Current Directions in the Auricular Vagus Nerve Stimulation I – A Physiological Perspective

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

Current Directions in the Auricular

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.European Cooperation in Science and TechnologyThe Austrian Research Promotion Agenc

Detection of Hearing Loss in Audiological Frequencies from Transient Evoked Otoacoustic Emissions

Transient evoked otoacoustic emissions (TEOAEs) have been analyzed for objective assessment of hearing function and monitoring of the influence of noise exposure and ototoxic drugs. This paper presents a novel application of the Hilbert-Huang transform (HHT) for detection and time-frequency mapping of TEOAEs. Since the HHT does not distinguish between signal and noise, it is combined with ensemble correlation in order to extract signal information in intervals with correlated activity. High resolution time-frequency mapping could predict 30 dB(HL), or higher hearing loss, at different audiological frequencies in 63-90% of the cases and normal hearing in 75-90% of the cases. The proposed method offers TEOAE time-frequency mapping by constraining the analysis to regions with high signal-to-noise ratios. The results suggest that the HI-IT is suitable for hearing loss detection at individual frequencies and characterization of the fine structures of TEOAEs

Reservoir computing for extraction of low amplitude atrial activity in atrial fibrillation

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 (ESN) 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 performance is evaluated on ECG signals, with simulated f-waves of low amplitude added, by determining the root mean square error P 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 equal to mean and standard deviation of PESN 24.8±7.3 and PABS 34.2±17.9 μV (p < 0.001). The novel method is particularly well-suited for implementation in mobile health systems where monitoring of AF during extended time periods is of interest

An Echo State Neural Network for QRST Cancellation During Atrial Fibrillation

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

Multiscale detection of transient evoked otoacoustic emissions

This paper presents a unified approach to multiscale detection of transient evoked otoacoustic emissions (TEOAEs). Using statistical detection theory, it is shown that the optimal detector involves a time windowing operation where the window can be estimated from ensemble correlation information. The detector performs adaptive splitting of the signal into different frequency bands using either wavelet or wavelet packet decomposition. A simplified detector is proposed in. which signal energy is omitted. The results show that the simplified detector performs significantly better than existing TEOAE detectors based on wave reproducibility or the modified variance ratio, whereas the. detector involving signal energy does not offer such a performance advantage

A noise-adaptive method for detection of brief episodes of paroxysmal atrial fibrillation

The aim of this work is to develop a method for detection of brief episode paroxysmal atrial fibrillation (PAF). The proposed method utilizes four different features: RR interval irregularity, absence of P waves, presence of f-waves and noise level. The obtained features are applied to the Mamdani-type fuzzy inference method for decisionmaking. The performance was evaluated on one hundred 90 s long surrogate ECG signals with brief PAF episodes (5-30 beats). The robustness to noise in ECGs where noise level in each set is incremented in steps of 0.01 mV from 0 to 0.2 mV was examined as well. When compared to the coefficient of sample entropy, our method showed considerably better performance for low and moderate noise levels (< 0.06 mV) with an area under the receiver operating characteristic curve of 0.9 and 0.94, respectively. Similar performance is expected for higher noise levels as atrial activity is less used in the detection process. Finally, the results suggest that our method is more robust to false alarms due to ectopic beats or other irregular rhythms than the method under comparison