Spectral and Time Domain Parameters for The Classification of Atrial Fibrillation

2015 ◽  
Author(s):  
Diana Batista ◽  
Ana Fred
Keyword(s):  
Atrial Fibrillation ◽  
Time Domain

scholarly journals Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)

Proceedings ◽  
2020 ◽  
Vol 70 (1) ◽  
pp. 109
Author(s):  
Jimy Oblitas ◽  
Jorge Ruiz
Keyword(s):  
Machine Learning ◽  
Time Domain ◽  
Electromagnetic Pulse ◽  
Machine Learning Algorithms ◽  
Classification Models ◽  
Terahertz Time Domain Spectroscopy ◽  
Time Domain Spectroscopy ◽  
Svm Algorithm ◽  
Classification Of Images

Terahertz time-domain spectroscopy is a useful technique for determining some physical characteristics of materials, and is based on selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify cocoa percentages in chocolates, the terahertz spectra (0.5–10 THz) of five chocolate samples (50%, 60%, 70%, 80% and 90% of cocoa) were examined. The acquired data matrices were analyzed with the MATLAB 2019b application, from which the dielectric function was obtained along with the absorbance curves, and were classified by using 24 mathematical classification models, achieving differentiations of around 93% obtained by the Gaussian SVM algorithm model with a kernel scale of 0.35 and a one-against-one multiclass method. It was concluded that the combined processing and classification of images obtained from the terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to successfully classify chocolates with different percentages of cocoa.

scholarly journals B-PO03-094 DEEP LEARNING-BASED CLASSIFICATION OF UNIPOLAR ELECTROGRAMS IN HUMAN ATRIAL FIBRILLATION: APPLICATION IN FOCAL SOURCE AND TRIGGER (FAST) MAPPING AND ABLATION

Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S226-S227
Author(s):  
Vijay S. Chauhan ◽  
Shun Liao ◽  
Don Ragot ◽  
Sachin Nayyar ◽  
Adrian Suszko ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Fast Mapping ◽  
Unipolar Electrograms ◽  
Human Atrial Fibrillation

Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest

2021 ◽  
Author(s):  
Balajee Alphonse ◽  
Venkatesan Rajagopal ◽  
Sudhakar Sengan ◽  
Kousalya Kittusamy ◽  
Amudha Kandasamy ◽  
...  
Keyword(s):  
Random Forest ◽  
Time Domain ◽  
Multi Class Classification

Increased prevalence of psychosis in patients who get admitted with atrial fibrillation with worse outcomes

2017 ◽  
Vol 41 (S1) ◽  
pp. S575-S576
Author(s):  
Z. Mansuri ◽  
S. Patel ◽  
P. Patel ◽  
O. Jayeola ◽  
A. Das ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Hospital Admissions ◽  
Temporal Trends ◽  
International Classification Of Diseases ◽  
Trend Test ◽  
Classification Of Diseases ◽  
Competing Interest ◽  
Armitage Trend Test ◽  
The Impact

ObjectiveTo determine trends and impact on outcomes of atrial fibrillation (AF) in patients with pre-existing psychosis.BackgroundWhile post-AF psychosis has been extensively studied, contemporary studies including temporal trends on the impact of pre-AF psychosis on AF and post-AF outcomes are largely lacking.MethodsWe used Nationwide Inpatient Sample (NIS) from the healthcare cost and utilization project (HCUP) from year's 2002–2012. We identified AF and psychosis as primary and secondary diagnosis respectively using validated international classification of diseases, 9th revision, and Clinical Modification (ICD-9-CM) codes, and used Cochrane–Armitage trend test and multivariate regression to generate adjusted odds ratios (aOR).ResultsWe analyzed total of 3.887.827AF hospital admissions from 2002–2012 of which 1.76% had psychosis. Proportion of hospitalizations with psychosis increased from 5.23% to 14.28% (P trend < 0.001). Utilization of atrial-cardioversion was lower in patients with psychosis (0.76%v vs. 5.79%, P < 0.001). In-hospital mortality was higher in patients with Psychosis (aOR 1.206; 95%CI 1.003–1.449; P < 0.001) and discharge to specialty care was significantly higher (aOR 4.173; 95%CI 3.934–4.427; P < 0.001). The median length of hospitalization (3.13 vs. 2.14 days; P < 0.001) and median cost of hospitalization (16.457 vs. 13.172; P < 0.001) was also higher in hospitalizations with psychosis.ConclusionsOur study displayed an increasing proportion of patients with Psychosis admitted due to AF with higher mortality and extremely higher morbidity post-AF, and significantly less utilization of atrial-cardioversion. There is a need to explore reasons behind this disparity to improve post-AF outcomes in this vulnerable population.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Deep Neural Network Classification of In Vivo Burn injuries with Different Etiologies Using THz Time-Domain Spectral Imaging

2021 ◽  
Author(s):  
Omar B. Osman ◽  
Zachery B. Harris ◽  
Juin-Wan Zhou ◽  
Mahmoud E. Khani ◽  
Andrew Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Deep Neural Network ◽  
Spectral Imaging ◽  
Burn Injuries ◽  
Neural Network Classification

Alternative Time-Domain P-wave Analysis for Precise Information on Substrate Alteration After Pulmonary Vein Isolation for Atrial Fibrillation

2021 ◽  
Author(s):  
Aikaterini Vraka ◽  
Vicente Bertomeu-Gonzalez ◽  
Fernando Hornero ◽  
Leif Sornmo ◽  
Raul Alcaraz ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Pulmonary Vein ◽  
Pulmonary Vein Isolation ◽  
Time Domain ◽  
P Wave ◽  
Wave Analysis ◽  
Precise Information

Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG

2022 ◽  
pp. 105168
Author(s):  
Hassan Serhal ◽  
Nassib Abdallah ◽  
Jean-Marie Marion ◽  
Pierre Chauvet ◽  
Mohamad Oueidat ◽  
...  
Keyword(s):  
Atrial Fibrillation

Abstract 16199: Trend of Hypertensive Emergencies in Atrial Fibrillation Hospitalization; National Estimate

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Mae'n Al-Dabbas ◽  
Jay Shah ◽  
devina adalja ◽  
Archana Gundabolu ◽  
Ashish Kumar ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
International Classification Of Diseases ◽  
Primary Diagnosis ◽  
Hypertensive Emergency ◽  
P Value ◽  
National Inpatient Sample ◽  
Common Risk Factor ◽  
The Past ◽  
Classification Of Diseases

Introduction: Hypertension has been reported as one of the most common risk factor causing atrial fibrillation (AF). Also, hypertension is associated with more persistent AF. However the epidemiology for incidence of hypertensive emergency in AF hospitalization is not well documented. Hypothesis: We hypothesize that frequency of hypertensive emergency has been increasingly associated with Atrial fibrillation. Methods: National inpatient sample from 2005 -2015 was used for the present analysis. Patients over 18 years, with AF as the primary diagnosis were identified using International Classification of Diseases, Ninth Revision-Clinical Modification (ICD-9-CM) code, 427.31. Patients with hypertensive emergency were identified using ICD-9-CM code. 401.0, 402.0, 403.0, 404.0, 405.0. Results: A total of 4,988,269 AF patients over 18 years of age were included in the present analysis. Of these hospitalization 49,423 had hypertensive emergency during hospitalizations and the rest 4,938,846 had no hypertensive emergency. There was a monotonic increase in the trend of rates of hypertensive emergency per 1000 AF hospitalizations, from 2005 - 2015. (P value <0.001) [Figure 1]. Conclusion: There has been a significant increase in the rates of hypertensive emergency among AF hospitalization, over the past decade. Further research is needed to investigate the reason for this increased trend.

scholarly journals Effect of EEG Time Domain Features on the Classification of Sleep Stages

2016 ◽  
Vol 9 (25) ◽  
Author(s):  
Sule Yucelbas ◽  
Seral Ozsen ◽  
Cuneyt Yucelbas ◽  
Gulay Tezel ◽  
Serkan Kuccukturk ◽  
...  
Keyword(s):  
Time Domain ◽  
Sleep Stages
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