Adaptive Deep Brain Stimulation in Advanced Parkinson's Disease: Bridging the Gap beetween Concept and Clinical Application

Parkinson’s disease (PD) is a common neurodegenerative disorder. Recent evidence points towards increased synchronous neuronal oscillations of the cortico-thalamic-basal ganglia circuits in the beta band (12–30 Hz) as the main pathophysiological abnormality associated with PD. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving PD motor symptoms. However, the current DBS systems have several limitations, mainly related to the fixed and continuous application of stimulation. Especially in the long-term, DBS can only partially control clinical fluctuations and can exacerbate undesirable adverse effects often reversible with a change of stimulation parameters. A new strategy called adaptive DBS (aDBS) allows for continuous adaptation of STN stimulation to the patient’s clinical state by directly harnessing the recordings of the STN pathological oscillatory activity or local field potentials (LFPs). With this project, we aimed to accelerate the clinical translational process by suggesting a pathway to the clinical practice. To do so, we developed an external portable LFPs-based aDBS device for clinical investigations in acute experimental sessions. We then conducted a proof of concept study investigating the functioning of the device and comparing aDBS and conventional DBS (cDBS) and how they interacted with the concurrent pharmacological treatment. Then, we monitored the clinical and neurophysiological fluctuations over a period of eight hours with and without aDBS. We thus investigated the preservation of LFPs-clinical state correlation and the aDBS management of motor fluctuations during daily activities. Because in the clinical practice the DBS therapy is provided by means of implantable pulse generators (IPGs), we evaluated whether the proposed aDBS approach, based on real-time LFPs processing, fits the power constraints of implantable devices. Finally, we contextualized our results and proposed an overview of the possible pathways toward the clinical practice

Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware

Ultra-low power mixed-signal frontend for wearable EEGs

Electronics circuits are ubiquitous in daily life, aided by advancements in the chip design industry, leading to miniaturised solutions for typical day to day problems. One of the critical healthcare areas helped by this advancement in technology is electroencephalography (EEG). EEG is a non-invasive method of tracking a person's brain waves, and a crucial tool in several healthcare contexts, including epilepsy and sleep disorders. Current ambulatory EEG systems still suffer from limitations that affect their usability. Furthermore, many patients admitted to emergency departments (ED) for a neurological disorder like altered mental status or seizures, would remain undiagnosed hours to days after admission, which leads to an elevated rate of death compared to other conditions. Conducting a thorough EEG monitoring in early-stage could prevent further damage to the brain and avoid high mortality. But lack of portability and ease of access results in a long wait time for the prescribed patients. All real signals are analogue in nature, including brainwaves sensed by EEG systems. For converting the EEG signal into digital for further processing, a truly wearable EEG has to have an analogue mixed-signal front-end (AFE). This research aims to define the specifications for building a custom AFE for the EEG recording and use that to review the suitability of the architectures available in the literature. Another critical task is to provide new architectures that can meet the developed specifications for EEG monitoring and can be used in epilepsy diagnosis, sleep monitoring, drowsiness detection and depression study. The thesis starts with a preview on EEG technology and available methods of brainwaves recording. It further expands to design requirements for the AFE, with a discussion about critical issues that need resolving. Three new continuous-time capacitive feedback chopped amplifier designs are proposed. A novel calibration loop for setting the accurate value for a pseudo-resistor, which is a crucial block in the proposed topology, is also discussed. This pseudoresistor calibration loop achieved the resistor variation of under 8.25%. The thesis also presents a new design of a curvature corrected bandgap, as well as a novel DDA based fourth-order Sallen-Key filter. A modified sensor frontend architecture is then proposed, along with a detailed analysis of its implementation. Measurement results of the AFE are finally presented. The AFE consumed a total power of 3.2A (including ADC, amplifier, filter, and current generation circuitry) with the overall integrated input-referred noise of 0.87V-rms in the frequency band of 0.5-50Hz. Measurement results confirmed that only the proposed AFE achieved all defined specifications for the wearable EEG system with the smallest power consumption than state-of-art architectures that meet few but not all specifications. The AFE also achieved a CMRR of 131.62dB, which is higher than any studied architectures.Open Acces