Office Action 1: Non-Final Rejection (2024-07-08)
Rejection types: 103 - Obviousness

CLAIMS:
What is claimed is:
1\. An apparatus for generating a quality diagnostic of analog ECG (electrocardiogram) data, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive ECG data; extract a plurality of ECG parameters from the ECG data; convert the ECG data to one or more digitized ECG signals; validate the one or more digitized ECG signals, wherein validating comprises: classifying the one or more digitized ECG signals to a plurality of preliminary ECG parameters; and determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters; and generate a quality diagnostic of the ECG data based on validation of the one or more digitized ECG signals.
2\. The apparatus of claim 1, wherein the ECG data comprises an ECG printout comprising one or more ECG signals and the plurality of ECG parameters.
3\. The apparatus of claim 1, further comprising a machine vision system, wherein the memory contains instructions further configuring the at least a processor to: capture an image of the ECG data; and extract the plurality of ECG parameters from the image.
4\. The apparatus of claim 3, wherein: the machine vision system comprises an optical character recognition reader; and the memory contains instructions further configuring the at least a processor to extract the plurality of ECG parameters from the image using the optical character recognition reader.
5\. The apparatus of claim 1, wherein classifying the plurality of digitized ECG signals comprises: training a validation classifier using training data comprising a plurality of digitized ECG signals correlated to a plurality of textbook validators of ECG parameters; inputting the plurality of digitized ECG signals into the validation classifier; and outputting, by the validation classifier, the plurality of preliminary ECG parameters.
6\. The apparatus of claim 1, wherein determining an accuracy status comprises using a scoring algorithm using a threshold to determine the accuracy status, wherein the accuracy status comprises one of a pass status and a fail status.
7\. The apparatus of claim 6, wherein the memory contains instructions further configuring the at least a processor to determine the threshold by: training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds; inputting the analog ECG data into the threshold classifier; and outputting, by the threshold classifier, the threshold.
8\. The apparatus of claim 1, wherein the quality diagnostic comprises an auditory representation comprising sounds to indicate a pass status or a fail status regarding an overall quality of the analog ECG data.
9\. The apparatus of claim 1, wherein generating the quality diagnostic comprises generating a visual representation regarding the quality status of the analog ECG data by: training a diagnostic classifier with training data correlating analog ECG data to a plurality of flags; inputting the analog ECG data into the diagnostic classifier; and outputting, by the diagnostic classifier, one or more flags indicating an issue with the analog ECG data.
10\. The apparatus of claim 1, wherein validating the digitized ECG signals comprises receiving ECG verification data from a user comprising a threshold for a scoring algorithm.
11\. A method for generating a quality diagnostic of analog ECG (electrocardiogram) data, wherein the method comprises: receiving, by at least a processor, ECG data; extracting, by the at least a processor, a plurality of ECG parameters from the ECG data; converting, by the at least a processor, the ECG data to one or more digitized ECG signals; validating, by the at least a processor, the digitized ECG signals, wherein validating comprises: classifying, by the at least a processor, the plurality of digitized ECG signals to a plurality of preliminary ECG parameters; and determining, by the at least a processor, an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters; and generating, by the at least a processor, a quality diagnostic of the ECG data based on validation of the digitized ECG signals.
12\. The method of claim 11, wherein the ECG data comprises an ECG printout comprising one or more ECG signals and the plurality of ECG parameters.
13\. The method of claim 11, wherein receiving the ECG data comprises: capturing, using a machine vison system, an image of the ECG data; and extracting, using the machine vision system, the plurality of ECG parameters from the image.
14\. The method of claim 13, wherein extracting, using the machine vision system, the plurality of ECG parameters further comprises using an using an optical character recognition reader.
15\. The method of claim 11, wherein classifying the plurality of digitized ECG signals comprises: training a validation classifier using training data comprising a plurality of digitized ECG signals correlated to a plurality of textbook validators of ECG parameters; inputting the plurality of digitized ECG signals into the validation classifier; and outputting, by the validation classifier, the plurality of preliminary ECG parameters.
16\. The method of claim 11, wherein determining an accuracy status comprises using a scoring algorithm using a threshold to determine the accuracy status, wherein the accuracy status comprises one of a pass status and a fail status.
17\. The method of claim 16, wherein using the scoring algorithm comprises determining the threshold by: training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds; inputting the analog ECG data into the threshold classifier; and outputting, by the threshold classifier, the threshold.
18\. The method of claim 11, wherein the quality diagnostic comprises an auditory representation comprising sounds to indicate a pass status or a fail status regarding an overall quality of the analog ECG data.
19\. The method of claim 11, wherein generating the quality diagnostic comprises generating a visual representation regarding the quality status of the analog ECG data by: training a diagnostic classifier with training data correlating analog ECG data to a plurality of flags; inputting the analog ECG data into the diagnostic classifier; and outputting, by the diagnostic classifier, one or more flags indicating an issue with the analog ECG data.
20\. The method of claim 11, wherein validating the digitized ECG signals comprises receiving ECG verification data from user comprising a threshold for a scoring algorithm.

OFFICE ACTION:
DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/8/2024 was filed after the mailing date of 3/8/2024.  The submission is in compliance with the provisions of 37 CFR 1.97.  Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA  35 U.S.C. 102 and 103 (or as subject to pre-AIA  35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA  to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6, 10, 11, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brockway et al (US20130289424) in view of Taha et al (US6304772).
Regarding claim 1, Brockway discloses an apparatus for generating a quality diagnostic of analog ECG (electrocardiogram) data, wherein the apparatus comprises:
at least a processor (para. [0072], microprocessor); and
a memory communicatively connected to the at least a processor (para. [0072]), wherein the memory contains instructions configuring the at least a processor to:
receive ECG data (201C and 201G in fig. 2);
extract a plurality of ECG parameters from the ECG data (para. [0049], [0078]);
convert the ECG data to one or more digitized ECG signals (203C and 203G in fig. 2, para. [0042], [0048]);
validate the one or more digitized ECG signals (para. [0082], determining the validity of a detected event), wherein validating comprises:
classifying the one or more digitized ECG signals to a plurality of preliminary ECG parameters (para. [0063], Each event is classified as one of valid, invalid, or uncertain based upon said confidence signal); and
generate a quality diagnostic of the ECG data based on validation of the one or more digitized ECG signals (para. [0082], detecting alarm and notification conditions; para. [0111]; para. [0130], communicate a notification that such as event occurred).
Brockway fails to teach determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters.
However Taha teaches classifying one or more digitized ECG signals to a plurality of preliminary ECG parameters (abstract, analyzing the digital ECG data to generate second pacemaker pulse markers); and
determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to extracted plurality of ECG parameters (abstract, comparing the first pacemaker pulse markers and energy measures to the second pacemaker pulse markers).
Therefore taking the combined teachings of Brockway and Taha as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Taha into the apparatus of Brockway. The motivation to combine Taha and Brockway would be to provide a highly specific, highly sensitive patient monitoring and diagnostic system for detecting pacemaker pulses in an ECG waveform (col. 3 lines 3-6 of Taha).
Regarding claim 6, the modified invention of Brockway teaches an apparatus wherein determining an accuracy status comprises using a scoring algorithm using a threshold to determine the accuracy status (para. [0062] of Brockway), wherein the accuracy status comprises one of a pass status and a fail status (para. [0063] of Brockway, Each event is classified as one of valid, invalid, or uncertain based upon said confidence signal).
Regarding claim 10, the modified invention of Brockway teaches an apparatus wherein validating the digitized ECG signals comprises receiving ECG verification data from a user comprising a threshold for a scoring algorithm (para. [0110], [0136] of Brockway).
Regarding claim 11, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 16, the claim recites similar subject matter as claim 6 and is rejected for the same reasons as stated above.
Regarding claim 20, the claim recites similar subject matter as claim 10 and is rejected for the same reasons as stated above.
Claim(s) 2-5 and 12-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brockway et al (US20130289424) and Taha et al (US6304772) in view of Lyman et al (US20200160980).
Regarding claim 2, the modified invention of Brockway fails to teach an apparatus wherein the ECG data comprises an ECG printout comprising one or more ECG signals and the plurality of ECG parameters.
However Lyman teaches ECG data (para. [0266], ECG entry) comprising an ECG printout comprising one or more ECG signals (para. [0262]) and a plurality of ECG parameters (para. [0267], diagnosis data including measurements).
Therefore taking the combined teachings of Brockway and Taha with Lyman as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Lyman into the apparatus of Brockway and Taha. The motivation to combine Lyman, Taha and Brockway would be to aid medical professionals or other users in diagnosing, triaging, classifying, ranking, and/or otherwise reviewing medical scans (para. [0038] of Lyman).
Regarding claim 3, the modified invention of Brockway fails to teach an apparatus further comprising a machine vision system, wherein the memory contains instructions further configuring the at least a processor to:
capture an image of the ECG data; and
extract the plurality of ECG parameters from the image.
However Lyman teaches a machine vision system (para. [0050]); and
a processor (para. [0050]) for capturing an image of ECG data and extracting a plurality of ECG parameters from the image (para. [0269], [0275]).
Therefore taking the combined teachings of Brockway and Taha with Lyman as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Lyman into the apparatus of Brockway and Taha. The motivation to combine Lyman, Taha and Brockway would be to aid medical professionals or other users in diagnosing, triaging, classifying, ranking, and/or otherwise reviewing medical scans (para. [0038] of Lyman).
Regarding claim 4, the modified invention of Brockway teaches an apparatus wherein:
the machine vision system comprises an optical character recognition reader (para. [0210] of Lyman, text extracted from the image data); and
the memory contains instructions further configuring the at least a processor to extract the plurality of ECG parameters from the image using the optical character recognition reader (para. [0267] of Lyman, Some or all of the measurement data can be generated by the ECG interpretation system 4002 and/or can be extracted from text included in the ECG printout).
Regarding claim 5, the modified invention of Brockway fails to teach an apparatus wherein classifying the plurality of digitized ECG signals comprises:
inputting the plurality of digitized ECG signals into the validation classifier (para. [0042], [0045], [0063] of Brockway); and
outputting, by the validation classifier, the plurality of preliminary ECG parameters (para. [0042], [0045], [0063 of Brockway).
Brockway fails to teach training a validation classifier using training data comprising a plurality of digitized ECG signals correlated to a plurality of textbook validators of ECG parameters.
However Lyman teaches training a validation classifier using training data comprising a plurality of digitized ECG signals correlated to a plurality of textbook validators of ECG parameters (para. [0104]).
Therefore taking the combined teachings of Brockway and Taha with Lyman as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Lyman into the apparatus of Brockway and Taha. The motivation to combine Lyman, Taha and Brockway would be to aid medical professionals or other users in diagnosing, triaging, classifying, ranking, and/or otherwise reviewing medical scans (para. [0038] of Lyman).
Regarding claim 12, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Regarding claim 13, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Regarding claim 14, the claim recites similar subject matter as claim 4 and is rejected for the same reasons as stated above.
Regarding claim 15, the claim recites similar subject matter as claim 5 and is rejected for the same reasons as stated above.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brockway et al (US20130289424) and Taha et al (US6304772) in view of Sullivan et al (US20180116537).
Regarding claim 7, the modified invention of Brockway fails to teach an apparatus wherein the memory contains instructions further configuring the at least a processor to determine the threshold by:
training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds;
inputting the analog ECG data into the threshold classifier; and
outputting, by the threshold classifier, the threshold.
However Sullivan teaches training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds (para. [0073], determining a predetermined threshold score with a machine learning classifier (block 103), that has been trained with a sample data set; para. [0094], The training data set may include 60 noisy normal sinus rhythm signals (i.e., false positive detections) and 60 tachyarrhythmia signals);
inputting the analog ECG data into the threshold classifier (100 in fig. 4); and
outputting, by the threshold classifier, the threshold (para. [0073], determining a predetermined threshold score with a machine learning classifier).
Therefore taking the combined teachings of Brockway and Taha with Sullivan as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Sullivan into the apparatus of Brockway and Taha. The motivation to combine Sullivan, Taha and Brockway would be to reduce the frequency of false detections of arrhythmia (para. [0002] of Sullivan).
Regarding claim 17, the claim recites similar subject matter as claim 7 and is rejected for the same reasons as stated above.
Claim(s) 8, 9, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brockway et al (US20130289424) and Taha et al (US6304772) in view of Newton et al (US20210259778).
Regarding claim 8, the modified invention of Brockway fails to teach an apparatus wherein the quality diagnostic comprises an auditory representation comprising sounds to indicate a pass status or a fail status regarding an overall quality of the analog ECG data.
However Newton teaches auditory representation comprising sounds to indicate a pass status or a fail status regarding an overall quality of the analog ECG data (para. [0011], trigger an alert to an operator where the signal quality is not adequate; para. [0087]; para. [0093], For example, the alert may be an audible alert).
Therefore taking the combined teachings of Brockway and Taha with Newton as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Newton into the apparatus of Brockway and Taha. The motivation to combine Newton, Taha and Brockway would be to allow for more automation and minimal intervention (para. [0093] of Newton).
Regarding claim 9, the modified invention of Brockway fails to teach an apparatus wherein generating the quality diagnostic comprises generating a visual representation regarding the quality status of the analog ECG data by:
training a diagnostic classifier with training data correlating analog ECG data to a plurality of flags;
inputting the analog ECG data into the diagnostic classifier; and
outputting, by the diagnostic classifier, one or more flags indicating an issue with the analog ECG data.
However Newton teaches generating a visual representation (para. [0093], change of display colour or other visual alert) regarding the quality status of an analog ECG data (para. [0011]) by:
training a diagnostic classifier with training data correlating analog ECG data to a plurality of flags (para. [0087], The model can be derived by training either a support vector machine algorithm or a neural network architecture with ECG data that is specifically labelled with classes: ‘clean’, ‘no signal’, and ‘noisy signal’);
inputting the analog ECG data into the diagnostic classifier (para. [0085], The FFT can be analysed in the frequency domain to determine quality of the signal based on frequency response within a target expected range, centred around 3 Hz for a clean signal, very high frequency responses are characteristic of noisy signals); and
outputting, by the diagnostic classifier, one or more flags indicating an issue with the analog ECG data (para. [0085], Where the FFT does not display the anticipated characteristics for a clean signal, an alert may be triggered to warn the clinician; para. [0088]).
Therefore taking the combined teachings of Brockway and Taha with Newton as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Newton into the apparatus of Brockway and Taha. The motivation to combine Newton, Taha and Brockway would be to allow for more automation and minimal intervention (para. [0093] of Newton).
Regarding claim 18, the claim recites similar subject matter as claim 8 and is rejected for the same reasons as stated above.
Regarding claim 19, the claim recites similar subject matter as claim 9 and is rejected for the same reasons as stated above.
Conclusion


NEXT OFFICE ACTION: DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Allowable Subject Matter
Claims 1-20 are allowed.
The following is an examiner’s statement of reasons for allowance:
None of the prior art made of record teaches determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters, wherein:
the accuracy status further comprises a variable indicating accuracy of the plurality of preliminary ECG parameters;
determining an accuracy status comprises using a scoring algorithm using a threshold to determine the accuracy status, wherein the accuracy status comprises one of a pass status and a fail status, and wherein the apparatus is configured to generate the threshold by:
training a threshold classifier with training data correlating a plurality of annotations indicating abnormalities to a plurality of thresholds;
inputting the analog ECG data into the threshold classifier; and
outputting, by the threshold classifier, the threshold; and
generating a quality diagnostic of the image of ECG data based on validation of the one or more digitized ECG signals, wherein the quality diagnostic represents a degree of quality of the image of ECG data.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

CITED REFERENCE SUMMARIES: Ref US6304772B1: [content unavailable]
Ref US20210259778A1: Claims:
A computer-implemented method of using machine learning to automatically determine catheter locations in patients, the method comprising: obtaining a training data set comprising electrocardiogram (ECG) data and catheter location data collected from electrodes associated with catheters inserted in patients; pre-processing the ECG data to automatically select and extract features of P-waves and R-waves; using the extracted features of P-waves and R-waves and the catheter location data to automatically train a predictive machine learning model to automatically predict catheter locations in patients.
obtaining a training data set comprising electrocardiogram (ECG) data and catheter location data collected from electrodes associated with catheters inserted in patients;
pre-processing the ECG data to automatically select and extract features of P-waves and R-waves;
using the extracted features of P-waves and R-waves and the catheter location data to automatically train a predictive machine learning model to automatically predict catheter locations in patients.
The method of claim 1 , wherein the pre-processing comprises one or more of segmentation into time windows, fast Fourier transformation, dimensionality reduction, noise reduction, normalisation, outlier handling, and handling missing values.
The method of claim 1 , wherein the predictive machine learning model comprises one or more of regression analysis, neural networks, deep learning, support vector machines, Bayesian networks, and decision trees.
The method of claim 1 , wherein the electrodes comprise surface electrodes or intravascular electrodes.
The method of claim 1 , wherein the patients comprise adult patients or paediatric patients.
A computer-implemented method for automatically determining a catheter location in a patient, the method comprising: receiving electrocardiogram (ECG) data from an electrode associated with a catheter when indwelling in the patient; pre-processing the ECG data to automatically select and extract features of P-waves peaks and R-waves; inputting the extracted features of P-waves and R-waves to the previously trained predictive machine learning model of claim 1 to automatically predict the catheter location in the patient.
receiving electrocardiogram (ECG) data from an electrode associated with a catheter when indwelling in the patient;
pre-processing the ECG data to automatically select and extract features of P-waves peaks and R-waves;
inputting the extracted features of P-waves and R-waves to the previously trained predictive machine learning model of claim 1 to automatically predict the catheter location in the patient.
The method of claim 6 , further comprising providing a user interface configured to display a visualisation of the output of the previously trained predictive machine learning model.
A system for determining catheter tip location in a patient, the system comprising: an electrode configured to associate with a catheter to collect ECG data when the catheter is indwelling in the patient; and
an electrode configured to associate with a catheter to collect ECG data when the catheter is indwelling in the patient; and
the previously trained predictive machine learning model of claim 1 .
The system of claim 8 , further comprising a user interface configured to display a visualisation of the output of the previously trained predictive machine learning model.
A method of determining catheter tip location in a patient, the method comprising: inserting a catheter in the patient; monitoring catheter tip location in the patient; and while the catheter is indwelling, determining catheter tip location in the patient using the system of claim 8 .
inserting a catheter in the patient;
monitoring catheter tip location in the patient; and
while the catheter is indwelling, determining catheter tip location in the patient using the system of claim 8 .
Ref US20200160980A1: Claims:
An electrocardiogram (ECG) interpretation system, comprising: at least one processor; and a memory that stores operational instructions that, when executed by the at least one processor, cause the ECG interpretation system to: receive, via a receiver, a captured image of an ECG printout; perform a waveform detection function on the captured image to determine a plurality of locations of a plurality of ECG waveforms in the captured image; generate a plurality of waveform images by partitioning the captured image based on the plurality of locations, wherein each of the plurality of waveform images includes one of the plurality of ECG waveforms; generate a plurality of pseudo-raw ECG signal data by performing a signal reconstruction function on each of the plurality of waveform images, wherein each of the plurality of pseudo-raw ECG signal data corresponds to one of the plurality of waveform images; generate diagnosis data by performing a diagnosing function on the plurality of pseudo-raw ECG signal data; and transmit, via a transmitter, the diagnosis data to a client device for display via a display device.
at least one processor; and
a memory that stores operational instructions that, when executed by the at least one processor, cause the ECG interpretation system to:
receive, via a receiver, a captured image of an ECG printout;
perform a waveform detection function on the captured image to determine a plurality of locations of a plurality of ECG waveforms in the captured image;
generate a plurality of waveform images by partitioning the captured image based on the plurality of locations, wherein each of the plurality of waveform images includes one of the plurality of ECG waveforms;
generate a plurality of pseudo-raw ECG signal data by performing a signal reconstruction function on each of the plurality of waveform images, wherein each of the plurality of pseudo-raw ECG signal data corresponds to one of the plurality of waveform images;
generate diagnosis data by performing a diagnosing function on the plurality of pseudo-raw ECG signal data; and
transmit, via a transmitter, the diagnosis data to a client device for display via a display device.
The ECG interpretation system of claim 1 , wherein the ECG printout was generated by an ECG machine based on electrical activity recorded by the ECG machine, and wherein the captured image was generated by a utilizing an image capture device to generate the captured image from the ECG printout.
The ECG interpretation system of claim 2 , wherein the client device corresponds to a mobile device, wherein the image capture device corresponds to a camera, wherein the camera is coupled to the mobile device, wherein the captured image is transmitted to the ECG interpretation system by the mobile device.
The ECG interpretation system of claim 2 , wherein the image capture device corresponds to a scanner.
The ECG interpretation system of claim 1 , wherein the waveform detection function utilizes a computer vision model trained on a training set of waveform images.
The ECG interpretation system of claim 1 , wherein the waveform detection function utilizes standardized layout information, wherein the standardized layout information indicates relative positions of waveforms in standard ECG printouts.
The ECG interpretation system of claim 6 , wherein the standardized layout information indicates relative locations of exactly twelve waveforms, wherein each of the exactly twelve waveforms corresponds to one of twelve ECG leads, and wherein the plurality of waveform images includes exactly twelve waveform images corresponding to the twelve ECG leads.
The ECG interpretation system of claim 1 , wherein the waveform detection function utilizes standardized formatting feature information, wherein the standardized formatting feature information indicates at least one relative position of at least one additional feature in standard ECG printouts, and wherein the performing the waveform detection function includes detecting the at least one additional feature in the ECG printout.
The ECG interpretation system of claim 1 , wherein the signal reconstruction function determines a plurality of discrete points for one of the plurality of waveform images, and wherein a corresponding one of the plurality of pseudo-raw ECG signal data includes the plurality of discrete points, wherein the diagnosing function is trained on a training set of raw ECG signal data, and wherein each raw ECG signal data in the training set includes discrete points characterizing the raw ECG signal data.
The ECG interpretation system of claim 1 , wherein performing the diagnosing function on the plurality of pseudo-raw ECG signal data includes: generating twelve sub-diagnosis data, wherein each of the twelve sub-diagnosis data is generated by performing one of twelve inference functions on a corresponding one of twelve pseudo-raw ECG signal data, wherein each of the twelve inference functions corresponds to one of twelve ECG leads and is trained on a training set of raw ECG signal data for the corresponding one of the twelve ECG leads; and performing a final inference function on the twelve sub-diagnosis data to generate the diagnosis data.
generating twelve sub-diagnosis data, wherein each of the twelve sub-diagnosis data is generated by performing one of twelve inference functions on a corresponding one of twelve pseudo-raw ECG signal data, wherein each of the twelve inference functions corresponds to one of twelve ECG leads and is trained on a training set of raw ECG signal data for the corresponding one of the twelve ECG leads; and
performing a final inference function on the twelve sub-diagnosis data to generate the diagnosis data.
The ECG interpretation system of claim 1 , wherein the captured image is received from the client device, wherein the ECG printout corresponds to a patient, and wherein the client device is associated with the patient.
The ECG interpretation system of claim 1 , wherein the captured image is received from the client device, wherein the ECG printout corresponds to a patient, and wherein the client device is associated with a medical professional responsible for the patient.
The ECG interpretation system of claim 1 , wherein the ECG printout is received in an first email transmission from a first email address associated with a requestor to a second email address associated with the ECG interpretation system, wherein the diagnosis data is transmitted in a second email transmission from the second email address to the first email address, and wherein the requestor is a user of the client device.
The ECG interpretation system of claim 13 , wherein the second email transmission includes a document for display to the requestor via the client device, and wherein the document includes the diagnosis data and further includes the plurality of pseudo-raw ECG signal data.
The ECG interpretation system of claim 1 , wherein the diagnosis data is displayed via the display device in conjunction with the captured image.
The ECG interpretation system of claim 1 , wherein the diagnosis data is displayed via the display device in conjunction with the plurality of pseudo-raw ECG signal data via a user interface.
The ECG interpretation system of claim 16 , wherein the diagnosis data indicates at least one abnormality in at least one of the plurality of pseudo-raw ECG signal data, and wherein the at least one abnormality is visually indicated in the display of the at least one of the plurality of pseudo-raw ECG signal data.
The ECG interpretation system of claim 17 , wherein the operational instructions, when executed by the at least one processor, further cause the ECG interpretation system to: generating normal pseudo-raw ECG signal data by removing the at least one abnormality from the at least one of the plurality of pseudo-raw ECG signal data, wherein the normal pseudo-raw ECG signal data is displayed via the display device in conjunction with the diagnosis data and the plurality of pseudo-raw ECG signal data.
generating normal pseudo-raw ECG signal data by removing the at least one abnormality from the at least one of the plurality of pseudo-raw ECG signal data, wherein the normal pseudo-raw ECG signal data is displayed via the display device in conjunction with the diagnosis data and the plurality of pseudo-raw ECG signal data.
The ECG interpretation system of claim 1 , wherein the operational instructions, when executed by the at least one processor, further cause the ECG interpretation system to: receive, via the receiver, a second captured image of a patient's skin; generate second diagnosis data by performing a second diagnosing function on the second captured image, wherein the second diagnosing function utilizes a computer vision model trained on a plurality of images of skin corresponding to a plurality of skin conditions; and transmit, via the transmitter, the second diagnosis data to a second client device for display via a second display device.
receive, via the receiver, a second captured image of a patient's skin;
generate second diagnosis data by performing a second diagnosing function on the second captured image, wherein the second diagnosing function utilizes a computer vision model trained on a plurality of images of skin corresponding to a plurality of skin conditions; and
transmit, via the transmitter, the second diagnosis data to a second client device for display via a second display device.
A method for execution by an ECG interpretation system, the method comprising: receiving, via a receiver, a captured image of an ECG printout; performing a waveform detection function on the captured image to determine a plurality of locations of a plurality of ECG waveforms in the captured image; generating a plurality of waveform images by partitioning the captured image based on the plurality of locations, wherein each of the plurality of waveform images includes one of the plurality of ECG waveforms; generating a plurality of pseudo-raw ECG signal data by performing a signal reconstruction function on each of the plurality of waveform images, wherein each of the plurality of pseudo-raw ECG signal data corresponds to one of the plurality of waveform images; generating diagnosis data by performing a diagnosing function on the plurality of pseudo-raw ECG signal data; and transmitting, via a transmitter, the diagnosis data to a client device for display via a display device.
receiving, via a receiver, a captured image of an ECG printout;
performing a waveform detection function on the captured image to determine a plurality of locations of a plurality of ECG waveforms in the captured image;
generating a plurality of waveform images by partitioning the captured image based on the plurality of locations, wherein each of the plurality of waveform images includes one of the plurality of ECG waveforms;
generating a plurality of pseudo-raw ECG signal data by performing a signal reconstruction function on each of the plurality of waveform images, wherein each of the plurality of pseudo-raw ECG signal data corresponds to one of the plurality of waveform images;
generating diagnosis data by performing a diagnosing function on the plurality of pseudo-raw ECG signal data; and
transmitting, via a transmitter, the diagnosis data to a client device for display via a display device.
Ref US20180116537A1: Claims:
A system comprising: an ambulatory medical device comprising: a digital signal processing module configured to: detect an abnormal rhythm from an electrocardiogram (ECG) signal of a patient using a first signal processing routine; and generate a first flag indicating an abnormal rhythm is detected; and a noise detector module configured to: receive the ECG signal from the digital signal processing module; execute a second signal processing routine to classify the abnormal rhythm as one of an arrhythmia event and a noise event; and, if the abnormal rhythm is classified as a noise event, initiate a preconfirmation period during which the noise detector module continues to evaluate the abnormal rhythm and classify the abnormal rhythm as one of an arrhythmia event and a noise event using the second signal processing routine; and generate a second flag indicating the start of the preconfirmation period; and a server configured to: receive the ECG signal, the first flag indicating the abnormal rhythm, and the second flag indicating the start of the preconfirmation period; and provide a visual indication of the preconfirmation period.
an ambulatory medical device comprising: a digital signal processing module configured to: detect an abnormal rhythm from an electrocardiogram (ECG) signal of a patient using a first signal processing routine; and generate a first flag indicating an abnormal rhythm is detected; and a noise detector module configured to: receive the ECG signal from the digital signal processing module; execute a second signal processing routine to classify the abnormal rhythm as one of an arrhythmia event and a noise event; and, if the abnormal rhythm is classified as a noise event, initiate a preconfirmation period during which the noise detector module continues to evaluate the abnormal rhythm and classify the abnormal rhythm as one of an arrhythmia event and a noise event using the second signal processing routine; and generate a second flag indicating the start of the preconfirmation period; and
a digital signal processing module configured to: detect an abnormal rhythm from an electrocardiogram (ECG) signal of a patient using a first signal processing routine; and generate a first flag indicating an abnormal rhythm is detected; and
detect an abnormal rhythm from an electrocardiogram (ECG) signal of a patient using a first signal processing routine; and
generate a first flag indicating an abnormal rhythm is detected; and
a noise detector module configured to: receive the ECG signal from the digital signal processing module; execute a second signal processing routine to classify the abnormal rhythm as one of an arrhythmia event and a noise event; and, if the abnormal rhythm is classified as a noise event, initiate a preconfirmation period during which the noise detector module continues to evaluate the abnormal rhythm and classify the abnormal rhythm as one of an arrhythmia event and a noise event using the second signal processing routine; and generate a second flag indicating the start of the preconfirmation period; and
receive the ECG signal from the digital signal processing module;
execute a second signal processing routine to classify the abnormal rhythm as one of an arrhythmia event and a noise event; and,
if the abnormal rhythm is classified as a noise event, initiate a preconfirmation period during which the noise detector module continues to evaluate the abnormal rhythm and classify the abnormal rhythm as one of an arrhythmia event and a noise event using the second signal processing routine; and
generate a second flag indicating the start of the preconfirmation period; and
a server configured to: receive the ECG signal, the first flag indicating the abnormal rhythm, and the second flag indicating the start of the preconfirmation period; and provide a visual indication of the preconfirmation period.
receive the ECG signal, the first flag indicating the abnormal rhythm, and the second flag indicating the start of the preconfirmation period; and
provide a visual indication of the preconfirmation period.
The system of claim 1 , wherein the ambulatory medical device is configured to monitor and treat the patient for arrhythmia.
The system of claim 1 , wherein the ambulatory medical device is a wearable defibrillator.
The system of claim 1 , wherein the digital signal processing module is configured to receive the ECG signal of the patient from an electrode assembly removably attached to the patient.
The system of claim 1 , wherein the second signal processing routine co
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