Physiological signal acquisition system and method with improved noise and common mode rejection performance and signal quality
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Publication Number
US-12647146-B1
Publication Date
2026-06-02
Expiration Date
Abstract
The present invention relates to the acquisition, processing, and monitoring of signals, and particularly to the acquisition, processing, and monitoring of electrophysiological signals. More particularly, the present invention relates to the acquisition, processing, and monitoring electroencephalography (EEG) signals representing cortical/brain activity. Further, the present invention relates to a method and apparatus for acquiring such signals in the presence of electrical interference and noise. More particularly, the present invention relates to systems and methods for filtering out and rejecting electrical interference and noise while maintaining or improving the quality of the underlying physiological signal and preventing perturbation or introduction of artifacts into the physiological signal.
Core Innovation
The invention relates to an electrophysiological physiological signal acquisition system, particularly for electroencephalography (EEG), that removes common-mode signal and maintains a high common mode rejection ratio (CMRR) during active signal acquisition. The problem addressed is common-mode interference, including 50/60 Hz power interference, together with electrode/lead impedance mismatch and electrode-skin impedance variation that degrade signal quality and common-mode rejection. The system includes analog front-end circuitry configured to remove common-mode signal from the EEG physiological signal prior to the signal entering an instrumentation amplifier.
The system uses at least two bootstrapped filters, each having a resistor, a first capacitor and a second capacitor, and a unity gain buffer. The resistor and first capacitor form a low-pass filter, while the second capacitor is driven to have substantially the same upper and lower AC voltage as when the EEG physiological signal enters the input. The second capacitor is driven by a common mode signal or a physiological signal potential, and the bootstrapped filters are external to the instrumentation amplifier and electrically interposed between a respective EEG electrode and an input of the instrumentation amplifier.
The bootstrapped filters are dynamically controlled during active signal acquisition to compensate for electrode-skin impedance variation. In the disclosed circuitry, the second capacitor of each bootstrapped filter is driven by a feedback signal derived from a node downstream of an input stage of the instrumentation amplifier, so the analog front-end compensation tracks time-varying sensor-to-subject impedance. The system is adapted to have a common mode rejection ratio of 95 dB or greater, and the processor quantifies and displays a filtered, quantified physiological signal such as depth of anesthesia or sedation.
The disclosure further extends to measuring a physiological brain signal with deep brain electrodes using analog front-end circuitry that includes bootstrapped filters and instrumentation amplifiers for common-mode signal removal. In that embodiment, bootstrapped filters are each directly connected to a common node for each of the amplifiers within each instrumentation amplifier, and the system is adapted to allow additional channels by adding an additional bootstrapped filter and using an existing channel as a reference. The system also supports processor computation of physiological metrics, including quantitative EEG (QEEG) indices, and optionally includes identifying artifacts and removing artifacts to provide denoised, artifact-free physiological signals.
Claims Coverage
The partial content includes three independent claims (clm-00001, clm-00006, clm-00011). Each claim centers on dynamically driven bootstrapped impedance networks/filters that compensate for time-varying electrode/sensor impedance and remove common-mode signal prior to instrumentation amplification, combined with downstream quantification/metrics and display.
Bootstrapped filters external to instrumentation amplifier for EEG common-mode rejection
Bootstrapped filters external to the instrumentation amplifier and electrically interposed between a respective EEG electrode and an input of the instrumentation amplifier, wherein the second capacitor is driven by a feedback signal derived from a node downstream of an input stage of the instrumentation amplifier and the bootstrapped filters are dynamically controlled during active signal acquisition to compensate for electrode-skin impedance variation.
Common-mode signal removal prior to instrumentation amplification with high CMRR
The system is adapted to remove common-mode signal from the EEG physiological signal prior to the signal entering the instrumentation amplifier, and the system is adapted to have a common mode rejection ratio of 95 or greater dB.
Quantifying filtered EEG for depth of anesthesia or sedation with display
A processor adapted for quantifying the filtered EEG physiological signal and a monitor for displaying a quantified, filtered EEG signal as part of a system for measuring depth of anesthesia or sedation.
Deep brain electrodes with common node bootstrapped filters and scalable channels
Bootstrapped filters external to each instrumentation amplifier and adapted to remove common mode signal prior to the signal entering the instrumentation amplifier, wherein the bootstrapped filters are each directly connected to a common node for each of the at least two amplifiers for each of the at least two instrumentation amplifiers, and the system is adapted to allow additional channels to be added by adding an additional bootstrapped filter and using an existing channel as a reference.
Physiological metric computation from instrumentation amplifier output
A physiological signal acquisition system wherein a processor is configured to compute at least one physiological metric from an output of the instrumentation amplifier.
Dynamically driven bootstrapped impedance networks driven by downstream feedback to compensate sensor-to-subject impedance
Analog front-end circuitry including dynamically controlled bootstrapped impedance networks electrically interposed between the physiological sensors and an instrumentation amplifier, wherein the bootstrapped impedance networks are dynamically driven by feedback derived from a node downstream of an input stage of the instrumentation amplifier to compensate for time-varying sensor-to-subject impedance during active signal acquisition.
Across the independent claims, the core claim coverage is for dynamically controlled bootstrapped impedance networks/filters external to the instrumentation amplifier that use capacitors driven by common-mode signal/physiological signal potential and feedback from a downstream node to compensate time-varying sensor/electrode impedance. The claims also require common-mode signal removal prior to instrumentation amplification with a specified CMRR threshold (95 dB or greater, with dependent higher limits), together with processor computation/quantification of physiological metrics and display.
Stated Advantages
Improved common-mode rejection by adapting the system to remove common-mode signal prior to entering the instrumentation amplifier and to have a common mode rejection ratio of 95 dB or greater.
Compensation for electrode-skin or sensor-to-subject impedance variation during active signal acquisition via dynamically controlled bootstrapped filters/impedance networks.
Providing quantified, filtered EEG physiological signals for measurement such as depth of anesthesia or sedation via processor quantification and monitor display.
Support for scalable channel addition by adding an additional bootstrapped filter and using an existing channel as a reference.
Documented Applications
Measuring depth of anesthesia or sedation using quantified, filtered EEG physiological signals displayed by a monitor.
Measuring a physiological brain signal with deep brain electrodes, with output for displaying the physiological brain signal.
Computing and displaying physiological metrics from an instrumentation amplifier output, including at least one physiological metric (e.g., quantitative EEG (QEEG) index reflecting brain/cortical activity level as described in dependent claims).
Identifying artifacts in a physiological signal and removing artifacts as part of the processing chain to support denoised, artifact-free physiological signals (as described in dependent claims).
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