A Novel Wave Decomposition for Oscillatory Signals

Rueda, Cristina; Rodríguez-Collado, Alejandro; Larriba, Yolanda

doi:10.1109/tsp.2021.3051428

Cited by 12 publications

(17 citation statements)

References 47 publications

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“…The ODE representation of the FMM signal, obtained in Rueda et al (2021a) [8] and reproduced here in Theorem 1 below, describes how the underlying system evolves dynamically over time, and is related to the phase space where a signal is a trajectory or an orbit. Theorem 1.…”

Section: The Fmm Signal As the Solution Of A System Of Odesmentioning

confidence: 92%

“…AP signals have been analyzed using the FMM approach in a series of papers [8,10,11], and we have proved that the FMM model represents Hodgking-Huxley signals accurately and achieves a quasi-perfect fit for different real AP patterns.…”

Section: Neurosciencementioning

confidence: 93%

“…Rueda et al (2019) [7] The single-component FMM model Rueda et al (2021a) [8] The multi-component FMM model Rueda et al (2021b) [9] The FMM approach for describing ECG signals Rodríguez-Collado & Rueda (2021a) [10] The FMM representation of the Hodgkin-Huxley model Rodríguez-Collado & Rueda (2021b) [11] The potential of FMM features to classify neurons…”

Section: Reference Descriptionmentioning

confidence: 99%

“…In particular, it is interesting in the case of signals where there exists a dominant component. For more details see Rueda et al (2021a) [8].…”

Section: The Complex Signal Associated With the Fmm M Signalmentioning

confidence: 99%

“…A backfitting algorithm solves the optimizing problem (3) fitting an FMM 1 model to the residue at each step. This is repeated until a stop criterion is attained (see Rueda et al (2021a) [8] for details).…”

Section: Estimation Algorithm and Softwarementioning

confidence: 99%

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The FMM Approach to Analyze Biomedical Signals: Theory, Software, Applications and Future

et al. 2021

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Oscillatory systems arise in the different biological and medical fields. Mathematical and statistical approaches are fundamental to deal with these processes. The Frequency Modulated Mobiüs approach (FMM), reviewed in this paper, is one of these approaches. Little known as it has been recently developed, it solves a variety of exciting questions with real data; some of them, such as the decomposition of the signal into components and their multiple uses, are of general application, others are specific. Among the exciting specific applications is the automatic interpretation of the electrocardiogram signal. In this paper, a summary of the theoretical, statistical and computational properties of the FMM approach are revised. Additionally, as a novelty, the FMM approach’s usefulness for the analysis of blood pressure signals is shown. For the latter, a new robust estimation algorithm is proposed using FMM models with restrictions. The paper ends with a view about challenges for the future.

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Section: The Fmm Signal As the Solution Of A System Of Odesmentioning

confidence: 92%

Section: Neurosciencementioning

confidence: 93%