Patent US10566092

Methods and systems using mathematical analysis and machine learning to diagnose disease
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US10566092

1. A system comprising: (10) (2)

a biopotential measuring equipment configured to non-invasively capture, from one or more electrical leads placed on a subject, biopotential signals as a biopotential signal data set; and

a storage area network device configured to store the biopotential signal data set, wherein the biopotential signal data set are transmitted from the biopotential measuring equipment, and wherein the biopotential signal data set is 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.

2. The system of claim 1, wherein the first fractional derivative signal data set is generated by performing a first numeric fractional derivative operation of a first pre-defined order value on the biopotential signal data set in the frequency domain and by performing an inversed transformation operation on the first fractional derived signal data set to convert the first fractional derived signal data set to a time domain signal data set. (1) (0)

4. The system of claim 1, wherein the second fractional derivative signal data set is generated by performing a second numeric fractional derivative operation of a second pre-defined order value on the biopotential signal data set and by performing an inversed transformation operation on the second fractional derived signal data to convert the second fractional derived signal data set to a time domain signal data set. (0)

5. The system of claim 1, wherein each of the first fractional derivative signal data set and the second fractional derivative signal data set comprises a time domain signal data. (0)

6. The system of claim 1, wherein the first fractional derivative signal data set is generated by performing a fractional derivative operation of an order of pi/2 or an order of 0.5. (0)

7. The system of claim 1, wherein the geometric features and/or the dynamical properties of the three-dimensional space data set are generated by performing a modified matching pursuit (MMP) algorithm operation on the three-dimensional point in said space data set. (0)

8. The system of claim 1, wherein the biopotential signal data set is associated with a biopotential signal collected from a single electrical lead. (0)

9. The system of claim 1, wherein the electrical-lead-collected measurements of the at least one biopotential signal are collected at a location selected from the group consisting of a chest line of the subject, a waistline of the subject, a wrist of the subject, a pelvic line of the subject, a neck of the subject, an ankle of the subject, a forehead of the subject, and an arm line of the subject. (0)

10. The system of claim 1, wherein the detection operation comprises: (0) (1)

11. The system of claim 1, wherein the biopotential signal data set comprises electrocardiographic data. (0)

12. The system of claim 1, wherein the machine learning operation comprises an artificial neural network algorithm or a regression random forest algorithm. (0)

13. A method comprising: (7) (2)

capturing, via a biopotential measuring equipment, as a biopotential signal data set, one or more biopotential signals from one or more electrical leads placed on a subject; and

storing, in a network, at a storage area network (SAN), the biopotential signal data set, wherein the stored biopotential signal data set 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

14. The method of claim 13 further comprising: (0) (1)

15. The method of claim 13, wherein the one or more diagnosable pathologies include a congenital heart defect (CHD) in a mammal. (0)

16. The method of claim 13, (0) (2)

17. The method of claim 13, wherein the electrical-lead-collected measurements of the at least one biopotential signal are collected at a location selected from the group consisting of a chest line of the subject, a waistline of the subject, a wrist of the subject, a pelvic line of the subject, a neck of the subject, an ankle of the subject, a forehead of the subject, and an arm line of the subject. (0)

18. The method of claim 13, wherein the geometric features and/or the dynamical properties of the three-dimensional space data set are generated by performing a modified matching pursuit (MMP) algorithm operation on the three-dimensional point in said space data set. (0)

19. The method of claim 13, wherein the machine learning operation comprises an artificial neural network algorithm or a regression random forest algorithm. (0)

20. The method of claim 13, wherein the detection operation comprises generating, via the processor, an alpha shape or a Delaunay triangle mesh of the three-dimensional point in said space data set, wherein the geometric features and/or the dynamical properties of the three-dimensional space data set include the geometric features of the alpha shape or the Delaunay triangle mesh. (0)

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Patent US10566092

Methods and systems using mathematical analysis and machine learning to diagnose disease

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