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Optimal control of non-invasive neuromodulation for the treatment of sleep apnea syndromes

Abstract : Sleep apnea syndrome (SAS) is a multifactorial disease characterized by recurrent episodes of breathing pauses or significant reductions in respiratory amplitude during sleep. These episodes may provoke acute cardiorespiratory responses along with alterations of the sleep structure, which may be deleterious in the long term. Several therapies have been proposed for the treatment of SAS, being continuous positive airway pressure the gold standard treatment. Despite its excellent results in symptomatic patients, there is a 15% initial refusal rate and long term adherence is difficult to achieve in minimally symptomatic patients. Therefore, the development of non-invasive SAS treatment methods, with improved acceptability, is of major importance. The objective of this PhD thesis is to propose new signal processing and control methods of non-invasive neuromodulation for the treatment of SAS. The hypothesis underlying this work is that bursts of kinesthetic stimulation delivered during the early phase of apneas or hypopneas may elicit a controlled startle response that can activate sub-cortical centers controlling upper airways muscles and the autonomic nervous system, stopping respiratory events without generating a cortical arousal. In this context, the first part of this manuscript is dedicated to the description of a novel real-time monitoring and therapeutic neuromodulation system, which functions as a multi-purpose device for SAS diagnosis and treatment through kinesthetic stimulation. This system has been developed in the framework of an ANR TecSan project led by our laboratory, with the participation of Sorin CRM SAS. The main contributions in this thesis are focused on the signal processing and control aspects of this system, as well as the electronics associated. Another contribution is related to the evaluation of these methods and devices through specific clinical protocols. In a second part, we propose a first optimal On/Off control method for delivering kinesthetic stimulation, using as control variable the output of a real-time respiratory event detector. A unique stimulation strategy where a constant stimulation amplitude is applied upon event detention was implemented in a first clinical protocol, dedicated to assessing the patient response to therapy. Results showed that 75% of the patients responded correctly to therapy, showing statistically significant reductions in respiratory event durations. Also, significant decreases in the SaO2 variability were also found when implementing a novel acute analysis method. Since we hypothesized that inappropriate patient selection could explain the observed lack of response in 25% of patients, we proposed a method to differentiate patients who could benefit from this therapy based on the estimation of complexity-based indexes of heart rate variability. Results of these analyses showed that the effectiveness of this therapy seems correlated to a functional autonomic nervous system. Finally, an improved closed-loop control method integrating concurrent, coupled proportional-derivative (PD) controllers in order to adaptively change the kinesthetic stimulation was proposed. It uses as control variables three physiological signals recorded in real-time: Nasal pressure, oxygen saturation and the electrocardiogram signal. A second clinical protocol with the main objective of validating the control algorithm for patient-specific adaptive kinesthetic stimulation was launched. Several improvements to the first version of the system were developed to allow the integration of the proposed controller. Preliminary results from the first phase of this study validated the proposed controller operation and showed that the controller was able to provide adaptive kinesthetic stimulation in function of the patient-specific responses. A second phase of this study implementing the proposed controller and the set of the selected control parameters from the first phase is currently ongoing.
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  • HAL Id : tel-01838213, version 1

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Diego Oswaldo Pérez Trenard. Optimal control of non-invasive neuromodulation for the treatment of sleep apnea syndromes. Signal and Image processing. Université Rennes 1, 2018. English. ⟨NNT : 2018REN1S014⟩. ⟨tel-01838213⟩

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