P 51 A factor graph-based change point detection with an application to semg- Onset and activity detection

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1 S116 P 51 A factor graph-based change point detection with an application to semg- Onset and activity detection Christian Hoffmann, Institut für Medizinische Elektrotechnik, Universität zu Lübeck, Lübeck, Deutschland, christian.hoffmann@uni-luebeck.de Eike Petersen, Institut für Medizinische Elektrotechnik, Universität zu Lübeck, Lübeck, Deutschland, Eike.Petersen@uni-luebeck.de Thomas Handzsuj, Institut für Medizinische Elektrotechnik, Universität zu Lübeck, Lübeck, Deutschland, Thomas.Handzsuj@draeger.com Giacomo Bellani, Institut für Medizinische Elektrotechnik, Universität zu Lübeck, Lübeck, Deutschland, giacomo.bellani1@unimib.it Philipp Rostalski, Institut für Medizinische Elektrotechnik, Universität zu Lübeck, Lübeck, Deutschland, Philipp.Rostalski@uni-luebeck.de Change point detection (CPD) algorithms are relevant tools to achieve triggering of various functions, e.g., in medical support devices. In the context of mechanical ventilation, one such application exists in detecting muscle activity based on electromyographic (EMG) measurements of the diaphragm. Change point detection algorithms to be applied in this setting are required to reliably detect the onset of the EMG signal in real-time and are usually desired to operate on the raw signal, thus minimizing the required effort for prior signal processing. In turn, information about the periods of muscular activity facilitates a wide range of subsequent signal processing and estimation algorithms. A novel algorithm for EMG-onset and activity detection is proposed based on a probabilistic graphical model, formulated as a factor graph. Factor graphs form a class of probabilistic graphical models representing the factorization of probability density functions as bipartite graphs. They can be used to exploit the conditional independence structure of the underlying model to effciently solve inference problems by message passing on graphs. Hence, the factor graph framework is capable of recovering a wide range of classical results in signal processing, estimation and control in a unified framework. The present work advocates the use of this class of models in the field of change point detection and activity estimation. Based on a combined factor graph representation of both the Kalman filter and the expectation maximization algorithm, regularized signal estimation is achieved. In conjunction with a simple dynamic model, the sparsity of the estimated input results in a filtered state estimate denoting the estimated activity level. Thresholding on this signal yields the desired detection of the onset and activity of the EMG signal. Possible extensions are outlined for automated adaptation of the threshold levels. The presented example highlights the efficacy of the proposed method on clinical data.

2 S117 P 53 Experimental workflow for determining psychological stress from physiological biosignals Alexander Pilling, Laboratory for Biomechanics, OTH Regensburg, Regensburg, Deutschland, alexander.pilling@st.oth-regensburg.de Franz Suess, Regensburg Center of Biomedical Engineering, OTH and University Regensburg, Regensburg, Deutschland, franz.suess@oth-regensburg.de Simone Kubowitsch, Laboratory for Biomechanics, OTH Regensburg, Regensburg, Deutschland, simone.kubowitsch@extern.oth-regensburg.de Sebastian Dendorfer, Laboratory for Biomechanics, OTH Regensburg, Regensburg, Deutschland, sebastian.dendorfer@oth-regensburg.de Contemporary, stress is a major growing concern impacting both individuals and population. Stress investigation has a beneficial wide range for the society resulting in an interesting area with many social advantages, e.g. improving learning, personal operations, and increasing work productivity. Mental stress is reflected by dynamic activity transformation of the autonomic nervous system (ANS). The examination of heart rate variability (HRV) and skin conductance response (SCR) is a common tool for the evaluation of the ANS. The main focus of this scientific investigation is to determine ways of monitoring, measuring and detecting psychological stress from physiologically obtained biosignals. The aim of this project is to establish a workflow in order to determine mental stress based on physiological behavior. The experimental workflow includes 1) the monitoring and physiological interpretation of biosignals, 2) signal processing (obtainment and calculation of stress indicating parameters, HRV frequency analysis methods, e.g. fast Fourier transformation, power spectral density and continuous wavelet transformation), 3) grouping of physiological reaction type comportment by clustering with the single-linkage and ward concept, 4) quantitative scaling of stress intensity based on mental stress mapping and an own established SCR parameter. Besides HRV and SCR, which are most sensitive to mental changes, the respiration, blood volume pulse and temperature were also considered for stress detection. The experimental workflow will be represented by physiological data from a previous study and a pre-study validation for future investigations. Significant stress recognition is rather possible on subject-dependent fashion, whereas in the case of subject-independence a more difficult problem is provided, due to the between-subject differences that typically appear in biosignals. Systems for automatic stress detection in general are a challenge for the years to come.

3 S118 P 54 Monitoring of drilling induced noise during ear surgeries Matthias Bornitz, Medizinische Fakultät, HNO-Klinik, TU Dresden, Dresden, Deutschland, matthias.bornitz@uniklinikum-dresden.de Mario Fleischer, Medizinische Fakultät, HNO-Klinik, TU Dresden, Dresden, Deutschland, mario.fleischer@uniklinikum-dresden.de Thomas Zahnert, Medizinische Fakultät, HNO-Klinik, TU Dresden, Dresden, Deutschland, thomas.zahnert@uniklinikum-dresden.de It has been argued, that during surgery at the head, drilling induced noise can exceed recommended daily exposure limits. Because of general anaesthesia of the patients, evaluation of bone conducted noise level is limited to indirect and objective measurements. Therefore, we have developed a system which allows real-time estimation of the equivalent sound pressure level during surgery. The system is built up of a calibration device (piezoelectric shaker connected to a force sensor) and a piezoelectric uniaxial acceleration sensor. The acceleration sensor is attached to the skull and calibrated by an explicit broadband force signal at the mastoid (generated by the calibration device). The equivalent sound pressure level L in db(spl) can be obtained from the force by using the standard for bone conduction hearing tests (EN ISO 389-3:1998). The acceleration measured during drilling can thus be converted to an equivalent sound pressure level, which then is subsequently weighted in the frequency domain using A- & C-Filter and in the time domain with a time constant of 125 ms (fast) according to DIN EN leading to L AF and L CPeak, respectively. In seven monitored cochlear implant (CI) operations, we found equivalent sound pressure values L AF of up to 120 db(spl) in maximum and L CPeak of up to db(spl). In three out of seven surgeries, the recommended daily exposure limits were exceeded. Greatest values had been observed during mastoidectomy and preparation of the implant bed. We conclude that for extensive drilling, such as during CI-operations, a potential risk for temporary hearing impairment is given. Especially in single side affected ears, the contralateral, healthy ear is in danger of hearing disorders through bone conducted noise. This is in accordance to clinical results where after mastoidectomy temporary threshold shifts for bone conduction have been observed.

4 S119 P 57 Performance evaluation of state-of-the-art neural recorder SoCs Michael Haas, Universität Ulm, Ulm, Deutschland, michael.haas@uni-ulm.de Mahdi Rajabzadeh, Universität Ulm, Ulm, Deutschland, mahdi.rajabzadeh@uni-ulm.de Maurits Ortmanns, Universität Ulm, Ulm, Deutschland, maurits.ortmanns@uni-ulm.de The State-of-the-Art (SoA) in integrated, neural interfaces made great process over the last few years, producing new and powerful instruments for the study of the central nervous system and the treatment of neurological disorders. Scaling of CMOS technology led to a higher level of integration, allowing power and area efficient System-on-Chip (SoC) designs with large number of recording channels, stimulation capability and even singlechip closed-loop operation. Additionally, the ongoing research in low noise analog front-ends led to different architectures for neural amplifiers with variations in key performance parameters, like input noise, power and area consumption. Different noise reduction schemes are reported in literature to improve especially the low-frequency noise behavior of the recorder which is dominant in the recording of Local Field Potentials (LFP). The most prominent approach is the chopping technique, were the signal is converted into a higher frequency band, amplified, filtered and shifted back to the original band. Thereby an efficient suppression of the amplifiers low-frequency 1/f-noise is possible. However, this method increases the power consumption of the system since all amplifiers must be operated at a higher frequency and requires additional area for the chopping circuitry. A second approach is the precise adjustment of the recorder bandwidth to the expected signal, which reduces not only electronic in-band noise, but also neural background noise. Several systems offer the possibility of digital or analog tuning even in the implanted state. Besides the improved noise performance, this technique yields the advantage of a reduced blind time after a stimulation event. Since most of the SoA systems have contradicting key parameters, a suitable trade-off must be found for each individual application. Therefore an overview of available SoCs is given, together with a comparison of their key features and their performance regarding noise, channel size and recording bandwidth.

5 S120 P 59 Statistical assessment of cardiac excitation by morphology-based clustering of local activation waves Tobias Oesterlein, Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Deutschland, tobias.oesterlein@kit.edu Axel Loewe, Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Deutschland, axel.loewe@kit.edu Gustavo Lenis, Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Deutschland, gustavo.lenis@kit.edu Olaf Dössel, Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Deutschland, olaf.doessel@kit.edu Cardiac excitation during atrial fibrillation (AFib) is changing dynamically, compromising the ability to identify underlying mechanisms by intracardiac catheter mapping. Statistical analysis of dominant excitation patterns may help to identify and subsequently eliminate the drivers of this tachycardia. As the morphology of local bipolar intracardiac electrograms (EGMs) depends on the orientation of the propagating excitation wave, its evaluation for a fixed multichannel catheter position can provide information about the stability of the depolarization pattern. Up to date, analysis of morphology is most often done by computing a similarity index or the recurrence rate of individual EGMs, reflecting how often similar excitations appear. We sougth to extend this approach to a classification based analysis technique. In each multichannel EGM, local activation waves (LAWs) were automatically detected by assessing instantaneous signal energy. A greedy algorithm was implemented to cluster LAWs based on their similiarity. New clusteres were formed when similarity fell below a predefined threshold. The concept was tested using simulated EGM data (quadratic patch of cardiac tissue, bidomain simulation, both planar and focal excitations, various catheter types). Results demonstrated that the algorithm correctly identified and classified the simulated excitation patterns. Subsequent quantitative analysis allowed to both discard singular classes of excitation and identify dominant excitations. The presented method forms the basis for statistical assessment of prevailing depolarization patterns, and for computation of additional features like conduction velocity, presence of focal sources, or dissociation when applied on multichannel data.

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