Development and use of bioanalytical instrumentation and signal analysis methods for rapid sampling microdialysis monitoring of neuro-intensive care patients

This thesis focuses on the development and use of analysis tools to monitor brain injury patients. For this purpose, an online amperometric analyzer of cerebral microdialysis samples for glucose and lactate has been developed and optimized within the Boutelle group. The initial aim of this thesis was to significantly improve the signal-to-noise ratio and limit of detection of the assay to allow reliable quantification of the analytical data. The first approach was to re-design the electronic instrumentation of the assay. Printed-circuit boards were fabricated and proved very low noise, stable and much smaller than the previous potentiostats. The second approach was to develop generic data processing algorithms to remove three complex types of noise that commonly contaminate analytical signals: spikes, non-stationary ripples and baseline drift. The general strategy consisted in identifying the types of noise, characterising them, and subsequently subtracting them from the otherwise unprocessed data set. Spikes were effectively removed with 96.8% success and ripples were removed with minimal distortion of the signal resulting in an increased signal-to-noise ratio by up to 250%. This allowed reliable quantification of traces from ten patients monitored with the online microdialysis assay. Ninety-six spontaneous metabolic events in response to spreading depolarizations were resolved. These were characterized by a fall in glucose by -32.0 μM and a rise in lactate by +23.1 μM (median values) for over a 20-minute time-period. With frequently repeating events, this led to a progressive depletion of brain glucose. Finally, to improve the temporal coupling between the metabolic data and the electro-cortical signals, a flow-cell was engineered to integrate a potassium selective electrode into the microdialysate flow stream. With good stability over hours of continuous use and a 90% response time of 65 seconds, this flow cell was used for preliminary in vivo experiments the Max Planck Institute in Cologne

Personal-by-design: a 3D Electromechanical Model of the Heart Tailored for Personalisation

International audienceIn this work we present a coupled electromechanical model of the heart for patient-specific simulations, and in particular cardiac resynchronisation therapy. To this end, we propose a fast fully autonomous and flexible pipeline to generate and optimise the data required to run the mechanical simulation. After the meshing of the biventricular segmentation image and the construction of the associated fibres arrangement, we compute the electrical potential propagation in the myocardial tissue from selected onset points on the endocardium. We generate a 12-lead electrocardiogram corresponding to the latter activation map by extrapolating the electrical potential on a virtual torso. This electrical activation is coupled to a mechanical model, featuring a small set of interpretable parameters. We also propose an efficient algorithm to optimise the model parameters, based on patient data. The whole pipeline including a cardiac cycle is computed in 30 minutes, enabling to use this digital twin for diagnosis and therapy planning

Development and use of bioanalytical instrumentation and signal analysis methods for rapid sampling microdialysis monitoring of neuro-intensive care patients

This thesis focuses on the development and use of analysis tools to monitor brain injury patients. For this purpose, an online amperometric analyzer of cerebral microdialysis samples for glucose and lactate has been developed and optimized within the Boutelle group. The initial aim of this thesis was to significantly improve the signal-to-noise ratio and limit of detection of the assay to allow reliable quantification of the analytical data. The first approach was to re-design the electronic instrumentation of the assay. Printed-circuit boards were fabricated and proved very low noise, stable and much smaller than the previous potentiostats. The second approach was to develop generic data processing algorithms to remove three complex types of noise that commonly contaminate analytical signals: spikes, non-stationary ripples and baseline drift. The general strategy consisted in identifying the types of noise, characterising them, and subsequently subtracting them from the otherwise unprocessed data set. Spikes were effectively removed with 96.8% success and ripples were removed with minimal distortion of the signal resulting in an increased signal-to-noise ratio by up to 250%. This allowed reliable quantification of traces from ten patients monitored with the online microdialysis assay. Ninety-six spontaneous metabolic events in response to spreading depolarizations were resolved. These were characterized by a fall in glucose by -32.0 μM and a rise in lactate by +23.1 μM (median values) for over a 20-minute time-period. With frequently repeating events, this led to a progressive depletion of brain glucose. Finally, to improve the temporal coupling between the metabolic data and the electro-cortical signals, a flow-cell was engineered to integrate a potassium selective electrode into the microdialysate flow stream. With good stability over hours of continuous use and a 90% response time of 65 seconds, this flow cell was used for preliminary in vivo experiments the Max Planck Institute in Cologne.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Securing Next Generation Multinodal Leadless Cardiac Pacemaker System: A Proof of Concept in a Single Animal

As the next generation of implanted medical devices for cardiac rhythm management moves towards multi-nodal leadless systems that do without the limitations of transvenous leads, new security threats arise from the wireless communication between the systems' nodes. Key management and the key distribution problem used in traditional cryptographic methods are considered to be too computationally expensive for small implanted medical devices. Instead, inherent human biometrics could provide a reliable alternative. In this work, we tested the key generation process across different nodes of a mimicked dual-chamber leadless cardiac pacemaker system and a subcutaneous implantable relay (S-relay). The proposed key generation process utilizes the randomness available from inter beat intervals (IBIs). A pre-clinical in-vivo experiment was performed in one dog in order to validate the concept by implanting conventional bipolar cardiac pacemaker leads in the right atrium, the right ventricle and the subcutaneous space. Based on the available randomness and entropy of recorded IBIs, 3-bits were extracted per IBI by approximating a sequence of intervals with a normal distribution. This allowed for the generation of a 128-bit key string across the nodes with an average bit mismatch rate of about 3%. Parity check methods were used to reconciliate the keys across the multiple nodes of a multi-nodal leadless pacemaker and subcutaneous device system. The findings are encouraging and demonstrate that IBIs can be used to generate secure keys for data encryption across different nodes of a leadless pacemaker system and S-relay

Patient specific strategies to enhance leadless pacemaker lifetime in synchronized dual chamber system

Dual chamber leadless pacemakers are multi-unit, battery-driven implants utilized for treating patients with bradyarrhythmias and sino-atrial dysfunctions. Establishing synchronization between the units provides coordination between the atrium and ventricular contraction, and this mechanism depletes battery energy. Due to implant size constraints, reducing the synchronization energy consumed to enhance the lifetime of the implant is crucial. In this paper, a set of strategies are proposed and evaluated to indicate the best strategy to enhance the lifetime of atrial unit based on the patient's heart condition. Beat selective pulse transmission is employed instead of pulse tr 4?0ansmission on every beat to reduce energy consumption. The characteristics of interbeat contraction timing of the atrium and ventricle from the patient data is modeled as time series. The designed model is extended to model synchronization strategies with sufficient synchronization accuracy and reduction in energy consumption. It is found that the implant lifetime is dependent on the natural atrial contraction probability, which is patient specific. A relation between the transmission duty-cycle and natural atrial contraction probability is derived for all the strategies, and this analysis is used in a case study to quantify the longevity. The proposed strategies show improved lifetime in comparison to the reference strategy. In the case study, for natural atrial contraction probability of 0.1, longevity is increased by two orders in relation to the reference strategy with the longevity of 4 years. However, there is no one best strategy; instead, the most energy-efficient strategy is determined from patient's natural atrial contraction probability and tolerance to suboptimal coordination

Modeling Cardiac Stimulation by a Pacemaker, with Accurate Tissue-Electrode Interface

International audienceAn implantable pacemaker aims to restore a cardiac beat when the intrisic conduction system fails. It sends energy to the heart in the form of a voltage pulse and it is programmed to deliver enough energy to trigger a cardiac depolarization (which is called capture). We present a 0D model of a cardiac pacemaker with a cardiac tissue. We take into account electrochemical phenomena observed during pacing, like electrode polarization. To validate it, we compare numerical results with ex-vivo experimental data of stimulation threshold detection

Modeling Cardiac Stimulation by a Pacemaker, with Accurate Tissue-Electrode Interface

International audienceIn this paper we model a cardiac pacemaker placed in a bath with a cardiac excitable tissue. We take into account electrochemical phenomena observed at the electrodes during pacing by using equivalent circuits, whose parameters are calibrated with respect to bench tests data. The complete model consists of a pacemaker model coupled to a re-scaled cardiac ionic model through these circuits. It is compared with ex-vivo experimental data of stimulation threshold detection. We perform an additional study of the influence of the scaling parameters, that can help matching experimental results

Continuous online microdialysis using microfluidic sensors: dynamic neurometabolic changes during spreading depolarization

Microfluidic glucose biosensors and potassium ion selective electrodes were used in an in vivo study to measure the neurochemical effects of spreading depolarizations (SD), which have been shown to be detrimental to the injured human brain. A microdialysis probe implanted in the cortex of rats was connected to a microfluidic PDMS chip containing the sensors. The dialysate was also analyzed using our gold standard, rapid sampling microdialysis (rsMD). The glucose biosensor performance was validated against rsMD with excellent results. The glucose biosensors successfully monitored concentration changes, in response to SD wave induction, in the range of 10–400 ?M with a second time-resolution. The data show that during a SD wave, there is a time delay of 62 ± 24.8 s (n = 4) between the onset of the increase in potassium and the decrease in glucose. This delay can be for the first time demonstrated, thanks to the high-temporal resolution of the microfluidic sensors sampling from a single tissue site (the microdialysis probe), and it indicates that the decrease in glucose is due to the high demand of energy required for repolarization

From experimental data to 3D realistic simulations: a pacemaker example

An implantable pacemaker aims to restore a cardiac beat when the intrisic conduction system fails. It sends energy to the heart in the form of a voltage pulse during a fixed duration and it is programmed to deliver enough energy to trigger a cardiac depolarization.We present results of a 3D model of a cardiac pacemaker with a tissue surrounded by blood. We take into account electrochemical phenomena observed during pacing, like electrode polarization, which is crucial to make realistic simulations. We explain how 2 we use experimental data to feed our model with valid inputs. To validate the model, we will compare numerical results with ex-vivo experimental data of stimulation threshold detection and voltage measurements.Simulation of Cardiac Devices & Drugs for in-silico Testing and Certificatio