Analysis of heart rate variability (HRV) can be applied to assess the autonomic nervous system (ANS) sympathetic and parasympathetic activity. Since living systems are non-linear, evaluation of ANS activity is difficult by means of linear methods. We propose to apply the Higuchi fractal dimension (HFD) method for assessment of ANS activity. HFD measures complexity of the HRV signal. We analyzed 45 RR time series of 84 min duration each from nine healthy and five diabetic subjects with clinically confirmed long-term diabetes mellitus type II and with diabetic foot ulcer lasting more than 6 weeks. Based on HRV time series complexity analysis we have shown that HFD: (1) discriminates healthy subjects from patients with diabetes mellitus type II; (2) assesses the impact of percutaneous auricular vagus nerve stimulation (pVNS) on ANS activity in normal and diabetic conditions. Thus, HFD may be used during pVNS treatment, to provide stimulation feedback for on-line regulation of therapy in a fast and robust way.

Development of wearable point-of-care medical devices faces many challenges. Besides technological and clinical issues, demands on robustness, miniaturization, and user interface design are of paramount importance. However, a systematic assessment of these non-functional but essential requirements is often impossible within the first product cycle. Later, surveys on user satisfaction with existing devices and user demands can offer significant input for device re-development and improvement. In this paper, we present a survey on satisfaction with and demands for a wearable medical device for percutaneous auricular vagus nerve stimulation (pVNS). We analyzed 36 responses from patients treated with pVNS and five responses from experienced physicians in order to devise a future concept of pVNS. Main shortcomings of a current pVNS device were identified to be lacking water resistance and mechanical robustness, both impairing daily activities. Painful sensation during pVNS application, unwanted side effects like skin irritations and strongly varying perception of the stimulation were reported. Results urge for more patient self-governance and an (automatic) adjustment of the stimulation to the current physiological state of the patient. Attained results support a strategic approach for future developments of pVNS towards personalized health care.

Electrical impedance tomography (EIT) is a promising imaging technique for bedside monitoring of lung function. It is easily applicable, cheap and requires no ionizing radiation, but clinical interpretation of EIT-images is still not standardized. One of the reasons for this is the ill-posed nature of EIT, allowing a range of possible images to be produced–rather than a single explicit solution. Thus, to further advance the EIT technology for clinical application, thorough examinations of EIT-image reconstruction settings–i.e., mathematical parameters and addition of a priori (e.g., anatomical) information–is essential. In the present work, regional ventilation distribution profiles derived from different EIT finite-element reconstruction models and settings (for GREIT and Gauss Newton) were compared to regional aeration profiles assessed by the gold-standard of 4-dimensional computed tomography (4DCT) by calculating the root mean squared error (RMSE). Specifically, non-individualized reconstruction models (based on circular and averaged thoracic contours) and individualized reconstruction models (based on true thoracic contours) were compared. Our results suggest that GREIT with noise figure of 0.15 and non-uniform background works best for the assessment of regional ventilation distribution by EIT, as verified versus 4DCT. Furthermore, the RMSE of anteroposterior ventilation profiles decreased from 2.53±0.62% to 1.67±0.49% while correlation increased from 0.77 to 0.89 after embedding anatomical information into the reconstruction models. In conclusion, the present work reveals that anatomically enhanced EIT-image reconstruction is superior to non-individualized reconstruction models, but further investigations in humans, so as to standardize reconstruction settings, is warranted.

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Objective. Recent research suggests that percutaneous auricular vagus nerve stimulation (pVNS) beneficially modulates the autonomic nervous system (ANS). Bursted pVNS seems to be efficient for nerve excitation. Bursted pVNS effects on cardiac autonomic modulation are not disclosed yet. Approach. For the first time, the present study evaluates the effect of pVNS on cardiac autonomic modulation in healthy subjects (n = 9) using two distinct bursted stimulation patterns (biphasic and triphasic stimulation) and heart rate variability analysis (HRV). Stimulation was delivered via four needle electrodes in vagally innervated regions of the right auricle. Each of the two bursted stimulation patterns was applied twice in randomized order over four consecutive stimulation sessions per subject. Main results. Bursted pVNS did not change heart rate, blood pressure, and inflammatory parameters in study subjects. pVNS significantly increased the standard deviation of heart inter-beat intervals, from 46.39 ± 10.4 ms to 63.46 ± 22.47 ms (p < 0.05), and the total power of HRV, from 1475.7 ± 616.13 ms2 to 3190.5 ± 2037.0 ms2 (p < 0.05). The high frequency (HF) power, the low frequency (LF) power, and the LF/HF ratio did not change during bursted pVNS. Both stimulation patterns did not show any significant differences in cardiac autonomic modulation. Stimulation intensity to reach a tingling sensation was significantly lower in triphasic compared to biphasic stimulation (p < 0.05). Bursted stimulation was well tolerated. Significance. Bursted pVNS seems to affect cardiac autonomic modulation in healthy subjects, with no difference between biphasic and triphasic stimulation, the latter requiring lower stimulation intensities. These findings foster implementation of more efficient pVNS stimulation.

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An accurate detection of anatomical structures in electrical impedance tomography (EIT) is still at an early stage. Aorta detection in EIT is of special interest, since it would favor non-invasive assessment of hemodynamic processes in the body. Here, diverse EIT reconstruction parameters of the GREIT algorithm were systematically evaluated to detect the aorta after saline bolus injection in apnea. True aorta position and size were taken from computed tomography (CT). A comparison with CT showed that the smallest error for aorta displacement was attained for noise figure nf = 0.7, weighting radius rw = 0.15, and target size ts = 0.01. The spatial extension of the aorta was most precise for nf = 0.7, rw = 0.25, and ts = 0.07. Detection accuracy (F1-score) was highest with nf = 0.6, rw = 0.15, and ts = 0.04. This work provides algorithm-related evidence for potentially accurate aorta detection in EIT after injection of a saline bolus.

It is possible to detect the descending aorta at different thoracic levels by EIT using an intra-aortic bolus of hypertonic saline. No significant differences in the position of the descending aorta on EIT images compared to CT images were obtained for both EIT belts.