Channel integrity detection

• US 9,470,728 B2
• Filed: 05/08/2013
• Issued: 10/18/2016
• Est. Priority Date: 05/09/2012
• Status: Active Grant

• Current Assignee: CardioInsight Technologies Inc. (Medtronic Plc)
• Original Assignee: CardioInsight Technologies Inc. (Medtronic Plc)
• Inventors: George, Brian P., Ramanathan, Charulatha, Jia, Ping, Zeng, Qingguo, Vasudevan, Venkatesh, Strom, Maria, Bokan, Ryan, Dubois, Remi
• Primary Examiner(s): Raymond, Edward
• Application Number:
  US13/890,058
• Publication Number:
  US 20130304407A1
• Time in Patent Office: 1,259 Days
• Field of Search: 702/72, 702 64- 66, 702182-185
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0035 adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography
• CPC Class Codes: A61B 5/282 Holders for multiple electrodes for introduction into the body A61B5/287
• CPC Class Codes: A61B 5/304 Switching circuits
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/35 by template matching
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 6/5247 combining images from an ionising-radiation diagnostic technique and a non-ionising radiation diagnostic technique, e.g. X-ray and ultrasound
• CPC Class Codes: A61B 8/5261 combining images from different diagnostic modalities, e.g. ultrasound and X-ray
• CPC Class Codes: G01R 25/00 Arrangements for measuring phase angle between a voltage and a current or between voltages or currents
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A computer-implemented method can include determining an amplitude for each of a plurality of input channels, corresponding to respective nodes. A measure of similarity can be computed between the input channel of each node and the input channel of its neighboring nodes. The method can also include comparing an amplitude for each node relative to other nodes to determine temporary bad channels. For each of the temporary bad channels, a measure of similarity can be computed between the input channel of each node and the input channel of its neighboring nodes. Channel integrity can then be identified based on the computed measures of similarity.

Claims

1. A non-transitory computer readable medium having instructions, the instructions comprising:

a preprocessing stage to analyze input channel data for a plurality of input channels to detect channels having an integrity that is considered one of bad or good, each of the plurality of input channels corresponding to a respective one of a plurality of nodes;

a first spatial similarity measurement function to compute a measure of similarity between the input channel data for each of the plurality of nodes and a set of neighboring nodes to identify a spatial correlated set of channels having an integrity that is considered one of bad or good;

an amplitude analyzer to determine a subset of channels meeting amplitude criteria;

a second spatial similarity measurement function to compute, for each channel in the subset of channels meeting the amplitude criteria, a measure of similarity between the input channel data for each node and a set of neighboring nodes to identify an amplitude correlated set of channels having an integrity that is considered one of bad or good; and

a combiner to store output data representing the integrity of the plurality of input channels based on the integrity of the channels detected by the preprocessing stage, the spatial correlated set of channels and the amplitude correlated set of channels.

2. The medium of claim 1, wherein the amplitude analyzer is further programmed to compare an amplitude value for each node relative the amplitude values for at least a substantial portion of the other nodes to determine temporary bad channels, which defines the subset of channels meeting the amplitude criteria.

3. The medium of claim 2, wherein the second spatial similarity measurement function comprises a correlation calculator programmed to compute a cross correlation between the input channel data for each node, corresponding to the temporary bad channels, and the set of neighboring nodes.

4. The medium of claim 2, further comprising an amplitude calculator to compute the amplitude values for each of the plurality of nodes based on the input channel data for each respective node.

5. The medium of claim 4, wherein the second spatial similarity measurement function comprises a correlation calculator programmed to compute a correlation coefficient value between the computed amplitude value of each of the temporary bad channels and its local neighboring nodes, the amplitude correlated set of channels being determined based on a comparison of the correlation coefficient value computed for each node relative to a threshold value.

6. The medium of claim 5, wherein the threshold value is one of programmable in response to a user input or a predetermined default value.

7. The medium of claim 1, wherein the first spatial similarity measurement function comprises a correlation calculator programmed to compute correlation coefficient values from a cross correlation computed between each of the plurality of nodes and its local neighboring nodes, the spatial correlated set of channels being determined based on a comparison of the correlation coefficient value for each node relative to a threshold value.

8. The medium of claim 7, wherein the threshold value is one of programmable in response to a user input or a predetermined default value.

9. The medium of claim 1, further comprising a node distance analyzer to compute distance between nodes, each set of neighboring nodes being determined based on the distance between nodes.

10. The medium of claim 9, wherein node distance is computed based on locations of nodes determined from geometry data that represents locations for the plurality of nodes.

11. The medium of claim 10, wherein the geometry data is computed from imaging data for a plurality of sensors corresponding to the nodes.

12. The medium of claim 11, further comprising a de-trend filter applied to the input data to provide de-trended input data, each of the first and second spatial correlation functions being performed on the de-trended input data.

13. The medium of claim 1, wherein the preprocessing stage further comprises a saturated channel detector programmed to identify a disconnected condition of a sensor based on the input data prior to de-trending, the output data including channels identified by the saturated channel detector.

14. The medium of claim 1, wherein the preprocessing stage further comprises a low amplitude detector programmed to identify each channel having an amplitude value that resides below a low amplitude threshold, the output data including channels identified by the low amplitude detector.

15. The medium of claim 1, wherein the preprocessing stage further comprises a high amplitude detector programmed to identify each channel having an amplitude value that resides above a high amplitude threshold, the output data including channels identified by the high amplitude detector.

16. The medium of claim 1, further comprising:

a resolution calculator to compute coefficients of a transformation matrix for at least a substantially portion of plurality of input channels based on the data representing the integrity of plurality of input channels; and

an evaluator to identify a low resolution spatial region based on an evaluation of the coefficients of the transformation matrix.

17. The medium of claim 16, further comprising generating a graphical map depicting the low resolution spatial region.

18. The medium of claim 1, further comprising a mapping system programmed to generate a reconstructed set of signals on an envelope based on input channel data and the output data, such that an interpolated value is used for each bad channel.

19. One or more non-transitory computer readable media that, when executed by one or more processors, perform a method comprising:

determining an amplitude for each of a plurality of input channels, corresponding to respective nodes;

computing a measure of similarity between the input channel of each node and the input channel of its neighboring nodes;

comparing the amplitude for each node relative to other nodes to determine temporary bad channels;

for each of the temporary bad channels, computing a measure of similarity between the input channel of each node and the input channel of its neighboring nodes;

identifying channel integrity for each of the plurality of input channels based on the computed measures of similarity; and

storing data associated with the identified channel integrity for each of the plurality of input channels in a memory.

20. The non-transitory computer readable media of claim 19, further comprising determining a disconnected condition of a sensor for a given input channel to identify at least one bad channel.

21. The non-transitory computer readable media of claim 19, further comprising comparing the amplitude of each input channel relative to a low amplitude threshold to identify at least one bad channel.

22. The non-transitory computer readable media of claim 19, further comprising comparing the amplitude of each input channel relative to a high amplitude threshold to identify at least one bad channel.

23. The non-transitory computer readable media of claim 19, wherein the measure of similarity computed for each of the temporary bad channels further comprises:

computing a correlation coefficient value between the computed amplitude value of each of the temporary bad channels and its neighboring nodes; and

comparing the correlation coefficient value computed for each node relative to a threshold value.

24. The non-transitory computer readable media of claim 23, wherein the threshold value is a first threshold value and wherein the measure of similarity computed for each of the input channels further comprises:

computing a correlation coefficient value between the computed amplitude value of each of the temporary bad channels and its neighboring nodes; and

comparing the correlation coefficient value computed for each node relative to a second threshold value.

25. The non-transitory computer readable media of claim 24, wherein the first threshold value is greater than the second threshold value such that the measure of similarity computed for each of the temporary bad channels is more strict.

26. The non-transitory computer readable media of claim 19, further comprising computing a distance between nodes, the neighboring nodes for each respective node being determined based on the computed distance.

27. The non-transitory computer readable media of claim 26, wherein node distance is computed based on locations of nodes determined from geometry data that represents locations for the plurality of nodes.

28. The non-transitory computer readable media of claim 19, further comprising:

computing a resolution of reconstructed signals based on the plurality of input channels; and

comparing the computed resolution relative to a threshold to identify at least one region of low resolution resulting from the identified bad channels.

30. The non-transitory computer readable media of claim 19, further comprising generating a reconstructed set of signals using an inverse method based on the plurality of input channels, in which the identified bad channels are excluded from the generating and an interpolated channel value is used in place each identified bad channel.

29. One or more non-transitory computer readable media that, when executed by one or more processors, perform a method comprising:

determining an amplitude for each of a plurality of input channels, corresponding to respective nodes;

computing a measure of similarity between the input channel of each node and the input channel of its neighboring nodes;

comparing the amplitude for each node relative to other nodes to determine temporary bad channels;

for each of the temporary bad channels, computing a measure of similarity between the input channel of each node and the input channel of its neighboring nodes;

identifying channel integrity for each of the plurality of input channels based on the computed measures of similarity; and

computing a resolution of reconstructed signals based on the plurality of input channels;

identifying at least one region of low resolution resulting based on the computed resolution; and

generating a display of a graphical map representing the region of low resolution.