Stephen Faul

Ambulatory Signal Processing Algorithm Development

Stephen has worked, in his PhD and as a postdoc, in the area of biomedical signal processing. With EEDSP he is using his experience in algorithm development to implement algorithms efficiently in hardware for use in ambulatory monitoring. These algorithms comprise of routines from frequency, time-frequency, information theory, blind source separation and classification techniques.

One of the primary tasks of Stephen’s research is to investigate the use of algorithms developed in UCC for use in ambulatory devices. Part of this work was recently presented at the IEEE Engineering in Medicine and Biology Society Annual Conference in Minneapolis, USA. The paper was entitled “Age-independent Seizure Detection” and tested the hypothesis that an appropriately designed algorithm could be used to detect both adult and neonatal seizures. Stephen also continues to carry out his work on EEG signal processing and on evaluating differences in adult and neonatal EEG. Stephen is also working on algorithms for activity measurement, heart rate analysis and short-term epileptiform activity analysis with a number of PhD students.