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US 12,097,032 B2Granted
Machine differentiation of abnormalities in bioelectromagnetic fields
AssigneeGenetesis, Inc.
InventorsRaj Muchhala, Emmanuel T. Setegn, Benjamin Donaldson Moore
Life of this patent
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Abstract
Abnormalities in electromagnetic fields in the heart, brain, and stomach, among other organs and tissues of the human body, can be indicative of serious health conditions. Described herein are methods, software, systems and devices for detecting the presence of an abnormality in an organ or tissue of a subject by analysis of the electromagnetic fields generated by the organ or tissue.
Claims21
A diagnostic device configured to detect a cardiac abnormality of a heart of an individual, comprising:
- (i) an electromagnetic field sensor configured to sense an electromagnetic field measurement associated with the heart of the individual;
- (ii) a processor operably coupled to the electromagnetic field sensor;
- and
- (iii) a non-transitory computer-readable storage medium encoded with software comprising a trained machine learning software module, wherein the trained machine learning software module is configured to process an electromagnetic field measurement of the individual to reconstruct a reference spatiotemporal representation for the individual comprising a spatiotemporal representation of heart-related electromagnetic field (EMF) signal segments that is representative of an absence of the cardiac abnormality, wherein the software is executable by the processor and causes the processor to:
- (a) receive the electromagnetic field measurement from the electromagnetic field sensor;
- (b) generate a test spatiotemporal representation for the individual using the electromagnetic field measurement, wherein the test spatiotemporal representation comprises (i) a spatiotemporal representation of magnetic activation of the heart from base to apex, or (ii) a spatiotemporal representation of magnetic activation of the heart from left to right;
- (c) process the electromagnetic field measurement using the trained machine learning software module to reconstruct the reference spatiotemporal representation for the individual comprising the spatiotemporal representation of heart-related EMF signal segments that is representative of the absence of the cardiac abnormality;
- (d) determining a difference between the test spatiotemporal representation for the individual and the reference spatiotemporal representation for the individual;
- (e) detect the cardiac abnormality of the heart of the individual based at least in part on the difference determined in (d);
- and
- (f) generate an output based on the detection, the output including one or more of an abnormality, a disease state, an imbalance, a diagnosis, a prognosis, a prediction of a change in health status, or a therapy suggestion including preventative therapy for the individual.
8 more elements
A computer-implemented method for detecting a cardiac abnormality of a heart of an individual, comprising:
- (a) sensing, by an electromagnetic field sensor, an electromagnetic field measurement associated with the heart of the individual;
- (b) generating a test spatiotemporal representation for the individual using the electromagnetic field measurement, wherein the test spatiotemporal representation comprises (i) a spatiotemporal representation of magnetic activation of the heart from base to apex, or (ii) a spatiotemporal representation of magnetic activation of the heart from left to right;
- (c) processing the electromagnetic field measurement using a trained machine learning algorithm to reconstruct a reference spatiotemporal representation for the individual comprising a spatiotemporal representation of heart-related EMF signal segments that is representative of an absence of the cardiac abnormality;
- (d) determining a difference between the test spatiotemporal representation for the individual and the reference spatiotemporal representation for the individual;
- (e) detecting the cardiac abnormality of the heart of the individual based at least in part on the difference determined in (d);
- and
- (f) generating an output based on the detection, the output including one or more of an abnormality. a disease state, an imbalance, a diagnosis, a prognosis, a prediction of a change in health status, or a therapy suggestion including preventative therapy for the individual.
4 more elements
A non-transitory computer-readable storage medium comprising machine-executable instructions that, when executed by one or more processors, implements a method for detecting a cardiac abnormality of a heart of an individual, the method comprising:
- (a) sensing, by an electromagnetic field sensor, an electromagnetic field measurement associated with the heart of the individual;
- (b) generating a test spatiotemporal representation for the individual using the electromagnetic field measurement, wherein the test spatiotemporal representation comprises (i) a spatiotemporal representation of magnetic activation of the heart from base to apex, or (ii) a spatiotemporal representation of magnetic activation of the heart from left to right;
- (c) processing the electromagnetic field measurement using a trained machine learning algorithm to reconstruct a reference spatiotemporal representation for the individual comprising a spatiotemporal representation of heart-related EMF signal segments that is representative of an absence of the cardiac abnormality;
- (d) determining a difference between the test spatiotemporal representation for the individual and the reference spatiotemporal representation for the individual;
- (g) detecting the cardiac abnormality of the heart of the individual based at least in part on the difference determined in (d);
- and
- (h) generating an output based on the detection, the output including one or more of an abnormality, a disease state, an imbalance, a diagnosis, a prognosis, a prediction of a change in health status, or a therapy suggestion including preventative therapy for the individual.
4 more elements
Dependencies are inferred from the claim text.