scholarly journals Blind Source Separation In The Analysis Of Electrocardiogram Pre-Shock Waveforms During Ventricular Fibrillation

2021 ◽  
Author(s):  
Marzieh Rasooli
Keyword(s):  
Ventricular Fibrillation ◽  
Blind Source Separation ◽  
Treatment Option ◽  
Electric Shock ◽  
Classification Accuracy ◽  
Source Separation ◽  
Temporal Phase ◽  
Spatio Temporal ◽  
The Given ◽  
Extract Information

Ventricular fibrillation (VF) is a lethal cardiac arrhythmia and electric shock is the only available treatment option for it. Existing works focus on predicting shock success to help improve cardiac resuscitation outcomes. It is desirable to extract information from the electrograms that relates to the current theories on VF mechanism and associate them to the prediction of shock outcomes. To this effect this study used a unique human VF database to evaluate the independent sources (ISs) extracted from Blind Source Separation approach (BSS) and a correlation of 88% was observed between the dominant ISs extracted using a single lead ECG with the number of rotors (i.e., sources identified using multi-channel spatio-temporal phase maps) supporting the hypothesis that the ISs are associated with the rotors. In predicting the shock outcomes using features extracted from the ISs for the given database, we achieved a classification accuracy of 68%.

scholarly journals Blind Source Separation In The Analysis Of Electrocardiogram Pre-Shock Waveforms During Ventricular Fibrillation

2021 ◽  
Author(s):  
Marzieh Rasooli
Keyword(s):  
Ventricular Fibrillation ◽  
Blind Source Separation ◽  
Treatment Option ◽  
Electric Shock ◽  
Classification Accuracy ◽  
Source Separation ◽  
Temporal Phase ◽  
Spatio Temporal ◽  
The Given ◽  
Extract Information

Ventricular fibrillation (VF) is a lethal cardiac arrhythmia and electric shock is the only available treatment option for it. Existing works focus on predicting shock success to help improve cardiac resuscitation outcomes. It is desirable to extract information from the electrograms that relates to the current theories on VF mechanism and associate them to the prediction of shock outcomes. To this effect this study used a unique human VF database to evaluate the independent sources (ISs) extracted from Blind Source Separation approach (BSS) and a correlation of 88% was observed between the dominant ISs extracted using a single lead ECG with the number of rotors (i.e., sources identified using multi-channel spatio-temporal phase maps) supporting the hypothesis that the ISs are associated with the rotors. In predicting the shock outcomes using features extracted from the ISs for the given database, we achieved a classification accuracy of 68%.

scholarly journals Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 425
Author(s):  
Krzysztof Gajowniczek ◽  
Iga Grzegorczyk ◽  
Michał Gostkowski ◽  
Tomasz Ząbkowski
Keyword(s):  
Blind Source Separation ◽  
Cardiac Arrhythmias ◽  
Classification Accuracy ◽  
Recursive Partitioning ◽  
Source Separation ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Physiological Signals ◽  
New Approach ◽  
Model Aggregation

In this work, we present an application of the blind source separation (BSS) algorithm to reduce false arrhythmia alarms and to improve the classification accuracy of artificial neural networks (ANNs). The research was focused on a new approach for model aggregation to deal with arrhythmia types that are difficult to predict. The data for analysis consisted of five-minute-long physiological signals (ECG, BP, and PLETH) registered for patients with cardiac arrhythmias. For each patient, the arrhythmia alarm occurred at the end of the signal. The data present a classification problem of whether the alarm is a true one—requiring attention or is false—should not have been generated. It was confirmed that BSS ANNs are able to detect four arrhythmias—asystole, ventricular tachycardia, ventricular fibrillation, and tachycardia—with higher classification accuracy than the benchmarking models, including the ANN, random forest, and recursive partitioning and regression trees. The overall challenge scores were between 63.2 and 90.7.

BLIND SOURCE SEPARATION OF SPATIO-TEMPORAL MIXED SIGNALS USING PHASE INFORMATION OF ANALYTIC WAVELET TRANSFORM

2010 ◽  
Vol 08 (04) ◽  
pp. 575-594 ◽  
Author(s):  
RYUICHI ASHINO ◽  
TAKESHI MANDAI ◽  
AKIRA MORIMOTO
Keyword(s):  
Wavelet Transform ◽  
Blind Source Separation ◽  
Source Separation ◽  
Cocktail Party ◽  
Phase Information ◽  
Separation Problem ◽  
Time Frequency ◽  
Cocktail Party Problem ◽  
Analytic Wavelet ◽  
Spatio Temporal

The cocktail party problem deals with the specialized human listening ability to focus one's listening attention on a single talker among a cacophony of conversations and background noises. The blind source separation problem is how to enable computers to solve the cocktail party problem in a satisfactory manner. The simplest version of spatio-temporal mixture problem, which is a type of blind source separation problem, has been solved by a generalized version of the quotient signal estimation method based on the analytic wavelet transform, under the assumption that the time delays are integer multiples of the sampling period. The analytic wavelet transform is used to represent time-frequency information of observed signals. Without the above assumption, improved algorithms, utilizing phase information of the analytic wavelet transforms of the observed signals, are proposed. A series of numerical simulations is presented.

Blind source separation of spatio-temporal mixed signals using time-frequency analysis

2009 ◽  
Vol 88 (3) ◽  
pp. 425-456 ◽  
Author(s):  
Ryuichi Ashino ◽  
Takeshi Mandai ◽  
Akira Morimoto ◽  
Fumio Sasaki
Keyword(s):  
Blind Source Separation ◽  
Frequency Analysis ◽  
Source Separation ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Spatio Temporal

Multilayered spatio‐temporal gradient analysis for acoustic blind source separation

2006 ◽  
Vol 120 (5) ◽  
pp. 3047-3047
Author(s):  
Kenbu Teramoto ◽  
Md. Tawhidul Islam Khan ◽  
Seiichirou Torisu ◽  
Akito Uekihara
Keyword(s):  
Blind Source Separation ◽  
Gradient Analysis ◽  
Source Separation ◽  
Temporal Gradient ◽  
Spatio Temporal

Extracting Low-Frequency Spatio-Temporal Patterns in Ambient Power System Data Using Blind Source Separation

2018 ◽  
Vol 46 (2) ◽  
pp. 230-241 ◽  
Author(s):  
José de Jesús Nuño Ayón ◽  
Julián Sotelo Castañon ◽  
Carlos Alberto López de Alba
Keyword(s):  
Power System ◽  
Blind Source Separation ◽  
Source Separation ◽  
Low Frequency ◽  
Temporal Patterns ◽  
System Data ◽  
Spatio Temporal
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