Abstract 185: Optimization of Public Access Defibrillators Compared to Actual Deployment: An In Silico Trial

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Christopher Sun ◽  
Lena Karlsson ◽  
Christian Thorp-Pedersen ◽  
Fredrik Folke ◽  
Timothy C Chan
Keyword(s):  
Real Time ◽  
Confidence Intervals ◽  
In Silico ◽  
Time Horizon ◽  
Mathematical Optimization ◽  
Public Access ◽  
Optimization Models ◽  
Time Interval ◽  
Automated External Defibrillators ◽  
Risk Areas

Introduction: Unguided placement of automated external defibrillators (AEDs) often leads to placements in low risk areas and locations with limited temporal availability. Mathematical optimization may improve AED placements and increase AED use in out-of-hospital cardiac arrests (OHCAs). Aim: To conduct the first in silico public AED location trial to determine whether optimization models (interventions) trained on historical OHCA data will recommend AED locations that significantly improve OHCA coverage on prospective OHCAs, compared to locations of actually deployed AEDs (control). Methods: We identified all public OHCAs of presumed cardiac cause (1994-2016) and already deployed AEDs (2007-2016) in Copenhagen, Denmark. We computed the number of OHCAs that occurred within 100m of a temporally available AED after it was deployed (“OHCA coverage”). We then divided 2007-2016 into 30-day intervals and determined the number of AEDs deployed in each interval. Using previously validated optimization models, we determined an equal number of optimal AED locations in each time interval, either indoor locations with actual availability (intervention #1) or outdoor locations with 24/7 availability (intervention #2). OHCA coverage was calculated for the interventions similarly to the already deployed AEDs. Finally, we repeated the analysis 25 times to evaluate sensitivity and generate confidence intervals, by randomizing the location and time of the OHCAs. Results: A total of 2,149 public OHCAs (744 between 2007-2016) and 1,573 registered AEDs were identified. OHCA coverage of actually deployed AEDs was 22.3% (166 of 744 OHCAs). For optimally located indoor AEDs, mean OHCA coverage was 32.6% (mean: 242.5 OHCAs; 95% CI: 239.7 - 245.3). For optimally located outdoor AEDs, mean OHCA coverage was 43.9% (mean: 326.6 OHCAs; 95% CI: 324.0 - 329.2). Conclusions: Optimizing AED locations in a real-time deployment approach mimicking the time horizon of actual AED deployment in Copenhagen, Denmark results in significantly higher OHCA coverage compared to the actual AEDs deployed. Between the two interventions, optimal locations that are 24/7 available significantly outperform optimal indoor locations with more limited temporal availability.

scholarly journals The Role of Automated External Defibrillators in Athletics

2009 ◽  
Vol 1 (1) ◽  
pp. 16-20 ◽  
Author(s):  
Justin D. Rothmier ◽  
Jonathan A. Drezner
Keyword(s):  
Cardiac Arrest ◽  
Action Plan ◽  
Sudden Cardiac Arrest ◽  
Public Access ◽  
Time Interval ◽  
Athletic Programs ◽  
Early Defibrillation ◽  
Automated External Defibrillators ◽  
External Defibrillator

Context: Sudden cardiac arrest is the leading cause of death in young athletes. The purpose of this review is to summarize the role of automated external defibrillators and emergency planning for sudden cardiac arrest in the athletic setting. Evidence Acquisition: Relevant studies on automated external defibrillators, early defibrillation, and public-access defibrillation programs were reviewed. Recommendations from consensus guidelines and position statements applicable to automated external defibrillators in athletics were also considered. Results: Early defibrillation programs involving access to automated external defibrillators by targeted local responders have demonstrated a survival benefit for sudden cardiac arrest in many public and athletic settings. Conclusion: Schools and organizations sponsoring athletic programs should implement automated external defibrillators as part of a comprehensive emergency action plan for sudden cardiac arrest. In a collapsed and unresponsive athlete, sudden cardiac arrest should be suspected and an automated external defibrillator applied as soon as possible, as decreasing the time interval to defibrillation is the most important priority to improve survival in sudden cardiac arrest.

Abstract 9876: Optimizing Public Naloxone Kit Locations Through Mathematical Modeling

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
K.H. Benjamin Leung ◽  
Brian Grunau ◽  
May K Lee ◽  
Jane Buxton ◽  
Jennie Helmer ◽  
...  
Keyword(s):  
British Columbia ◽  
Public Transit ◽  
Mathematical Optimization ◽  
Public Access ◽  
Walking Distance ◽  
Opioid Overdose ◽  
Automated External Defibrillators ◽  
Metro Vancouver ◽  
Out Of Sample ◽  
Bus Stops

Introduction: Use of bystander-administered naloxone may lead to improved likelihood of recovery from opioid overdose. We sought to determine the accessibility of public access naloxone kits on nearby opioid overdose incidents if placed at public transit stops, compared to placing kits outside pharmacies or with existing public access automated external defibrillators (PADs). Methods: We included all incidents in Metro Vancouver, British Columbia responded to by British Columbia Emergency Health Services coded as a drug overdose with naloxone administered on-scene (Dec. 2014 to Aug. 2020). We geo-coded all public transit bus stops and used a mathematical optimization model to select bus stops where publicly accessible naloxone kits could be placed to maximize accessibility (defined as ≤100 m walking distance) to opioid overdoses. We evaluated accessibility on out-of-sample OHCAs using five-fold cross validation and compared against two baseline policies: placing publicly accessible naloxone kits at all pharmacies identified by the College of Pharmacists of British Columbia, and placing kits at all PADs identified by the British Columbia AED Registry. Statistical analysis was conducted using McNemar’s test. Results: We identified 14,318 opioid overdoses, 8,972 bus stops, 736 pharmacies, and 425 PADs. Accessibility of public naloxone kits for opioid overdose locations was 5.1% when placed at all pharmacies and 3.5% when placed with all existing PADs. Optimized naloxone kit placement using bus stops as candidate locations resulted in significantly higher accessibility than both pharmacy and PAD-based placement at 14.8% with 10 optimized locations (P<0.001), increasing to 36.7% with 500 locations (P<0.001). Conclusion: Optimizing placement of public access naloxone kits at select public transit locations can provide significantly higher accessibility to opioid overdose locations compared to placement at pharmacies or at existing PAD locations.

scholarly journals Improving the Stochastic Model of Ionospheric Delays for BDS Long-Range Real-Time Kinematic Positioning

2021 ◽  
Vol 13 (14) ◽  
pp. 2739
Author(s):  
Huizhong Zhu ◽  
Jun Li ◽  
Longjiang Tang ◽  
Maorong Ge ◽  
Aigong Xu
Keyword(s):  
Real Time ◽  
Long Range ◽  
Ionospheric Delay ◽  
Time Interval ◽  
Short Time Interval ◽  
Dual Frequency ◽  
Triple Frequency ◽  
Ambiguity Fixing ◽  
Power Spectral ◽  
Observation Noise

Although ionosphere-free (IF) combination is usually employed in long-range precise positioning, in order to employ the knowledge of the spatiotemporal ionospheric delays variations and avoid the difficulty in choosing the IF combinations in case of triple-frequency data processing, using uncombined observations with proper ionospheric constraints is more beneficial. Yet, determining the appropriate power spectral density (PSD) of ionospheric delays is one of the most important issues in the uncombined processing, as the empirical methods cannot consider the actual ionosphere activities. The ionospheric delays derived from actual dual-frequency phase observations contain not only the real-time ionospheric delays variations, but also the observation noise which could be much larger than ionospheric delays changes over a very short time interval, so that the statistics of the ionospheric delays cannot be retrieved properly. Fortunately, the ionospheric delays variations and the observation noise behave in different ways, i.e., can be represented by random-walk and white noise process, respectively, so that they can be separated statistically. In this paper, we proposed an approach to determine the PSD of ionospheric delays for each satellite in real-time by denoising the ionospheric delay observations. Based on the relationship between the PSD, observation noise and the ionospheric observations, several aspects impacting the PSD calculation are investigated numerically and the optimal values are suggested. The proposed approach with the suggested optimal parameters is applied to the processing of three long-range baselines of 103 km, 175 km and 200 km with triple-frequency BDS data in both static and kinematic mode. The improvement in the first ambiguity fixing time (FAFT), the positioning accuracy and the estimated ionospheric delays are analysed and compared with that using empirical PSD. The results show that the FAFT can be shortened by at least 8% compared with using a unique empirical PSD for all satellites although it is even fine-tuned according to the actual observations and improved by 34% compared with that using PSD derived from ionospheric delay observations without denoising. Finally, the positioning performance of BDS three-frequency observations shows that the averaged FAFT is 226 s and 270 s, and the positioning accuracies after ambiguity fixing are 1 cm, 1 cm and 3 cm in the East, North and Up directions for static and 3 cm, 3 cm and 6 cm for kinematic mode, respectively.

scholarly journals An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction

2020 ◽  
Vol 10 (11) ◽  
pp. 3788 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Short Term Memory ◽  
Historical Data ◽  
Time Interval ◽  
Information Coding ◽  
Time Data ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Real Time Data

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

2013 ◽  
Vol 333-335 ◽  
pp. 650-655
Author(s):  
Peng Hui Niu ◽  
Yin Lei Qin ◽  
Shun Ping Qu ◽  
Yang Lou
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Phase Difference ◽  
Time Signal ◽  
Time Interval ◽  
Time Varying ◽  
Signal Model ◽  
Time Frequency ◽  
Mass Flowmeter ◽  
Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

scholarly journals Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yaghoub Dabiri ◽  
Alex Van der Velden ◽  
Kevin L. Sack ◽  
Jenny S. Choy ◽  
Julius M. Guccione ◽  
...  
Keyword(s):  
Real Time ◽  
Material Properties ◽  
In Silico ◽  
Prediction Models ◽  
Heart Function ◽  
Active Material ◽  
Experimental Studies ◽  
Left Ventricular ◽  
Feed Forward ◽  
Ventricular Mechanics

AbstractAn understanding of left ventricle (LV) mechanics is fundamental for designing better preventive, diagnostic, and treatment strategies for improved heart function. Because of the costs of clinical and experimental studies to treat and understand heart function, respectively, in-silico models play an important role. Finite element (FE) models, which have been used to create in-silico LV models for different cardiac health and disease conditions, as well as cardiac device design, are time-consuming and require powerful computational resources, which limits their use when real-time results are needed. As an alternative, we sought to use deep learning (DL) for LV in-silico modeling. We used 80 four-chamber heart FE models for feed forward, as well as recurrent neural network (RNN) with long short-term memory (LSTM) models for LV pressure and volume. We used 120 LV-only FE models for training LV stress predictions. The active material properties of the myocardium and time were features for the LV pressure and volume training, and passive material properties and element centroid coordinates were features of the LV stress prediction models. For six test FE models, the DL error for LV volume was 1.599 ± 1.227 ml, and the error for pressure was 1.257 ± 0.488 mmHg; for 20 LV FE test examples, the mean absolute errors were, respectively, 0.179 ± 0.050 for myofiber, 0.049 ± 0.017 for cross-fiber, and 0.039 ± 0.011 kPa for shear stress. After training, the DL runtime was in the order of seconds whereas equivalent FE runtime was in the order of several hours (pressure and volume) or 20 min (stress). We conclude that using DL, LV in-silico simulations can be provided for applications requiring real-time results.

scholarly journals Experimental assessment of harmonic contributions using a ternary pulse sequence

2021 ◽  
Vol 18 (3) ◽  
pp. 271-289
Author(s):  
Evgeniia Bulycheva ◽  
Sergey Yanchenko
Keyword(s):  
Real Time ◽  
Pulse Sequence ◽  
Wide Band ◽  
Voltage Source ◽  
Time Interval ◽  
Time Frequency ◽  
Grid Impedance ◽  
Set Up ◽  
Grid Voltage Distortion ◽  
Operational Modes

Harmonic contributions of utility and customer may feature significant variations due to network switchings and changing operational modes. In order to correctly define the impacts on the grid voltage distortion the frequency dependent impedance characteristic of the studied network should be accurately measured in the real-time mode. This condition can be fulfilled by designing a stimuli generator measuring the grid impedance as a response to injected interference and producing time-frequency plots of harmonic contributions during considered time interval. In this paper a prototype of a stimuli generator based on programmable voltage source inverter is developed and tested. The use of ternary pulse sequence allows fast wide-band impedance measurements that meet the requirements of real-time assessment of harmonic contributions. The accuracy of respective analysis involving impedance determination and calculation of harmonic contributions is validated experimentally using reference characteristics of laboratory test set-up with varying grid impedance.

scholarly journals Internet of medical things – integrated, ultrasound-based respiration monitoring system for incubators

2020 ◽  
Author(s):  
◽  
Tareq Abdulqader
Keyword(s):  
Real Time ◽  
Respiration Rate ◽  
Surface Measurement ◽  
Time Interval ◽  
Portable Devices ◽  
Important Indicator ◽  
The Us ◽  
Respiratory Signal ◽  
Central Apnoea ◽  
Respiration Signal

The study's aim was to develop a non-contact, ultrasound (US) based respiration rate and respiratory signal monitor suitable for babies in incubators. Respiration rate indicates average number of breaths per minute and is higher in young children than adults. It is an important indicator of health deterioration in critically ill patients. The current incubators do not have an integrated respiration monitor due to complexities in its adaptation. Monitoring respiratory signal assists in diagnosing respiration rated problems such as central Apnoea that can affect infants. US sensors are suitable for integration into incubators as US is a harmless and cost-effective technology. US beam is focused on the chest or abdomen. Chest or abdomen movements, caused by respiration process, result in variations in their distance to the US transceiver located at a distance of about 0.5 m. These variations are recorded by measuring the time of flight from transmitting the signal and its reflection from the monitored surface. Measurement of this delay over a time interval enables a respiration signal to be produced from which respiration rate and pauses in breathing are determined. To assess the accuracy of the developed device, a platform with a moving surface was devised. The magnitude and frequency of its surface movement were accurately controlled by its signal generator. The US sensor was mounted above this surface at a distance of 0.5 m. This US signal was wirelessly transmitted to a microprocessor board to digitise. The recorded signal that simulated a respiratory signal was subsequently stored and displayed on a computer or an LCD screen. The results showed that US could be used to measure respiration rate accurately. To cater for possible movement of the infant in the incubator, four US sensors were adapted. These monitored the movements from different angles. An algorithm to interpret the output from the four US sensors was devised and evaluated. The algorithm interpreted which US sensor best detected the chest movements. An IoMT system was devised that incorporated NodeMcu to capture signals from the US sensor. The detected data were transmitted to the ThingSpeak channel and processed in real-time by ThingSpeak’s add-on Matlab© feature. The data were processed on the cloud and then the results were displayed in real-time on a computer screen. The respiration rate and respiration signal could be observed remotely on portable devices e.g. mobile phones and tablets. These features allow caretakers to have access to the data at any time and be alerted to respiratory complications. A method to interpret the recorded US signals to determine respiration patterns, e.g. intermittent pauses, were implemented by utilising Matlab© and ThingSpeak Server. The method successfully detected respiratory pauses by identifying lack of chest movements. The approach can be useful in diagnosing central apnoea. In central apnoea, respiratory pauses are accompanied by cessation of chest or abdominal movements. The devised system will require clinical trials and integration into an incubator by conforming to the medical devices directives. The study demonstrated the integration of IoMT-US for measuring respiration rate and respiratory signal. The US produced respiration rate readings compared well with the actual signal generator's settings of the platform that simulated chest movements.

scholarly journals Geographic Information and Geo-visualisation in support of Disaster Resilience

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Edward Kurwakumire ◽  
Paul Muchechetere ◽  
Shelter Kuzhazha ◽  
Guy Blachard Ikokou
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Disaster Preparedness ◽  
Geographic Information ◽  
Public Access ◽  
Data Sets ◽  
Disaster Resilience ◽  
Data Systems ◽  
Information Infrastructures ◽  
Timely Access

<p><strong>Abstract.</strong> Society continues to become more spatially enabled as spatial data becomes increasingly available and accessible. This is partly due to democratisation of data achieved through open access of framework data sets. On the other hand, mobile devices such as smartphones have become more accessible, giving the public access to applications that use spatial data. This has tremendously increased the consumption of spatial data at the level of the general public. Spatial data has a history in planning and decision making as detailed in literature on promises and benefits of geographic information. We extend these promises to sustainability and disaster resilience. It is our belief that geographic information (GI) and geographic information infrastructures (GIIs) contribute positively towards the achievement of sustainability in cities and nations and in disaster resilience. This study carries out a review of geo-visualisation and GI applications in order to determine their suitability and impact in disaster resilience. Real-time GI are significant for cities to ensure sustainability and to increase disaster preparedness. Geographic information infrastructures need to be integrated with BIG data systems to ensure that local government agencies have timely access to real time geographic information so that decisions on sustainability and disaster resilience can be effectively done.</p>

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