From frequency content to signal dynamics using DNNs
This study developed a novel method for analyzing and decomposing a signal into its main dynamics for small and large timescales. Our proposal is based on a decoupled hybrid system of convolutional and recurrent neural networks that uses as inputs the power spectrum and spectrogram of a given signal...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/60352 |
| Acceso en línea: | http://hdl.handle.net/10017/60352 https://dx.doi.org/10.1109/ACCESS.2022.3224426 |
| Access Level: | acceso abierto |
| Palabra clave: | Biological signals DNN architecture Dynamic behavior Power spectrum Spectrogram Timescales Electrónica Electronics |
| Sumario: | This study developed a novel method for analyzing and decomposing a signal into its main dynamics for small and large timescales. Our proposal is based on a decoupled hybrid system of convolutional and recurrent neural networks that uses as inputs the power spectrum and spectrogram of a given signal, giving as output the dynamic behavior. We define the dynamic classification predicted of the signal using previously known dynamics characterized through training signals: periodic, quasi-periodic, aperiodic, chaotic, and randomness. We created a synthetic dataset comprising more than 50 training signals from different categories. For the real-world dataset, we used photoplethysmographic signals from 40 students obtained from a Spanish medical study. We tested the developed system?s performance in real biological and synthetical signals, obtaining noteworthy results. All the results are evaluated qualitatively and quantitatively. Still, given the novelty and the lack of similar works, we cannot compare reliably and rigorously our results with other works, at least quantitatively. We can retrieve from the exposed results in this work three key ideas: the DNN-based solutions are capable of learning and generalizing the dynamics behavior of signals; the proposal learned correctly to distinguish between the reference dynamics provided and find some unidirectional similarities in the aperiodicity cases; and the results obtained using real-world PPG signals reveal that biological signals seem to exhibit a multi-dynamic behavior that changes depending on the used timescale, being quasi-periodically dominant in the short-term and aperiodically dominant in the long-term. |
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