Heart rate extraction using neural wavelet adaptive gain control and neural pattern processing
First Claim
1. A heart rate measurement device comprising:
- a wavelet template;
an instrumentation amplifier;
a set of two electrodes configured to be coupled to a subject'"'"'s skin, and in electrical communication with and providing a set of signals to said instrumentation amplifier;
an adaptive gain control (AGC) algorithm;
an analog or digital filter in a feedback path of said instrumentation amplifier for at least one of removal of saturating signals and unwanted waveform components responsive to said AGC algorithm;
wherein said AGC algorithm utilizes said wavelet template and responds to the correlation of said wavelet template to a measured signal obtained from said set of signals by said instrumentation amplifier; and
wherein said device is configured to capture said set of signals utilizing a high common-mode resection ratio (CMRR) termination, which does not include a DC path to ground or overload said electrodes.
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Abstract
An improved heart rate monitor is provided that can detect and distinguish a heartbeat from an otherwise contaminated system with noise components potentially larger than the signal of interest. Embodiments of the inventive monitor have an amplification system that eliminates large noise components so as not to saturate the system during detection of a desired low amplitude signal. In embodiments the elimination of noise components is accomplished through wavelet decomposition, and the removal of undesired components including interference components during adaptive gain control (AGC), in addition to hunting algorithms which minimize the error with techniques such as neural network least mean squares type back propagation algorithms.
9 Citations
20 Claims
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1. A heart rate measurement device comprising:
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a wavelet template; an instrumentation amplifier; a set of two electrodes configured to be coupled to a subject'"'"'s skin, and in electrical communication with and providing a set of signals to said instrumentation amplifier; an adaptive gain control (AGC) algorithm; an analog or digital filter in a feedback path of said instrumentation amplifier for at least one of removal of saturating signals and unwanted waveform components responsive to said AGC algorithm; wherein said AGC algorithm utilizes said wavelet template and responds to the correlation of said wavelet template to a measured signal obtained from said set of signals by said instrumentation amplifier; and wherein said device is configured to capture said set of signals utilizing a high common-mode resection ratio (CMRR) termination, which does not include a DC path to ground or overload said electrodes. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A method for conditioning a measured signal of a heart rate measurement device comprising:
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providing a heart rate measurement device comprising; a wavelet template; an instrumentation amplifier; a set of two electrodes configured to be coupled to a subject'"'"'s skin, and in electrical communication with and providing a set of signals to said instrumentation amplifier; an adaptive gain control (AGC) algorithm; an analog or digital filter in a feedback path of said instrumentation amplifier for at least one of removal of saturating signals and unwanted waveform components responsive to said AGC algorithm; wherein said AGC algorithm utilizes said wavelet template and responds to the correlation of said wavelet template to a measured signal obtained from said set of signals by said instrumentation amplifier; and wherein said device is configured to capture said set of signals utilizing a high common-mode rejection ratio (CMRR) termination, which does not include a DC path to ground or overload said electrodes; removing artifacts outside a heart rate window by applying one or more Bessel filters to create filtered waveform; smoothing said filtered waveform with a smoothing function by averaging points around each point to create a baseline; subtracting the baseline from the filtered waveform; and performing adaptive gain control (AGC) on the filtered signal in conformance with a three or more dimensional image produced using iterative decomposition wavelet template algorithm.
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19. A method of heart waveform extraction comprising:
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providing a wavelet template responsive to PQRST components of said heart waveform; providing a contaminated signal containing said heart waveform as well non-ideal components measured by set of two electrodes configured to be coupled to a subject'"'"'s skin; using a processor to provide an at least three dimensional image created by deconstructing said contaminated signal utilizing said wavelet template by said processor; and providing a pattern recognition neural network trained to recognize heart beat components from an image created from similar wavelet deconstructed signals including noise components, wherein said at least three dimensional image is applied to said neural network trained to produce an output vector responsive to recognized heart beats.
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20. A heart rate measurement device comprising:
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a wavelet; an instrumentation amplifier; a set of two electrodes configured to be coupled to a subject'"'"'s skin, and in electrical communication with and providing a set of signals to said instrumentation amplifier; an adaptive gain control (AGC) algorithm; an analog or digital filter in a feedback path of said instrumentation amplifier for at least one of removal of saturating signals and unwanted waveform components responsive to said AGC algorithm; wherein said AGC algorithm utilizes said wavelet and responds to the correlation of said wavelet to a measured signal obtained from said set of signals by said instrumentation amplifier; wherein said measured signal is analyzed by a wavelet decomposition that builds up a three or more dimensional image of said measured signal, said three or more dimensional image analyzed with pattern recognition methods to determine heart beat components responsive to a training algorithm; and wherein said pattern recognition methods are a neural network trained to recognize heart beat components from said measured signal.
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Specification