| NSF Org: |
IIS Div Of Information & Intelligent Systems |
| Recipient: |
|
| Initial Amendment Date: | March 23, 2017 |
| Latest Amendment Date: | May 29, 2018 |
| Award Number: | 1657260 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Wendy Nilsen
wnilsen@nsf.gov (703)292-2568 IIS Div Of Information & Intelligent Systems CSE Direct For Computer & Info Scie & Enginr |
| Start Date: | March 15, 2017 |
| End Date: | February 28, 2021 (Estimated) |
| Total Intended Award Amount: | $174,998.00 |
| Total Awarded Amount to Date: | $190,998.00 |
| Funds Obligated to Date: |
FY 2018 = $16,000.00 |
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
601 S KNOLES DR RM 220 FLAGSTAFF AZ US 86011 (928)523-0886 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
AZ US 86011-0001 |
| Primary Place of Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): |
CRII CISE Research Initiation, Smart and Connected Health |
| Primary Program Source: |
01001819DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The following outcomes have been generated during this project:
( * identifies the graduate students and ** identifies the undergraduate students.)
1. F. Afghah, A. Razi, K. Najarian, "False Alarm Suppression in Intensive Care Units", Issued Patent, Patent No. 16/195,585, May 12, 2020.
2. F. Afghah, S. Mousavi*, "Novel ECG Language Processing for Detection and Prediction of Cardiac Events", U.S. Provisional Pat. App. No. 62801881, Filed February 6, 2019.
3. F. Afghah, S. Mousavi*, "Patient ECG Heartbeat Classification and Analysis for Arrhythmia Detection", U.S. Provisional Pat. App. No. 62801881, Filed February 6, 2019.
4. S. Mousavi, “ECG Signal Processing for Heart Arrhythmia Detection”, PhD thesis, Northern Arizona University, May 2020.
5. S. Mousavi*, F. Afghah and U. R. Acharya, "HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks", Elsevier Computers in Biology and Medicine Journal, Volume 127, 104057, December 2020.
6. J. Belen**, S. Mousavi*, A. Shamsoshoara*, F. Afghah, "An Uncertainty Estimation Framework for Risk Assessment in Deep Learning-based AFib Classification", IEEE Asilomar Conference on Signals, Systems, and Computers ASILOMAR, 2020.
7. B. Ghazanfari*, F. Afghah, M. Haghiaghayi, "Inverse Feature Learning: Feature learning based on Representation Learning of Error", IEEE Access, 2020.
8. S. Mousavi*, F. Afghah, F. Khadem and U. R. Acharya, "HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks", Elsevier Computers in Biology and Medicine Journal, Volume 127, 104057, December 2020.
9. Fotoohinasab*, T. Hocking, F. Afghah, "A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS-Complex Detection", Elsevier Computers in Biology and Medicine Journal, 2021.
10. Fotoohinasab*, T. Hocking, F. Afghah, "A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection", IEEE Asilomar Conference on Signals, Systems, and Computers ASILOMAR, 2020.
11. Fotoohinasab*, T. Hocking, F. Afghah, “A Graph-constrained Changepoint Detection Approach for ECG Segmentation", 42th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 2020.
12. S. Mousavi*, A. Fotoohinasab*, F. Afghah, Single-modal and multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks, PLOS ONE, 15(1):e0226990 2020.
13. S. Mousavi*, F. Afghah, and R. Acharya, ''SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach'', PLOS ONE, 14(5): e0216456, 2019.
14. Ghazanfari*, S. Zhang*, F. Afghah, and N. Payton- McCauslin**, "Simultaneous Multiple Features Tracking of Beats: A Representation Learning Approach to Reduce False Alarm Rate in ICUs", IEEE International Conference on Bioinformatics and Biomedicine (BIBM), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics (DLB2H), San Diego, CA 2019.
15. Ghazanfari*, F. Afghah, K. Najarian, S. Mousavi*, J. Gryak, and J. Todd**, "An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs", 40th International Conference of the IEEE Engineering in Medicine and Biology Society, (EMBC'19) 2019.
16. S. Mousavi*, F. Afghah, ''Inter- and Intra-patient ECG Heartbeat Classification for Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach'', International Conference on Acoustics, Speech, and Signal Processing (ICASSP'19), May 2019.
17. S. Mousavi*, F. Afghah, A. Razi, R. Acharya, "ECGNET: Learning Where to Attend for Detection of Paroxysmal Atrial Fibrillation with Deep Visual Attention", IEEE-EMBS International Conferences on Biomedical and Health Informatics (BHI'19), May 2019.
18. J. Cheng*, A. Valehi, A. Afghah, A. Razi, ''A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation'', IEEE-EMBS International Conferences on Biomedical and Health Informatics (BHI'19), May 2019.
19. M. Zaeri-Amirani*, F. Afghah, S. Mousavi*, ''A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs, A Genetic-Algorithm Approach'' , submitted to 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18), July 2018.
20. F. Afghah, A. Razi, R. Soroushmehr, H. Ghanbari, K. Najarian, "Game Theory for Systematic Selection of Wavelet-based Features; Application in False Alarm Detection in Intensive Care Units", to appear in Entropy, Special Issue on Information Theory in Game Theory, 2018.
21. M. Zaeri-Amirani*, F. Afghah, J. Gryak, R. Soroushmehr, K. Najarian, “Roulette Transform for Biomedical Signal Processing”, to be submitted to BMC Informatics and Decision Making, 2019.
22. Fatemeh Afghah; Abolfazl Razi; Kayvan Najarian. “Roulette Transform for Signal Processing. “Invention Disclosure, Disclosure ID: 2018-007, Filed Jan. 2018.
23. Nathan Payton-McCauslin**, Alexander Grzesiak**, James Todd**, Fatemeh Afghah, “ A Graphical User Interface for False Alarm Detection in ICUs”, Software package (Capstone project), 2019.
Last Modified: 06/02/2021
Modified by: Fatemeh Afghah
Please report errors in award information by writing to: awardsearch@nsf.gov.
