Volker M. Koch

Visual field representations and locations of visual areas V1/2/3 in human visual cortex

The position, surface area and visual field representation of human visual areas V1, V2 and V3 were measured using fMRI in 7 subjects (14 hemispheres). Cortical visual field maps of the central 12 deg were measured using rotating wedge and expanding ring stimuli. The boundaries between areas were identified using an automated procedure to fit an atlas of the expected visual field map to the data. All position and surface area measurements were made along the boundary between white matter and gray matter. The representation of the central 2 deg of visual field in areas V1, V2, V3 and hV4 spans about 2100 mm2 and is centered on the lateral-ventral aspect of the occipital lobes at Talairach coordinates -29, -78, -11 and 25, -80, -9. The mean area between the 2-deg and 12-deg eccentricities for the primary visual areas was: V1: 1470 mm2; V2: 1115 mm2; and V3: 819 mm2. The sizes of areas V1, V2 and V3 varied by about a factor of 2.5 across individuals; the sizes of V1 and V2 are significantly correlated within individuals, but there is a very low correlation between V1 and V3. These in vivo measurements of normal human retinotopic visual areas can be used as a reference for comparison to unusual cases involving developmental plasticity, recovery from injury, identifying homology with animal models, or analyzing the computational resources available within the visual pathways.

A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients

This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-LODEC (ElectroMyoGram LOng-term DEComposition) is especially designed for multichannel long-term recordings of signals of slight muscle movements. A wavelet-based, hierarchical cluster analysis algorithm estimates the number of classes [motor units (MUs)], distinguishes single MUAPs from superpositions, and sets up the shape of the template for each class. Using three channels and a weighted averaging method to track action potential (AP) shape changes improve the analysis. In the last step, nonclassified segments, i.e., segments containing superimposed APs, are decomposed into their units using class-mean signals. Based on experiments on simulated and long-term recorded EMG signals, our software is capable of providing reliable decompositions with satisfying accuracy. EMG-LODEC is suitable for the study of MU discharge patterns and recruitment order in healthy subjects and patients during long-term measurements.

Energy harvesting from the cardiovascular system, or how to get a little help from yourself.

Human energy harvesting is envisioned as a remedy to the weight, the size, and the poor energy density of primary batteries in medical implants. The first implant to have necessarily raised the idea of a biological power supply was the pacemaker in the early 1960s. So far, review articles on human energy harvesting have been rather unspecific and no tribute has been given to the early role of the pacemaker and the cardiovascular system in triggering research in the field. The purpose of the present article is to provide an up-to-date review of research efforts targeting the cardiovascular system as an alternative energy source for active medical implants. To this end, a chronological survey of the last 14 most influential publications is proposed. They include experimental and/or theoretical studies based on electromagnetic, piezoelectric, or electrostatic transducers harnessing various forms of energy, such as heart motion, pressure gradients, and blood flow. Technical feasibility does not imply clinical applicability: although most of the reported devices were shown to harvest an interesting amount of energy from a physiological environment, none of them were tested in vivo for a longer period of time.

Design and realization of an energy harvester using pulsating arterial pressure

Most medical implants run on batteries, which require costly and tedious replacement or recharging. It is believed that micro-generators utilizing intracorporeal energy could solve these problems. However, such generators do not, at this time, meet the energy requirements of medical implants.This paper highlights some essential aspects of designing and implementing a power source that scavenges energy from arterial expansion and contraction to operate an implanted medical device. After evaluating various potentially viable transduction mechanisms, the fabricated prototype employs an electromagnetic transduction mechanism. The artery is inserted into a laboratory-fabricated flexible coil which is permitted to freely deform in a magnetic field. This work also investigates the effects of the arterial walls material properties on energy harvesting potential. For that purpose, two types of arteries (Penrose X-ray tube, which behave elastically, and an artery of a Göttinger minipig, which behaves viscoelastically) were tested. No noticeable difference could be observed between these two cases. For the pig artery, average harvestable power was 42 nW. Moreover, peak power was 2.38 μW. Both values are higher than those of the current state of the art (6 nW/16 nW). A theoretical modelling of the prototype was developed and compared to the experimental results.

Performance Analysis of a Miniature Turbine Generator for Intracorporeal Energy Harvesting

Replacement intervals of implantable medical devices are commonly dictated by battery life. Therefore, intracorporeal energy harvesting has the potential to reduce the number of surgical interventions by extending the life cycle of active devices. Given the accumulated experience with intravascular devices such as stents, heart valves, and cardiac assist devices, the idea to harvest a small fraction of the hydraulic energy available in the cardiovascular circulation is revisited. The aim of this article is to explore the technical feasibility of harvesting 1 mW electric power using a miniature hydrodynamic turbine powered by about 1% of the cardiac output flow in a peripheral artery. To this end, numerical modelling of the fluid mechanics and experimental verification of the overall performance of a 1:1 scale friction turbine are performed in vitro. The numerical flow model is validated for a range of turbine configurations and flow conditions (up to 250 mL/min) in terms of hydromechanic efficiency; up to 15% could be achieved with the nonoptimized configurations of the study. Although this article does not entail the clinical feasibility of intravascular turbines in terms of hemocompatibility and impact on the circulatory system, the numerical model does provide first estimates of the mechanical shear forces relevant to blood trauma and platelet activation. It is concluded that the time-integrated shear stress exposure is significantly lower than in cardiac assist devices due to lower flow velocities and predominantly laminar flow.

Signal processing with factor graphs: examples

Graphical models such as factor graphs allow to model complex systems and help to derive practical detection/estimation algorithms as message passing in the graph. In this paper, we outline three examples of on-going work of this type. For an introduction to factor graphs, we refer to F. R. Kschischang (Feb. 2001) and H. A. Loeliger (Jan. 2004). We use the notation of H. A. Loeliger (Jan. 2004).

Decomposition of electromyographic signals by iterative message passing in a graphical model

The problem of decomposing electromyographic (EMG) signals is considered. Starting from a graphical model (factor graph) of the problem, a new iterative decomposition algorithm is proposed. It is based on the sum-product (or belief/probability propagation) algorithm and is able to decompose heavily superimposed single- and multi-channel signals.

Data fusion for a hand prosthesis tactile feedback system

Current myoelectrically controlled hand prostheses normally lack tactile feedback, thus limiting their functionalities and user acceptance. We propose a non-invasive tactile sensory feedback system, consisting miniaturized sensors, wireless communication module and vibrotactile actuators, aiming at providing a natural sense of touch to amputees. We model the tactile feedback chain from tactile sensors to actuators. This model includes a 2D hand model, a sensor data fusion model, and a phantom map model. We implemented the sensor data fusion model by different techniques and compared their performance.

Evaluation and Real-Time Monitoring of Data Quality in Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a noninvasive method to image conductivity distributions within a body. One promising application of EIT is to monitor ventilation in patients as a real-time bedside tool. Thus, it is essential that an EIT system reliably provide meaningful information, or alert clinicians when this is impossible. Because the reconstructed images are very sensitive to system instabilities (primarily from electrode connection variability and movement), EIT systems should continuously monitor and, if possible, correct for such errors. Motivated by this requirement, we describe a novel approach to quantitatively measure EIT data quality. Our goals are to define the requirements of a data quality metric, develop a metric q which meets these requirements, and an efficient way to calculate it. The developed metric q was validated using data from saline tank experiments and a retrospective clinical study. Additionally, we show that q may be used to compare the performance of EIT systems using phantom measurements. Results suggest that the calculated metric reflects well the quality of reconstructed EIT images for both phantom and clinical data. The proposed measure can thus be used for real-time assessment of EIT data quality and, hence, to indicate the reliability of any derived physiological information.

Tactile prosthetics in WiseSkin

The use of prosthetic hands is limited in part by the lack of sensory feedback to the wearer. In order to provide sensory feedback, an adequate number of sensors must be integrated with the prosthesis. The WiseSkin project targets the use of artificial skin embedding ultra-low power wireless sensor nodes. This presentation provides an overview of the WiseSkin project and the current status of the developments.