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.

2016, Zalmai, Nour, Wildhaber, Reto, Clausen, Desiree, Loeliger, Hans-Andrea

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.

2015, Thanks, Marisa, Niederhauser, Thomas, Haeberlin, Andreas, Wildhaber, Reto, Vogel, Rolf, Jacomet, Marcel, Goette, Josef

Asynchronous level crossing sampling analog-to-digital converters (ADCs) are known to be more energy efficient and produce fewer samples than their equidistantly sampling counterparts. However, as the required threshold voltage is lowered, the number of samples and, in turn, the data rate and the energy consumed by the overall system increases. In this paper, we present a cubic Hermitian vector-based technique for online compression of asynchronously sampled electrocardiogram signals. The proposed method is computationally efficient data compression. The algorithm has complexity O(n), thus well suited for asynchronous ADCs. Our algorithm requires no data buffering, maintaining the energy advantage of asynchronous ADCs. The proposed method of compression has a compression ratio of up to 90% with achievable percentage root-mean-square difference ratios as a low as 0.97. The algorithm preserves the superior feature-to-feature timing accuracy of asynchronously sampled signals. These advantages are achieved in a computationally efficient manner since algorithm boundary parameters for the signals are extracted a priori.

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.

2016, Haeberlin, Andreas, Lacheta, Lucca, Niederhauser, Thomas, Marisa, Thanks, Wildhaber, Reto, Goette, Josef, Jacomet, Marcel, Seiler, Jens, Fuhrer, Juerg, Roten, Laurent, Tanner, Hildegard, Vogel, Rolf

Purpose Paroxysmal atrial fibrillation (PAF) often remains undiagnosed. Long-term surface ECG is used for screening, but has limitations. Esophageal ECG (eECG) allows recording high quality atrial signals, which were used to identify markers for PAF.

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.

2016, Niederhauser, Thomas, Marisa, Thanks, Kohler, Lukas, Haeberlin, Andreas, Wildhaber, Reto, Abächerli, Roger, Goette, Josef, Jacomet, Marcel, Vogel, Rolf

Long-term electrocardiogram (ECG) signals might suffer from relevant baseline disturbances during physical activity. Motion artifacts in particular are more pronounced with dry surface or esophageal electrodes which are dedicated to prolonged ECG recording. In this paper we present a method called baseline wander tracking (BWT) that tracks and rejects strong baseline disturbances and avoids concurrent saturation of the analog front-end. The proposed algorithm shifts the baseline level of the ECG signal to the middle of the dynamic input range. Due to the fast offset shifts, that produce much steeper signal portions than the normal ECG waves, the true ECG signal can be reconstructed offline and filtered using computationally intensive algorithms. Based on Monte Carlo simulations we observed reconstruction errors mainly caused by the non-linearity inaccuracies of the DAC. However, the signal to error ratio of the BWT is higher compared to an analog front-end featuring a dynamic input ranges above 15 mV if a synthetic ECG signal was used. The BWT is additionally able to suppress (electrode) offset potentials without introducing long transients. Due to its structural simplicity, memory efficiency and the DC coupling capability, the BWT is dedicated to high integration required in long-term and low-power ECG recording systems.