NSF-CRII: False Alarm Suppression

False alarms are widely considered the number one hazard imposed by the use of medical technologies. The Emergency Care Research Institute named alarm hazards as the number one of the 'Top 10 Health Technology Hazards' for 2012, 2013 and 2015. Healthcare providers are usually overwhelmed with 350 alarm conditions per patient per day, of which 80-99% are meaningless or false. These false alarms can be due to several factors such as patient movement, malfunction of individual sensors and imperfections in the patient-equipment contact, resulting in alarm fatigue among healthcare providers and the possibility of missing a true life-threatening event lost in a cacophony of multiple alarms. These false alarms can also cause patient anxiety, inferior sleep structure and depressed immune systems. Thereby, alarm safety has been determined as a national patient safety goal by The Joint Commission, which accredits and certifies nearly 21,000 health care organizations and programs in the United States.

This project will develop a multifaceted framework to reduce the false alarm rate in Intensive Care Units (ICUs) by integrating principles from information theory, game theory, graph theory and signal processing. The alarms in ICUs are mostly created based on the measurements made by individual machine/monitors, while the majority of the alarms produced by these individual machines are considered false. The majority of current methods to suppress the false alarm rate attempt to design new monitors or create more accurate sensors. These methods are often tailored to specific devices or datasets and the significant intrinsic correlations among the extracted features from different sensors are overlooked in these methods. A real-time and accurate yet general method will be developed to reduce the number of false alarms while avoiding the suppression of true alarms through integrating information from a variety of devices and considering the non-linear correlations and mutual information among the features collected from these devices using a new game theoretic approach. The performance of this proposed method will be evaluated using PhysionNet's publicly available MIMIC II dataset considering the three vital signals of ECG, PLETH, and APB. The proposed false alarm detection method can potentially save many patients' lives and significantly reduce medical costs. This work can advance the research program and practice of teaching at the newly established Northern Arizona University School of Informatics, Computing and Cyber System (SICCS) by developing a new course in game theoretical optimizations, integration of this research in several undergraduate-level course modules and training of graduate students.

Mousavi, Sajad and Fotoohinasab, Atiyeh and Afghah, Fatemeh and Bacciu, Davide "Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks" PLOS ONE, v.15 , 2020. https://doi.org/10.1371/journal.pone.0226990.

Ghazanfari, Behzad and Zhang, Sixian and Afghah, Fatemeh and Payton-McCauslin, Nathan "Simultaneous Multiple Features Tracking of Beats: A Representation Learning Approach to Reduce False Alarm Rate in ICUs" 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019. https://doi.org/10.1109/BIBM47256.2019.8983408

Ghazanfari, Behzad and Afghah, Fatemeh and Najarian, Kayvan and Mousavi, Sajad and Gryak, Jonathan and Todd, James "An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs" 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019. https://doi.org/10.1109/EMBC.2019.8857034

Mousavi, Sajad and Afghah, Fatemeh "Inter- and Intra- Patient ECG Heartbeat Classification for Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach" ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019. https://doi.org/10.1109/ICASSP.2019.8683140

Zaeri-Amirani, Mohammad and Afghah, Fatemeh and Mousavi, Sajad "A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs A Genetic-Algorithm Approach" 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018. https://doi.org/10.1109/EMBC.2018.8512266

Chen, Jiaming and Valehi, Ali and Afghah, Fatemeh and Razi, Abolfazl "A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation" 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2019. https://doi.org/10.1109/BHI.2019.8834617.

Mousavi, Sajad and Afghah, Fatemeh and Razi, Abolfazl and Acharya, U. Rajendra "ECGNET: Learning Where to Attend for Detection of Atrial Fibrillation with Deep Visual Attention" 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2019. https://doi.org/10.1109/BHI.2019.8834637

Mousavi, Sajad and Afghah, Fatemeh and Acharya, U. Rajendra and Pawiak, Pawe "SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach" PLOS ONE, v.14, 2019. https://doi.org/10.1371/journal.pone.0216456

Mousavi, Sajad and Afghah, Fatemeh and Khadem, Fatemeh and Acharya, U. Rajendra "ECG Language processing (ELP): A new technique to analyze ECG signals" Computer Methods and Programs in Biomedicine, v.202, 2021 https://doi.org/10.1016/j.cmpb.2021.105959 

Fotoohinasab, Atiyeh and Hocking, Toby and Afghah, Fatemeh "A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection" Computers in Biology and Medicine, v.130, 2021 https://doi.org/10.1016/j.compbiomed.2021.104208

Mousavi, Sajad and Afghah, Fatemeh and Acharya, U. Rajendra "HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks." Computers in Biology and Medicine, v.127, 2020. https://doi.org/10.1016/j.compbiomed.2020.104057

Fotoohinasab, Atiyeh and Hocking, Toby and Afghah, Fatemeh "A Graph-constrained Changepoint Detection Approach for ECG Segmentation" 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020. https://doi.org/10.1109/EMBC44109.2020.9175333

Ghazanfari, Behzad and Afghah, Fatemeh and Hajiaghayi, Mohammadtaghi "Inverse Feature Learning: Feature Learning Based on Representation Learning of Error." IEEE Access, v.8, 2020. https://doi.org/10.1109/ACCESS.2020.3009902

Belen, James and Mousavi, Sajad and Shamsoshoara, Alireza and Afghah, Fatemeh "An Uncertainty Estimation Framework for Risk Assessment in Deep Learning-based Atrial Fibrillation Classification" IEEE Asilomar Conference on Signals, Systems, and Computers, 2020. https://doi.org/10.1109/IEEECONF51394.2020.9443466

Fotoohinasab, Atiyeh and Hocking, Toby and Afghah, Fatemeh "A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection." IEEE Asilomar Conference on Signals, Systems, and Computers ASILOMAR, 2020. https://doi.org/10.1109/IEEECONF51394.2020.9443307

Afghah, Fatemeh and Razi, Abolfazl and Soroushmehr, Reza and Ghanbari, Hamid and Najarian, Kayvan "Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units" Entropy, v.20, 2018. https://doi.org/10.3390/e20030190.

The Computational Framework to False Alarm Suppression in Intensive Care Units project was funded by the National Science Foundation, CISE Research Initiation Initiative (NSF-CRII), NSF award #1657260