Multi-Level Optimization of an Ultra-Low Power BrainWave System for Non-Convulsive Seizure Detection.

We present a systematic evaluation and optimization of a complex bio-medical signal processing application on the BrainWave prototype system, targeted towards ambulatory EEG monitoring within a tiny power budget of 1 mW. The considered BrainWave processor is completely programmable, while maintaining energy-efficiency by means of a Coarse-Grained Reconfigurable Array (CGRA). This is demonstrated through the mapping and evaluation of a state-of-the-art non-convulsive epileptic seizure detection algorithm, while ensuring real-time operation and seizure detection accuracy.

A configurable and low-power mixed signal SoC for portable ECG monitoring applications.

This paper describes a mixed-signal ECG System-on-Chip (SoC) that is capable of implementing configurable functionality with low-power consumption for portable ECG monitoring applications. A low-voltage and high performance analog front-end extracts 3-channel ECG signals and single channel electrode-tissue-impedance (ETI) measurement with high signal quality. This can be used to evaluate the quality of the ECG measurement and to filter motion artifacts.

A lightweight security scheme for wireless body area networks: design, energy evaluation and proposed microprocessor design.

In order for wireless body area networks to meet widespread adoption, a number of security implications must be explored to promote and maintain fundamental medical ethical principles and social expectations. As a result, integration of security functionality to sensor nodes is required. Integrating security functionality to a wireless sensor node increases the size of the stored software program in program memory, the required time that the sensor's microprocessor needs to process the data and the wireless network traffic which is exchanged among sensors.