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Browsing Electrical and Computer Engineering by Subject "Ambulatory EEG monitoring"
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Item Open Access Wireless Implantable ICs for Energy-Efficient Long-Term Ambulatory EEG Monitoring(2022-08-08) Freeman, Al; Kassiri, HosseinThis thesis presents the design, development, and experimental characterization of wireless subcutaneous implantable integrated circuits and systems for long-term ambulatory EEG monitoring. Application-, system- and circuit-level requirements for such a device are discussed and a critical review of the state-of-the-art academic and currently available commercial solutions are provided. Two prototypes are presented: The first prototype presented in Chapter 2 is an 8-channel wireless implantable device with a 2.5×1.5 mm2 custom-designed integrated circuit implemented using CMOS 180nm technology at its core. The microchip is fabricated and the measurement results showing its efficacy in EEG signal recording in terms of input-referred noise, voltage gain, signal-to-noise ratio, and power consumption are presented. The chip is implemented together with a BLE 5.0 module on the same platform. Our vision and discussions on biocompatible encapsulation of this system, as well as its integration with a microelectrode array as also provided. The second prototype, also implemented in CMOS 180nm technology and presented in Chapter 3, employs a novel EEG recording channel architecture that enables long-term implantation of EEG monitoring devices through significant improvement of their energy efficiency. The channel leverages the inherent sparsity of the EEG signals and conducts recording in an activity-dependent adaptive manner. Thanks to the proposed fully dynamic spectral-compressing architecture, the recording channels power consumption is drastically reduced. More importantly, the proposed architecture reduces the required wireless transmission throughput by more than an order of magnitude. Our test results on 10 different patients’ pre-recorded human EEG data shows an average of 12.6× improvement in the device’s energy efficiency.