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.

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.

No abstract

No abstract

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.

BackgroundHyperechogenicity of the substantia nigra (SN+), detected by transcranial sonography (TCS), was reported as a characteristic finding in Parkinson's disease (PD), with high diagnostic accuracy values, when compared mainly to healthy controls or essential tremor (ET) group. However, some data is accumulating that the SN + could be detected in other neurodegenerative and even in non-neurodegenerative disorders too. Our aim was to estimate the diagnostic accuracy of TCS, mainly focusing on the specificity point, when applied to a range of the parkinsonian disorders, and comparing to the degenerative cognitive syndromes.MethodsA prospective study was carried out at the Hospital of Lithuanian University of Health Sciences from January until September 2011. Initially, a TCS and clinical examination were performed on 258 patients and 76 controls. The General Electric Voluson 730 Expert ultrasound system was used. There were 12.8% of cases excluded with insufficient temporal bones, and 4.3% excluded with an unclear diagnosis. The studied sample consisted of the groups: PD (n = 71, 33.2%), ET (n = 58, 27.1%), PD and ET (n = 10, 4.7%), atypical parkinsonian syndromes (APS) (n = 3, 1.4%), hereditary neurodegenerative parkinsonism (HDP) (n = 3, 1.4%), secondary parkinsonism (SP) (n = 23, 10.8%), mild cognitive impairment (MCI) (n = 33, 15.4%), dementia (n = 13, 6.1%), and control (n = 71).ResultsThere were 80.3% of PD patients at stages 1 & 2 according to Hoehn and Yahr. At the cut-off value of 0.20 cm2 of the SN+, the sensitivity for PD was 94.3% and the specificity - 63.3% (ROC analysis, AUC 0.891), in comparison to the rest of the cohort. At the cut-off value of 0.26 cm2, the sensitivity was 90% and the specificity 82.4%.The estimations for the lowest specificity for PD, in comparison to the latter subgroups (at the cut-off values of 0.20 cm2 and 0.26 cm2, respectively) were: 0% and 33.3% to APS, 33.3% and 66.7% to HDP, 34.8% and 69.6% to SP, 55.2% and 82.8% to ET, 75% and 91.7% to dementia.ConclusionsThe high sensitivity of the test could be employed as a valuable screening tool. But TCS is more useful as a supplementary diagnostic method, due to the specificity values not being comprehensive.

In this paper, the development of an accelerometer based sensor and algorithms to extract useful information for evaluation of sportsman performance in swimming sport is presented. Several parameters were identified as useful and feasible to estimate from registered acceleration curves: "number of strokes per lap", "instantaneous stroke rate" also durations of various swimming process intervals, periods and phases. Possible applications of extracted parameters were discussed.