Medical systems and methods for detecting changes in electrophysiological evoked potentials

1. A medical method of automatically improving signals received from a patient's physiological system comprising:

delivering stimulation signals to a nerve pathway of a patient with electrical pulses via electrodes placed over the nerve pathway to generate a plurality of resultant evoked potentials (EPs) based on a plurality of electrophysiological responses (ERs);

recording the plurality of resultant EPs;

generating an ensemble average waveform (EA), the generating comprising averaging a subset of the plurality of ERs;

denoising the EA to generate a denoised EA comprising a denoised signal, the denoising comprising:

decomposing, hierarchically, the EA using a series of filter banks, wherein filter coefficients of the series of filter banks used in the hierarchical decomposition are derived from a mother wavelet derived from an electrophysiological response of interest, and wherein the decomposing comprises applying a first wavelet transform;

iterating the hierarchical decomposition of the EA, the hierarchical decomposition of the EA filtering high-frequency noise from the EA;

applying a dynamic coefficient threshold to the filter coefficients, the dynamic coefficient threshold determined with the EA; and

applying a second wavelet transform to the EA, the second wavelet transform comprising an inverse wavelet transform using the mother wavelet derived from the electrophysiological response of interest;

comparing the denoised EA to a previously denoised EA;

determining whether a change has occurred in the denoised EA relative to the previously denoised EA; and

generating, based on the determination that the change has occurred, an alert.

2. The method of claim 1 , wherein the denoising further comprises: attenuating noise components from the transformed EA by decomposing the transformed EA; and wherein the second wavelet transform comprises an inverse transform applied to the transformed EA to generate the denoised EA.

3. The method of claim 1 , further comprising comparing the denoised EA to a threshold EA.

4. The method of claim 3 , wherein the threshold EA includes the previously denoised EA.

5. The method of claim 3 , further comprising determining a change between the denoised EA and the threshold EA.

6. The method of claim 5 , further comprising indicating an alert that the change between the denoised EA and the threshold EA has occurred.

7. The method of claim 1 , further comprising transmitting information to other devices in a surgical environment thereby allowing the devices to manually or automatically identify changes between the denoised EA and the previously denoised EA.

8. The method of claim 1 , further comprising: obtaining information from an anesthesia or blood pressure machine; and determining when changes in EPs are due to anesthesia or blood pressure changes.

9. The method of claim 1 , further comprising displaying the denoised EA on a monitor device.

10. An automated electrical waveforms (EPs) analysis system for improved monitoring, detecting and identifying changes to a patient's physiological system, wherein the system comprises:

an input device for obtaining electrical potential data from the patient's physiological system after application of stimulation to a patient's nerve pathway;

at least one processor; and

at least one memory storing instructions which, when executed by the at least one processor, result in operations comprising:

causing stimulation of a nerve pathway of a patient with electrical pulses via electrodes placed over the nerve pathway to generate a plurality of resultant electrical waveforms (EPs) based on a plurality of electrophysiological responses (ERs);

recording the plurality of resultant EPs;

generating an ensemble average waveform (EA), the generating comprising averaging a subset of the plurality of ERs;

denoising the EA to generate a denoised EA comprising a denoised signal, the denoising comprising:

decomposing, hierarchically, the EA using a series of filter banks, wherein filter coefficients of the series of filter banks used in the hierarchical decomposition are derived from a mother wavelet derived from an electrophysiological response of interest, and wherein the decomposing comprises applying a first wavelet transform;

iterating the hierarchical decomposition of the EA, the hierarchical decomposition of the EA filtering high-frequency noise from the EA;

applying a dynamic coefficient threshold to the filter coefficients, the dynamic coefficient threshold determined with the EA; and

applying a second wavelet transform to the EA, the second wavelet transform comprising an inverse wavelet transform using the mother wavelet derived from the electrophysiological response of interest;

comparing the denoised EA to a previously denoised EA;

determining whether a change has occurred in the denoised EA relative to the previously denoised EA; and

generating, based on the determination that the change has occurred, an alert.

11. The system of claim 10 , wherein the denoising further comprises: attenuating noise components from the transformed EA by decomposing the transformed EA; and wherein the second wavelet transform comprises an inverse transform applied to the transformed EA to generate the denoised EA.

12. The system of claim 10 , wherein the operations further comprises comparing the denoised EA to a threshold EA.

13. The system of claim 12 , wherein the threshold EA includes the previously denoised EA.

14. The system of claim 13 , wherein the operations further comprise determining a change between the denoised EA and the threshold EA.

15. The system of claim 12 , wherein the operations further comprise indicating an alert that a change between the denoised EA and the threshold EA has occurred.

16. The system of claim 10 , wherein the operations further comprise transmitting information to other devices in a surgical environment thereby allowing the devices to manually or automatically identify changes between the denoised EA and the previously denoised EA.

17. The system of claim 10 , wherein the operations further comprise: obtaining information from an anesthesia or blood pressure machine; and determining when changes in EPs are due to anesthesia or blood pressure changes.