bluuebunny/crossref_metadata_2025_split huggingface dataset:
A practical example of an integrated interoperable neuromonitoring system based on IEEE 11073 SDC and HL7
Authors: Weßbecher Lea , Berger Johann , Neumann Juliane , Lente Kornelius , Krüger Thilo B. 
Published: September 2023

Abstract
Purpose: With the ongoing work on the Health Level Seven (HL7) standards and the IEEE 11073 Serviceoriented Device Connectivity (SDC) family, the demand for open integration of medical devices in the operating room (OR) has become clear. Nevertheless, there are very few interoperable medical devices available to date. This work describes a practical example of connecting an intraoperative neuromonitoring (IONM) device with a radiofrequency (RF) surgical device based on SDC and with the hospital information system (HIS) based on HL7. Methods: Before starting the surgery, patient-related data must be entered into the neuromonitoring system. To minimize manual input, we integrated an automized patient data query based on HL7, which completes all other necessary data provided by the HIS after entering the patient identification number. During the surgery, while mapping neural structures in the situs using IONM, the parallel operation of RF devices for coagulation generates artifacts in the neuromonitoring signals, which makes a reliable interpretation of the IONM signals impossible. Therefore, we developed an IEEE 11073 SDC interface for the neuromonitoring device and implemented an SDC-based OR control panel. While placing the hand probe for mapping neural structures in the situs, the OR control panel suppresses the coagulation of the electrosurgical instrument and only reenables it, after the mapping has been terminated. After the surgery, the generated IONM report can be uploaded into the HIS using HL7. Therewith it is assigned automatically to the previously selected patient. Result: With the SDC- and HL7-enabled neuromonitoring system, we showed a practical use case of interoperable medical devices to optimize surgical workflow.
Method for CAD/CAM-based production of patient-specific anatomical bone surrogates from polyurethane rigid foams of different densities for various clinical applications
Authors: Sembdner Philipp , Blaurock Karl , Paetzold-Byhain Kristin 
Published: September 2023

Abstract
Physical models of bony areas are used in the medical environment for training, patient information or for various examinations. These are idealized average models which, however, do not reflect the individuality of a respective patient. Furthermore, the mechanical properties of the bony structure with cortical bone and cancellous bone are not considered in most models. A few manufacturers, such as SawBone, offer models and material blocks that approximate the properties of the bone. However, these are not patientspecific either. This paper presents a proposal for a method to create patient-specific physical bone models with CAD/CAM technologies that approximate shape and mechanical properties. The models are made of two different rigid polyurethane foams for cortical bone and cancellous bone. The methodical development includes the elaboration of a framework starting from the patient's CT data, through design derivation, to fabrication. Furthermore, a possible process for fabrication in the form of a demonstrator and its application in sawing tests will be demonstrated.
Model-based Tracking of Magnetic Sensor Gloves in Real Time
Authors: Boueke Moritz , Hoffmann Johannes , Schmidt Tobias , Bald Christin , Bergholz Robert , Schmidt Gerhard 
Published: September 2023

Abstract
While mechanical tracking is commonly seen in robot-assisted surgery, contactless gesture control leads to a more intuitive approach. Magnetic localization systems might be able to provide an untethered access to the necessary hand movement data. A model-based system exploiting a priori knowledge to improve the accuracy and robustness can be beneficial in this context. We present a proof of concept, in which the index finger of a digital twin of a hand is tracked with three simulated sensors. Based on the physiological composition, the finger is modeled as a kinematic chain with rotational degrees of freedom between the segments according to the corresponding finger joints. We applied an extended Kalman filter using this description to enhance position and rotation estimates for the sensors on the finger segments. We achieved mean absolute errors < 1 cm for the positions and < 10 for the local rotations with first simulations of a bending motion.
A Concept for Myocardial Current Density Estimation with Magnetoelectric Sensors
Authors: Engelhardt Erik , Elzenheimer Eric , Hoffman Johannes , Schmidt Tobias , Zaman Adrian , Frey Norbert , Schmidt Gerhard 
Published: September 2023

Abstract
In this paper we present a novel noninvasive approach to estimate current densities in the heart from magnetocardiography. The proposed algorithm uses nested optimization to model current densities in equally-sized voxels of myocardial tissue. First-order Thiran all-pass filters are used to describe the propagation between voxels.We demonstrate feasibility of the algorithm for a noise-free single-layer simulation. However, challenges remain, such as addressing measurement noise and optimizing propagation velocity. Overall, this approach has the potential to complement or replace invasive catheter-based electrophysiological studies for localization of arrhythmogenic tissue.
Combined Method for Segmentation of Multiple Needles in 3D Liver CT
Authors: Moravová Adéla , Bale Reto , Harders Matthias 
Published: September 2023

Abstract
Radiofrequency ablation is a minimally invasive technique for treating liver or kidney tumors, where precisely inserted ablation probes destroy the tumor tissue with induced heat around their tips. Automatic segmentation of needles from the abdominal computed tomography (CT) scans enables large-scale procedure evaluation and creates a baseline ground truth for automated planning approaches. Our method for needle segmentation enables the detection of multiple needles from unlabeled CT scans by sequential combination of two approaches, a classical method and a neural network. The former method detects intensity-prominent needles in 3D. The latter focuses on the detection of needles with lower intensity, that remained undetected by the classical method. A well-known encoder-decoder approach on axial slices of the CT scan gives a segmentation mask for the needles prominent in slices. To train the network, we create a synthetic dataset, adding needles to clean abdominal CT slices. Our network trained on a small synthetic dataset is able to generalize to clinical CT scans. The performance of the combined method is evaluated on ten anonymous patients from the Medical University of Innsbruck each with up to 18 needles inserted in the liver without any template.
Muscle stimulation for peripheral venous oxygen saturation estimation using photoplethysmography: a proof-of-concept
Authors: Badiola Idoia , Lyu Chenglin , Ferchland Arne , Comes Fabian , Blazek Vladimir , Leonhardt Steffen , Lueken Markus 
Published: September 2023

Abstract
The body’s ability to balance oxygen supply and demand can be compromised in conditions such as shock, sepsis, and heart failure. Thus, measuring venous oxygen saturation (SvO2) simultaneously with the well-established peripheral arterial oxygen saturation can help in the clinical management of these conditions. Some authors have suggested a non-invasive SvO2 estimation method that acquires venous blood volume variations generated through the calf muscle pump using photoplethysmography (PPG): the Venous Muscle Pump Test (VMPT). However, the technique presents significant variability in the rhythm and speed of the foot dorsal flexions needed for the VMPT and cannot be performed on unconscious subjects and those with reduced mobility. This study proposes using functional electrical stimulation (FES) to stimulate the calf muscle and generate rhythmic and reproducible muscle contractions. A human proof-of-concept study was conducted with three healthy young male participants. The PPG signals achieved through the VMPT with conventionally active and FES-induced movements were compared. We found that FES-induced movement produced reproducible venous blood volume variations comparable to the ones induced by the active movement. However, it also leads to lower venous refilling time and lower muscle power. Although further individualized tuning of the stimulation parameters is needed to achieve more conclusive results, FES-induced movement proves to be a promising alternative to the conventional VMPT technique to measure venous oxygen saturation and assess venous insufficiency in specific clinical situations.
Conception And Implementation of an virtual Reality application for the evaluation of different types of commercially available haptic gloves
Authors: Schmücker Vanessa , Grensing Florian , Jakob Rebekka , Gießer Christian , Brück Rainer , Eiler Tanja 
Published: September 2023

Abstract
Immersion and presence are important aspects of virtual reality (VR). Efforts are therefore being made to enhance these effects in order to increase the impact of VR and its transferability to reality. This is particularly important in research areas such as medical education and psychology. The development of haptic gloves has therefore increased significantly in recent years. These are immersive input devices designed to make virtual objects tangible. However, the research field is still very new and there is insufficient research on their handling and effect. For this reason, an application was developed to compare different haptic data gloves with different haptic technologies. A glove with vibrotactile feedback (Manus Prime X Haptic VR) and a glove with additional force feedback (SenseGlove) were used and compared in multiple tasks. These include finger tracking and gesture recognition, as well as hand tracking and the behaviour of gloves when grasping objects with and without haptics. The aim is to determine how well the haptics could be integrated and which is more accepted by the test subjects. For this purpose, a questionnaire has been designed to evaluate the effect of the haptic gloves on users. It aims to get a first impression of the tactile gloves available on the market and to advance research in the area.
Limitations of Pacemaker Spike Detection in Capacitive ECGs via Deep Learning
Authors: Rohr Maurice , Huang Zhaolan , Umutcan Uguz Durmus , Dettori Rosalia , Napp Andreas , Walter Marian , Leonhardt Steffen , Hoog Antink Christoph 
Published: September 2023

Abstract
Pacemaker spike detection is an important step in monitoring paced patients. Capacitive ECG facilitates unobtrusive monitoring of subjects during daily routines such as driving. Robust algorithms are required to deal with low signal quality and artifacts, e.g. by employing fusion of multiple signal channels. Due to the low signal-to-noise ratio of the measurement, there are limitations to detection accuracy compared to conventional ECG monitors. Especially low voltage stimulations such as bipolar pacemaker spikes are hard to detect. We present a convolutional network approach to improve on recent signal processing algorithms.We show a realistic evaluation of its performance using leave-one-subject-out cross validation (LOOCV), its dependence on the size of the receptive field, and an estimation of an upper performance bound.
Multi-Height Extraction of Clinical Parameters Improves Classification of Craniosynostosis
Authors: Becker Anna Maria , Schaufelberger Matthias , Kühle Reinald Peter , Freudlsperger Christian , Nahm Werner 
Published: September 2023

Abstract
Introduction: 3D surface scan-based diagnosis of craniosynostosis is a promising radiation-free alternative to traditional diagnosis using computed tomography. The cranial index (CI) and the cranial vault asymmetry index (CVAI) are well-established clinical parameters that are widely used. However, they also have the benefit of being easily adaptable for automatic diagnosis without the need of extensive preprocessing. Methods: We propose a multi-height-based classification approach that uses CI and CVAI in different height layers and compare it to the initial approach using only one layer. We use ten-fold cross-validation and test seven different classifiers. The dataset of 504 patients consists of three types of craniosynostosis and a control group consisting of healthy and non-synostotic subjects. Results: The multi-height-based approach improved classification for all classifiers. The k-nearest neighbors classifier scored best with a mean accuracy of 89% and a mean F1-score of 0.75. Conclusion: Taking height into account is beneficial for the classification. Based on accepted and widely used clinical parameters, this might be a step towards an easy-to-understand and transparent classification approach for both physicians and patients.
Determination of the water content of an ex vivo porcine liver
Authors: Busch Christoph , Rupitsch Stefan J. , Moeller Knut 
Published: September 2023

Abstract
When modeling High-Frequency (HF) surgical applications the choice of the right dielectric tissue parameters is a key factor for success. The water content of biological tissue is of particular importance since the dielectric tissue properties highly depend on it. Therefore, we investigate a simple and fast method for the determination of relative tissue water content using small samples of an ex vivo porcine liver. To identify the water content, we utilized a desiccation and weighing experiment. It is assumed that the weight loss can be directly attributed to the loss of water in the case of desiccation. Based on the ratio between the initial and dry weight of a sample, the relative water content can be determined. The drying procedure lasted 28 hours, with 16 small samples taken from an ex vivo porcine liver. At the end of the drying procedure, a relative tissue water content of 69.6 % ± 1.1 % was observed. Using a curve fit to the measured data, it can be assumed that approximately 99.3 % of the tissue water content had evaporated by the end of the drying process. With the drying and weighing method used, we were able to determine the water content of the ex vivo porcine liver relatively easily. However, our results also suggest that optimizations of the applied method should be considered for future measurements. Especially, if a faster determination of the water content is required, which in a broader sense should afterward serve for the validation of a simulation model.
Proof-of-Concept: Conceptual design and realisation of an educational augmented reality (AR) application for anaesthesia induction
Authors: Maleszka Leonie , Breuer-Kaiser Andreas , Schäfer Marcel , Lehn Karsten , Felderhoff Thomas 
Published: September 2023

Abstract
Increasing patient safety during operations is an inevitable topic in anaesthesiology. The cockpit strategy presented by Vogelsang et al. addresses this issue with elements such as closed loop communication and the use of checklists. In this paper, the use of checklists is taken up further. An AR application is presented that augments checklists for anaesthesia induction into the user’s field of view. The application is to be implemented in teaching and help prospective physicians to internalise the workflows in anaesthesiology more quickly and sustainably. A working prototype has been created and loaded onto the HoloLens 2. The prototype is being assessed and evaluated by an anaesthetist. The feedback will be included into the following development steps. The application will be further adapted to the needs and requirements of the users. In order to evaluate the usability and the user experience, students and medical professionals should test and evaluate the application repeatedly.
Generation and validation of a PREP sample response
Authors: Rölle Annika , Felderhoff Thomas , Enax-Krumova Elena , Eitner Lynn , Höffken Oliver , Maier Christoph , Simal Özgül Özüm 
Published: September 2023

Abstract
PREP are typical signal courses in the EEG, which are used to investigate the influence of neuropathologies or, for example, post-traumatic headaches on pain processing. The medically meaningful peaks are detected manually for further analysis. To develop a more time-efficient method, an automatic, latency-independent way of detection was created using the cross-correlation function, which generates a sample response for each data set. The comparison of automatic and manual methods shows advantages and disadvantages of both methods, but no significant deviation of the automatic method can be found.
Towards non-invasive Wilson’s disease progression monitoring based on corneal copper content
Authors: Mayer Juliane , Narr Julia , Pott Peter P. 
Published: September 2023

Abstract
Patients suffering from Wilson’s disease accumulate copper in their body. Copper load may become visible as Kayser-Fleischer (KF) rings - colorful copper-sulfur granules in the base of the cornea. Reoccurrence of KF rings in patients under anti-copper medication may be a sign for ineffective treatment. This work proposes a hand-held sensing system based on infrared reflectivity measurement of the cornea as a potential method for regular at-home progression monitoring. The intensity of the LEDs used is safe for operation on the human eye. As proof of concept, reflectivity was measured when placing the sensor onto samples of white paper that had copper tape of different sizes centered within the sensitive area. Further, reflectivity was determined for different solutions of copper sulfate in saline that were poured into adapter bowels fitting the sensor head, with Cu2+ concentrations of the samples deducted from literature. While the prototype system can be used to differentiate between two copper strips of 6 and 12 mm2 or larger, a different measuring principle would be needed to also determine the concentration of copper sulfate solutions. Since these two in-vitro models cannot fully represent the complex in-vivo molecular conditions of patients’ corneas, additional measurements on patients’ eyes are necessary to evaluate the potential of the proposed sensing system.
A triangle-shape region of interest in cardiorespiratory estimation during sleep monitoring
Authors: Haghi Mostafa , Asadov Akhmadbek , Boiko Andrei , Seepold Ralf 
Published: September 2023

Abstract
Introduction: With advancements in sensor and communication technologies, sleep monitoring is moving out of specialized clinics and into everyday homes. Extracting sleep-related data using far less complicated tools and procedures is possible than polysomnography. Respiratory and cardiovascular data are extracted from the signals such as the electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) to identify the aberrant respiratory events of apnea/hypopnea as well as to estimate sleep parameters. However, due to the different sleeping positions, such systems lack accuracy and/or complicated sensor network topology. In this work, we proposed an optimal topology of forcesensitive resistor (FSR) sensors to simplify the system design by identifying the region of interest for estimating cardiorespiratory parameters with minimal error. The sensors are deployed under the mattress and located on the bed frame. Methods: We proposed a low-cost, unobtrusive, non-invasive, and reliable solution with robust signal processing algorithms for cardiorespiratory measurements and automatic signal validation based on signal quality. The solution is established based on a multi-physical layer (MPL) and sensor interfaces coping with the embedded system’s specifications, and signal processing is performed onboard with two independent and simultaneous pipelines for heart rate and respiratory rate using discrete wavelet transform (DWT) and bandpass filter, respectively. Results: We identified the three most contributing FSR sensors forming a triangle shape covering the left upper side of the subject (in the supine position) as the region of interest. We reduced the mean absolute error (MAE) to as low as 3.94 and 2.35 for heart rate and respiratory rate. Conclusions: The approach with the topology of triangle-shaped performs appropriately in estimating the cardiorespiratory parameters in all four regular sleeping positions, i.e. supine, prone, left lateral, and right lateral.
Removing noise and overlapping spikes from extracellular recordings using a regularized denoising autoencoder
Authors: Samann Fars , Meyer Luca , Schanze Thomas 
Published: September 2023

Abstract
One of the difficulties in sorting extracellularly recorded neuronal action potentials, known as spikes, is noise from several sources superimposed on the target spike. For example, the activity of other neurons is often overlaid on the recorded spikes, denoted as neuronal noise interference (NNI), and interference from neighbouring neurons firing at the same time is known as overlapping spikes. In addition, the noise induced by the recording device is known as Gaussian white noise (GWN). Thus, the appropriate signal denoising process of extracellular recordings is an extremely challenging task, where the classical filtering approach may fail specially in removing the overlapping spikes due to the similarity between the spike waveforms of the neighbouring neurons and the wanted single-unit waveform. This issue is tackled in this work by training regularized denoising autoencoder (RDAE) model with the average spike-waveforms of previously identified single-units, denoted as ground truth. To capture the distinct features of the wanted single-unit waveforms, and weight regularization were employed at the encoder/decoder of the RDAE, respectively. With a suitable regularization parameter, the -RDAE model outperforms the -RDAE model. In general, RDAE shows extraordinary performance not only in removing the NNI and GWN, but also the overlapping spikes.
Development of an Intelligent Walking Aid for Fall Detection
Authors: Schröter Eliane , Thanh Nghi Doan , Schneider Armin 
Published: September 2023

Abstract
The medical field has made significant improvements over the past decades, leading to an older and healthier population. One of the main core problems being tackled is the detection of falling events. The quick detection of a fall contributes greatly to the fast arrival of help and prevents further damages. Therefore an intelligent crutch was developed with the purpose of detecting a fall and the following call for help if needed, as well as conveying the location of the user to relatives and care givers. The crutch was evaluated in a series of experiments during which all falls were identified correctly and alerting was initiated.
The ARTEMIS project: Mixed-Signal IC for Edge-AI-based Classification of ECG Signals
Authors: Hoyer Ingo , Roßman Özgü , Utz Alexander , Straczek Lukas , Akboyraz Onur , Hessel Sebastian , Lüdecke André , Seidl Karsten 
Published: September 2023

Abstract
Atrial fibrillation (AF) is a common heart arrhythmia and is closely associated with causing strokes. Diagnosis is usually performed with Holter monitors over longer periods of time, causing discomfort to the patient. The proposed mixed-signal integrated circuit (IC) is designed for small patch electrocardiogram (ECG) devices and combines, an analog front-end (AFE) with tailored recording channel characteristics and 12-bit successive-approximation-register analog digital converter (SAR ADC) as well as an RISC-V based microcontroller (μC) for edge artificial intelligence (AI)-based AF-detection. The digital signal processing is supported with hardware accelerators. Including 160 kB of SRAM, the system on chip (SoC) requires 25.56 mm² in silicon area in a 180 nm technology. The recording channel shows promising simulation results with an input impedance of 230 MΩ, an input referred noise of below 1.6 μVrms and a CMMR of 95 dB. The digital part enables the integration of AI-based classification on the IC. Due to the flexibility of the software-based classification approach, this IC can also be used to detect other arrhythmias.