2023, Kieninger-Graefitsch, Andrea, Bigler, Marius Reto, Waldmann, Frédéric, Wildhaber, Reto, Seiler, Christian

Coronary pressure-derived fractional flow reserve (FFR) measurements are recommended for hemodynamic coronary stenosis assessment. Given temporary paralysis of the coronary microcirculation during hyperemia, pressure is, in theory, directly related to coronary flow. Pressure drop during hyperemia across a coronary stenosis, thus, provides an estimate of its restrictive effect on flow. FFR during reactive hyperemia induced by a proximal, 1-minute coronary artery balloon occlusion has been shown non-inferior to FFR as obtained by adenosine-induced hyperemia. Intracoronary ECG (icECG) is more sensitive in detecting myocardial ischemia than the surface ECG, and can be easily obtained.

2020, Wildhaber, Reto, Ren, Elizabeth, Waldmann, Frederic, Loeliger, Hans-Andrea

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.

2018, Wildhaber, Reto, Bruegger, Dominik, Zalmai, Nour, Malmberg, Hampus, Goette, Josef, Jacomet, Marcel, Tanner, Hildegard, Haeberlin, Andreas, Loeliger, Hans-Andrea

The rapid progress of invasive therapeutic options for cardiac arrhythmias increases the need for accurate diagnostics. The surface electrocardiogram (ECG) is still the standard of noninvasive diagnostics but lacks atrial signal resolution. By contrast, esophageal electrocardiography (EECG) yields atrial signals of high amplitude and with a high signal-to-noise ratio. Esophageal electrocardiography has become fast and safe, but the mechanical constraints of esophageal measuring catheters and the “random” motion of the catheter inside the subject's esophagus limit the spatial resolution of EECG signals. In this paper, we propose a method to estimate the electrical field projected onto the esophagus with an increased spatial resolution, using commonly available esophageal catheters. In a first step, we estimate the time-varying catheter position, and in a second step, we estimate the projected electrical field with enhanced spatial resolution. The proposed algorithm comprises several consecutive optimization steps, where each intermediate step produces not just a single point estimate, but a cost function over multiple solutions, which reduces the information loss at each processing step. We conclude with examples from a clinical trial, where the fields of cardiac arrhythmias are presented as two-dimensional contour plots.

2017, Zalmai, Nour, Wildhaber, Reto, Loeliger, Hans-Andrea

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.

2023, Knecht, Sven, Waldmann, Frédéric, Kuhn, Raffael, Mannhart, Diego, Kühne, Michael, Sticherling, Christian, Badertscher, Patrick, Wildhaber, Reto

2019, Sweda, Romy, Wildhaber, Reto, Mortier, Simone, Bruegger, Dominik, Niederhauser, Thomas, Goette, Josef, Jacomet, Marcel, Tanner, Hildegard, Haeberlin, Andreas

Supraventricular arrhythmia diagnosis using the surface electrocardiogram (sECG) is often cumbersome due to limited atrial signal quality. In some instances, use of esophageal electrocardiography (eECG) may facilitate the diagnosis. Here, we present a novel approach to reconstruct cardiac activation maps from eECG recordings.

2017, Wildhaber, Reto, Zalmai, Nour, Jacomet, Marcel, Loeliger, Hans-Andrea

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.

2022, Wildhaber, Reto, Baeriswil, Christof, Bertrand, Alexander

2018, Wildhaber, Reto, Zalmai, Nour, Jacomet, Marcel, Loeliger, Hans-Andrea

This paper introduces a toolbox for model-based detection, separation, and reconstruction of signals that is especially suited for biomedical signals, such as electrocardiograms (ECGs) or electromyograms (EMGs). The modeling is based on autonomous linear state space models (LSSMs), which are localized with flexible windows. The models are fit to observations by minimizing the squared error while the use of LSSMs leads to efficient recursive error computations and minimizations. Multisection windows enable complex models, and per-sample weights enable multistage processing or adaptive smoothing. This paper is motivated by, and intended for, practical applications, for which several examples and tabulated cost computations are given.

2017, Thanks, Marisa, Niederhauser, Thomas, Haeberlin, Andreas, Wildhaber, Reto, Vogel, Rolf, Goette, Josef, Jacomet, Marcel

A new pseudo asynchronous level crossing analogue-to-digital converter (ADC) architecture targeted for low-power, implantable, long-term biomedical sensing applications is presented. In contrast to most of the existing asynchronous level crossing ADC designs, the proposed design has no digital-to-analogue converter (DAC) and no continuous time comparators. Instead, the proposed architecture uses an analogue memory cell and dynamic comparators. The architecture retains the signal activity dependent sampling operation by generating events only when the input signal is changing. The architecture offers the advantages of smaller chip area, energy saving and fewer analogue system components. Beside lower energy consumption the use of dynamic comparators results in a more robust performance in noise conditions. Moreover, dynamic comparators make interfacing the asynchronous level crossing system to synchronous processing blocks simpler. The proposed ADC was implemented in 0.35 μm complementary metal-oxide-semiconductor (CMOS) technology, the hardware occupies a chip area of 0.0372 mm 2 and operates from a supply voltage of 1.8 V to 2.4 V. The ADC's power consumption is as low as 0.6 μW with signal bandwidth from 0.05 Hz to 1 kHz and achieves an equivalent number of bits (ENOB) of up to 8 bits.