ARTIFACT AS A FEATURE IN NEURO DIAGNOSTICS

• US 20160029965A1
• Filed: 03/12/2014
• Published: 02/04/2016
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: Cerora, Inc.
• Original Assignee: Adam J. Simon, Cerora, Inc.
• Inventors: SIMON, Adam J.
• Application Number:
  US14/777,012
• Publication Number:
  US 20160029965A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1103 Detecting eye twinkling
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/377 using evoked responses
• CPC Class Codes: A61B 5/4088 Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
• CPC Class Codes: A61B 5/6897 Computer input devices, e.g. mice or keyboards
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7289 Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition

Abstract

A multi-modal physiological assessment device and method enables the simultaneous recording and then subsequent analysis of multiple data streams of biological signal measurements to assess the health and function of the brain. Means and methods are provided to identify and leverage artifact samples within ID and 2D bio signal data streams to help create more accurate predictors and classifiers of brain health states and conditions. One sensor'"'"'s data is used to gate the relevant portion of another bio sensor'"'"'s data in order to reduce the noise and increase the signal-to-noise ratio. This is a form of phase locking for multimodal data streams for brain health assessment.

Claims

1. An analysis method for pre-processing biological sensor data from one of a plurality of concurrently collected independent biological sensor data streams, comprising:

identifying and flagging areas of artifact in said one biological sensor data stream; and

analytically characterizing features of the artifact as candidate predictor variables for predictive statistical models for analysis of said plurality of biological sensor data streams.

2. An analysis method as in claim 1, wherein said areas of artifact are used as time markers for analysis of said biological sensor data streams.

3. An analysis method as in claim 1, wherein a flagged artifact in said one biological sensor data stream is used to temporally gate data from another biological sensor data stream.

4. An analysis method as in claim 1, wherein the flagged artifact results from a manual action of a patient during a neuropsychiatric, neuropsychological, or cognition test.

5. An analysis method as in claim 4, wherein the manual action of the patient comprises clicks on one or more buttons or key strokes on one or more keys of a keyboard to mark times at the beginning and end of a time frame or period of interest.

6. An analysis method as in claim 1, wherein the artifact is automatically flagged without patient input.

7. An analysis method as in claim 6, wherein an acoustic microphone time series is analyzed to automatically determine when the first value is read and when a last value is read during oral testing of a patient.

8. An analysis method as in claim 1, wherein analytically characterizing features of the artifact comprises analyzing the artifact data for putative predictor variables to create additional features to be used as putative diagnostic information alone or to be used in development of multi-variate predictive statistical models.

9. An analysis method as in claim 1, wherein identifying and flagging areas of artifact comprises extracting a number N of artifacts in a block of data.

10. An analysis method as in claim 1, wherein identifying and flagging areas of artifact comprises determining a set of locations of artifacts within one-dimensional or two-dimensional data stream and recording the set of locations to a storage device.

11. An analysis method as in claim 1, wherein identifying and flagging areas of artifact comprises determining a central value of an artifact for a one-dimensional data stream or an equivalent for each dimension in a two-dimensional data stream.

12. An analysis method as in claim 1, wherein identifying and flagging areas of artifact comprises using a weighted value of an amplitude within a window of an artifact to understand how large or small the artifact is in relation to other artifacts.

13. An analysis method as in claim 1, wherein identifying and flagging areas of artifact comprises calculating distribution of extracted lengths L of the artifact in terms of individual samples from a time series, where the distribution is calculated for each artifact i by L_i=x_l−

X_f.

14. An analysis method as in claim 13, wherein identifying and flagging areas of artifact comprises determining a mean value of the data in the one sensor data stream signal over a region of the artifact.

15. An analysis method as in claim 13, wherein identifying and flagging areas of artifact comprises using a nonlinearly calculated median value of values in the one sensor data stream within an artifact window taken as a central value after an ascending or descending sort of the values in the one sensor data stream has occurred.

16. An analysis method as in claim 1, wherein identifying and flagging areas of artifact comprises calculating a standard deviation and higher order moments of a distribution of sample amplitudes from zones in the one sensor data stream containing an artifact.

17. An analysis method as in claim 16, further comprising calculating a standard deviation and higher order moments of a distribution of sample amplitudes on a per artifact basis, a number of artifacts N, a distribution of artifacts, and various moments of the distribution of various artifacts for possible extraction as a candidate predictor variable.

18. An analysis method as in claim 1, further comprising using a relative position of artifacts in a first sensor data stream relative to presentation of external sensory and cognitive stimuli presentation of a physical motion challenge to a patient to identify a feature of interest in a second sensor data streams that has been temporally synchronized with the first sensor data stream.