Wildhaber, Reto

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Wildhaber
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Reto
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Wildhaber, Reto

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2016 - 201932020 - 20222
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  • Vorschaubild nicht verfügbar
    Publikation
    Windowed state space filters for peak interference suppression in neural spike sorting
    (IEEE, 2022) Wildhaber, Reto; Baeriswil, Christof; Bertrand, Alexander
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Signal analysis using local polynomial approximations
    (IEEE, 2020) Wildhaber, Reto; Ren, Elizabeth; Waldmann, Frederic; Loeliger, Hans-Andrea [in: 2020 28th European Signal Processing Conference (EUSIPCO)]
    Local polynomial approximations represent a versatile feature space for time-domain signal analysis. The parameters of such polynomial approximations can be computed by efficient recursions using autonomous linear state space models and often allow analytical solutions for quantities of interest. The approach is illustrated by practical examples including the estimation of the delay difference between two acoustic signals and template matching in electrocardiogram signals with local variations in amplitude and time scale.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Signal detection and discrimination for medical devices using windowed state space filters
    (IEEE, 2017) Wildhaber, Reto; Zalmai, Nour; Jacomet, Marcel; Loeliger, Hans-Andrea [in: 2017 13th IASTED International Conference on Biomedical Engineering (BioMed)]
    We introduce a model-based approach for computationally efficient signal detection and discrimination, which is relevant for biological signals. Due to its low computational complexity and low memory need, this approach is well-suited for low power designs, as required for medical devices and implants. We use linear state space models to gain recursive, efficient computation rules and obtain the model parameters by minimizing the squared error on discrete-time observations. Furthermore we combine multiple models of different time-scales to match superpositions of signals of variable length. To give immediate access to our method, we highlight the use in several practical examples on standard and on esophageal ECG signals. This method was adapted and improved as part of a research and development project for medical devices.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Autonomous state space models for recursive signal estimation beyond least squares
    (IEEE, 2017) Zalmai, Nour; Wildhaber, Reto; Loeliger, Hans-Andrea [in: 2017 25th European Signal Processing Conference (EUSIPCO)]
    The paper addresses the problem of fitting, at any given time, a parameterized signal generated by an autonomous linear state space model (LSSM) to discrete-time observations. When the cost function is the squared error, the fitting can be accomplished based on efficient recursions. In this paper, the squared error cost is generalized to more advanced cost functions while preserving recursive computations: first, the standard sample-wise squared error is augmented with a sampledependent polynomial error; second, the sample-wise errors are localized by a window function that is itself described by an autonomous LSSM. It is further demonstrated how such a signal estimation can be extended to handle unknown additive and/or multiplicative interference. All these results rely on two facts: first, the correlation function between a given discrete-time signal and a LSSM signal can be computed by efficient recursions; second, the set of LSSM signals is a ring.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Inferring depolarization of cells from 3D-electrode measurements using a bank of linear state space models
    (IEEE, 2016) Zalmai, Nour; Wildhaber, Reto; Clausen, Desiree; Loeliger, Hans-Andrea [in: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)]
    Cell depolarization runs essentially in a uniform motion along the muscular tissue, which creates transient electrical potential differences measurable by nearby electrodes. Inferring the depolarization speed and direction from measurements is of great interest for physicians. In cardiology, this is part of the inverse ECG problem which often requires a large number of electrodes and intense computational power even if the simple common model of the single equivalent moving dipole (SEMD) is applied. In this paper, we model a depolarization process as a straight-line movement of a SEMD. We provide an efficient algorithm based on linear state space models that infers the SEMD movement using only 3 measurement channels from a tetrahedral electrode and with the presence of interferences. Our algorithm is tested both on simulated and experimental data.
    04B - Beitrag Konferenzschrift