Multi-domain motion estimation and plethysmographic recognition using fuzzy neural-nets
First Claim
1. A pulse oximeter comprising:
- a first optical signal source operable to emit an optical signal characterized by a first wavelength;
a second optical signal source operable to emit an optical signal characterized by a second wavelength different than said first wavelength;
a detector operable to receive said first and second optical signals after said first and second optical signals are attenuated by a patient tissue site of a patient, said detector being further operable to provide a detector output signal representative of said attenuated first and second optical signals; and
a processor enabled to obtain first and second time domain plethysmographic signals from the detector output signal and classify at least one of the first and second time domain plethysmographic signals using a neural network, said neural network receiving input coefficients derived from at least one transform of said at least one of said first and second time domain plethysmographic signals.
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Abstract
Pulse oximetry is improved through classification of plethysmographic signals by processing the plethysmographic signals using a neural network that receives input coefficients from multiple signal domains including, for example, spectral, bispectral, cepstral and Wavelet filtered signal domains. In one embodiment, a plethysmographic signal obtained from a patient is transformed (240) from a first domain to a plurality of different signal domains (242, 243, 244, 245) to obtain a corresponding plurality of transformed plethysmographic signals. A plurality of sets of coefficients derived from the transformed plethysmographic signals are selected and directed to an input layer (251) of a neural network (250). The plethysmographic signal is classified by an output layer (253) of the neural network (250) that is connected to the input layer (251) by one or more hidden layers (252).
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Citations
45 Claims
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1. A pulse oximeter comprising:
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a first optical signal source operable to emit an optical signal characterized by a first wavelength;
a second optical signal source operable to emit an optical signal characterized by a second wavelength different than said first wavelength;
a detector operable to receive said first and second optical signals after said first and second optical signals are attenuated by a patient tissue site of a patient, said detector being further operable to provide a detector output signal representative of said attenuated first and second optical signals; and
a processor enabled to obtain first and second time domain plethysmographic signals from the detector output signal and classify at least one of the first and second time domain plethysmographic signals using a neural network, said neural network receiving input coefficients derived from at least one transform of said at least one of said first and second time domain plethysmographic signals. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method of processing a plethysmographic signal obtained from a patient, the plethysmographic signal being obtained in a first signal domain, said method comprising the steps of:
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transforming the plethysmographic signal from the first domain to a plurality of signal domains different from the first domain to obtain a corresponding plurality of transformed plethysmographic signals, each transformed plethysmographic signal being in one of the different signal domains;
selecting a plurality of sets of coefficients, each set of coefficients being derived from a corresponding one of the transformed plethysmographic signals;
inputting the sets of coefficients to a neural network; and
classifying the plethysmographic signal based on an output from the neural network. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31)
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32. A method of training a neural network to classify a plethysmographic signal obtained from a patient, said method comprising the steps of:
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selecting a plurality of first domain plethysmographic signal data sets associated with a plurality of different types of predetermined signal conditions from a database of plethysmographic signal data sets;
transforming the first domain plethysmographic signal data sets to other signal domains different than the first domain to obtain a corresponding plurality of transformed plethysmographic signal data sets;
extracting a plurality of sets of coefficients from the transformed plethysmographic signal data sets, each set of coefficients being extracted from a corresponding one of the transformed plethysmographic signal data sets;
using the sets of extracted coefficients as inputs to the neural network; and
adjusting weighting values associated with connections between neurons in the neural network in accordance with a learning procedure. - View Dependent Claims (33, 34, 35, 36, 37, 38, 39, 40)
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41. A method of providing information relating to a physiological condition of a patient based on at least one plethysmographic signal obtained from the patient, the plethysmographic signal being obtained in a first signal domain, said method comprising the steps of:
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transforming the plethysmographic signal from the first domain to a plurality of signal domains different from the first domain to obtain a corresponding plurality of transformed plethysmographic signals, each transformed plethysmographic signal being in one of the different signal domains;
classifying the plethysmographic signal based on an output from a neural network, wherein the output of the neural network is based on input coefficients derived from at least one of the transformed plethysmographic signals; and
selecting a technique for determining the physiological condition of the patient based on the classification. - View Dependent Claims (42, 43, 44, 45)
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Specification