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Sinno ZC, Shay D, Kruppa J, Klopfenstein SA, Giesa N, Flint AR, Herren P, Scheibe F, Spies C, Hinrichs C, Winter A, Balzer F, Poncette AS. The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study. Sci Rep 2022; 12:21801. [PMID: 36526892 PMCID: PMC9758124 DOI: 10.1038/s41598-022-26261-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Intensive care units (ICU) are often overflooded with alarms from monitoring devices which constitutes a hazard to both staff and patients. To date, the suggested solutions to excessive monitoring alarms have remained on a research level. We aimed to identify patient characteristics that affect the ICU alarm rate with the goal of proposing a straightforward solution that can easily be implemented in ICUs. Alarm logs from eight adult ICUs of a tertiary care university-hospital in Berlin, Germany were retrospectively collected between September 2019 and March 2021. Adult patients admitted to the ICU with at least 24 h of continuous alarm logs were included in the study. The sum of alarms per patient per day was calculated. The median was 119. A total of 26,890 observations from 3205 patients were included. 23 variables were extracted from patients' electronic health records (EHR) and a multivariable logistic regression was performed to evaluate the association of patient characteristics and alarm rates. Invasive blood pressure monitoring (adjusted odds ratio (aOR) 4.68, 95%CI 4.15-5.29, p < 0.001), invasive mechanical ventilation (aOR 1.24, 95%CI 1.16-1.32, p < 0.001), heart failure (aOR 1.26, 95%CI 1.19-1.35, p < 0.001), chronic renal failure (aOR 1.18, 95%CI 1.10-1.27, p < 0.001), hypertension (aOR 1.19, 95%CI 1.13-1.26, p < 0.001), high RASS (aOR 1.22, 95%CI 1.18-1.25, p < 0.001) and scheduled surgical admission (aOR 1.22, 95%CI 1.13-1.32, p < 0.001) were significantly associated with a high alarm rate. Our study suggests that patient-specific alarm management should be integrated in the clinical routine of ICUs. To reduce the overall alarm load, particular attention regarding alarm management should be paid to patients with invasive blood pressure monitoring, invasive mechanical ventilation, heart failure, chronic renal failure, hypertension, high RASS or scheduled surgical admission since they are more likely to have a high contribution to noise pollution, alarm fatigue and hence compromised patient safety in ICUs.
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Affiliation(s)
- Zeena-Carola Sinno
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany
| | - Denys Shay
- grid.189504.10000 0004 1936 7558Harvard T.H. Chan School of Public Health, Department of Epidemiology, Boston, MA USA
| | - Jochen Kruppa
- grid.434095.f0000 0001 1864 9826Hochschule Osnabrück, University of Applied Sciences, Osnabrück, Germany
| | - Sophie A.I. Klopfenstein
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany ,grid.484013.a0000 0004 6879 971XBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Core Facility Digital Medicine and Interoperability, Charitéplatz 1, 10117 Berlin, Germany
| | - Niklas Giesa
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany
| | - Anne Rike Flint
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany
| | - Patrick Herren
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany
| | - Franziska Scheibe
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany ,grid.517316.7NeuroCure Clinical Research Center, Charitéplatz 1, 10117 Berlin, Germany
| | - Claudia Spies
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine, Charitéplatz 1, 10117 Berlin, Germany
| | - Carl Hinrichs
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Nephrology and Medical Intensive Care, Charitéplatz 1, 10117 Berlin, Germany
| | - Axel Winter
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Surgery, Charitéplatz 1, 10117 Berlin, Germany
| | - Felix Balzer
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany
| | - Akira-Sebastian Poncette
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany ,grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine, Charitéplatz 1, 10117 Berlin, Germany
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Sološenko A, Paliakaitė B, Marozas V, Sörnmo L. Training Convolutional Neural Networks on Simulated Photoplethysmography Data: Application to Bradycardia and Tachycardia Detection. Front Physiol 2022; 13:928098. [PMID: 35923223 PMCID: PMC9339964 DOI: 10.3389/fphys.2022.928098] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/15/2022] [Indexed: 11/23/2022] Open
Abstract
Objective: To develop a method for detection of bradycardia and ventricular tachycardia using the photoplethysmogram (PPG). Approach: The detector is based on a dual-branch convolutional neural network (CNN), whose input is the scalograms of the continuous wavelet transform computed in 5-s segments. Training and validation of the CNN is accomplished using simulated PPG signals generated from RR interval series extracted from public ECG databases. Manually annotated real PPG signals from the PhysioNet/CinC 2015 Challenge Database are used for performance evaluation. The performance is compared to that of a pulse-based reference detector. Results: The sensitivity/specificity were found to be 98.1%/97.9 and 76.6%/96.8% for the CNN-based detector, respectively, whereas the corresponding results for the pulse-based detector were 94.7%/99.8 and 67.1%/93.8%, respectively. Significance: The proposed detector may be useful for continuous, long-term monitoring of bradycardia and tachycardia using wearable devices, e.g., wrist-worn devices, especially in situations where sensitivity is favored over specificity. The study demonstrates that simulated PPG signals are suitable for training and validation of a CNN.
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Affiliation(s)
- Andrius Sološenko
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
- *Correspondence: Andrius Sološenko ,
| | - Birutė Paliakaitė
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
- Department of Electronics Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | - Leif Sörnmo
- Department of Biomedical Engineering, Lund University, Lund, Sweden
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Shih YS, Lee TT, Mills ME. Critical Care Nurses' Perceptions of Clinical Alarm Management on Nursing Practice. Comput Inform Nurs 2022; 40:389-395. [PMID: 35234706 DOI: 10.1097/cin.0000000000000886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
The alarm management of physiological monitoring systems is a key responsibility of critical care nurses. However, the high numbers of false and nonactionable (true but clinically irrelevant) alarms cause distractions to healthcare professionals, interruptions to nursing workflow, and ignoring of crucial tasks. Therefore, understanding how nurses manage large amounts of alarms in their daily work could provide a direction to design interventions to prevent adverse patient care effects. A qualitative design with focus group interviews was conducted with 37 nurses in Taiwan. Content analysis was performed to analyze the interview data, and four main themes were derived: (1) the foundation stone of critical care nursing practice; (2) a trajectory adaptation of alarms management; (3) adverse impacts on the quality of care and patient safety; and (4) a hope for multimodal learning alternatives and wireless technology. Nurses manage alarm parameter settings influenced not only by their knowledge and skills of patient care, but also in accordance with the three dimensions of technology, human, and organization evaluation framework. Customized alarm management training alternatives, patient-centered care values, and application of wireless technology are the suggested approaches to enhance nursing care and minimize the risk of adverse events.
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Affiliation(s)
- Yu-Shan Shih
- Author Affiliations: College of Nursing, National Yang Ming Chiao Tung University (Ms Shih and Dr Lee); Nursing Department, Shin Kong Wu Memorial Hospital (Ms Shih); and School of Nursing, University of Maryland, Baltimore, Maryland, MD (Dr Mills)
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Bollepalli SC, Sevakula RK, Au-Yeung WTM, Kassab MB, Merchant FM, Bazoukis G, Boyer R, Isselbacher EM, Armoundas AA. Real-Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks. J Am Heart Assoc 2021; 10:e023222. [PMID: 34854319 PMCID: PMC9075394 DOI: 10.1161/jaha.121.023222] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening arrhythmia detection in the ICUs using a deep learning approach. Methods and Results This study involves a total of 953 independent life-threatening arrhythmia alarms generated from the ICU bedside monitors of 410 patients. Specifically, we used the ECG (4 channels), arterial blood pressure, and photoplethysmograph signals to accurately detect the onset and offset of various arrhythmias, without prior knowledge of the alarm type. We used a hybrid convolutional neural network based classifier that fuses traditional handcrafted features with features automatically learned using convolutional neural networks. Further, the proposed architecture remains flexible to be adapted to various arrhythmic conditions as well as multiple physiological signals. Our hybrid- convolutional neural network approach achieved superior performance compared with methods which only used convolutional neural network. We evaluated our algorithm using 5-fold cross-validation for 5 times and obtained an accuracy of 87.5%±0.5%, and a score of 81%±0.9%. Independent evaluation of our algorithm on the publicly available PhysioNet 2015 Challenge database resulted in overall classification accuracy and score of 93.9% and 84.3%, respectively, indicating its efficacy and generalizability. Conclusions Our method accurately detects multiple arrhythmic conditions. Suitable translation of our algorithm may significantly improve the quality of care in ICUs by reducing the burden of false alarms.
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Affiliation(s)
| | - Rahul K Sevakula
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | | | - Mohamad B Kassab
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | | | - George Bazoukis
- Second Department of Cardiology Evangelismos General Hospital of Athens Athens Greece
| | - Richard Boyer
- Anesthesia Department Massachusetts General Hospital Boston MA
| | | | - Antonis A Armoundas
- Cardiovascular Research Center Massachusetts General Hospital Boston MA.,Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA
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Zahid MU, Kiranyaz S, Ince T, Devecioglu OC, Chowdhury MEH, Khandakar A, Tahir A, Gabbouj M. Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network. IEEE Trans Biomed Eng 2021; 69:119-128. [PMID: 34110986 DOI: 10.1109/tbme.2021.3088218] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. METHODS In this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model to reduce the number of false alarms. This CNN architecture consists of an encoder block and a corresponding decoder block followed by a sample-wise classification layer to construct the 1D segmentation map of R-peaks from the input ECG signal. Once the proposed model has been trained, it can solely be used to detect R-peaks possibly in a single channel ECG data stream quickly and accurately, or alternatively, such a solution can be conveniently employed for real-time monitoring on a lightweight portable device. RESULTS The model is tested on two open-access ECG databases: The China Physiological Signal Challenge (2020) database (CPSC-DB) with more than one million beats, and the commonly used MIT-BIH Arrhythmia Database (MIT-DB). Experimental results demonstrate that the proposed systematic approach achieves 99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-DB, which is the best R-peak detection performance ever achieved. Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99.83% F1-score, 99.85% recall, and 99.82% precision. SIGNIFICANCE Compared to all competing methods, the proposed approach can reduce the false-positives and false-negatives in Holter ECG signals by more than 54% and 82%, respectively. CONCLUSION Finally, the simple and invariant nature of the parameters leads to a highly generic system and therefore applicable to any ECG dataset.
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Towards better heartbeat segmentation with deep learning classification. Sci Rep 2020; 10:20701. [PMID: 33244078 PMCID: PMC7692498 DOI: 10.1038/s41598-020-77745-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 10/29/2020] [Indexed: 11/24/2022] Open
Abstract
The confidence of medical equipment is intimately related to false alarms. The higher the number of false events occurs, the less truthful is the equipment. In this sense, reducing (or suppressing) false positive alarms is hugely desirable. In this work, we propose a feasible and real-time approach that works as a validation method for a heartbeat segmentation third-party algorithm. The approach is based on convolutional neural networks (CNNs), which may be embedded in dedicated hardware. Our proposal aims to detect the pattern of a single heartbeat and classifies them into two classes: a heartbeat and not a heartbeat. For this, a seven-layer convolution network is employed for both data representation and classification. We evaluate our approach in two well-settled databases in the literature on the raw heartbeat signal. The first database is a conventional on-the-person database called MIT-BIH, and the second is one less uncontrolled off-the-person type database known as CYBHi. To evaluate the feasibility and the performance of the proposed approach, we use as a baseline the Pam-Tompkins algorithm, which is a well-known method in the literature and still used in the industry. We compare the baseline against the proposed approach: a CNN model validating the heartbeats detected by a third-party algorithm. In this work, the third-party algorithm is the same as the baseline for comparison purposes. The results support the feasibility of our approach showing that our method can enhance the positive prediction of the Pan-Tompkins algorithm from \documentclass[12pt]{minimal}
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\begin{document}$$95.71\%$$\end{document}95.71% on the MIT-BIH/CYBHi databases.
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Xiao R, Do D, Ding C, Meisel K, Lee R, Hu X. Generalizability of SuperAlarm via Cross-Institutional Performance Evaluation. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:132404-132412. [PMID: 33747677 PMCID: PMC7971165 DOI: 10.1109/access.2020.3009667] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Bedside patient monitors are ubiquitous tools in modern critical care units to provide timely patient status. However, current systems suffer from high volume of false alarms leading to alarm fatigue, one of top technical hazards in clinical settings. Many studies are racing to develop improved algorithms towards precision patient monitoring, while little has been done to investigate the aspect of algorithm generalizability across different health institutions. Our group has been developing an evolving framework termed SuperAlarm that extracts multivariate patterns in data streams (monitor alarms, electronic health records and physiologic waveforms) of modern health enterprise to predict patient deterioration and has demonstrated great potential in mitigating alarm fatigue. In this study, we further investigate the generalizability of SuperAlarm by designing a comprehensive approach to achieve performance comparison in predicting in-hospital code blue (CB) events across two health institutions. SuperAlarm model trained with alarm data in one institution is tested on both internal and external test sets. Results show comparable performance with sensitivity up to 80% within one-hour window of events and over 90% in reduction of false alarms in both institutions. Cross-institutional performance agreement can be further improved by predicting a more stringent CB subtype (cardiopulmonary arrest), with internal sensitivity lying within 95% confident interval of external one up to 8-hour before event onset. The cross-institutional performance comparison offers first-hand knowledge on both advantages and challenges in generalizing a prediction algorithm across different institutions, which hold key information to guide the design of model training and deployment strategy.
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Affiliation(s)
- Ran Xiao
- School of Nursing, University of California San Francisco, San Francisco, CA 94143 USA
- School of Nursing, Duke University, Durham, NC 27708 USA
| | - Duc Do
- UCLA Cardiac Arrhythmia Center, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095 USA
| | - Cheng Ding
- School of Nursing, University of California San Francisco, San Francisco, CA 94143 USA
| | - Karl Meisel
- School of Medicine, University of California San Francisco, San Francisco, CA 94143 USA
| | - Randall Lee
- School of Medicine, University of California San Francisco, San Francisco, CA 94143 USA
| | - Xiao Hu
- School of Nursing, University of California San Francisco, San Francisco, CA 94143 USA
- School of Nursing, Duke University, Durham, NC 27708 USA
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Dev C, M S S, Reddy Manne S, Goud A, Bashar SB, Richhariya A, Chhablani J, Vupparaboina KK, Jana S. Diagnostic Quality Assessment of Ocular Fundus Photographs: Efficacy of Structure-Preserving ScatNet Features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:2091-2094. [PMID: 31946313 DOI: 10.1109/embc.2019.8857046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Various ophthalmic procedures critically depend on high-quality images. For instance, efficiency of teleophthalmology, a framework to bring advanced eye care to remote regions, is determined by the capability of assessing diagnostic quality of ocular fundus photographs (FPs), and rejecting poor-quality ones at the source. In this context, we study algorithmic methods of classifying high- and low-quality FPs. Crucially, diagnostic quality (DQ) - determined by clinically, but not necessarily perceptually, significant structures - is not synonymous with perceptual appeal. Yet, traditional methods handpick features individually (or in small subsets) to meet certain ad hoc perceptual requirements. In contrast, we investigate the efficacy of a comprehensive set of structure-preserving features, systematically generated by a deep scattering network (ScatNet). Specifically, we consider three advanced machine learning classifiers, train each using ScatNet as well as traditional features separately, and demonstrate that the former ensure significantly superior performance for each classifier under multiple criteria including classification accuracy.
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10
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Joshi R, Peng Z, Long X, Feijs L, Andriessen P, Van Pul C. Predictive Monitoring of Critical Cardiorespiratory Alarms in Neonates Under Intensive Care. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2019; 7:2700310. [PMID: 32166052 PMCID: PMC6906083 DOI: 10.1109/jtehm.2019.2953520] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 10/11/2019] [Accepted: 11/10/2019] [Indexed: 11/07/2022]
Abstract
We aimed at reducing alarm fatigue in neonatal intensive care units by developing a model using machine learning for the early prediction of critical cardiorespiratory alarms. During this study in over 34,000 patient monitoring hours in 55 infants 278,000 advisory (yellow) and 70,000 critical (red) alarms occurred. Vital signs including the heart rate, breathing rate, and oxygen saturation were obtained at a sampling frequency of 1 Hz while heart rate variability was calculated by processing the ECG - both were used for feature development and for predicting alarms. Yellow alarms that were followed by at least one red alarm within a short post-alarm window constituted the case-cohort while the remaining yellow alarms constituted the control cohort. For analysis, the case and control cohorts, stratified by proportion, were split into training (80%) and test sets (20%). Classifiers based on decision trees were used to predict, at the moment the yellow alarm occurred, whether a red alarm(s) would shortly follow. The best performing classifier used data from the 2-min window before the occurrence of the yellow alarm and could predict 26% of the red alarms in advance (18.4s, median), at the expense of 7% additional red alarms. These results indicate that based on predictive monitoring of critical alarms, nurses can be provided a longer window of opportunity for preemptive clinical action. Further, such as algorithm can be safely implemented as alarms that are not algorithmically predicted can still be generated upon the usual breach of the threshold, as in current clinical practice.
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Affiliation(s)
- Rohan Joshi
- 2Department of Family Care SolutionsPhilips Research5656AZEindhovenThe Netherlands.,3Department of Industrial DesignEindhoven University of Technology5612AZEindhovenThe Netherlands.,5Department of Clinical PhysicsMáxima Medical Center5504DBVeldhovenThe Netherlands
| | - Zheng Peng
- 1Department of Electrical EngineeringEindhoven University of Technology5612AZEindhovenThe Netherlands
| | - Xi Long
- 1Department of Electrical EngineeringEindhoven University of Technology5612AZEindhovenThe Netherlands.,2Department of Family Care SolutionsPhilips Research5656AZEindhovenThe Netherlands
| | - Loe Feijs
- 3Department of Industrial DesignEindhoven University of Technology5612AZEindhovenThe Netherlands
| | - Peter Andriessen
- 4Department of NeonatologyMáxima Medical Center5504DBVeldhovenThe Netherlands
| | - Carola Van Pul
- 5Department of Clinical PhysicsMáxima Medical Center5504DBVeldhovenThe Netherlands.,6Department of Applied PhysicsEindhoven University of Technology5612AZEindhovenThe Netherlands
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Ghazanfari B, Zhang S, Afghah F, Payton-McCauslin N. Simultaneous multiple features tracking of beats: A representation learning approach to reduce false alarm rates in ICUs. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2019; 2019:2350-2355. [PMID: 33062389 PMCID: PMC7552433 DOI: 10.1109/bibm47256.2019.8983408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rule-based and machine learning-based techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm. That calls for novel methods that can accurately detect the cardiac events by only accessing one signal (e.g., ECG) with a low level of computation and sensors requirement. We propose a novel and robust representation learning framework for ECG analysis that only rely on a single lead ECG signal and yet achieves considerably better performance compared to the state-of-the-art works in this domain, without relying on an expert knowledge. We evaluate the performance of this method using the "2015 Physionet computing in cardiology challenge" dataset. To the best of our knowledge, the best previously reported performance is based on both expert knowledge and machine learning where all available signals of ECG, ABP and PPG are utilized. Our proposed method reaches the performance of 97.3%, 95.5 %, and 90.8 % in terms of sensitivity, specificity, and the challenge's score, respectively for the detection of five arrhythmias when only one single ECG lead signals is used without any expert knowledge.
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Affiliation(s)
- Behzad Ghazanfari
- School of Informatics, Computing and Cyber Security, Northern Arizona University, Flagstaff, AZ
| | - Sixian Zhang
- School of Informatics, Computing and Cyber Security, Northern Arizona University, Flagstaff, AZ
| | - Fatemeh Afghah
- School of Informatics, Computing and Cyber Security, Northern Arizona University, Flagstaff, AZ
| | - Nathan Payton-McCauslin
- School of Informatics, Computing and Cyber Security, Northern Arizona University, Flagstaff, AZ
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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. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:319-323. [PMID: 30440402 DOI: 10.1109/embc.2018.8512266] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
High false alarm rate in intensive care units (ICUs) has been identified as one of the most critical medical challenges in recent years. This often results in overwhelming the clinical staff by numerous false or unurgent alarms and decreasing the quality of care through enhancing the probability of missing true alarms as well as causing delirium, stress, sleep deprivation and depressed immune systems for patients. One major cause of false alarms in clinical practice is that the collected signals from different devices are processed individually to trigger an alarm, while there exists a considerable chance that the signal collected from one device is corrupted by noise or motion artifacts. In this paper, we propose a low-computational complexity yet accurate game-theoretic feature selection method which is based on a genetic algorithm that identifies the most informative biomarkers across the signals collected from various monitoring devices and can considerably reduce the rate of false alarms 1.
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Hu X. An algorithm strategy for precise patient monitoring in a connected healthcare enterprise. NPJ Digit Med 2019; 2:30. [PMID: 31304377 PMCID: PMC6550269 DOI: 10.1038/s41746-019-0107-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Accepted: 04/09/2019] [Indexed: 11/08/2022] Open
Abstract
This perspective paper describes the building elements for realizing a precise patient monitoring algorithm to fundamentally address the alarm fatigue problem. Alarm fatigue is well recognized but no solution has been widely successful. Physiologic patient monitors are responsible for the lion's share of alarms at the bedside, most of which are either false or non-actionable. Algorithms on patient monitors lack precision because they fail to leverage multivariate relationship among variables monitored, to integrate rich patient clinical information from electronic health record system, and to utilize temporal patterns in data streams. Therefore, a solution to patient monitor alarm fatigue is to open the black-box of patient monitors to integrate physiologic data with clinical data from EHR under a four-element algorithm strategy to be described in this paper. This strategy will be presented in this paper in the context of its current status as described in our prior publications.
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Affiliation(s)
- Xiao Hu
- Department of Physiological Nursing, Department of Neurological Surgery, Bakar Computational Health Sciences Institue, UCB-UCSF Joint Bioengieering Graduate Program, University of California, San Francisco, San Francisco, CA 94122 USA
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Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems. Crit Care Clin 2019; 35:483-495. [PMID: 31076048 DOI: 10.1016/j.ccc.2019.02.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in the tele-ICU, applications of machine learning (ML) algorithms to critical care, and opportunities to integrate ML with tele-ICU CDSS. The enormous quantities of data generated by tele-ICU systems is a major driver in the development of the large, comprehensive, heterogeneous, and granular data sets necessary to develop generalizable ML CDSS algorithms, and deidentification of these data sets expands opportunities for ML CDSS research.
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Nandi M, Venton J, Aston PJ. A novel method to quantify arterial pulse waveform morphology: attractor reconstruction for physiologists and clinicians. Physiol Meas 2018; 39:104008. [PMID: 30256216 PMCID: PMC6372136 DOI: 10.1088/1361-6579/aae46a] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Current arterial pulse monitoring systems capture data at high frequencies (100-1000 Hz). However, they typically report averaged or low frequency summary data such as heart rate and systolic, mean and diastolic blood pressure. In doing so, a potential wealth of information contained in the high-fidelity waveform data is discarded, data which has long been known to contain useful information on cardiovascular performance. Here we summarise a new mathematical method, attractor reconstruction, which enables the quantification of arterial waveform shape and variability in real-time. The method can handle long streams of non-stationary data and does not require preprocessing of the raw physiological data by the end user. Whilst the detailed mathematical proofs have been described elsewhere (Aston et al 2008 Physiol. Meas. 39), the authors were motivated to write a summary of the method and its potential utility for biomedical researchers, physiologists and clinician readers. Here we illustrate how this new method may supplement and potentially enhance the sensitivity of detecting cardiovascular disturbances, to aid with biomedical research and clinical decision making.
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Affiliation(s)
- Manasi Nandi
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, Franklin Wilkins Building, 150 Stamford Street, London SE1 9NH, United Kingdom. School of Cardiovascular Medicine and Sciences, Faculty of Life Sciences and Medicine, King's College London, Franklin Wilkins Building, 150 Stamford Street, London SE1 9NH, United Kingdom
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Lehman EP, Krishnan RG, Zhao X, Mark RG, Lehman LWH. Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2018; 85:571-586. [PMID: 31723938 PMCID: PMC6853621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
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 Supervised Denoising Autoencoder (SDAE) to detect false alarms using a low-dimensional representation of ECG dynamics learned by minimizing a combined reconstruction 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 improve over previous entries from the 2015 PhysioNet Challenge.
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Affiliation(s)
- Eric P Lehman
- College of Computer and Information Science, Northeastern University, Boston, MA
| | - Rahul G Krishnan
- CSAIL & Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN
| | - Roger G Mark
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Li-Wei H Lehman
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA
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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 (BASEL, SWITZERLAND) 2018; 20:E190. [PMID: 33265281 PMCID: PMC7512707 DOI: 10.3390/e20030190] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 02/27/2018] [Accepted: 03/05/2018] [Indexed: 01/19/2023]
Abstract
Intensive Care Units (ICUs) are equipped with many sophisticated sensors and monitoring devices to provide the highest quality of care for critically ill patients. However, these devices might generate false alarms that reduce standard of care and result in desensitization of caregivers to alarms. Therefore, reducing the number of false alarms is of great importance. Many approaches such as signal processing and machine learning, and designing more accurate sensors have been developed for this purpose. However, the significant intrinsic correlation among the extracted features from different sensors has been mostly overlooked. A majority of current data mining techniques fail to capture such correlation among the collected signals from different sensors that limits their alarm recognition capabilities. Here, we propose a novel information-theoretic predictive modeling technique based on the idea of coalition game theory to enhance the accuracy of false alarm detection in ICUs by accounting for the synergistic power of signal attributes in the feature selection stage. This approach brings together techniques from information theory and game theory to account for inter-features mutual information in determining the most correlated predictors with respect to false alarm by calculating Banzhaf power of each feature. The numerical results show that the proposed method can enhance classification accuracy and improve the area under the ROC (receiver operating characteristic) curve compared to other feature selection techniques, when integrated in classifiers such as Bayes-Net that consider inter-features dependencies.
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Affiliation(s)
- Fatemeh Afghah
- School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Abolfazl Razi
- School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Reza Soroushmehr
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hamid Ghanbari
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kayvan Najarian
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
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Implementation and Operational Analysis of an Interactive Intensive Care Unit within a Smart Health Context. SENSORS 2018; 18:s18020389. [PMID: 29382148 PMCID: PMC5854996 DOI: 10.3390/s18020389] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 01/23/2018] [Accepted: 01/26/2018] [Indexed: 11/17/2022]
Abstract
In the context of hospital management and operation, Intensive Care Units (ICU) are one of the most challenging in terms of time responsiveness and criticality, in which adequate resource management and signal processing play a key role in overall system performance. In this work, a context aware Intensive Care Unit is implemented and analyzed to provide scalable signal acquisition capabilities, as well as to provide tracking and access control. Wireless channel analysis is performed by means of hybrid optimized 3D Ray Launching deterministic simulation to assess potential interference impact as well as to provide required coverage/capacity thresholds for employed transceivers. Wireless system operation within the ICU scenario, considering conventional transceiver operation, is feasible in terms of quality of service for the complete scenario. Extensive measurements of overall interference levels have also been carried out, enabling subsequent adequate coverage/capacity estimations, for a set of Zigbee based nodes. Real system operation has been tested, with ad-hoc designed Zigbee wireless motes, employing lightweight communication protocols to minimize energy and bandwidth usage. An ICU information gathering application and software architecture for Visitor Access Control has been implemented, providing monitoring of the Boxes external doors and the identification of visitors via a RFID system. The results enable a solution to provide ICU access control and tracking capabilities previously not exploited, providing a step forward in the implementation of a Smart Health framework.
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Ruppel H, Funk M, Whittemore R. Measurement of Physiological Monitor Alarm Accuracy and Clinical Relevance in Intensive Care Units. Am J Crit Care 2018; 27:11-21. [PMID: 29292271 DOI: 10.4037/ajcc2018385] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
BACKGROUND Alarm fatigue threatens patient safety by delaying or reducing clinician response to alarms, which can lead to missed critical events. Interventions to reduce alarms without jeopardizing patient safety target either inaccurate or clinically irrelevant alarms, so assessment of alarm accuracy and clinical relevance may enhance the rigor of alarm intervention studies done in clinical units. OBJECTIVES To (1) examine approaches used to measure accuracy and/or clinical relevance of physiological monitor alarms in intensive care units and (2) compare the proportions of inaccurate and clinically irrelevant alarms. METHODS An integrative review was used to systematically search the literature and synthesize resulting articles. RESULTS Twelve studies explicitly measuring alarm accuracy and/or clinical relevance on a clinical unit were identified. In the most rigorous studies, alarms were annotated retrospectively by obtaining alarm data and parameter waveforms rather than being annotated in real time. More than half of arrhythmia alarms in recent studies were inaccurate. However, contextual data were needed to determine alarms' clinical relevance. Proportions of clinically irrelevant alarms were high, but definitions of clinically irrelevant alarms often included inaccurate alarms. CONCLUSIONS Future studies testing interventions on clinical units should include alarm accuracy and/or clinical relevance as outcome measures. Arrhythmia alarm accuracy should improve with advances in technology. Clinical interventions should focus on reducing clinically irrelevant alarms, with careful consideration of how clinical relevance is defined and measured.
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Affiliation(s)
- Halley Ruppel
- Halley Ruppel is a doctoral candidate, Marjorie Funk is the Helen Porter Jayne and Martha Prosser Jayne professor of nursing, and Robin Whittemore is a professor at Yale School of Nursing, West Haven, Connecticut
| | - Marjorie Funk
- Halley Ruppel is a doctoral candidate, Marjorie Funk is the Helen Porter Jayne and Martha Prosser Jayne professor of nursing, and Robin Whittemore is a professor at Yale School of Nursing, West Haven, Connecticut
| | - Robin Whittemore
- Halley Ruppel is a doctoral candidate, Marjorie Funk is the Helen Porter Jayne and Martha Prosser Jayne professor of nursing, and Robin Whittemore is a professor at Yale School of Nursing, West Haven, Connecticut
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Hoog Antink C, Leonhardt S, Walter M. A synthesizer framework for multimodal cardiorespiratory signals. Biomed Phys Eng Express 2017. [DOI: 10.1088/2057-1976/aa76ee] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Andreotti F, Graser F, Malberg H, Zaunseder S. Non-invasive Fetal ECG Signal Quality Assessment for Multichannel Heart Rate Estimation. IEEE Trans Biomed Eng 2017; 64:2793-2802. [PMID: 28362581 DOI: 10.1109/tbme.2017.2675543] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The noninvasive fetal ECG (NI-FECG) from abdominal recordings offers novel prospects for prenatal monitoring. However, NI-FECG signals are corrupted by various nonstationary noise sources, making the processing of abdominal recordings a challenging task. In this paper, we present an online approach that dynamically assess the quality of NI-FECG to improve fetal heart rate (FHR) estimation. METHODS Using a naive Bayes classifier, state-of-the-art and novel signal quality indices (SQIs), and an existing adaptive Kalman filter, FHR estimation was improved. For the purpose of training and validating the proposed methods, a large annotated private clinical dataset was used. RESULTS The suggested classification scheme demonstrated an accuracy of Krippendorff's alpha in determining the overall quality of NI-FECG signals. The proposed Kalman filter outperformed alternative methods for FHR estimation achieving accuracy. CONCLUSION The proposed algorithm was able to reliably reflect changes of signal quality and can be used in improving FHR estimation. SIGNIFICANCE NI-ECG signal quality estimation and multichannel information fusion are largely unexplored topics. Based on previous works, multichannel FHR estimation is a field that could strongly benefit from such methods. The developed SQI algorithms as well as resulting classifier were made available under a GNU GPL open-source license and contributed to the FECGSYN toolbox.
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Antink CH, Leonhardt S, Walter M. Reducing false alarms in the ICU by quantifying self-similarity of multimodal biosignals. Physiol Meas 2016; 37:1233-52. [PMID: 27454256 DOI: 10.1088/0967-3334/37/8/1233] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
False arrhythmia alarms pose a major threat to the quality of care in today's ICU. Thus, the PhysioNet/Computing in Cardiology Challenge 2015 aimed at reducing false alarms by exploiting multimodal cardiac signals recorded by a patient monitor. False alarms for asystole, extreme bradycardia, extreme tachycardia, ventricular flutter/fibrillation as well as ventricular tachycardia were to be reduced using two electrocardiogram channels, up to two cardiac signals of mechanical origin as well as a respiratory signal. In this paper, an approach combining multimodal rhythmicity estimation and machine learning is presented. Using standard short-time autocorrelation and robust beat-to-beat interval estimation, the signal's self-similarity is analyzed. In particular, beat intervals as well as quality measures are derived which are further quantified using basic mathematical operations (min, mean, max, etc). Moreover, methods from the realm of image processing, 2D Fourier transformation combined with principal component analysis, are employed for dimensionality reduction. Several machine learning approaches are evaluated including linear discriminant analysis and random forest. Using an alarm-independent reduction strategy, an overall false alarm reduction with a score of 65.52 in terms of the real-time scoring system of the challenge is achieved on a hidden dataset. Employing an alarm-specific strategy, an overall real-time score of 78.20 at a true positive rate of 95% and a true negative rate of 78% is achieved. While the results for some categories still need improvement, false alarms for extreme tachycardia are suppressed with 100% sensitivity and specificity.
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Affiliation(s)
- Christoph Hoog Antink
- Philips Chair for Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
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