Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study

Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings

COMLEX: Visualizing Communication for Research and Saving Lives

One of the major causes of patient harm in hospital is poor communication. We are developing a video review and visualization platform to research and improve medics' communication skills. It intended for use by experimenters, as a deployable training tool for medics, and also for forensic review of communication. It supports pluggable analysis modules and visualizations for research teams, and configurable workflow for educators and hospital administrators

Towards a diagnostic toolbox for medical communication , 2010, Volume 335/2010, 169-176, DOI:

Poor communication is a major cause of adverse patient events in hospitals. Although sophisticated simulators are in use for performing medical operations, there is comparatively little technology support being used for improving communication skills including patient history taking. Artificial Intelligence and Natural Language Processing researchers have developed sophisticated algorithms for analysing conversations. We are experimentally developing software that can visualise the combined output of these algorithms, as a diagnostic toolkit for medical communication

Towards a Diagnostic Toolbox for Medical Communication

Poor communication is a major cause of adverse patient events in hospitals. Although sophisticated simulators are in use for performing medical operations, there is comparatively little technology support being used for improving communication skills including patient history taking. Artificial Intelligence and Natural Language Processing researchers have developed sophisticated algorithms for analysing conversations. We are experimentally developing software that can visualise the combined output of these algorithms, as a diagnostic toolkit for medical communication

Towards a Diagnostic Toolbox for Medical

Abstract. Poor communication is a major cause of adverse patient events in hospitals. Although sophisticated simulators are in use for performing medical operations, there is comparatively little technology support being used for improving communication skills including patient history taking. Artificial Intelligence and Natural Language Processing researchers have developed sophisticated algorithms for analysing conversations. We are experimentally developing software that can visualise the combined output of these algorithms, as a diagnostic toolkit for medical communication