All ETDs from UAB

Advisory Committee Chair

Mohammad R Haider

Advisory Committee Members

Karthikeyan Lingasubramanian

Rotem Elgavish

Nasim Uddin

B Earl Wells

Document Type


Date of Award


Degree Name by School

Doctor of Philosophy (PhD) School of Engineering


Bio-electric signals convey information and phenomenon of the physiological activities of organisms, such as gene, protein sequences, neural, and cardiac rhythms. Various types of bio-electrical signals are extensively used for diagnostics as well as therapeutic interventions. Bio-electrical signals are low-amplitude, low-frequency and numerous unique properties (spiking, small oscillation, local field potential interference). The original signal's amplitude is so small that directly lower the output resolution of the analog to digital converter (ADC). Furthermore, the detected signals are corrupted with, local field potential, white noise, system noise, and other interference, so the relevant information could be easily missed during the digitization. For such reasons, before the digitization, an analog signal processing unit is usually required. The one of the most challenge part in the analog signal processing unit design, for implantable signal recording system, is power consumption. The low-power consumption implants are one of the most efficiency ways to reduce the risk of tissue damage from the heat distribution. In this dissertation, a low-power analog signal processing unit for real-time bio-electric signals recording is performed. The analog signal processing unit has two major blocks, an amplifier filter bank for signals amplification and band selection, and a continuous wavelet transform filter for biosignals' feature extraction. The amplifier-filter bank is inspired by the silicon neurons' current sinking and charging feature. The technology, such as current bias and weak inversion CMOS, presented in the design significantly reduce the power consumption into the microwatt level. The new design method produces an analog continuous wavelet function filter with high performance, low circuit complexity and low-power consumption.

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