Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging technology in the field of bioelectronic medicine with applications in therapy. Modulation of the afferent vagus nerve affects a large number of physiological processes and bodily states associated with information transfer between the brain and body. These include disease mitigating effects and sustainable therapeutic applications ranging from chronic pain diseases, neurodegenerative and metabolic ailments to inflammatory and cardiovascular diseases. Given the current evidence from experimental research in animal and clinical studies we discuss basic aVNS mechanisms and their potential clinical effects. Collectively, we provide a focused review on the physiological role of the vagus nerve and formulate a biology-driven rationale for aVNS. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the framework of EU COST Action “European network for innovative uses of EMFs in biomedical applications (BM1309).” Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on physiological aspects – a discussion of engineering aspects is provided by our accompanying article ( Kaniusas et al., 2019 ). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.

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Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging electroceutical technology in the field of bioelectronic medicine with applications in therapy. Artificial modulation of the afferent vagus nerve – a powerful entrance to the brain – affects a large number of physiological processes implicating interactions between the brain and body. Engineering aspects of aVNS determine its efficiency in application. The relevant safety and regulatory issues need to be appropriately addressed. In particular, in silico modeling acts as a tool for aVNS optimization. The evolution of personalized electroceuticals using novel architectures of the closed-loop aVNS paradigms with biofeedback can be expected to optimally meet therapy needs. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the scope of EU COST Action “European network for innovative uses of EMFs in biomedical applications (BM1309).” Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on engineering aspects – a discussion of physiological aspects is provided by our accompanying article ( Kaniusas et al., 2019 ). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.

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This work introduces a novel approach to the detection of brief episodes of paroxysmal atrial fibrillation (PAF). The proposed detector is based on four parameters which characterize RR interval irregularity, P-wave absence, f-wave presence, and noise level, of which the latter three are determined from a signal produced by an echo state network. The parameters are used for fuzzy logic classification where the decisions involve information on prevailing signal quality; no training is required. The performance is evaluated on a large set of test signals with brief episodes of PAF. The results show that episodes with as few as five beats can be reliably detected with an accuracy of 0.88, compared to 0.82 for a detector based on rhythm information only (the coefficient of sample entropy); this difference in accuracy increases when atrial premature beats are present. The results also show that the performance remains essentially unchanged at noise levels up to [Formula: see text] RMS. It is concluded that the combination of information on ventricular activity, atrial activity, and noise leads to substantial improvement when detecting brief episodes of PAF.

A novel method for QRST cancellation during atrial fibrillation (AF) is introduced for use in recordings with two or more leads. The method is based on an echo state neural network which estimates the time-varying, nonlinear transfer function between two leads, one lead with atrial activity and another lead without, for the purpose of canceling ventricular activity. The network has different sets of weights that define the input, hidden, and output layers, of which only the output set is adapted for every new sample to be processed. The performance is evaluated on ECG signals, with simulated f-waves added, by determining the root mean square error between the true f-wave signal and the estimated signal, as well as by evaluating the dominant AF frequency. When compared to average beat subtraction (ABS), being the most widely used method for QRST cancellation, the performance is found to be significantly better with an error reduction factor of 0.24-0.43, depending on f-wave amplitude. The estimates of dominant AF frequency are considerably more accurate for all f-wave amplitudes than the AF estimates based on ABS. The novel method is particularly well suited for implementation in mobile health systems where monitoring of AF during extended time periods is of interest.

Objective: The aim of this work is to evaluate and compare five fiducial points for the temporal location of each pulse wave from forehead and finger photoplethysmographic pulse waves signals (PPG) to perform pulse rate variability (PRV) analysis as a surrogate of heart rate variability (HRV) analysis.Approach: Forehead and finger PPG signals were recorded during tilt-table test simultaneously to the ECG. Artifacts were detected and removed and, five fiducial points were computed: apex, middle-amplitude and foot points of the PPG signal, apex point of the first derivative signal and, the intersection point of the tangent to the PPG waveform at the apex of the derivative PPG signal and the tangent to the foot of the PPG pulse defined as intersecting tangents method. Pulse period (PP) time intervals series were obtained from both PPG signals and compared to the RR intervals obtained from the ECG. Heart and pulse rate variability signals (HRV and PRV) were estimated and, classical time and frequency domain indices were computed.Main Results: The middle-amplitude point of the PPG signal (n M ), the apex point of the first derivative (n * A ), and the tangents intersection point (n T ) are the most suitable fiducial points for PRV analysis, which result in the lowest relative errors estimated between PRV and HRV indices, higher correlation coefficients and reliability indexes. Statistically significant differences according to the Wilcoxon test between PRV and HRV signals were found for the apex and foot fiducial points of the PPG, as well as the lowest agreement between RR and PP series according to Bland-Altman analysis. Hence, they have been considered less accurate for variability analysis. In addition, the relative errors are significantly lower for n M and n * A features by using Friedman statistics with Bonferroni multiple-comparison test and, we propose n M as the most accurate fiducial point. Based on our results, forehead PPG seems to provide more reliable information for a PRV assessment than finger PPG.Significance: The accuracy of the pulse wave detections depends on the morphology of the PPG. There is therefore a need to widely define the most accurate fiducial point to perform a PRV analysis under non-stationary conditions based on different PPG sensor locations and signal acquisition techniques. 2