Abstract
In this work, the implementation of a dynamic cubic neural unit for the prediction of heartbeats using a wireless method is presented. The data were recorded with the BITalino biomedical acquisition card using its ECG input and output module via Bluetooth. This paper aims to predict a prediction horizon according to the learning rate, the number of samples used to train the model, and the specified times required for training. The signal (input) was acquired from electrodes, which were placed on the surface of the chest near the heart. The signal was visualized and presented through a graphical interface. For the interface evaluation, tests are performed using the obtained signal in real time.
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Acknowledgements
This project is supported by Jan Evangelista Purkyně University. Title of the project - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.
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Rodríguez-Jorge, R., Huerta-Solis, P., Bíla, J., Škvor, J. (2022). Adaptive Analysis of Electrocardiogram Prediction Using a Dynamic Cubic Neural Unit. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_41
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