Techn. Univ., Diss.--Ilmenau, 2007. / Enth. außerdem: Thesen.

Universiẗat, Diss., 1997--Jena.

3D printed on-skin electrodes are of notable interest because, unlike traditional wet silver/silver chloride (Ag/AgCl) on-skin electrodes, they can be personalized and 3D printed using a variety of materials with distinct properties such as stretchability, conformal interfaces with skin, biocompatibility, wearable comfort, and, finally, low-cost manufacturing. Dry on-skin electrodes, in particular, have the additional advantage of replacing electrolyte gel, which dehydrates and coagulates with prolonged use. However, issues arise in performance optimization with the recently discovered dry materials. These challenges become even more critical when the on-skin electrodes are scaled down to a miniaturized size, making the detection of various biosignals while keeping mechanical resilience under several conditions crucial. Thus, this thesis focuses on designing, fabricating, optimizing, and applying a personalized, fully 3D-printed permeable skin patch with miniaturized octopus-like suckers and embedded microchannels for enhanced mechanical strengths, breathability, and biosignal monitoring. The developed device showcases a rapid, cost-effective fabrication process of porous skin patches and the printing process of ink metal-based materials that expands its applications to low-resource settings and environments.

In order to handle the increasing costs of healthcare more of the care and monitoring will take place in the patient’s home. It is therefore desirable to develop smaller and portable systems that can record important biosignals such as the electrical activity of the heart in the form of an ECG. This project is a continuation on a previous project that developed a shield that can be connected to the GPIO pins of a Raspberry Pi, a credit-card sized computer. The shield contains an ADAS1000, a low power and compact device that can record the electrical activity of the heart along with respiration. The aim of this project was to develop an application that can run on the Raspberry Pi in order to display the captured data from the shield on a screen along with storing the data for further processing. The project was successful in the way that the requirements for the software have been fulfilled. / För att hantera den ökande kostnaden för hälso- och sjukvård kommer en större del av övervakning samt vård att ske i patientens hem. Det kommer därför att vara önskvärt att utveckla mindre system som är lättare att hantera än de större traditionella apparaterna för att samla in vanliga biosignaler som exempelvis ett EKG. Detta projekt är en fortsättning på ett tidigare projekt vars syfte var att framställa en ”sköld” som kan kopplas ihop med en Raspberry Pi via dess GPIO pinnar. Det föregående projektet var lyckat och en sköld innehållande en ADAS1000 som kan samla in bl.a. ett EKG samt andningen framställdes. Syftet med detta projekt var att utveckla en applikation som kan köras på en Raspberry Pi och på så sätt visa den data som samlas in från skölden på en skärm. Det skulle även vara möjligt att spara insamlad data för senare användning. Projektet resulterade i en applikation som uppfyllde dessa krav.

Graph Signal Processing (GSP) has been used in the analysis of functional Magnetic Resonance Imaging(fMRI). As a holistic view of brain function and the connections between and within brain regions, by structuring data as node points within the brain and modelling the edge connections between nodes. Many studies have used GSP with Blood Oxygenation Level Dependent (BOLD) imaging of the brain and brain activation. Meanwhile, the methodology has seen little use in muscle imaging. Similar to brain BOLD, muscle BOLD (mBOLD) also aims to demonstrate muscle activation. Muscle BOLD depends on oxygenation, vascularization, fibre type, blood flow, and haemoglobin count. Nevertheless the mBOLD signal still follows muscle activation closely. Electromyography (EMG) is another modality for measuring muscle activation. Both mBOLD and EMG can be represented and analyzed with GSP. In order to better understand muscle activation during contraction the proposed method focused on using GSP to model mBOLD data both alone and jointly with EMG. Simultaneous mBOLD imaging and EMG recording of the calf muscles was performed, creating a multimodal dataset. A generalized filtering methodology was developed for the removal of the MRI gradient artifact in EMG sensors within the MR bore. The filtered data was then used to generate a GSP model of the muscle, focusing on gastrocnemius, soleus, and tibialis anterior muscles. The graph signals were constructed along two edge connection dimensions; coherence and fractility. For the standalone mBOLD graph signal models, the models’ goodness of fits were 1.3245 × 10-05 and 0.06466 for coherence and fractility respectively. The multimodal models showed values of 2.3109 × -06 and 0.0014799. These results demonstrate the promise of modelling muscle activation with GSP and its ability to incorporate multimodal data into a singular model. These results set the stage for future investigations into using GSP to represent muscle with mBOLD, EMG, and other biosignal modalities. / Thesis / Master of Applied Science (MASc) / Magnetic Resonance Imaging(MRI) and electromyography (EMG) are techniques used in the analysis of muscle, for detecting injury or deepening the understanding of muscle function. Graph Signal Processing (GSP) is a methodology used to represent data and the information flow between positions. While GSP has been used in modelling the brain, applications to muscle are scarce. This work aimed to model muscle activation using GSP methods, using both MRI and EMG data. To do so, a method for being able to simultaneously record MRI and EMG data was developed through hardware construction and the software implementation of EMG signal filtering. The collected data were then used to construct multiple GSP models based on the coherence and complexity of the signals, the goodness of fit for each of the constructed models were then compared. In conclusion, it is feasible to use GSP to model muscle activity with multimodal MRI and EMG data. This shows promise for future investigations into the applications of GSP to muscle research.

Effective game music is typically adaptive, interactive, or both. Changes in game music are usually influenced by the current state of the game or the actions of the player. To provide another dimension of interactivity, it would be useful to know the affective state of the human player. Biosignals are continuous signals generated by a person that can be measured over time, and have been shown to reflect affective state. This project demonstrates that control signals can be gathered from the player and mapped to musical parameters. Using a heart rate sensor and galvanic skin response sensor built from open source designs, we have used biosignals to control music playback while playing four games from different genres. A system for controlling game music with biosignals is computationally cheap, and can provide data that is useful to other game systems. The prototype developed for this project is basic, but with further research and development, we believe such a system will greatly improve the immersive experience of video games by involving the player on a new level. / text

Techn. Universiẗat, Diss., 2001--Berlin.

No description available.

abstract: The recording of biosignals enables physicians to correctly diagnose diseases and prescribe treatment. Existing wireless systems failed to effectively replace the conventional wired methods due to their large sizes, high power consumption, and the need to replace batteries. This thesis aims to alleviate these issues by presenting a series of wireless fully-passive sensors for the acquisition of biosignals: including neuropotential, biopotential, intracranial pressure (ICP), in addition to a stimulator for the pacing of engineered cardiac cells. In contrast to existing wireless biosignal recording systems, the proposed wireless sensors do not contain batteries or high-power electronics such as amplifiers or digital circuitries. Instead, the RFID tag-like sensors utilize a unique radiofrequency (RF) backscattering mechanism to enable wireless and battery-free telemetry of biosignals with extremely low power consumption. This characteristic minimizes the risk of heat-induced tissue damage and avoids the need to use any transcranial/transcutaneous wires, and thus significantly enhances long-term safety and reliability. For neuropotential recording, a small (9mm x 8mm), biocompatible, and flexible wireless recorder is developed and verified by in vivo acquisition of two types of neural signals, the somatosensory evoked potential (SSEP) and interictal epileptic discharges (IEDs). For wireless multichannel neural recording, a novel time-multiplexed multichannel recording method based on an inductor-capacitor delay circuit is presented and tested, realizing simultaneous wireless recording from 11 channels in a completely passive manner. For biopotential recording, a wearable and flexible wireless sensor is developed, achieving real-time wireless acquisition of ECG, EMG, and EOG signals. For ICP monitoring, a very small (5mm x 4mm) wireless ICP sensor is designed and verified both in vitro through a benchtop setup and in vivo through real-time ICP recording in rats. Finally, for cardiac cell stimulation, a flexible wireless passive stimulator, capable of delivering stimulation current as high as 60 mA, is developed, demonstrating successful control over the contraction of engineered cardiac cells. The studies conducted in this thesis provide information and guidance for future translation of wireless fully-passive telemetry methods into actual clinical application, especially in the field of implantable and wearable electronics. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

The main objective of this work was the development of a computer-based Expert Sleep Analysis Methodology (ESAM) to aid sleep care physicians in the diagnosis of pre-Parkinson's disease symptoms using polysomnogram data. ESAM is significant because it streamlines the analysis of the human sleep cycles and aids the physician in the identification, treatment, and prediction of sleep disorders. In this work four aspects of computer-based human sleep analysis were investigated: polysomnogram interpretation, pre-processing, sleep event classification, and abnormal sleep detection. A review of previous developments in these four areas is provided along with their relationship to the establishment of ESAM. Polysomnogram interpretation focuses on the ambiguities found in human polysomnogram analysis when using the rule based 1968 sleep staging manual edited by Rechtschaffen and Kales (R&K). ESAM is presented as an alternative to the R&K approach in human polysomnogram interpretation. The second area, pre-processing, addresses artifact processing techniques for human polysomnograms. Sleep event classification, the third area, discusses feature selection, classification, and human sleep modeling approaches. Lastly, abnormal sleep detection focuses on polysomnogram characteristics common to patients suffering from Parkinson's disease. The technical approach in this work utilized polysomnograms of control subjects and pre-Parkinsonian disease patients obtained from the Emory Clinic Sleep Disorders Center (ECSDC) as inputs into ESAM. The engineering tools employed during the development of ESAM included the Generalized Singular Value Decomposition (GSVD) algorithm, sequential forward and backward feature selection algorithms, Particle Swarm Optimization algorithm, k-Nearest Neighbor classification, and Gaussian Observation Hidden Markov Modeling (GOHMM). In this study polysomnogram data was preprocessed for artifact removal and compensation using band-pass filtering and the GSVD algorithm. Optimal features for characterization of polysomnogram data of control subjects and pre-Parkinsonian disease patients were obtained using the sequential forward and backward feature selection algorithms, Particle Swarm Optimization, and k-Nearest Neighbor classification. ESAM output included GOHMMs constructed for both control subjects and pre-Parkinsonian disease patients. Furthermore, performance evaluation techniques were implemented to make conclusions regarding the constructed GOHMM's reflection of the underlying nature of the human sleep cycle.