Noise Efficient Integrated Amplifier Designs for Biomedical Applications

The recording of neural signals with small monolithically integrated amplifiers is of high interest in research as well as in commercial applications, where it is common to acquire 100 or more channels in parallel. This paper reviews the recent developments in low-noise biomedical amplifier design based on CMOS technology, including lateral bipolar devices. Seven major circuit topology categories are identified and analyzed on a per-channel basis in terms of their noise-efficiency factor (NEF), input-referred absolute noise, current consumption, and area. A historical trend towards lower NEF is observed whilst absolute noise power and current consumption exhibit a widespread over more than five orders of magnitude. The performance of lateral bipolar transistors as amplifier input devices is examined by transistor-level simulations and measurements from five different prototype designs fabricated in 180 nm and 350 nm CMOS technology. The lowest measured noise floor is 9.9 nV/√Hz with a 10 µA bias current, which results in a NEF of 1.2

In Situ Raman Analysis of CO\u2082-Assisted Drying of Fruit-Slices

This work explores the feasibility of applying in situ Raman spectroscopy for the online monitoring of the supercritical carbon dioxide (SC-CO\u2082) drying of fruits. Specifically, we investigate two types of fruits: mango and persimmon. The drying experiments were carried out inside an optical accessible vessel at 10 MPa and 313 K. The Raman spectra reveal: (i) the reduction of the water from the fruit slice and (ii) the change of the fruit matrix structure during the drying process. Two different Raman excitation wavelengths were compared: 532 nm and 785 nm. With respect to the quality of the obtained spectra, the 532 nm excitation wavelength was superior due to a higher signal-to-noise ratio and due to a resonant excitation scheme of the carotenoid molecules. It was found that the absorption of CO\u2082 into the fruit matrix enhances the extraction of water, which was expressed by the obtained drying kinetic curve

Geochemical wolframite fingerprinting - the likelihood ratio approach for laser ablation ICP-MS data

Wolframite has been specified as a ‘conflict mineral’ by a U.S. Government Act, which obliges companies that use these minerals to report their origin. Minerals originating from conflict regions in the Democratic Republic of the Congo shall be excluded from the market as their illegal mining, trading, and taxation are supposed to fuel ongoing violent conflicts. The German Federal Institute for Geosciences and Natural Resources (BGR) developed a geochemical fingerprinting method for wolframite based on laser ablation inductively coupled plasma-mass spectrometry. Concentrations of 46 elements in about 5300 wolframite grains from 64 mines were determined. The issue of verifying the declared origins of the wolframite samples may be framed as a forensic problem by considering two contrasting hypotheses: the examined sample and a sample collected from the declared mine originate from the same mine (H 1 ), and the two samples come from different mines (H 2 ). The solution is found using the likelihood ratio (LR) theory. On account of the multidimensionality, the lack of normal distribution of data within each sample, and the huge within-sample dispersion in relation to the dispersion between samples, the classic LR models had to be modified. Robust principal component analysis and linear discriminant analysis were used to characterize samples. The similarity of two samples was expressed by Kolmogorov-Smirnov distances, which were interpreted in view of H 1 and H 2 hypotheses within the LR framework. The performance of the models, controlled by the levels of incorrect responses and the empirical cross entropy, demonstrated that the proposed LR models are successful in verifying the authenticity of the wolframite samples

Effects of invisible particle emission on global inclusive variables at hadron colliders

We examine the effects of invisible particle emission in conjunction with QCD initial state radiation (ISR) on quantities designed to probe the mass scale of new physics at hadron colliders, which involve longitudinal as well as transverse final-state momenta. This is an extension of our previous treatment, arXiv:0903.2013, of the effects of ISR on global inclusive variables. We present resummed results on the visible invariant mass distribution and compare them to parton-level Monte Carlo results for top quark and gluino pair-production at the LHC. There is good agreement as long as the visible pseudorapidity interval is large enough (eta ~ 3). The effect of invisible particle emission is small in the case of top pair production but substantial for gluino pair production. This is due mainly to the larger mass of the intermediate particles in gluino decay (squarks rather than W-bosons). We also show Monte Carlo modelling of the effects of hadronization and the underlying event. The effect of the underlying event is large but may be approximately universal.Comment: 22 pages, expanded sections and other minor modifications. Version published in JHE

Ketogenic diet and fasting diet as Nutritional Approaches in Multiple Sclerosis (NAMS): protocol of a randomized controlled study

BACKGROUND: Multiple sclerosis (MS) is the most common inflammatory disease of the central nervous system in young adults that may lead to progressive disability. Since pharmacological treatments may have substantial side effects, there is a need for complementary treatment options such as specific dietary approaches. Ketone bodies that are produced during fasting diets (FDs) and ketogenic diets (KDs) are an alternative and presumably more efficient energy source for the brain. Studies on mice with experimental autoimmune encephalomyelitis showed beneficial effects of KDs and FDs on disease progression, disability, cognition and inflammatory markers. However, clinical evidence on these diets is scarce. In the clinical study protocol presented here, we investigate whether a KD and a FD are superior to a standard diet (SD) in terms of therapeutic effects and disease progression. METHODS: This study is a single-center, randomized, controlled, parallel-group study. One hundred and eleven patients with relapsing-remitting MS with current disease activity and stable immunomodulatory therapy or no disease-modifying therapy will be randomized to one of three 18-month dietary interventions: a KD with a restricted carbohydrate intake of 20-40 g/day; a FD with a 7-day fast every 6 months and 14-h daily intermittent fasting in between; and a fat-modified SD as recommended by the German Nutrition Society. The primary outcome measure is the number of new T2-weighted MRI lesions after 18 months. Secondary endpoints are safety, changes in relapse rate, disability progression, fatigue, depression, cognition, quality of life, changes of gut microbiome as well as markers of inflammation, oxidative stress and autophagy. Safety and feasibility will also be assessed. DISCUSSION: Preclinical data suggest that a KD and a FD may modulate immunity, reduce disease severity and promote remyelination in the mouse model of MS. However, clinical evidence is lacking. This study is the first clinical study investigating the effects of a KD and a FD on disease progression of MS

Slow breathing reduces sympathoexcitation in COPD

Neurohumoral activation has been shown to be present in hypoxic patients with chronic obstructive pulmonary disease (COPD). The aims of the present study were to investigate whether there is sympathetic activation in COPD patients in the absence of hypoxia and whether slow breathing has an impact on sympathoexcitation and baroreflex sensitivity. Efferent muscle sympathetic nerve activity, blood pressure, cardiac frequency and respiratory movements were continuously measured in 15 COPD patients and 15 healthy control subjects. Baroreflex sensitivity was analysed by autoregressive spectral analysis and the alpha-angle method. At baseline, sympathetic nerve activity was significantly elevated in COPD patients and baroreflex sensitivity was decreased (5.0+/-0.6 versus 8.9+/-0.8 ms.mmHg(-1)). Breathing at a rate of 6 breaths.min(-1) caused sympathetic activity to drop significantly in COPD patients (from 61.3+/-4.6 to 53.0+/-4.3 bursts per 100 heartbeats) but not in control subjects (39.2+/-3.2 versus 37.5+/-3.3 bursts per 100 heartbeats). In both groups, slow breathing significantly enhanced baroreflex sensitivity. In conclusion, sympathovagal imbalance is present in normoxic chronic obstructive pulmonary disease patients. The possibility of modifying these changes by slow breathing may help to better understand and influence this systemic disease

Slow breathing reduces sympathoexcitation in COPD

Neurohumoral activation has been shown to be present in hypoxic patients with chronic obstructive pulmonary disease (COPD). The aims of the present study were to investigate whether there is sympathetic activation in COPD patients in the absence of hypoxia and whether slow breathing has an impact on sympathoexcitation and baroreflex sensitivity. Efferent muscle sympathetic nerve activity, blood pressure, cardiac frequency and respiratory movements were continuously measured in 15 COPD patients and 15 healthy control subjects. Baroreflex sensitivity was analysed by autoregressive spectral analysis and the alpha-angle method. At baseline, sympathetic nerve activity was significantly elevated in COPD patients and baroreflex sensitivity was decreased (5.0+/-0.6 versus 8.9+/-0.8 ms.mmHg(-1)). Breathing at a rate of 6 breaths.min(-1) caused sympathetic activity to drop significantly in COPD patients (from 61.3+/-4.6 to 53.0+/-4.3 bursts per 100 heartbeats) but not in control subjects (39.2+/-3.2 versus 37.5+/-3.3 bursts per 100 heartbeats). In both groups, slow breathing significantly enhanced baroreflex sensitivity. In conclusion, sympathovagal imbalance is present in normoxic chronic obstructive pulmonary disease patients. The possibility of modifying these changes by slow breathing may help to better understand and influence this systemic disease

Phase Noise of SAW Delay Line Magnetic Field Sensors

Surface acoustic wave (SAW) sensors for the detection of magnetic fields are currently being studied scientifically in many ways, especially since both their sensitivity as well as their detectivity could be significantly improved by the utilization of shear horizontal surface acoustic waves, i.e., Love waves, instead of Rayleigh waves. By now, low-frequency limits of detection (LOD) below 100 pT/Hz can be achieved. However, the LOD can only be further improved by gaining a deep understanding of the existing sensor-intrinsic noise sources and their impact on the sensor's overall performance. This paper reports on a comprehensive study of the inherent noise of SAW delay line magnetic field sensors. In addition to the noise, however, the sensitivity is of importance, since both quantities are equally important for the LOD. Following the necessary explanations of the electrical and magnetic sensor properties, a further focus is on the losses within the sensor, since these are closely linked to the noise. The considered parameters are in particular the ambient magnetic bias field and the input power of the sensor. Depending on the sensor's operating point, various noise mechanisms contribute to f0 white phase noise, f-1 flicker phase noise, and f-2 random walk of phase. Flicker phase noise due to magnetic hysteresis losses, i.e. random fluctuations of the magnetization, is usually dominant under typical operating conditions. Noise characteristics are related to the overall magnetic and magnetic domain behavior. Both calculations and measurements show that the LOD cannot be further improved by increasing the sensitivity. Instead, the losses occurring in the magnetic material need to be decreased

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes t=35 ms and consumes an average power of P≈140 μW. The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment