Classification of methods to reduce clinical alarm signals for remote patient monitoring : a critical review
- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew , Shenhan, Mai , Buyya, Rajkumar , Islam, Sardar
- Date: 2023
- Type: Text , Book chapter
- Relation: Cloud Computing in Medical Imaging Chapter 10 p. 173-194
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Clinically prioritized data visualization in remote patient monitoring
- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew , Neupane, Arun
- Date: 2023
- Type: Text , Conference paper
- Relation: 19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023, Montreal, Canada, 21-23 June 2023, International Conference on Wireless and Mobile Computing, Networking and Communications Vol. 2023-June, p. 5-12
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- Description: Understanding and integrating physiological data collected from wearable sensors in remote patient monitoring (RPM) is challenging. Data streams may be interrupted due to the sensor's sensitivity, movement, and electromagnetic interference leading to inconsistent, missing, and inaccurate data. Existing approaches to summarize data flows into a single score such as the traditional Modified early warning score (MEWS) is limited. Data visualization approaches have the potential to address this challenge, but few studies have focused on visualization of RPM streams. The study presents a transformation of observed raw RPM physiological data into parameters identified as trust, frequency, slope, and trend. This facilitated visualization and enabled automated assessments of prioritized alerts. Experimental results have shown that the transformations led to the prioritization of clinically significant conditions, and improved visualization has the potential to better support clinical decisions compared with traditional MEWS. © 2023 IEEE.
Missing health data pattern matching technique for continuous remote patient monitoring
- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew
- Date: 2023
- Type: Text , Conference paper
- Relation: 20th International Conference on Smart Living and Public Health, ICOST 2023, Wonju, Korea, 7-8 July 2023, Digital Health Transformation, Smart Ageing, and Managing Disability, 20th International Conference, ICOST 2023, Wonju, South Korea, July 7–8, 2023, Proceedings Vol. 14237 LNCS, p. 130-143
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- Description: Remote patient monitoring (RPM) has been gaining popularity recently. However, health data acquisition is a significant challenge associated with patient monitoring. In continuous RPM, health data acquisition may miss health data during transmission. Missing data compromises the quality and reliability of patient risk assessment. Several studies suggested techniques for analyzing missing data; however, many are unsuitable for RPM. These techniques neglect the variability of missing data and provide biased results with imputation. Therefore, a holistic approach must consider the correlation and variability of the various vitals and avoid biased imputation. This paper proposes a coherent computation pattern-matching technique to identify and predict missing data patterns. The performance of the proposed approach is evaluated using data collected from a field trial. Results show that the technique can effectively identify and predict missing patterns. © 2023, The Author(s).
A secured real-time IoMT application for monitoring isolated COVID-19 patients using edge computing
- Authors: Balasubramanian, Venki , Sulthana, Rehena , Stranieri, Andrew , Manoharan, G. , Arora, Teena , Srinivasan, Ram , Mahalakshmi, K. , Menon, Varun
- Date: 2021
- Type: Text , Conference paper
- Relation: 20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021, Shenyang, China, 20-22 October 2021, Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021 p. 1227-1234
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- Description: Internet of Medical Things (IoMT) is an emerging technology whose capabilities to self-organize itself on-the-fly, to monitor the patient's vital health data without any manual entry and assist early human intervention gave birth to smart healthcare applications. The smart applications can be used to remotely monitor isolated patients during this COVID-19 pandemic. Remote patient monitoring provides an opportunity for COVID-19 patients to have vital signs and other indicators recorded regularly and inexpensively to provide rapid and early warning of conditions that require medical attention using secured edge and cloud computing. However, to gain the confidence of the users over these applications, the performance of healthcare applications should be evaluated in real-time. Our real-time implementation of IoMT based remote monitoring application using edge and cloud computing, along with empirical evaluation, show that COVID-19 patients can be monitored effectively not only with mobility but also helps the health care professionals to generate consolidated health data of the patient that can guide them to obtain medical attention. © 2021 IEEE.