Fig 3 - uploaded by Dante Mújica-Vargas
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Signals from the selected database.

Signals from the selected database.

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This article proposes a method to classify atrial fibrillation signals using time-frequency characteristics through a BiLSTM network. The experiment was performed with the ECG signals, which are part of the PhysioNet CinC 2017 database. In addition to the BiLSTM network, machine learning algorithms such as k Nearest Neighbors, Linear SVM, RBF SVM,...

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... two classes that interest us for the classification tasks, these being: Normal (N), AFib (A), added to the fact that the characteristic of the ECG signals is that they are of a single derivation and the duration in seconds of an individual signal is mostly around 30 s. A graphic representation of the signals present in the ECG's can be seen in Fig. 3. ...

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