IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit

Baldassano, Steven N.; Roberson, Shawniqua Williams; Balu, Ramani; Scheid, Brittany H; Bernabei, John M.; Pathmanathan, Jay; Oommen, Brian S.; Leri, Damien; Echauz, Javier; Gelfand, Michael; Bhalla, Paulomi; Hill, Chloé E.; Christini, Amanda; Wagenaar, Joost B.; Litt, Brian

doi:10.1109/jbhi.2020.2965858

Cited by 14 publications

(9 citation statements)

References 55 publications

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“…The RAMSES system represents a significant starting point for future work in data-driven ICU electroencephalogram analysis. We have previously reported an integrated data management and caretaker notification platform for multimodal ICU data (20). Our vision involves a unifying data platform that is capable of incorporating any number of analytic engines, harnessing the power of cloud computing, and providing real-time clinical updates.…”

Section: Discussionmentioning

confidence: 99%

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring

Bernabei

Owoputi

Small

et al. 2021

Critical Care Explorations

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BACKGROUND: Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning–based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review. METHODS: We developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning–based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients. RESULTS We achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning–assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods.

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Section: Discussionmentioning

confidence: 99%

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring

Bernabei

Owoputi

Small

et al. 2021

Critical Care Explorations

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“…Second, continuous, and automated analysis using machine learning could increase detection of abnormal brain states which cannot be identified clinically (e.g., burst suppression, nonconvulsive seizures). Third, generation of automated alerts will prompt clinicians to evaluate patients at the bedside when abnormality is detected [39,40] (Fig. 1).…”

Section: Automated Tracking Of Sedation Delirium and Sleep With Elect...mentioning

confidence: 99%

Monitoring of sedation in mechanically ventilated patients using remote technology

Hanidziar

Westover

2022

Current Opinion in Critical Care

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Purpose of reviewTwo years of coronavirus disease 2019 (COVID-19) pandemic highlighted that excessive sedation in the ICU leading to coma and other adverse outcomes remains pervasive. There is a need to improve monitoring and management of sedation in mechanically ventilated patients. Remote technologies that are based on automated analysis of electroencephalogram (EEG) could enhance standard care and alert clinicians real-time when severe EEG suppression or other abnormal brain states are detected.Recent findingsHigh rates of drug-induced coma as well as delirium were found in several large cohorts of mechanically ventilated patients with COVID-19 pneumonia. In patients with acute respiratory distress syndrome, high doses of sedatives comparable to general anesthesia have been commonly administered without defined EEG endpoints. Continuous limited-channel EEG can reveal pathologic brain states such as burst suppression, that cannot be diagnosed by neurological examination alone. Recent studies documented that machine learning-based analysis of continuous EEG signal is feasible and that this approach can identify burst suppression as well as delirium with high specificity.SummaryPreventing oversedation in the ICU remains a challenge. Continuous monitoring of EEG activity, automated EEG analysis, and generation of alerts to clinicians may reduce drug-induced coma and potentially improve patient outcomes.

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“…That non-invasive sensor-based works can do Person Localization, Patient Identification, Patient Pose Classification, and Context Detection, Motion Analysis, and Mobility Classification. IRIS [47], an AI model for continuous monitoring and caretaker in the ICU was proposed. That model simultaneously monitors many activities in ICU including ECG electrode, intracranial pressure, etc.…”

Section: Review Of Some Recent State-of-the-artsmentioning

confidence: 99%

“…Recent progress in robotics technology is very fast. Many intelligent robots are applying as pilot trials, some are already using in real cases including the medical sector [47]. Many studies and experiments are already undergoing that how robots could assist medical practice including robotic surgery [50].…”

Section: Patient Care With Ai Assistantmentioning

confidence: 99%

Insights of Artificial Intelligence to Stop Spread of COVID-19

Sufian

Jat

Banerjee

2020

Studies in Big Data

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, a pandemic that has pushed down human civilization in a severe threat. As viruses of COVID-19 like diseases are transferable from human to human, so it becomes very challenging to stop spreading these pandemics. These challenges are not only limited to the treatment of infected patients but also maintaining systematic social distancing to stop spreading the disease. However, maintaining social distancing is not entirely possible everywhere, like in hospitals, emergency sectors, etc. Some critical issues such as: carefully handling the Intensive Care Unite (ICU), patient care, hygienic practice, and systematic social distancing have become very necessary to slow down the spread of the new virus as appropriate vaccines or drugs are not yet available. In this time of crisis, Artificial Intelligence (AI) could assists in many more ways in addition to assisting diagnosis, drug or vaccine discovery. Therefore, this AI, especially algorithms of machine learning, deep learning, and computer vision along with edge computing and IoT technologies could be smart solutions for such challenges. This chapter brings such solutions through some insights of AI to assist to stop these COVID-19 like pandemics.

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IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit

Cited by 14 publications

References 55 publications

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring

Monitoring of sedation in mechanically ventilated patients using remote technology

Insights of Artificial Intelligence to Stop Spread of COVID-19

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