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AI Biotech/Diagnostics: Cardio
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Patent US10566091
Issued 2020-02-18
Methods And Systems Using Mathematical Analysis And Machine Learning To Diagnose Disease
Exemplified method and system facilitates monitoring and/or evaluation of disease or physiological state using mathematical analysis and machine learning analysis of a biopotential signal collected from a single electrode. The exemplified method and system creates, from data of a singularly measured biopotential signal, via a mathematical operation (i.e., via numeric fractional derivative calculation of the signal in the frequency domain), one or more mathematically-derived biopotential signals (e.g., virtual biopotential signals) that is used in combination with the measured biopotential signals to generate a multi-dimensional phase-space representation of the body (e.g., the heart). By mathematically modulating (e.g., by expanding or contracting) portions of a given biopotential signal, in the frequency domain, the numeric-based operation gives emphasis or de-emphasis to certain measured frequencies of the biopotential signals, which, when coupled with machine learning, facilitates improved diagnostics of certain pathologies.
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- 1. A system comprising:
a storage area network device configured to store a captured biopotential signals of a subject, wherein the captured biopotential signals are transmitted from a biopotential measuring equipment that non-invasively captured, from one or more electrical leads placed on a subject, biopotential signals as a biopotential signal data set, wherein the captured biopotential signals are used to detect one or more diagnosable pathologies of the subject, wherein the detection operation comprises:
generating, via a processor, one or more fractional derivative data sets by numerically performing, for each of a first fractional derivative data set and a second fractional derivative signal data set of the one or more fractional derivative data sets, one or more fractional derivative operations on the biopotential signal data set in a frequency domain and converting a result of the one or more fractional derivative operations to a time domain signal data set, wherein each of the first generated fractional derivative signal data set and the second generated fractional derivative signal data set comprises a same length and a same sampling frequency as the biopotential signal data set; and
generating, via the processor, a three-dimensional space data set wherein each corresponding value of the biopotential signal data set, the first fractional derivative signal data set, and the second fractional derivative signal data set forms a three-dimensional point in said space data set, wherein geometric features and/or dynamical properties of the three-dimensional space data set are used as variables representative of the subject in a machine learning operation to detect one or more diagnosable pathologies of the subject.
- 15. A method comprising:
storing, in a network, at a storage area network (SAN), a biopotential signal data set of biopotential signals captured via a biopotential measuring equipment configured to capture one or more biopotential signals from one or more electrical leads placed on a subject, wherein the stored biopotential signal are analyzed to detect one or more diagnosable pathologies of the subject, wherein the detection operation comprises:
generating, via a processor, a first fractional derivative data set and a second fractional derivative signal data set by numerically performing, for each of the first fractional derivative data set and the second fractional derivative signal data set, one or more fractional derivative operations on the biopotential signal data set in a frequency domain and converting a result of the one or more fractional derivative operations to a time domain signal data set, wherein each of the first generated fractional derivative signal data set and the second generated fractional derivative signal data set comprises a same length and a same sampling frequency as the biopotential signal data set; and
generating, via the processor, a three-dimensional space data set wherein each corresponding value of the biopotential signal data set, the first fractional derivative signal data set, and the second fractional derivative signal data set forms a three-dimensional point in said space data set, wherein geometric features and/or dynamical properties of the three-dimensional space data set are used as variables representative of the subject in a machine learning operation to detect one or more diagnosable pathologies of the subject.
