scholarly journals Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm

2022 ◽  
Vol 38 ◽  
pp. 100954
Author(s):  
Shinya Suzuki ◽  
Jun Motogi ◽  
Hiroshi Nakai ◽  
Wataru Matsuzawa ◽  
Tsuneo Takayanagi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

scholarly journals A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation

Heart Rhythm ◽  
2020 ◽  
Vol 17 (5) ◽  
pp. 847-853 ◽  
Author(s):  
Erdong Chen ◽  
Jie Jiang ◽  
Rui Su ◽  
Meng Gao ◽  
Sainan Zhu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

scholarly journals Performance of an automated photoplethysmography-based artificial intelligence algorithm to detect atrial fibrillation

2020 ◽  
Vol 1 (2) ◽  
pp. 107-110
Author(s):  
Daniel Mol ◽  
Robert K. Riezebos ◽  
Henk A. Marquering ◽  
Marije E. Werner ◽  
Trudie C.A. Lobban ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

scholarly journals Performance of an artificial intelligence algorithm to detect atrial fibrillation on a 24-hour continuous photoplethysmography recording using a smartwatch: ACURATE study

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
H Gruwez ◽  
S Evens ◽  
L Desteghe ◽  
L Knaepen ◽  
P Dreesen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Clinical Performance ◽  
Daily Life ◽  
Heart Rhythm ◽  
Holter Monitoring ◽  
University Hospital ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm ◽  
The One

Abstract Background In the awakening era of mobile health, wearable devices capable of detecting atrial fibrillation (AF) are on the rise. Smartwatches and wristbands are equipped with photoplethysmography (PPG) technology that enables (semi)continuous rhythm monitoring. These devices have been pioneered already in a few screening trials. However, such devices are being spread among consumers at a pace that is not paralleled by the evidence supporting their clinical performance. This imbalance reflects the urgent need for validation studies. Purpose To determine the diagnostic performance of an artificial intelligence algorithm to detect AF using photoplethysmography acquired by a smartwatch. Methods One hundred patients (≥18 years) without a pacemaker-dependent heart rhythm who were referred to a university hospital or a large tertiary hospital for elective 24-hour ECG Holter monitoring were asked to wear a continuous PPG monitoring smartwatch (i.e. Samsung GWA2 or Empatica E4) simultaneously with the Holter. All activities of daily life were allowed. The ECG trace and PPG waveform were synchronised and fragmented in one-minute fragements. The one-minute ECG fragments were labelled as AF, non-AF, or insufficient quality based on the routine clinical interpretation of the 24-hour Holter (i.e. software + physician overreading). The one-minute PPG fragments were analysed by an artificial intelligence (AI) algorithm (i.e. FibriCheck) and were given the same labels. Diagnostic metrics of the PPG AI algorithm were calculated with respect to the ECG interpretation, for all fragments with sufficient quality for both PPG and ECG. Results Four patients had to be excluded due to technical error (3 Holter errors, 1 smartwatch error). The mean age in the remaining study population (n=96) was 59±16 years, 51 (53%) were men and 15 (15.6%) were known with permanent AF. In this population, simultaneous ECG and PPG monitoring was recorded for 115,245 one-minute fragments. Fragments of insufficient quality for ECG (n=1,454; 1.3%), PPG (n=25,704; 22.3%) or both (n=15,362; 13.3%) were excluded. PPG fragments were more frequently of insufficient quality (p<0.001). AF was present in 10,255 (14.1%) of the resulting 72,725 high-quality one-minute fragments. The sensitivity of PPG to detect AF was 93.4% (CI 92.9% - 93.8%). The specificity of PPG to exclude AF was 98.4% (CI 98.3% - 98.5%). As a result, the overall accuracy of the PPG algorithm on one-minute fragment level was 97.7% (CI 97.6%- 97.8%). Conclusion Continuous out-of-hospital PPG monitoring using a smartwatch in combination with an AI algorithm can accurately discriminate between AF and non-AF rhythms in a heterogenous patient population. PPG quality is more often affected than ECG quality during daily life activities. FUNDunding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Research Foundation-Flanders, Strategic Basic Research Fund

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

scholarly journals Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 14
Author(s):  
Mei Dong ◽  
Hongyu Wu ◽  
Hui Hu ◽  
Rafig Azzam ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Monitoring System ◽  
Slope Instability ◽  
Mountainous Area ◽  
Probabilistic Forecasting ◽  
Intelligence Algorithm ◽  
Good Prediction Accuracy ◽  
Artificial Intelligence Algorithm ◽  
Steep Slopes

With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

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