Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks
Autoři:
Sajad Mousavi aff001; Atiyeh Fotoohinasab aff001; Fatemeh Afghah aff001
Působiště autorů:
School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States of America
aff001
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0226990
Souhrn
This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multi- modal biosignals. Most of the current work in the literature are either rule-based methods, requiring prior knowledge of arrhythmia analysis to build rules, or classical machine learning approaches, depending on hand-engineered features. In this work, we apply convolutional neural networks to automatically extract time-invariant features, an attention mechanism to put more emphasis on the important regions of the segmented input signal(s) that are more likely to contribute to an alarm, and long short-term memory units to capture the temporal information presented in the signal segments. We trained our method efficiently using a two-step training algorithm (i.e., pre-training and fine-tuning the proposed network) on the dataset provided by the PhysioNet computing in cardiology challenge 2015. The evaluation results demonstrate that the proposed method obtains better results compared to other existing algorithms for the false alarm reduction task in ICUs. The proposed method achieves a sensitivity of 93.88% and a specificity of 92.05% for the alarm classification, considering three different signals. In addition, our experiments for 5 separate alarm types leads significant results, where we just consider a single-lead ECG (e.g., a sensitivity of 90.71%, a specificity of 88.30%, an AUC of 89.51 for alarm type of Ventricular Tachycardia arrhythmia).
Klíčová slova:
Algorithms – Cardiology – Electrocardiography – Machine learning algorithms – Deep learning – Heart rate – Arrhythmia – Tachycardia
Zdroje
1. Aboukhalil A, Nielsen L, Saeed M, Mark RG, Clifford GD. Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. Journal of biomedical informatics. 2008;41(3):442–451. doi: 10.1016/j.jbi.2008.03.003 18440873
2. Drew BJ, Harris P, Zègre-Hemsey JK, Mammone T, Schindler D, Salas-Boni R, et al. Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients. PloS one. 2014;9(10):e110274. doi: 10.1371/journal.pone.0110274 25338067
3. PhysioNet. Reducing False Arrhythmia Alarms in the ICU; 2015. Available from: http://www.physionet.org/challenge/2015/.
4. Ansari S, Belle A, Najarian K. Multi-modal integrated approach towards reducing false arrhythmia alarms during continuous patient monitoring: the PhysioNet Challenge 2015. In: 2015 Computing in Cardiology Conference (CinC). IEEE; 2015. p. 1181–1184.
5. Fallet S, Yazdani S, Vesin JM. A multimodal approach to reduce false arrhythmia alarms in the intensive care unit. In: 2015 Computing in Cardiology Conference (CinC). IEEE; 2015. p. 277–280.
6. Plesinger F, Klimes P, Halamek J, Jurak P. False alarms in intensive care unit monitors: detection of life-threatening arrhythmias using elementary algebra, descriptive statistics and fuzzy logic. In: Computing in Cardiology Conference (CinC), 2015. IEEE; 2015. p. 281–284.
7. Couto P, Ramalho R, Rodrigues R. Suppression of false arrhythmia alarms using ECG and pulsatile waveforms. In: Computing in Cardiology Conference (CinC), 2015. IEEE; 2015. p. 749–752.
8. He R, Zhang H, Wang K, Yuan Y, Li Q, Pan J, et al. Reducing false arrhythmia alarms in the ICU using novel signal quality indices assessment method. In: 2015 Computing in Cardiology Conference (CinC). IEEE; 2015. p. 1189–1192.
9. Antink CH, Leonhardt S, Walter M. Reducing false alarms in the ICU by quantifying self-similarity of multimodal biosignals. Physiological measurement. 2016;37(8):1233. doi: 10.1088/0967-3334/37/8/1233 27454256
10. Gajowniczek K, Grzegorczyk I, Zabkowski T. Reducing False Arrhythmia Alarms Using Different Methods of Probability and Class Assignment in Random Forest Learning Methods. Sensors. 2019;19(7):1588. doi: 10.3390/s19071588
11. Lehman EP, Krishnan RG, Zhao X, Mark RG, Li-wei HL. Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics. In: Machine Learning for Healthcare Conference; 2018. p. 571–586.
12. Kalidas V, Tamil LS. Enhancing accuracy of arrhythmia classification by combining logical and machine learning techniques. In: Computing in Cardiology Conference (CinC), 2015. IEEE; 2015. p. 733–736.
13. Afghah F, Razi A, Najarian K. A Shapley Value Solution to Game Theoretic-based Feature Reduction in False Alarm Detection. arXiv preprint arXiv:151201680. 2015;.
14. Zaeri-Amirani M, Afghah F, Mousavi S. A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs A Genetic-Algorithm Approach. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2018. p. 319–323.
15. Acharya UR, Fernandes SL, WeiKoh JE, Ciaccio EJ, Fabell MKM, Tanik UJ, et al. Automated Detection of Alzheimer’s Disease Using Brain MRI Images–A Study with Various Feature Extraction Techniques. Journal of Medical Systems. 2019;43(9):302. doi: 10.1007/s10916-019-1428-9 31396722
16. Mousavi S, Afghah F. Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2019. p. 1308–1312.
17. Mousavi S, Afghah F, Razi A, Acharya UR. ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention. In: 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE; 2019. p. 1–4.
18. Hooman OM, Al-Rifaie MM, Nicolaou MA. Deep Neuroevolution: Training Deep Neural Networks for False Alarm Detection in Intensive Care Units. In: 2018 26th European Signal Processing Conference (EUSIPCO). IEEE; 2018. p. 1157–1161.
19. Mozos I, Caraba A. Electrocardiographic predictors of cardiovascular mortality. Disease markers. 2015;2015. doi: 10.1155/2015/727401 26257460
20. Abdelghani SA, Rosenthal TM, Morin DP. Surface electrocardiogram predictors of sudden cardiac arrest. Ochsner Journal. 2016;16(3):280–289. 27660578
21. Lai D, Zhang Y, Zhang X, Su Y, Heyat MBB. An Automated Strategy for Early Risk Identification of Sudden Cardiac Death by Using Machine Learning Approach on Measurable Arrhythmic Risk Markers. IEEE Access. 2019;7:94701–94716. doi: 10.1109/ACCESS.2019.2925847
22. Shashikumar SP, Shah AJ, Clifford GD, Nemati S. Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM; 2018. p. 715–723.
23. Mousavi S, Afghah F, Acharya UR. SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach. PLoS ONE 14(5): e0216456 2019; https://doi.org/10.1371/journal.pone.0216456 31063501
24. Wang S, Liu W, Wu J, Cao L, Meng Q, Kennedy PJ. Training deep neural networks on imbalanced data sets. In: Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE; 2016. p. 4368–4374.
25. Clifford GD, Silva I, Moody B, Li Q, Kella D, Shahin A, et al. The PhysioNet/computing in cardiology challenge 2015: reducing false arrhythmia alarms in the ICU. In: 2015 Computing in Cardiology Conference (CinC). IEEE; 2015. p. 273–276.
26. Li AS, Johnson AE, Mark RG. False arrhythmia alarm reduction in the intensive care unit. arXiv preprint arXiv:170903562. 2017;.
27. Ansari S, Belle A, Ghanbari H, Salamango M, Najarian K. Suppression of false arrhythmia alarms in the ICU: a machine learning approach. Physiological measurement. 2016;37(8):1186. doi: 10.1088/0967-3334/37/8/1186 27454017
28. Afghah F, Razi A, Soroushmehr R, Ghanbari H, Najarian K. Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units. Entropy. 2018;20(3):190. doi: 10.3390/e20030190
Článok vyšiel v časopise
PLOS One
2020 Číslo 1
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
- Těžké menstruační krvácení může značit poruchu krevní srážlivosti. Jaký management vyšetření a léčby je v takovém případě vhodný?
Najčítanejšie v tomto čísle
- Psychometric validation of Czech version of the Sport Motivation Scale
- Comparison of Monocyte Distribution Width (MDW) and Procalcitonin for early recognition of sepsis
- Effects of supplemental creatine and guanidinoacetic acid on spatial memory and the brain of weaned Yucatan miniature pigs
- Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals