedings of the 3rd Machine Learning for Healthcare Conference}, pages = {571--586}, year = {2018}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {85}, series = {Proceedings of Machine Learning Research}, address = {Palo Alto, California}, month = {17--18 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v85/lehman18a/lehman18a.pdf}, url = {http://proceedings.mlr.press/v85/lehman18a.html}, abstract = {The high rate of intensive care unit false arrhythmia alarms can lead to disruption of care and slow response time due to desensitization of clinical staff. We study the use of machine learning models to detect false ventricular tachycardia (v-tach) alarms using ECG waveform recordings. We propose using a Superv sed Denoising Autoencoder (SDAE) to detect false alarms using a low-dimensional representation of ECG dynamics learned by minimizing a combined reconstruct on and classification loss. We evaluate our algorithms on the PhysioNet Challenge 2015 dataset, containing over 500 records (over 300 training and 200 testing) with v-tach alarms. Our results indicate that using the SDAE on Fast Fourier Transformed (FFT) ECG at a beat-by-beat level outperforms several competitive baselines on the task of v-tach false alarm classification. We show that it is important to exploit the underlying known physiological structure using beat-by-beat frequency distribution from multiple cardiac cycles of the ECG waveforms to obtain competitive results and improvement over previous entries from the 2015 PhysioNet Challenge.} }

%0 Conference Paper %T Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics %A Eric P. Lehman %A Rahul G. Krishnan %A Xiaopeng Zhao %A Roger G. Mark %A Li-wei H. Lehman %B Proceedings of the 3rd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2018 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v85-lehman18a %I PMLR %J Proceedings of Machine Learning Research %P 571--586 %U http://proceedings.mlr.press %V 85 %W PMLR %X The high rate of intensive care unit false arrhythmia alarms can lead to disruption of care and slow response time due to desensitization of clinical staff. We study the use of machine learning models to detect false ventricular tachycardia (v-tach) alarms using ECG waveform recordings. We propose using a Superv sed Denoising Autoencoder (SDAE) to detect false alarms using a low-dimensional representation of ECG dynamics learned by minimizing a combined reconstruct on and classification loss. We evaluate our algorithms on the PhysioNet Challenge 2015 dataset, containing over 500 records (over 300 training and 200 testing) with v-tach alarms. Our results indicate that using the SDAE on Fast Fourier Transformed (FFT) ECG at a beat-by-beat level outperforms several competitive baselines on the task of v-tach false alarm classification. We show that it is important to exploit the underlying known physiological structure using beat-by-beat frequency distribution from multiple cardiac cycles of the ECG waveforms to obtain competitive results and improvement over previous entries from the 2015 PhysioNet Challenge.

Lehman, E.P., Krishnan, R.G., Zhao, X., Mark, R.G. & Lehman, L.H.. (2018). Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics. Proceedings of the 3rd Machine Learning for Healthcare Conference, in PMLR 85:571-586