Comparison of the Efficacy and Noise Immunity of Algorithms for Recognition of Shockable Cardiac Rhythms

Gorbunov, B. B.; Гусев, А. Н.; Kamenskii, S. A.; Селищев, С. В.

doi:10.1023/b:bien.0000042105.69701.4c

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is important to note that for the original ECG signals obtained, it is necessary to perform an initial pre-processing (filtering, noise elimination, scaling, etc.) to be able to proceed in the next stages of characterization of them [6,7]. Once the signals were obtained, we proceeded to the construction of filters and pre-processing them (elimination of noise, etc.…”

Section: Data Base Of Ecg Signalmentioning

confidence: 99%

Wearable Intelligent System for the Diagnosis of Cardiac Diseases Working in Real Time and with Low Energy Cost

et al. 2018

View full text Add to dashboard Cite

Heart disease is currently one of the leading causes of death in developed countries. The electrocardiogram is an important source of information for identifying these conditions, therefore, becomes necessary to seek an advanced system of diagnosis based on these signals. In this paper we used samples of electrocardiograms of MIT-related database with ten types of pathologies and a rate corresponding to normal (healthy patient), which are processed and used for extraction from its two branches of a wide range of features. Next, various techniques have been applied to feature selection based on genetic algorithms, principal component analysis and mutual information. To carry out the task of intelligent classification, 3 different scenarios have been considered. These techniques allow us to achieve greater efficiency in the classification methods used, namely support vector machines (SVM) and decision trees (DT) to perform a comparative analysis between them. Finally, during the development of this contribution, the use of very non-invasive devices (2 channel ECG) was analyzed, we could practically classify them as wearable, which would not need interaction by the user, and whose energy consumption is very small to extend the average life of the user been on it.

show abstract

Section: Data Base Of Ecg Signalmentioning

confidence: 99%

Wearable Intelligent System for the Diagnosis of Cardiac Diseases Working in Real Time and with Low Energy Cost

et al. 2018

View full text Add to dashboard Cite

show abstract

“…

Various algorithms are used for automatic fibrillation detection [1,2]. Each algorithm has specific advantages and disadvantages.

…”

mentioning

confidence: 99%

A Complex Algorithm of Automatic Fibrillation Detection

Bazaev

Telyshev

2009

Biomed Eng

View full text Add to dashboard Cite

Various algorithms are used for automatic fibrillation detection [1,2]. Each algorithm has specific advantages and disadvantages. The goal of this work was to suggest a complex algorithm of automatic fibrillation detection. This algorithm combines four methods of fibrillation detection and reduces their disadvantages to a minimal level. The sensitivity and specificity of the complex algo rithm of automatic fibrillation detection is 94.3 and 98.4%, respectively. The sensitivity is the probability of correct detection of an event without fibrillation. The specificity is the probability of correct detection of a fib rillation event [3,4].

show abstract

Intelligent Systems to Autonomously Classify Several Arrhythmia Using Information from ECG

Valenzuela

Rojas

Herrera

et al. 2013

2013 International Conference on Social Computing

View full text Add to dashboard Cite

Comparison of the Efficacy and Noise Immunity of Algorithms for Recognition of Shockable Cardiac Rhythms

Cited by 3 publications

References 11 publications

Wearable Intelligent System for the Diagnosis of Cardiac Diseases Working in Real Time and with Low Energy Cost

Wearable Intelligent System for the Diagnosis of Cardiac Diseases Working in Real Time and with Low Energy Cost

A Complex Algorithm of Automatic Fibrillation Detection

Intelligent Systems to Autonomously Classify Several Arrhythmia Using Information from ECG

Contact Info

Product

Resources

About