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Real-time detection of atrial fibrillation on single board computer
Detección en tiempo real de fibrilación auricular en computador de placa reducida
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Real-time detection of atrial fibrillation on single board computer. (2022). Revista EIA, 19(38), 3823 pp. 1-14. https://doi.org/10.24050/reia.v19i38.1565
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Development of portable devices, that allows real-time detection of atrial fibrillation, requires the implementation of automatic pattern recognition algorithms and an appropriate methodology for their execution in embedded systems. In the present article, the performances of an artificial neural network, a machine vector support, a k-nearest neighbors algorithm and a hybrid classifier implemented on a single-board computer, were compared in terms of detection capacity of arrhythmia and time response associated with real-time execution. The MIT-BIH AFIB database was used to train and validate the algorithms. In advance, the extraction of parameters associated with the stationary wavelet transform was developed. Results between 92 % and 97 % for sensitivity and specificity, and time responses between 6 s and 7.1 s were found in this research.
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