Abstract 335: Predicting Pulse Status From the ECG Signal Without Interrupting CPR

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Heemun Kwok ◽  
Shiv Bhandari ◽  
Jennifer E Blackwood ◽  
Jason Coult ◽  
Peter Kudenchuk ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Real Time ◽  
Cardiac Rhythm ◽  
Logistic Model ◽  
Direct Care ◽  
Study Cohort ◽  
Palpable Pulse ◽  
Validation Set ◽  
The Individual ◽  
Pulse Check

Objective: Currently, cardiac arrest resuscitation requires interruptions in CPR every 2 minutes to assess cardiac rhythm and pulse. A method which analyzed the ECG during CPR to predict whether or not an organized rhythm generated a pulse could help to direct care and limit CPR interruptions. We evaluated a real-time method to predict pulse status from organized rhythm ECG segments with and without CPR. Methods: The study cohort received attempted resuscitation by a metropolitan EMS system following out-of-hospital ventricular fibrillation arrest. Two-minute rhythm/pulse checks on the continuous defibrillator recordings were annotated for CPR, rhythm, and pulse status using the ECG, impedance, and accelerometer signals, the audio recording, and EMS record. Pulse was defined by the presence of a palpable pulse by EMS. Paired ECG segments with and without CPR were extracted at each rhythm/pulse check. Using organized rhythm segments from one-third of cases for training, we developed four ECG features using wavelet analysis (median power values in three frequency bands and QRS rate) and a logistic model to predict pulse status. Predictive performances of each ECG feature and the logistic model were measured by AUC in the remaining validation cases with and without CPR. Results: There were 238 cases and 911 paired segments with a median of 3 (IQR 2,5) paired segments per case. Among 319 organized rhythm segments in the validation set, AUC for pulse prediction ranged from 0.67 to 0.79 for the individual ECG features (Figure). The logistic model was more predictive than any individual feature (AUC 0.84, 95% CI 0.80-0.89, p < 0.05 for each comparison). The model predicted pulse similarly regardless of CPR activity (p = 0.2). Conclusion: ECG features extracted by wavelet analysis were predictive of pulse status among organized rhythm segments with and without ongoing CPR. Further study is required to understand how pulse prediction could guide rescuer actions in real-time.

A Back Allocation Methodology to Estimate the Real-Time Flow and Assist Production Monitoring

2021 ◽  
Author(s):  
Gabriela Chaves ◽  
Danielle Monteiro ◽  
Virgilio José Martins Ferreira
Keyword(s):  
Real Time ◽  
Input Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Time Flow ◽  
Well Model ◽  
Operational Information ◽  
The Individual

Abstract Commingle production nodes are standard practice in the industry to combine multiple segments into one. This practice is adopted at the subsurface or surface to reduce costs, elements (e.g. pipes), and space. However, it leads to one problem: determine the rates of the single elements. This problem is recurrently solved in the platform scenario using the back allocation approach, where the total platform flowrate is used to obtain the individual wells’ flowrates. The wells’ flowrates are crucial to monitor, manage and make operational decisions in order to optimize field production. This work combined outflow (well and flowline) simulation, reservoir inflow, algorithms, and an optimization problem to calculate the wells’ flowrates and give a status about the current well state. Wells stated as unsuited indicates either the input data, the well model, or the well is behaving not as expected. The well status is valuable operational information that can be interpreted, for instance, to indicate the need for a new well testing, or as reliability rate for simulations run. The well flowrates are calculated considering three scenarios the probable, minimum and maximum. Real-time data is used as input data and production well test is used to tune and update well model and parameters routinely. The methodology was applied using a representative offshore oil field with 14 producing wells for two-years production time. The back allocation methodology showed robustness in all cases, labeling the wells properly, calculating the flowrates, and honoring the platform flowrate.

Prepare: P owe r -Awar e A p proximate Re a l-time Task Scheduling for Ene r gy-Adaptiv e QoS Maximization

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Shounak Chakraborty ◽  
Sangeet Saha ◽  
Magnus Själander ◽  
Klaus Mcdonald-Maier
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Processing Speed ◽  
Empirical Evaluation ◽  
Adaptive Mechanism ◽  
Approximate Computing ◽  
Thermal Safety ◽  
Time Task ◽  
Individual Task ◽  
The Individual

Achieving high result-accuracy in approximate computing (AC) based real-time applications without violating power constraints of the underlying hardware is a challenging problem. Execution of such AC real-time tasks can be divided into the execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve accuracy of the initially obtained result within the given time-limit. However, enhancing result-accuracy at the cost of increased execution length might lead to deadline violations with higher energy usage. We propose Prepare , a novel hybrid offline-online approximate real-time task-scheduling approach, that first schedules AC-based tasks and determines operational processing speeds for each individual task constrained by system-wide power limit, deadline, and task-dependency. At runtime, by employing fine-grained DVFS, the energy-adaptive processing speed governing mechanism of Prepare reduces processing speed during each last level cache miss induced stall and scales up the processing speed once the stall finishes to a higher value than the predetermined one. To ensure on-chip thermal safety, this higher processing speed is maintained only for a short time-span after each stall, however, this reduces execution times of the individual task and generates slacks. Prepare exploits the slacks either to enhance result-accuracy of the tasks, or to improve thermal and energy efficiency of the underlying hardware, or both. With a 70 - 80% workload, Prepare offers 75% result-accuracy with its constrained scheduling, which is enhanced by 5.3% for our benchmark based evaluation of the online energy-adaptive mechanism on a 4-core based homogeneous chip multi-processor, while meeting the deadline constraint. Overall, while maintaining runtime thermal safety, Prepare reduces peak temperature by up to 8.6 °C for our baseline system. Our empirical evaluation shows that constrained scheduling of Prepare outperforms a state-of-the-art scheduling policy, whereas our runtime energy-adaptive mechanism surpasses two current DVFS based thermal management techniques.

scholarly journals A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea

2020 ◽  
Author(s):  
Ben J. Brintz ◽  
Benjamin Haaland ◽  
Joel Howard ◽  
Dennis L. Chao ◽  
Joshua L. Proctor ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Predictive Performance ◽  
Data Availability ◽  
Clinical Prediction ◽  
Multiple Sources ◽  
Statistical Framework ◽  
Multiple Data ◽  
Post Test ◽  
The Individual

AbstractTraditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where “pre-test” epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.

scholarly journals Composite and Wearable Sensor Kit for Location Aware Healthcare Monitoring and Real-Time Trauma Scoring for Survival Prediction.

2018 ◽  
Author(s):  
Amit Walinjkar
Keyword(s):  
Critical Care ◽  
Real Time ◽  
Injury Severity ◽  
Vital Signs ◽  
Physiological Parameters ◽  
Care Team ◽  
Medical Emergency ◽  
Traumatic Experience ◽  
The Individual ◽  
Critical Care Team

With the availability of wearable health monitoring sensor modules like 3-Lead Electrocardiogram (ECG), Pulse Oximeter (SpO2), Galvanic Skin Response (GSR), Hall effect sensor (for measuring Respiratory Rate), Blood Pressure and Temperature measuring and sensing elements, it has now become possible to device a composite health status monitoring kit that can measure vital signs and other physiological parameters pertaining to human health in real time. Traditionally, the physiological parameters along with vital signs related examination was possible only in a hospitalized or ambulatory environment, however due to advances in sensing and embedded system technology and miniaturization of data acquisition and processing elements health monitoring has become possible even when individuals remain engaged in their day to day activities at the convenience of space and location. The patients or individuals subject to monitoring may suffer from a traumatic experience due to their medical condition and may need emergent incidence response and the critical care team may have to prepare for the treatment only after the patient arrives, which often is too late, as in case of cardiac arrests or severe injuries. The research focused on real-time health status monitoring and trauma scoring using standard physiological parameters along with standard telemetry protocols to make the critical care team aware of an emergent situation and prepare for a medical emergency. Vital signs and physiological parameters (heart rate, temperature, respiratory rate, and blood pressure, SpO2) were measured in real time from human subjects non-invasively. In order to enable monitoring of the patients engaged in day to day activities, errors due to the motion were removed using stationary wavelet transform correction (correlation coefficient of 0.9 after correction) and signals from various sensors were denoised, filtered and were encoded in a format suitable for further data analysis. A composite sensor kit capable of monitoring vital signs and physiological parameters can be very useful in incident response when an individual undergoes a traumatic experience related to stroke, cardiac arrest, fits or even injury, as along with monitoring information the kit can calculate scores related to trauma like the Injury Severity Score (ISS), National Early Warning Signs (NEWS), Revised Trauma Score (RTS). Trauma Injury Severity Score (TRISS), Probability of Survival (Ps) score. An open access database of vital signs and physiological parameters from Physionet, MIMIC 2 Numerics (mimicdb/numerics) database was used to calculate NEWS and RTS and to generate correlation and regression models using the vital signs/physiological parameters for a clinical class of patients with respiratory failure and admitted to Intensive Care Unit (ICU). NEWS and RTS scores showed no significant correlation (r = 0.25, p&lt;0.001) amongst themselves, however together NEWS and RTS showed significant correlation with Ps (blunt) (r = 0.70, p&lt;0.001). RTS and Ps (blunt) scores showed some correlation (r = 0.63, p&lt;0.001) and NEWS score showed significant correlation (r = 0.79, p&lt;0.001) with Ps (blunt) scores. Furthermore, since individuals have to be monitored regardless of location, these kits have to have a built-in capability to locate the individual so that the incident response team can locate the individual based on Global Positioning System coordinates (GPS). A Quantum GIS (Geographical Information System) application using real-time GPS coordinates (OpenStreetMap coordinates) was used to calculate the shortest path using QGIS Network Analysis tool to demonstrate the calculation of shortest path and direction to locate the nearest service provider in shortest time. Along with locating the nearest healthcare service provider, it would help if the critical care team could be made aware of the physiological parameters and trauma scores using standard protocols accepted across the globe. The physiological parameters from the sensors along with the calculated trauma scores were encoded according to a standard Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) coding system and International Code of Diseases (ICD) codes and the trauma information was logged to Electronic Health Records (EHR) using Fast Health Interoperability Resources (FHIR) servers. FHIR servers provided interoperable web services to log the event information in real time. It could be concluded that analytical models trained on existing datasets can help in analyzing a traumatic experience or an injury and the information can be logged using a standard telemetry protocol as a telemedicine initiative. These scores enable the healthcare service providers to estimate the extent of trauma and prepare for medical emergency procedures and find applications in general and military healthcare.

scholarly journals Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

2021 ◽  
Vol 11 (11) ◽  
pp. 68-75
Author(s):  
Enrique Lee Huamaní ◽  
◽  
Lilian Ocares Cunyarachi
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Face Detection ◽  
Face Mask ◽  
Economic Activities ◽  
Facial Mask ◽  
The Face ◽  
The Individual ◽  
A Company ◽  
Selection Of

Due to the pandemic caused by Covid-19, daily life has changed significantly. For this reason, biosecurity measures have been implemented to prevent the spread of the virus as an effective way to reactivate economic activities. In this sense, the present paper focuses on real-time face detection as a measure of control at the entrance to an entity, thus avoiding the spread of the virus while recognizing the identity of workers despite the use of masks and thus reducing the risk of entry of individuals outside the organization. Therefore, the objective is to contribute to the security of a company through the application of machine learning methodology. The selection of methodology is justified due to the adaptation of the same according to the interests of this project. Consequently, algorithms were used in a progressive manner, obtaining as a result the control system that was intended, since each particularity of the face of the individual was recognized in relation to its corresponding identification. Finally, the results of this article benefit the security of organizations regardless of their field or sector. Keywords— Control, Detection, Facial Recognition, Facial Mask, Face recognition, Machine learning.

scholarly journals Scheduling Schemes in Dynamic Multilevel Packet Scheduling in WSN

2019 ◽  
Vol 8 (2) ◽  
pp. 3800-3804
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Packet Scheduling ◽  
Turnaround Time ◽  
Priority Level ◽  
Average Waiting Time ◽  
Level 3 ◽  
Overall Performance ◽  
The Individual ◽  
Priority Levels

As focusing on the scheduling schemes, there are many scheduling schemes for multilevel. So the paper is concentrating to compare the scheduling schemes and producing the average waiting time and turnaround time. If it is minimized then the overall performance may shoot up. In this paper comparison is done between three scheduling schemes Enhanced Dynamic Multilevel Packet scheduling (EDMP), Circular Wait Dynamic Multilevel Packet scheduling (CW-DMP) and Starvation-Free Dynamic Multilevel Packet scheduling (SF-DMP). In all the above schemes there are three priority levels say priority level 1(Pr1), priority level 2(Pr2) and priority level 3(Pr3). Pr1 will comprise the real time tasks, Pr2 containing the non real time remote tasks and non real time local tasks are there in Pr3. In each and every scheme, each and every priority level will be using the individual scheduling technique to schedule the tasks. Also the comparison is done based on waiting time and the turnaround time of the task thereby the average waiting time and the average turnaround time are calculated.

scholarly journals The place of artificial intelligence in the risk management process

2021 ◽  
Vol 120 ◽  
pp. 02013
Author(s):  
Petya Biolcheva
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Risk Management ◽  
Real Time ◽  
Risk Identification ◽  
Management Process ◽  
Whole Process ◽  
Risk Management Process ◽  
Problematic Issue ◽  
The Individual

In recent years, there has been increasing talk of the rapid entry of artificial intelligence into risk management. All the benefits it would bring over the whole process are often commented on: real-time results, processing large amounts of data, more complete risk identification, more accurate risk assessment, etc. There are also negative moods that make various experts feel threatened by their need to be replaced by artificial intelligence. Another problematic issue that arises is related to the transparency of algorithms and the increase in cyber risks [6]. This material aims to identify the individual elements at the stages of risk management in which artificial intelligence (AI) can and should be applied alone, in combination with expert opinion or not. Here it is shown that because of the use of AI the efficiency of the whole process is significantly increased, first of all by conducting in-depth analyses, and the decisions are made by the risk management experts. This proves its usefulness and increases the confidence of experts in it.

scholarly journals Restoration of elective spine surgery during the first wave of COVID-19

2021 ◽  
Vol 2 (12) ◽  
pp. 1096-1101
Author(s):  
Riaz Mohammed ◽  
Pranav Shah ◽  
Alexander Durst ◽  
Naveen J. Mathai ◽  
Alexandru Budu ◽  
...  
Keyword(s):  
Spine Surgery ◽  
Collaborative Study ◽  
Length Of Hospital Stay ◽  
Recovery Phase ◽  
Primary Outcome Measure ◽  
Study Cohort ◽  
National Guidelines ◽  
The Mean ◽  
The Individual ◽  
The Uk

Aims With resumption of elective spine surgery services in the UK following the first wave of the COVID-19 pandemic, we conducted a multicentre British Association of Spine Surgeons (BASS) collaborative study to examine the complications and deaths due to COVID-19 at the recovery phase of the pandemic. The aim was to analyze the safety of elective spinal surgery during the pandemic. Methods A prospective observational study was conducted from eight spinal centres for the first month of operating following restoration of elective spine surgery in each individual unit. Primary outcome measure was the 30-day postoperative COVID-19 infection rate. Secondary outcomes analyzed were the 30-day mortality rate, surgical adverse events, medical complications, and length of inpatient stay. Results In all, 257 patients (128 males) with a median age of 54 years (2 to 88) formed the study cohort. The mean number of procedures performed from each unit was 32 (16 to 101), with 118 procedures (46%) done as category three prioritization level. The majority of patients (87%) were low-medium “risk stratification” category and the mean length of hospital stay was 5.2 days. None of the patients were diagnosed with COVID-19 infection, nor was there any mortality related to COVID-19 during the 30-day follow-up period, with 25 patients (10%) having been tested for symptoms. Overall, 32 patients (12%) developed a total of 34 complications, with the majority (19/34) being grade 1 to 2 Clavien-Dindo classification of surgical complications. No patient required postoperative care in an intensive care setting for any unexpected complication. Conclusion This study shows that safe and effective planned spinal surgical services can be restored avoiding viral transmission, with diligent adherence to national guidelines and COVID-19-secure pathways tailored according to the resources of the individual spinal units. Cite this article: Bone Jt Open 2021;2(12):1096–1101.

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