Berno J. E. Misgeld

Robust decentralised control of a hydrodynamic human circulatory system simulator

Abstract A novel feedback controlled hydrodynamic human circulatory system simulator, well-suited for in-vitro validation of cardiac assist devices, is presented in this paper. The cardiovascular system simulator consists of high-bandwidth actuators allowing a high precision hardware-in-the-loop hydrodynamic interface in connection with physiological circulatory models calculated in real-time. The hydrodynamically coupled process dynamics consist of several actuator loops and demand a multivariable control design approach in the face of system nonlinearities and uncertainties. Based on a detailed model employing the Lagrange formalism, a robust decentralised controller is designed. Fixed structural constraints and the minimisation of the H ∞ -norm necessitate the application of nonsmooth optimisation techniques. The robust decentralised norm-optimal controller is tested in extensive in-vitro experiments and shows good performance with regard to reference tracking and system coupling. In-vitro experiments include multivariable reference step tests and frequency analysis tests of the vascular impedance transfer function.

Multi-sensor calibration of low-cost magnetic, angular rate and gravity systems.

We present a new calibration procedure for low-cost nine degrees-of-freedom (9DOF) magnetic, angular rate and gravity (MARG) sensor systems, which relies on a calibration cube, a reference table and a body sensor network (BSN). The 9DOF MARG sensor is part of our recently-developed “Integrated Posture and Activity Network by Medit Aachen” (IPANEMA) BSN. The advantage of this new approach is the use of the calibration cube, which allows for easy integration of two sensor nodes of the IPANEMA BSN. One 9DOF MARG sensor node is thereby used for calibration; the second 9DOF MARG sensor node is used for reference measurements. A novel algorithm uses these measurements to further improve the performance of the calibration procedure by processing arbitrarily-executed motions. In addition, the calibration routine can be used in an alignment procedure to minimize errors in the orientation between the 9DOF MARG sensor system and a motion capture inertial reference system. A two-stage experimental study is conducted to underline the performance of our calibration procedure. In both stages of the proposed calibration procedure, the BSN data, as well as reference tracking data are recorded. In the first stage, the mean values of all sensor outputs are determined as the absolute measurement offset to minimize integration errors in the derived movement model of the corresponding body segment. The second stage deals with the dynamic characteristics of the measurement system where the dynamic deviation of the sensor output compared to a reference system is corrected. In practical validation experiments, this procedure showed promising results with a maximum RMS error of 3.89°.

Recurrence quantification analysis across sleep stages

In this work we employ a nonlinear data analysis method called recurrence quantification analysis (RQA) to analyze differences between sleep stages and wake using cardio-respiratory signals, only. The data were recorded during full-night polysomnographies of 313 healthy subjects in nine different sleep laboratories. The raw signals are first normalized to common time bases and ranges. Thirteen different RQA and cross-RQA features derived from ECG, respiratory effort, heart rate and their combinations are additionally reconditioned with windowed standard deviation filters and ZSCORE normalization procedures leading to a total feature count of 195. The discriminative power between Wake, NREM and REM of each feature is evaluated using the Cohens kappa coefficient. Besides kappa performance, sensitivity, specificity, accuracy and inter-correlations of the best 20 features with high discriminative power is also analyzed. The best kappa values for each class versus the other classes are 0.24, 0.12 and 0.31 for NREM, REM and Wake, respectively. Significance is tested with ANOVA F-test (mostly p <0.001). The results are compared to known cardio-respiratory features for sleep analysis. We conclude that many RQA features are suited to discriminate between Wake and Sleep, whereas the differentiation between REM and the other classes remains in the midrange.

Body sensor network-based spasticity detection

Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity Network by Medit Aachen body sensor network (BSN). For this, a new electromyography (EMG) sensor node was developed and employed in human locomotion. Following an analysis of the clinical gait data of patients with unilateral cerebral palsy, a novel algorithm was developed based on the idea to detect coactivation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated joint rigidity. The algorithm applies a cross-correlation function to the EMG signals of two antagonistically working muscles and subsequent weighting using a Blackman window. The result is a coactivation index which is also weighted by the signal equivalent energy to exclude positive detection of inactive muscles. Our experimental study indicates good performance in the detection of coactive muscles associated with spasticity from clinical data as well as measurements from a BSN in qualitative comparison with the Modified Ashworth Scale as classified by clinical experts. Possible applications of the new algorithm include (but are not limited to) use in robotic sensorimotor therapy to reduce the effect of spasticity.

An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors

BackgroundExtracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes.MethodsWe present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case.ResultsExtracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min −1 (0.3 min −1) and -0.7 bpm (1.7 bpm) (compared to -0.2 min −1 (0.4 min −1) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time.ConclusionsIt is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals.

Control of an electromechanical hydrocephalus shunt--a new approach.

Hydrocephalus is characterized by an excessive accumulation of cerebrospinal fluid (CSF). Therapeutically, an artificial pressure relief valve (so-called shunt) is implanted which opens in case of increased intracranial pressure (ICP) and drains CSF into another body compartment. Today, available shunts are of a mechanical nature and drainage depends on the pressure drop across the shunt. According to the latest data, craniospinal compliance is considered to be even more important than mean ICP alone. In addition, ICP is not constant but varies due to several influences. In fact, heartbeat-related ICP waveform patterns depend on volume changes in the cranial vessels during a heartbeat and changes its shape as a function of craniospinal compliance. In this paper, we present an electromechanical shunt approach, which changes the CSF drainage as a function of the current ICP waveform. A series of 12 infusion tests in patients were analyzed and revealed a trend between the compliance and specific features of the ICP waveform. For waveform analysis of patient data, an existing signal processing algorithm was improved (using a Moore machine) and was implemented on a low-power microcontroller within the electromechanical shunt. In a test rig, the ICP waveforms were replicated and the decisions of the ICP analysis algorithm were verified. The proposed control algorithm consists of a cascaded integral controller which determines the target ICP from the measured waveform, and a faster inner-loop integral controller that keeps ICP close to the target pressure. Feedforward control using measurement data of the patients position was implemented to compensate for changes in hydrostatic pressure during change in position. A model-based design procedure was used to lay out controller parameters in a simple model of the cerebrospinal system. Successful simulation results have been obtained with this new approach by keeping ICP within the target range for a healthy waveform.

Decentralised control of an electro-pneumatic adjustable impedance actuator

Abstract We present a decentralised controller design for the novel ElectroPneumatic Adjustable Impedance Actuator (EPAIA). EPAIA is designed as a rotational, back-drivable actuator, applying a rotary pneumatic element, used as a pneumatic spring in series to a brushless direct-current motor in the gear train. The actuator control consists of two coupled loops. Following a squaring down procedure, an impedance controller is presented for the torque loop using an ℋ2-optimal controller design employing additional constraints on passivity. The stiffness between motor and load is controlled by a PID-type controller including a decoupling pre-filter. Robust stability is verified by structured singular value analysis and the decentralised control strategy and the actuator are validated in simulations and on a test-bench.

Multivariable control design for artificial blood-gas exchange with heart-lung machine support

In this paper the design of a robust multivariable controller for the blood-gas exchange during cardiopulmonary-bypass with heart-lung machine support is presented. The proposed control strategy of the two-input-two-output oxygenation/decarbonisation process is divided into inner- and outercontrol loops. The inner-loop consists of input-output linearisation by state-feedback, which is extended with a Smith-like predictor for time-delay compensation and partially decouples the oxygenation exchange. The outer-control loop consists of a single linear multivariable controller, which is tuned via the ℋ∞-loop shaping approach for robust performance and accounts for plant, linearisation and time-delay uncertainties. The controller was tested with an experimentally validated model of blood-gas exchange over the whole operating range and shows good results with respect to reference changes and disturbance rejection. The control strategy is suggested to improve patients safety during the cardiopulmonary bypass (CPB) procedure.

Photoplethysmography-based in-ear sensor system for identification of increased stress arousal in everyday life

In this work, we present an in-ear system for physiological and psychological stress detection based on photoplethysmography, acceleration, and temperature measurements. The complete system is used to extract vital signs from healthy subjects, who are exposed to psychologically demanding tasks. The newly developed sensor system is integrated into our IPANEMA body sensor network and, thus, can be used in combination with several sensor modalities. The capability of the stress level estimation is validated in an human stress experiment. To obtain information on the current stress level, several well-known indicators are utilized like the heart rate variability, surgical stress index or the Oliva and Roztocil index.

Linearity of electrical impedance tomography during maximum effort breathing and forced expiration maneuvers

Electrical impedance tomography (EIT) provides global and regional information about ventilation by means of relative changes in electrical impedance measured with electrodes placed around the thorax. In combination with lung function tests, e.g. spirometry and body plethysmography, regional information about lung ventilation can be achieved. Impedance changes strictly correlate with lung volume during tidal breathing and mechanical ventilation. Initial studies presumed a correlation also during forced expiration maneuvers. To quantify the validity of this correlation in extreme lung volume changes during forced breathing, a measurement system was set up and applied on seven lung-healthy volunteers. Simultaneous measurements of changes in lung volume using EIT imaging and pneumotachography were obtained with different breathing patterns. Data was divided into a synchronizing phase (spontaneous breathing) and a test phase (maximum effort breathing and forced maneuvers). The EIT impedance changes correlate strictly with spirometric data during slow breathing with increasing and maximum effort ([Formula: see text]) and during forced expiration maneuvers ([Formula: see text]). Strong correlations in spirometric volume parameters [Formula: see text] ([Formula: see text]), [Formula: see text]/FVC ([Formula: see text]), and flow parameters PEF, [Formula: see text], [Formula: see text], [Formula: see text] ([Formula: see text]) were observed. According to the linearity during forced expiration maneuvers, EIT can be used during pulmonary function testing in combination with spirometry for visualisation of regional lung ventilation.