Bionova Technologies Inc.

Craig Robert Ries

Guy A Dumont

Mihai Huzmezan

Tatjana Zikov

Stéphane Bibian

Ernest Puil

Bernard Ansell Macleod

Hossain Cyrus Ahmadi

Hosaain Cyrus Ahmadi

A method and apparatus to monitor the neurologic state of a patient undergoing general anesthesia is provided. Previous automated systems to monitor the neurologic state of a patient undergoing general anesthesia involve a significant time delay between the patient's true hypnotic state and the computed indices. The present invention reduces this time delay by using a different analysis technique applied to spontaneous EEG. A wavelet decomposition and statistical analysis of the observed EEG is conducted and compared to reference data to provide a numerical indicator. In addition, this indicator is more consistent with the patient's loss of consciousness indicated by the loss of count event than previous systems.

Method and apparatus for the estimation of anesthetic depth using wavelet analysis of the electroencephalogram

The present invention relates to a method of signal processing of electrograms for use in medical devices, preferably by time-frequency transforms. The present invention additionally relates to a system for receiving and analyzing such signals. The present invention preferably is a method utilizing the time-frequency transforms, such as wavelet transforms, for the purpose of artifact removal from EGs. These transforms decompose a signal in both time and frequency domains, and therefore, are well suited for non-stationary signal analysis. As a result, dissimilar signal features are well localized both in time and frequency, which potentially provides a good separation between the signal of interest and artifacts. This particularly applies to large-amplitude artifacts corrupting EGs.

Method and system for the denoising of large-amplitude artifacts in electrograms using time-frequency transforms

A method and apparatus to monitor and control the anesthesia state of a patient undergoing general anesthesia is provided. Previous automated systems to monitor the anesthesia state of a patient undergoing general anesthesia involve a significant time delay between the patient's true anesthesia state and the computed indices. The present invention reduces this time delay by using a novel analysis technique applied to spontaneous electrophysiological activity. A transformation and statistical analysis of the observed electrophysiological activity is conducted and compared to reference data to provide numerical indicators. In addition, these indicators are consistent with the levels of depression of patients CNS and ANS states. This method is illustrated in detail by CNS monitoring of electroencephalogram signals.

Method and apparatus for the estimation of anesthesia depth

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Vancouver, Canada

Craig Robert Ries