초록 ▼

By redistributing the running totals for various preliminary classifications to other preliminary classifications based upon the values of the most recent cardiac events, the present invention biases the running totals (thereby biasing the duration criteria) to help overcome common discrimination pr

By redistributing the running totals for various preliminary classifications to other preliminary classifications based upon the values of the most recent cardiac events, the present invention biases the running totals (thereby biasing the duration criteria) to help overcome common discrimination problems which permits the stimulation device to make a correct and final therapy decision more quickly. To identify a patient's heart rhythm, various electrical events such as P-waves and R-waves, and their timing, relationship, and stability, are detected and a preliminary classification is made for each detected event. Running totals of the numbers of all events detected within each of the preliminary classifications are maintained, along with sliding totals covering only the most recently detected events. Then, the arrhythmia is identified based upon an analysis of both the running totals and the sliding totals. In a preferred embodiment, the arrhythmia is identified by first determining whether the sliding total of the number of detected events in any one of the preliminary classifications exceeds a corresponding sliding total threshold. If so, the running totals are then selectively redistributed based upon the sliding totals. The final decision is then made based upon whether the running total corresponding to any one of the preliminary classifications exceeds a corresponding running total threshold. Both method and apparatus embodiments are disclosed. Methods are further described for determining the preliminary classifications of the detected events.

대표청구항 ▼

By redistributing the running totals for various preliminary classifications to other preliminary classifications based upon the values of the most recent cardiac events, the present invention biases the running totals (thereby biasing the duration criteria) to help overcome common discrimination pr

By redistributing the running totals for various preliminary classifications to other preliminary classifications based upon the values of the most recent cardiac events, the present invention biases the running totals (thereby biasing the duration criteria) to help overcome common discrimination problems which permits the stimulation device to make a correct and final therapy decision more quickly. To identify a patient's heart rhythm, various electrical events such as P-waves and R-waves, and their timing, relationship, and stability, are detected and a preliminary classification is made for each detected event. Running totals of the numbers of all events detected within each of the preliminary classifications are maintained, along with sliding totals covering only the most recently detected events. Then, the arrhythmia is identified based upon an analysis of both the running totals and the sliding totals. In a preferred embodiment, the arrhythmia is identified by first determining whether the sliding total of the number of detected events in any one of the preliminary classifications exceeds a corresponding sliding total threshold. If so, the running totals are then selectively redistributed based upon the sliding totals. The final decision is then made based upon whether the running total corresponding to any one of the preliminary classifications exceeds a corresponding running total threshold. Both method and apparatus embodiments are disclosed. Methods are further described for determining the preliminary classifications of the detected events. odel to said calculated magnitude or relative magnitude to characterize said tissue layers. 2. The method of claim 1, wherein said step of processing said measured NIR absorbance spectrum comprises: projecting normalized spectra of said key indicators on said measured spectrum. 3. The method of claim 1, wherein said key indicators comprise chemical and structural components that are primary absorbers and scatterers within a particular tissue layer, and wherein said magnitude of said key indicators is greater in said particular layer of said tissue sample than in any other layer of said tissue sample, such that said magnitude of said key indicators is specific to said particular tissue layer, and whereby said particular tissue layer can be characterized according to said magnitudes of said key indicators. 4. The method of claim 3, wherein tissue layers that can be characterized by calculating said magnitudes of said key indicators include any of: subcutaneous tissue; dermis; epidermis; and stratum corneum. 5. The method of claim 3, wherein said key indicators are determined from a priori knowledge of the composition and structure of said tissue layers, and wherein structural and chemical components that can serve as said key indicators include any of: trigylcerides; collagen bundles; water; blood; keratinocytes; fatty acids; sterols; sphingolipids; pigments; corneocytes; keratinized cells; and sebum. 6. The method of claim 3, wherein said measuring step comprises the steps of: selecting a target tissue layer; selecting at least one target key indicator specific to said target tissue layer; and limiting said spectrum to a wavelength region wherein said at least one target key indicator absorbs and scatters, and wherein optimal penetration of transmitted energy to said target layer is possible. 7. The method of claim 3, wherein said plurality of key indicators comprises a basis set. 8. The method of claim 3, wherein said calculation step comprises the step of: applying a partial least squares regression to calculate said magnitudes. 9. The method of claim 3, wherein said calculated magnitudes of said key indicators provide relative concentrations of said structural and chemical components. 10. The method of claim 3, further comprising the step of: providing a calibration set of exemplary measurements. 11. The method of claim 10, wherein said step of applying at least one calibration model comprises the step of: applying at least one calibration model to said relative concentrations to determine an actual concentration in said target layer, wherein said calibration model is calculated from said calibration set. 12. The method of claim 10, wherein said step of applying at least one calibration model comprises the step of: applying said calibration model to said relative concentrations to determine absolute or relative thickness of said target layer, wherein thickness is any of physical and optical thickness; and wherein said calibration model is calculated from said calibration set. 13. The method of claim 12, further comprising the step of: summing thickness estimates. 14. The method of claim 13, wherein said calibration set comprises spectral measurements of a target tissue site and tissue layer thickness determinations from an exemplary population of subjects. 15. The method of claim 14, wherein multivariate regression analysis relates said exemplary spectral measurements to said exemplary tissue layer thickness determinations. 16. The method of claim 10, wherein said exemplary measurements comprise calculated relative concentrations of said chemical and structural components and tissue layer thickness determinations. 17. The method of claim 16, wherein said calibration model is calculated using any of multiple linear regression, partial least squares regression, and artificial neural networks. 18. The method of claim 10, wherein said calibration set comprises NIR s pectral measurements of an exemplary sample of skin tissue, tissue layer thickness measurements determined from biopsies of said exemplary sample, and determinations of chemical composition of said layers of said biopsy samples. 19. The method of claim 18, wherein multivariate regression analysis relates said NIR spectral measurements of said exemplary tissue sample to said layer thickness and chemical composition determinations from said biopsy samples. 20. The method of claim 10, wherein said calibration set comprises a tissue model that represents the fundamental absorbing and scattering characteristics of an in vivo tissue system. 21. The method of claim 20, wherein said tissue model employs a simulation method whereby photon propagation of light through said tissue model is simulated, and wherein said photon propagation simulation yields a simulated diffuse reflectance spectrum comparable to an actual reflectance spectrum. 22. The method of claim 21, wherein said simulation method is a Monte Carlo simulation. 23. A non-invasive method of estimating thickness of in vivo skin tissue comprising the steps of: measuring an NIR absorbance spectrum of a target layer at a tissue sample site; applying at least one calibration model to said absorbance spectrum; and determining a thickness estimate of said target layer of said tissue sample; and summing thickness estimates of individual target layers so that a total thickness of said tissue sample is calculated. 24. The method of claim 23, further comprising the step of: providing a calibration set of exemplary measurements. 25. The method of claim 24, wherein said calibration model is calculated from said calibration set using any of multiple linear regression, partial least squares regression, and artificial neural networks. et al., 600/411; US-6167296, 20001200, Shahidi, 600/427