Fast automatic blocking for physiological waveform display

Abstract

A method for automatically blocking a physiological waveform display to mitigate baseline drift includes: extracting a low-resolution preliminary baseline estimate from an input physiological waveform; extracting a high-resolution deviation average value from the input physiological waveform; subtracting the low-resolution preliminary baseline estimate from the high-resolution deviation average to extract an average deviation of the input physiological waveform; determining a weight factor in positive correlation with the average deviation; applying the weight factor to an average deviation of the input physiological waveform; applying the difference between 1 and the weight factor to the preliminary baseline estimation; combining the weighted average deviation of the input physiological waveform with the weighted preliminary baseline estimate to extract an adjusted baseline estimate; and subtracting the adjusted baseline estimate from the input physiological waveform to obtain an automatically blocked physiological waveform for display.

Description

Technical Field

[0001] The present disclosure relates generally to the field of medical monitoring of physiological parameters in a patient, and more particularly to monitoring and displaying physiological parameters indicative of a patient's condition. Background Art

[0002] This section of this document introduces information about and / or from the present technology, which can provide context for or be related to the subject matter described herein and / or claimed below. It provides background information to facilitate a better understanding of various aspects of the present invention. This is a discussion of "related" technology. Such technology is related in no way implies that it is also "prior" technology. Related technology may or may not be prior art. The discussion in this section of this document is to be read in this light and is not intended as an admission of prior art.

[0003] Traditional modern medicine often involves measuring and / or monitoring the physiological condition of a patient. Well-known physiological parameters that are measured and / or monitored may include, for example and without limitation, blood pressure, body temperature, vision, the amount of various substances in the blood, etc. Due to the value imparted by this information, medical monitoring technology has developed a range of instruments and procedures for obtaining such information.

[0004] For example, one well-known measurement / monitoring technique is the "electrocardiogram" ("ECG"). The ECG is commonly used to monitor the cardiac condition of a patient, and to detect or predict cardiac events and conditions. In a clinical setting, an ECG signal representing the patient's condition is captured as a waveform and analyzed by a physiological monitoring device. However, in addition to cardiac events and conditions, the ECG is also useful for obtaining other kinds of medical information.

[0005] An ECG is a graph that shows the voltage picked up from the human body over time. The voltage represents the electrical activity of the heart that causes it to beat. To pick up the voltage, sensors or "electrodes" are placed at selected points on the body. The number and location of the electrodes are fairly standardized, but can vary depending on the type of medical information of interest. For example, in one common nomenclature, the sensor is electrically connected to a physiological monitoring device via a cable. The sensor and cable together are then called a "lead."

[0006] Physiological parameter data, such as ECG data, is often displayed as a waveform on a patient monitor. In the present context, a patient monitor may also be referred to as a "physiological waveform monitor," and the physiological data may also be referred to as a "physiological parameter waveform." An ECG waveform is just one example of a "physiological parameter waveform." A physiological parameter waveform may also represent other physiological parameters, such as those set forth above, for example, such as and without limitation, blood pressure, body temperature, vision, the amount of various substances in the blood, and the like.

[0007] Physiological parameter waveform displays may be subject to problems associated with data collection and / or processing. One problem is known as "baseline drift," which may be a low-frequency artifact in the data that is unrelated to the patient's condition. For example, in an ECG display, baseline drift may be attributable to factors such as patient breathing, live electrodes, patient movement, or power line interference. Baseline drift is undesirable because it can obscure useful and / or desired information. Therefore, many techniques have been developed in the art to mitigate baseline drift in physiological waveforms. These baseline mitigation techniques are sometimes referred to as "autoblocking." Summary of the invention

[0008] In a first aspect, a method for automatically blocking a physiological waveform display to mitigate baseline drift comprises: extracting a low-resolution preliminary baseline estimate from an input physiological waveform; extracting a high-resolution deviation average from the input physiological waveform; subtracting the low-resolution preliminary baseline estimate from the high-resolution deviation average to extract a mean deviation of the input physiological waveform; determining a weight factor positively correlated with the mean deviation; applying the weight factor to the mean deviation of the input physiological waveform; applying the difference between 1 and the weight factor to the preliminary baseline estimate; combining the weighted mean deviation of the input physiological waveform with the weighted preliminary baseline estimate to extract an adjusted baseline estimate; and subtracting the adjusted baseline estimate from the input physiological waveform to obtain an automatically blocked physiological waveform for display.

[0009] In a second aspect, a method for automatically blocking a physiological waveform display to mitigate baseline drift comprises: extracting a low-resolution preliminary baseline estimate from an input physiological waveform; extracting a high-resolution deviation average from the input physiological waveform; subtracting the preliminary baseline estimate from the deviation average to extract the average deviation of the input physiological waveform; determining an average weight factor positively correlated with the average deviation by obtaining the average deviation divided by the square of the sum of the average deviation and a constant; extracting an adjusted baseline estimate by summing the average deviation weighted by the average weight factor and the preliminary baseline estimate weighted by 1 minus the average weight factor; subtracting the adjusted baseline estimate from the input physiological waveform to obtain an automatically blocked physiological waveform for display; subtracting the input physiological waveform from the high-resolution deviation average to extract the high-resolution deviation; determining a deviation suppression weight factor by utilizing the average weight factor and the high-resolution deviation with a suppression coefficient; and applying the deviation suppression weight factor to the automatically blocked physiological waveform; summing the weighted automatically blocked physiological waveform and the adjusted baseline estimate to obtain a deviation-suppressed corrected signal; and feeding the deviation-suppressed corrected signal back to the low-resolution preliminary baseline estimate generation.

[0010] In a third aspect, a method for automatically blocking a physiological waveform display to mitigate baseline drift comprises: determining a high-resolution average of an input physiological waveform; determining a preliminary baseline estimate; generating a plurality of weights from a difference between the high-resolution average and the input physiological waveform and from a difference between the high-resolution average and the preliminary baseline estimate; weighting the high-resolution average with a first weight of the plurality of weights and weighting the preliminary baseline estimate with a difference between 1 and the first weight of the plurality of weights; summing the weighted preliminary baseline estimate and the weighted high-resolution average and subtracting the input physiological waveform to obtain an automatically blocked physiological waveform; weighting the automatically blocked physiological waveform with a second weight of the plurality of weights; and feeding the weighted automatically blocked physiological waveform back to the preliminary baseline estimate.

[0011] In a fourth aspect, a physiological waveform monitor includes: a deviation estimation module, which generates a high-resolution average from an input physiological waveform in operation; a preliminary baseline estimation module, which generates a preliminary baseline estimate in operation; a weight generation module, which generates a plurality of weights in operation based on the signal amplitude of the deviation from the input physiological waveform and the average deviation in the preliminary baseline estimate; a signal correction module; a display, which displays an automatically blocked physiological waveform in operation; and feedback of a baseline-corrected signal to the preliminary baseline estimation module. In operation, the signal correction module generates an automatically blocked physiological waveform from a difference between the input physiological waveform and a final estimated baseline, the final estimated baseline being a combination of a high-resolution average weighted by a first weight of a plurality of weights and a preliminary baseline weighted by 1 minus the first weight of a plurality of weights; and generates a baseline-corrected signal from a combination of an automatically blocked physiological waveform weighted by a second weight of a plurality of weights and the final estimated baseline.

[0012] In a fifth aspect, a method for automatically blocking a physiological waveform display to mitigate baseline drift in a physiological waveform substantially as shown and described herein.

[0013] In a sixth aspect, a physiological monitoring device configured to mitigate baseline drift in a physiological waveform substantially as shown and described herein.

[0014] In a seventh aspect, a local physiological system configured to mitigate baseline drift in a physiological waveform substantially as shown and described herein.

[0015] In an eighth aspect, a larger physiological system configured to mitigate baseline drift in a physiological waveform substantially as shown and described herein.

[0016] The above presents a simplified summary of the invention as claimed below in order to provide a basic understanding of some aspects of the invention. This summary is not an exhaustive overview of the invention. It is not intended to identify key or important elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concepts in a simplified form as a prelude to a more detailed description discussed later. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In the drawings, like reference numbers generally indicate identical, functionally similar, and / or structurally similar elements.

[0018] Figure 1 A patient monitoring system according to one or more examples is presented.

[0019] Figure 2 A method for use in processing a physiological waveform representing a physiological parameter of a monitored patient is illustrated in accordance with one or more embodiments.

[0020] Figure 3 One particular embodiment of a physiological monitoring device is schematically depicted.

[0021] Figure 4 Illustrate a method in which Figure 2 The physiological waveforms generated by the automatic blocking method are used to adjust Figure 2 Initial baseline adjustment in the method.

[0022] Figure 5A and Figure 5B Another particular embodiment of a method for automatically blocking a physiological waveform display to mitigate baseline drift in a physiological waveform is illustrated.

[0023] Figure 6 is to describe such as can be used to implement Figure 1 - A schematic system block diagram of a system with the functions described in FIG5 .

[0024] Figure 7 A method for rapidly automatically blocking an input physiological waveform to mitigate baseline drift in another embodiment is illustrated.

[0025] Figure 8 The baseline reset responsiveness of one or more embodiments to large baseline drift is illustrated.

[0026] Fig. 9 Compare signal quality / fidelity in one or more embodiments.

[0027] Although the disclosed subject matter is susceptible to various modifications and alternative forms, the accompanying drawings illustrate specific embodiments described in detail by way of example. However, it should be understood that the description of specific examples herein is not intended to limit the claimed content to the specific form disclosed, but on the contrary, the present invention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the appended claims. DETAILED DESCRIPTION

[0028] Illustrative examples of the subject matter claimed below are disclosed. For the sake of clarity, not all features of an actual implementation for each example in this specification are described. It will be appreciated that in the development of any such actual implementation, many implementation-specific decisions may be made to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such development efforts will be a routine task, albeit complex and time-consuming, for one of ordinary skill in the art having the benefit of this disclosure.

[0029] In a physiological parameter waveform display on a patient monitor, baseline wander mitigation (a / k / a, auto-blocking or baseline restoration) can sometimes severely compromise signal quality by causing distortion or (multiple) artifacts if a fast response is expected. This can sometimes cause a significant delay to restore if good signal fidelity is required in a specific context. In addition, if auto-blocking is triggered by waveform clipping with display boundaries, design complexity may increase in cases where various display areas with different positions and scaling are distributed across multiple monitoring devices. For example, display control synchronization among network devices will take more effort in implementations including interface protocol design. This may affect the robustness (regulatory compliance) of the product in handling important clinical events (such as defibrillation).

[0030] The currently disclosed technology provides a fast automatic blocking with real-time baseline estimation. The currently disclosed technology provides automatic baseline drift detection and correction. The technology is a robust solution for rapidly mitigating baseline artifacts without detecting waveform clipping, and the adaptive baseline estimator can significantly improve signal quality, such as high fidelity and low distortion. More particularly, the fast automatic blocking with real-time baseline estimator technology disclosed herein avoids complex physiological waveform clipping detection when triggering baseline correction. This will simplify the design (cost saving) and reduce potential control errors and / or unwanted / unpredictable behaviors in clinical use, making the technology more robust. This technology also produces better signal fidelity / quality and less distortion, while fast response is a result based on clinical and regulatory needs, where if higher signal quality is required, traditional methods will cause significant delays in correcting the baseline. The system and method described herein provide improvements over previous methods. More particularly, the system and method described herein are configured to restore the baseline to normal levels in approximately 0.5 to 1.2 seconds, while also producing better signal quality with less distortion.

[0031] Fast automatic blocking with real-time baseline estimator includes adaptive weight baseline estimation and signal subtraction. Baseline estimation is based on dual-scale (time resolution) signal processing, and the low-resolution branch will extract low-frequency components for preliminary baseline estimation. The high-resolution branch will extract high-frequency components (e.g., clinical features or artifacts) by subtracting the preliminary estimate from the initial input signal. High-frequency change signals should be suppressed to reduce their impact on baseline estimation, which can be achieved by applying a weighting factor to the deviation (when exceeding a threshold), and using the weighted deviation to correct the preliminary baseline estimate, which will help improve the accuracy of the baseline estimator. Signal subtraction will subtract the estimated baseline from the initial input signal and generate a corrected output (the baseline is reset to zero level). Considering the use of a fixed-length time window or finite impulse filter ("FIR"), the maximum delay of fast automatic blocking can be well controlled (1 to 1.5 seconds), and signal quality / fidelity is guaranteed due to accurate baseline estimation.

[0032] The presently disclosed technology is deployed in the context of monitoring the physiological condition of a patient. Turning now to the accompanying drawings, Figure 1 A local physiological monitoring system 100 is shown in accordance with one or more embodiments. Figure 1 As shown in FIG. 1 , system 100 includes a physiological monitoring device 102 that, when deployed, is capable of receiving physiological data from various sensors 104 connected to a patient 106. In this example, multiple sensors 104 include electrocardiogram ("ECG") electrodes attached to the skin of patient 106.

[0033] In general, the present disclosure contemplates that the physiological monitoring device 102 includes electronic components and / or electronic computing devices operable to receive, transmit, process, store, and / or manage patient physiological data and information associated with performing the functions of the systems described herein. These electronic components and / or electronic computing devices encompass any suitable processing device adapted to perform computing tasks consistent with the execution of computer-readable instructions stored in a memory or computer-readable recording medium.

[0034] Furthermore, any, all, or some of the computing devices in physiological monitoring devices 102 may be adapted to execute any operating system, including Windows etc., and virtual machines suitable for virtualizing the execution of specific operating systems (including customized and proprietary operating systems). The physiological monitoring device 102 may be further equipped with components to facilitate communication with other computing devices through one or more network connections, which may include connections to local and wide area networks, wireless and wired networks, public and private networks, and any other communication network that enables communication in the system.

[0035] like Figure 1 As shown in , the physiological monitoring device 102 can be, for example, a patient monitor that is implemented to monitor various physiological parameters of a patient 106 via a sensor 104. The physiological monitoring device 102 can include a sensor interface 108, one or more processors 110, a display / graphical user interface ("GUI") 112, a communication interface 114, a memory 116, and a power source (or power connection) 118, all communicating via an internal bus 119. The sensor interface 108 can be implemented in hardware or a combination of hardware and software, and is used to connect to the sensor 104 via a wired and / or wireless connection for collecting physiological data from the patient 106. As noted, the sensor 104 in this example is an ECG electrode attached to the skin of the patient 106. A plurality of conductive leads 120 including a plurality of conductive cables are provided for coupling the sensor 104 to the sensor interface 108. In one or more examples, the conductive leads 120 include a plurality of ECG cables.

[0036] The data signals from the sensors 104 may include, for example, sensor data related to an ECG. One or more processors 110 may be used to control the general operation of the physiological monitoring device 102, and to process the sensor data received by the sensor interface 108. The one or more processors 110 may be any suitable processor-based resource. They may be, but are not limited to, a central processing unit ("CPU"), a hardware microprocessor, a multi-core processor, a single-core processor, a field programmable gate array ("FPGA"), a controller, a microcontroller, an application-specific integrated circuit ("ASIC"), a digital signal processor ("DSP"), or other similar processing devices capable of executing any type of instructions, algorithms, or software to control operations and perform the functions of the physiological monitoring device 102. In some embodiments, the one or more processors 110 may include a processor chipset, for example and without limitation, including one or more coprocessors.

[0037] The display / GUI 112 may be configured to display various patient data, sensor data, and hospital or patient care information, and include a user interface implemented to allow interaction and communication between a user and the physiological monitoring device 102. The display / GUI 112 may include a keyboard (not shown) and / or a pointing or tracking device (not shown), and a display such as a liquid crystal display (“LCD”), a cathode ray tube (“CRT”) display, a thin film transistor (“TFT”) display, a light emitting diode (“LED”) display, a high definition (“HD”) display, or other similar display devices that may include touch screen capabilities. The display / GUI 112 may provide a means for inputting instructions or information directly to the physiological monitoring device 102. The displayed patient information may be related to, for example, a measured physiological parameter (e.g., an ECG reading) of the patient 106.

[0038] The communication interface 114 may allow the physiological monitoring device 102 to communicate directly or indirectly (via, for example, a monitor dock) with one or more computing networks and devices, workstations, consoles, computers, monitoring equipment, alarm systems, and / or mobile devices (e.g., mobile phones, tablet computers, or other handheld display devices). The communication interface 114 may include various network cards, interfaces, communication channels, clouds, antennas, and / or circuits to allow wired and wireless communications with such computing networks and devices. The communication interface 114 may be used to implement, for example, communication with such computing networks and devices. connection, cellular network connection and / or Example wireless communication connections implemented using communication interface 114 include wireless communication according to, but not limited to, the IEEE 802.11 protocol, the Radio Frequency for Consumer Electronics ("RF4CE") protocol, and / or the IEEE 802.15.4 protocol (e.g., Essentially, any wireless communication protocol may be used.

[0039] In addition, the communication interface 114 can allow direct (i.e., device-to-device) communication (e.g., information transmission and reception, signal exchange, etc.), such as from a monitor base to the physiological monitoring device 102 using, for example, a universal serial bus ("USB") connection or other communication protocol interface. The communication interface 114 can also allow direct device-to-device connection to other devices (such as to a tablet, computer, or similar electronic device; or to an external storage device or memory).

[0040] The memory 116 may be a single memory device or one or more memory devices at one or more memory locations, which may include, without limitation, one or more of a random access memory (“RAM”), a memory buffer, a hard drive, a database, an erasable programmable read-only memory (“EPROM”), an electrically erasable programmable read-only memory (“EEPROM”), a read-only memory (“ROM”), flash memory, a hard disk, different layers of a memory hierarchy, or any other non-transitory computer-readable medium. Depending on the implementation of the one or more processors 110, the memory 116 may be on-chip or off-chip. The memory 116 may be used to store any type of instructions and patient data associated with algorithms, processes, or operations used to control the general functions and operations of the physiological monitoring device 102.

[0041] The power source 118 may include a self-contained power source (such as a battery pack) and / or include an interface for powering the device 102 directly or via a monitor base through an electrical outlet. The power source 118 may also be a rechargeable battery that can be removed to allow replacement. In the case of a rechargeable battery, a small built-in backup battery (or supercapacitor) may be provided to provide continuous power to the physiological monitoring device 102 during battery replacement. In this example, communication between the components of the physiological monitoring device 102 may be established using an internal bus 119.

[0042] Physiological monitoring device 102 may be attached to one or more of a number of different types of sensors 104 and may be configured to measure and read out physiological data related to patient 106. As noted, sensor 104 may be attached to physiological monitoring device 102 via conductive leads 120, which may be, for example, cables coupled to sensor interface 108. Additionally or alternatively, one or more sensors 104 may be connected to sensor interface 108 via a wireless connection. In this case, sensor interface 108 may include a method for using, for example, connection, cellular network connection and / or Circuitry connected to receive data from and send data to one or more devices.

[0043] The data signal received from the sensor 104 may be an analog signal. For example, the data signal for an ECG may be input to the sensor interface 108, which may include an ECG data acquisition circuit (in Figure 1 The ECG data acquisition circuit may include an amplification and filtering circuit and an analog-to-digital (A / D) circuit that converts analog signals into digital signals using amplification, filtering, and A / D conversion methods. If the ECG sensor is a wireless sensor, the sensor interface 108 may receive data from the wireless communication module ( Figure 1 Thus, the sensor interface 108 is a component that can be configured to interface with one or more sensors 104 and receive sensor data therefrom.

[0044] As further described herein, the processing performed by the physiological monitoring device 102 can generate an analog data waveform or a digital data waveform, which in this particular embodiment is processed by a microcontroller. However, other embodiments may use other types of processors disclosed above. The microcontroller can be one of the processors 110.

[0045] For example, the one or more processors 110 may analyze the waveform using one or more monitoring methods to identify certain waveform characteristics and threshold levels that indicate a condition (abnormal and normal) of the patient 106. One monitoring method may include comparing an analog or digital waveform characteristic or an analog or digital value to one or more thresholds and generating a comparison result based thereon. The microcontroller may be, for example, a processor, FPGA, ASIC, DSP, microcontroller, or similar processing device.

[0046] The microcontroller may include memory (on-chip memory) or use a separate memory 116 (off-chip memory). The memory may be, for example, a RAM, a memory buffer, a hard drive, a database, an EPROM, an EEPROM, a ROM, a flash memory, a hard disk, or any other non-transitory computer-readable medium. The memory 116 may store software or algorithms having executable instructions, and the microcontroller may execute a set of instructions of the software or algorithms associated with performing different operations and functions of the physiological monitoring device 102, such as analyzing a digital data waveform associated with a data signal from the sensor 104.

[0047] As alluded to above, in some embodiments, the physiological monitoring system 100 may also be deployed within the context of a larger patient monitoring system 130, also in Figure 11. The larger patient monitoring system 130 includes a computing system 133. The computing system 133 may be a distributed computing environment including a network or cloud 136 through which the physiological monitoring system 100 pushes and / or pulls data and information. Communications among the physiological monitoring devices 102, the computing system 136, and other resources of the patient monitoring system 130 may be wireless or wired, depending on the technical capabilities of the various components.

[0048] For example, a facility (not otherwise shown) in which patients 106 are located may include a central monitoring station 139 from which caregivers 142 can simultaneously monitor the physical conditions of multiple patients 106. Figure 1 The physiological monitoring device 102 of the communication interface 114 shown in FIG. 1 can push the sensed physiological parameters and / or signals representative thereof to the central monitoring station 139 through the computing system 136. The pushed data and information can then be displayed to the caregiver 142 for review and consideration.

[0049] For another example, the physiological monitoring device 100 may use, for example Figure 1 145 . The communication interface 114 shown in FIG. 14 pulls information from the electronic medical record 145 of the patient 106 from the record repository 148. The pulled information may then be displayed by the physiological monitoring device 102. Depending on the implementation and without limitation, such pulled information may include medication regimens, diagnosed medical conditions, medical history, historical medical data, etc. Similarly, data sensed by or input at the physiological monitoring device 102 may be pushed to, for example, the ERM 145 for storage and later retrieval. However, it is noted that in some embodiments, the patient monitoring system 100 may not be integrated into or in communication with a larger patient monitoring system (such as the larger patient monitoring system 130).

[0050] Therefore, and still referring to Figure 1 The presently disclosed technology processes physiological data from a patient 106 acquired from a sensor 104 via a sensor interface 108 for display on a display / GUI 112. The processing and display occurs in "real time" or "near real time." As used herein, the terms "real time" and "near real time" mean nearly simultaneous with acquisition as technically achievable, and within a time frame acceptable to clinical practice.

[0051] The techniques may be implemented in hardware or software. The embodiments disclosed herein perform processing in software, where one or more processors 110 execute software instructions stored, for example, in memory 116. Note that acquired physiological data may also be buffered and / or stored in memory 116. Although the illustrated embodiments perform the method in executable software, it is to be understood that other embodiments may perform the processing in hardware or some combination of hardware and software.

[0052] Figure 2 A method 200 for use in processing a physiological waveform representing a physiological parameter of a monitored patient is illustrated in accordance with one or more embodiments. Figure 3 A specific embodiment of a physiological monitoring device 300 is schematically depicted. The depiction of the physiological monitoring device 300 is from Figure 1 The matters shown in the drawings are simplified so as not to obscure those aspects of the claimed subject matter which are now being discussed. More particularly, as Figure 3 As shown in FIG. 1 , the processor-based resources 302 communicate with the memory 310 via an internal bus system 307. Therefore, for the purpose of this discussion, the details regarding the processor-based resources 302 and the memory 310 have been omitted. Figure 1 Discuss and Figure 1 Some details are shown in the specification, but such details will be relevant in most embodiments.

[0053] The processor-based resource 302 may be, for example, Figure 1 304 and 315. The processor-based resources 302 are one of the one or more processors 110 shown in FIG. 304 and in this particular embodiment are implemented in a microcontroller. The processor-based resources 302 operate under the programmed control of instructions 315. Thus, the processor-based resources 302 communicate with the memory 310 via the bus system 307 to, among other things, retrieve and execute the instructions 315 stored therein.

[0054] Execution of the instructions may cause processor-based resources 302 to perform certain tasks, such as Figure 2 However, depending on the circumstances, the processor-based resource 302 may also perform other tasks, such as pushing data to other computing resources, transmitting alarms, etc. Thus, in this sense, the processor-based resource 302, and the physiological monitoring device 300 as a whole, may be considered to be "programmed" or "configured" to perform certain functions as described herein.

[0055] Now refer to Figure 2 and Figure 3, method 200 assumes that physiological data has been acquired. Acquisition includes acquiring a time domain signal representing a physiological parameter. As described above, this time domain signal is acquired via the selected lead 120 and sensor 104. Note that sensor 104 is representative and other sensors ( Figure 3 In this particular embodiment, sensor 104 is an ECG sensor and is one of a plurality of ECG sensors in communication with physiological monitoring device 300. The acquired time domain signal is transmitted from sensor 104 to physiological monitoring device 300 via lead 120.

[0056] The acquired time domain signal as received by the physiological monitoring device 300 can be in analog or digital form. If received in analog form, it is converted to digital. Once digitized, the resulting data is buffered in the memory 310 as data 305. The memory 310 in this embodiment is off-chip and includes both RAM and ROM in both volatile and non-volatile memory. The data 305 can be buffered or stored in any suitable type of data structure.

[0057] Note that in most embodiments, information derived from the acquired time-domain signal will typically be displayed in real time or near real time. Therefore, the data 305 representing the acquired time-domain signal may be temporarily stored or buffered in the memory 310. However, some embodiments may store the data 305 representing the acquired time-domain signal in a portion of the memory 310 that is more suitable for long-term storage other than buffering. Some embodiments may even push such data 305 to other computing resources for longer-term storage, such as with respect to Figure 1 discussed.

[0058] The processing of data 305 can be performed by processor-based resources 302 in hardware (not shown) or in software. The illustrated embodiment performs this task in software. When data 305 begins to arrive, processor-based resources 302 begin processing data 305 by retrieving and executing instructions 315 to perform Figure 2 Thus, the processor-based resource 302 can be said to be "programmed" or "configured" to perform the method 200. Note that the processor-based resource 302 can process the data 305 immediately upon receipt, or can retrieve the data from a buffer in the memory 310, depending on the particular embodiment.

[0059] The method 200 begins by extracting (at 210) a low-resolution preliminary baseline estimate and extracting (at 220) a high-resolution deviation average from an input physiological waveform. The input physiological waveform is either an acquired physiological waveform or a baseline-corrected signal. Generally, if a baseline-corrected signal is available, the baseline-corrected signal is preferred over the acquired physiological waveform.

[0060] The method 200 then subtracts (at 230) the low-resolution preliminary baseline estimate from the high-resolution deviation average. This extracts the mean deviation of the input physiological waveform. The mean deviation is then used to determine (at 240) a weighting factor that is positively correlated with the mean deviation. In the illustrated embodiment, the weighting factor is between 0 and 1. In a particular embodiment, determining (at 240) the weighting factor that is positively correlated with the mean deviation includes obtaining the mean deviation divided by the square of the sum of the mean deviation and a constant. The constant may be, for example, 1 ± 10%.

[0061] Next, the method applies (at 250) a weighting factor to the mean deviation of the input physiological waveform and applies (at 260) the complement (ie, the difference between 1 and the weighting factor) to the preliminary baseline estimate. Note that these steps may be performed in any order.

[0062] The method 200 continues by combining (at 270) the weighted average deviation of the input physiological waveform with the weighted preliminary baseline estimate to extract an adjusted baseline estimate. This action suppresses the deviation in the preliminary baseline estimate. The method 200 then subtracts (at 280) the adjusted baseline estimate from the input physiological waveform to obtain an automatically blocked physiological waveform for display. The automatically blocked physiological waveform can be displayed on, for example, a local display / GUI of a physiological monitoring device. It is noted that some embodiments may store the automatically blocked physiological waveform for a longer period of time. Some embodiments may transmit the automatically blocked physiological waveform to a remote location, as discussed above for remote display.

[0063] In some embodiments, the automatically blocked physiological waveforms can be used to generate a low-resolution preliminary baseline estimate, such as Figure 4 This feedback 400 begins by suppressing (at 410) the deviation in the automatically blocked physiological waveform. In some embodiments, this can be accomplished by first subtracting the input physiological waveform from the high-resolution deviation average to extract the high-resolution deviation. Then, the deviation suppression weight factor is determined by using the average weight factor and the high-resolution deviation with the suppression coefficient. The deviation suppression weight factor is then applied to the automatically blocked physiological waveform. Then, return to Figure 4, the method 400 continues by: adding (at 420) the bias-suppressed auto-blocked physiological waveform to the adjusted baseline estimate; and feeding back (at 430) the adjusted baseline estimate to the low-resolution preliminary baseline estimate generation.

[0064] FIG. 5 illustrates another specific embodiment of a method for automatically blocking a physiological waveform display to mitigate baseline drift in a physiological waveform. The method 500 may be used in a system such as Figure 1 and Figure 3 The physiological parameters shown in the figure are performed on a monitor.

[0065] The method 500 begins by extracting (at 505) a low-resolution preliminary baseline estimate from an input physiological waveform. The input physiological waveform is either an acquired physiological waveform or a baseline-corrected signal. Generally, if a baseline-corrected signal is available, the baseline-corrected signal is preferred over the acquired physiological waveform. The method 500 then extracts (at 510) a high-resolution deviation average from the input physiological waveform.

[0066] Next, the method 500 subtracts (at 515) the preliminary baseline estimate from the deviation mean to extract the mean deviation of the input physiological waveform. This is followed by determining (at 520) an average weighting factor that is positively correlated with the mean deviation by taking the mean deviation divided by the square of the sum of the mean deviation and a constant. In the illustrated embodiment, the average weighting factor is between 0 and 1, and the constant is 1±10%.

[0067] The method 500 continues by extracting (at 525) an adjusted baseline estimate by summing the average deviation weighted by the average weighting factor and the preliminary baseline estimate weighted by 1 minus the average weighting factor. Next, the method 500 subtracts (at 530) the adjusted baseline estimate from the input physiological waveform to obtain an automatically blocked physiological waveform for display. The adjusted baseline estimate tracks baseline drift in a responsive manner.

[0068] The method 500 then proceeds to the optional feedback discussed above. The method 500 subtracts (at 535) the input physiological waveform from the high-resolution deviation average to extract the high-resolution deviation. Next, a deviation suppression weight factor is determined (at 540) by utilizing the average weight factor and the high-resolution deviation with a suppression coefficient. In the illustrated embodiment, the deviation suppression weight factor is between 0 and 1, and the suppression coefficient is 10±10%.

[0069] Next, the method 500 applies (at 545) the bias suppression weighting factor to the auto-occluded physiological waveform. The method 500 then sums (at 550) the weighted auto-occluded physiological waveform and the adjusted baseline estimate to obtain a bias-suppressed corrected signal. The bias-suppressed corrected signal is then fed back (at 555) to the low-resolution preliminary baseline estimate generation.

[0070] Figure 6 is a schematic system block diagram of a system 600 such as may be used to implement the functionality described above. The fast auto-blocking technology disclosed herein is designed based on clinical and regulatory considerations to achieve quality display of physiological parameter waveforms and mitigation of baseline drift artifacts in a responsive manner.

[0071] The original physiological signal 603 is obtained by the front-end system 606. This can be as described above with respect to Figure 1 and Figure 3 As mentioned above, the embodiments disclosed herein perform ECG acquisition, but the technology disclosed herein is not limited thereto. In various embodiments, the acquisition may be, for example and without limitation, the acquisition of blood pressure, body temperature, vision, the amount of various substances in the blood, etc. Therefore, the actual acquisition 606 will depend on the nature of the data being acquired and will be well known in the art. Therefore, for the sake of clarity, and in order not to obscure the content of the protection claimed below, further discussion of acquisition 606 will be omitted.

[0072] The raw physiological signal 603 is then preprocessed using an appropriate filter 609 to obtain the desired signal format for the input signal (i.e., input physiological waveform 612). As will be appreciated by those skilled in the art having the benefit of this disclosure, the nature and extent of the preprocessing, and therefore the nature of the filter 609, will depend to some extent on the type of data being acquired. The precise nature of the processing depends not only on the nature of the acquired data, but is also conventional and well known in the art. Therefore, for the sake of clarity, and so as not to obscure what is claimed below, further discussion of preprocessing will also be omitted.

[0073] The input physiological waveform 612 is then input to the deviation estimation module 615. The deviation estimation module 615 employs a high resolution, moving average finite impulse response ("FIR") filter with a small window size. Figure 6In the deviation estimation module 615 in FIG. 6 , D is the unit delay and M is the moving average window size. The illustrated embodiment uses a window size of 0.2 seconds for a typical physiological signal. The FIR filter generates a high-resolution average 618 of the input physiological waveform 612. The high-resolution average 618 is then subtracted from the input physiological waveform 612 at point 621 to obtain a signal deviation 624. The signal deviation 624 produces a clinical feature or sharp artifact.

[0074] The signal deviation 624 is then used in the weight generation module 627 to generate weight factors w and a. In this particular embodiment, the determination of the weight factors w, a begins with the average deviation, which can be represented as D m =|M h -B0|, where M h is the high-resolution average and B0 is the preliminary baseline estimate. The weighting factor for the estimated baseline is calculated as Where c is the unit signal amplitude (e.g., 1 mV). Given a signal deviation d = xM h (where x is the input physiological waveform), the weight factor can be expressed as Where K w is a deviation suppression threshold coefficient in the range of 10 + / - 1. If the signal deviation increases, the weight factor w will decrease to suppress the influence of the deviation.

[0075] The preliminary baseline estimation module 630 uses a low-resolution moving average FIR with a large window size. In the baseline estimation module 630, D is the unit delay and N is the moving average window size. In this context, the window size can be 1 to 2 seconds, depending on the type of physiological signal, to generate a low-resolution average of the corrected input physiological waveform. For example, 1.2 seconds is suitable for ECG waveforms. The preliminary baseline estimation module 630 generates a preliminary baseline estimate 633.

[0076] In order to track baseline drift in a responsive manner, a weight factor (a≤1) is derived based on the mean deviation 636, which is the difference between the high-resolution average 618 and the preliminary baseline estimate 633. The mean deviation 636 is positively correlated with the high-resolution average 618 to track rapid baseline changes. The weight factor is applied to the linear combination of the preliminary baseline estimate 633 and the high-resolution average 618, which is the final estimated baseline 639. The final estimated baseline 639 is subtracted from the input physiological waveform 612 in the signal correction module 642 to generate an output signal, namely the automatically blocked physiological waveform 645. The automatically blocked physiological waveform 645 then undergoes a waveform display process 648 for use in a parameter waveform display 651.

[0077] Still referring to the signal correction module 642, in order to mitigate the impact of high physiological features and high amplitude artifacts on the baseline estimate, a suppression weight factor w (w≤1) is derived based on the signal deviation and the weight factor a. The factor w is negatively correlated with the signal deviation and is applied to the input physiological waveform 612 to suppress the high resolution deviation. The weighted automatically blocked physiological waveform 654 is then combined with the weighted estimated baseline to generate a corrected signal 657. Figure 6 As shown in , the corrected signal 657 is then fed into the baseline estimation module 630. Note that, as mentioned above, the signal correction in the signal correction module 642 is optional and may not be performed in all embodiments.

[0078] Figure 7 A method 700 for rapidly and automatically blocking an input physiological waveform to mitigate baseline drift in another embodiment is shown. Figure 6 and Figure 7 , the method 700 begins by determining (at 710) a high-resolution average 618 of an input physiological waveform 612. The input physiological waveform 612 is either an acquired physiological waveform or a baseline-corrected signal. Generally speaking, if a baseline-corrected signal is available, the baseline-corrected signal is preferred over the acquired physiological waveform. The method 700 then determines (at 720) a preliminary baseline estimate 633.

[0079] Next, the method 700 generates (at 730) a plurality of weights w, a from the difference 624 between the input physiological waveform 612 and the high-resolution average 618 and from the difference 633 between the high-resolution average 618 and the preliminary baseline estimate 633. The difference 624 between the high-resolution average 618 and the input physiological waveform 612 represents a signal deviation. The difference 624 between the high-resolution average 618 and the preliminary baseline estimate 633 represents a mean deviation.

[0080] The method 700 then weights the high-resolution average 618 with a first weight in the plurality of weights (at 740), and weights the preliminary baseline estimate with a difference between 1 and the first weight a in the plurality of weights (at 740). The weighted preliminary baseline estimate and the weighted high-resolution average are then summed (at 750) and the input physiological waveform is subtracted from the sum to obtain the auto-occluded physiological waveform 645. The auto-occluded physiological waveform 645 is also weighted (at 760) with a second weight w in the plurality of weights. The weighted auto-occluded physiological waveform 654 is then combined (770) with the final baseline estimate 639, and the combination 657 is fed back (at 780) to the preliminary baseline estimate.

[0081] Figure 8The time response of different methods to large baseline excursions (e.g., caused by a defibrillation signal in an ECG signal) is shown. The fast automatic blocking and monitoring technique disclosed herein (IIR with a 0.67 Hz cutoff frequency) will restore the baseline to normal levels (red) in approximately 0.5 to 1.2 seconds, while turning off the filter (green) will take 15-20 seconds.

[0082] exist Fig. 9 A comparison of signal quality / fidelity is illustrated in Figure 2. Fast autoblock (red) and off filter (green) show good signal fidelity compared to input physiological signal (yellow). No attenuation and distortion are observed. The monitoring filter (light blue) shows significant signal attenuation and distortion, which is not suitable for clinical use cases requiring higher signal quality (e.g., ST segment analysis).

[0083] Therefore, in a first embodiment, a method for automatically blocking physiological waveform display to reduce baseline drift includes: extracting a low-resolution preliminary baseline estimate from an input physiological waveform; extracting a high-resolution deviation average from the input physiological waveform; subtracting the low-resolution preliminary baseline estimate from the high-resolution deviation average to extract the average deviation of the input physiological waveform; determining a weight factor that is positively correlated with the average deviation; applying the weight factor to the average deviation of the input physiological waveform; applying the difference between 1 and the weight factor to the preliminary baseline estimate; combining the weighted average deviation of the input physiological waveform with the weighted preliminary baseline estimate to extract an adjusted baseline estimate; and subtracting the adjusted baseline estimate from the input physiological waveform to obtain an automatically blocked physiological waveform for display.

[0084] In the second embodiment, the input physiological waveform in the first embodiment is an acquired physiological waveform or a baseline-corrected signal.

[0085] In the third embodiment, the weight factor in the first embodiment is between 0 and 1.

[0086] In a fourth embodiment, determining the weighting factor positively correlated with the average deviation in the first embodiment includes obtaining the average deviation divided by the square of the sum of the average deviation and a constant.

[0087] In the fifth embodiment, the constant in the first embodiment is 1±10%.

[0088] In a sixth embodiment, combining the weighted average deviation of the input physiological waveform with the weighted preliminary baseline estimate in the first embodiment suppresses the deviation in the preliminary baseline estimate.

[0089] In a seventh embodiment, the method of the first embodiment further comprises: suppressing deviations in the auto-occluded physiological waveform; adding the deviation-suppressed auto-occluded physiological waveform to the adjusted baseline estimate; and feeding the adjusted baseline estimate back to the low-resolution preliminary baseline estimate generation.

[0090] In an eighth embodiment, suppressing deviations in the physiological waveform of automatic blocking in the first embodiment further includes: subtracting the input physiological waveform from the high-resolution deviation average to extract the high-resolution deviation; determining a deviation suppression weight factor by utilizing the average weight factor and the high-resolution deviation with a suppression coefficient; and applying the deviation suppression weight factor to the physiological waveform of automatic blocking.

[0091] In the ninth embodiment, the deviation suppression weight factor in the first embodiment is between 0 and 1.

[0092] In the tenth embodiment, the suppression coefficient in the seventh embodiment is 10±10%.

[0093] In an eleventh embodiment, a method for automatically blocking a physiological waveform display to mitigate baseline drift includes: extracting a low-resolution preliminary baseline estimate from an input physiological waveform; extracting a high-resolution deviation average from the input physiological waveform; subtracting the preliminary baseline estimate from the deviation average to extract the average deviation of the input physiological waveform; determining an average weight factor positively correlated with the average deviation by obtaining the average deviation divided by the square of the sum of the average deviation and a constant; extracting an adjusted baseline estimate by summing the average deviation weighted by the average weight factor and the preliminary baseline estimate weighted by 1 minus the average weight factor; subtracting the adjusted baseline estimate from the input physiological waveform to obtain an automatically blocked physiological waveform for display; subtracting the input physiological waveform from the high-resolution deviation average to extract the high-resolution deviation; determining a deviation suppression weight factor by utilizing the average weight factor and the high-resolution deviation with a suppression coefficient; and applying the deviation suppression weight factor to the automatically blocked physiological waveform; summing the weighted automatically blocked physiological waveform and the adjusted baseline estimate to obtain a deviation-suppressed corrected signal; and feeding the deviation-suppressed corrected signal back to the low-resolution preliminary baseline estimate generation.

[0094] In the twelfth embodiment, the input physiological waveform in the eleventh embodiment is a raw acquired waveform or a baseline corrected signal.

[0095] In the thirteenth embodiment, the average weight factor in the eleventh embodiment is between 0 and 1.

[0096] In the fourteenth embodiment, the constant in the eleventh embodiment is 1±10%.

[0097] In a fifteenth embodiment, the adjusted baseline estimate in the eleventh embodiment tracks baseline drift in a responsive manner.

[0098] In the sixteenth embodiment, the deviation suppression weight factor in the eleventh embodiment is between 0 and 1.

[0099] In the seventeenth embodiment, the suppression coefficient in the eleventh embodiment is 10±10%.

[0100] In an eighteenth embodiment, the eleventh embodiment further comprises displaying a physiological waveform of automatic blocking.

[0101] In a nineteenth embodiment, a method for automatically blocking a physiological waveform display to mitigate baseline drift includes: determining a high-resolution average of an input physiological waveform; determining a preliminary baseline estimate; generating multiple weights from the difference between the high-resolution average and the input physiological waveform and from the difference between the high-resolution average and the preliminary baseline estimate; weighting the high-resolution average with a first weight from among the multiple weights, and weighting the preliminary baseline estimate with a difference between 1 and a first weight from among the multiple weights; summing the weighted preliminary baseline estimate and the weighted high-resolution average and subtracting the input physiological waveform to obtain an automatically blocked physiological waveform; weighting the automatically blocked physiological waveform with a second weight from among the multiple weights; combining the weighted automatically blocked physiological waveform with a final baseline estimate; and feeding the weighted automatically blocked physiological waveform back to the preliminary baseline estimate.

[0102] In a twentieth embodiment, the input physiological waveform in the nineteenth embodiment is a raw acquired waveform or a baseline corrected signal.

[0103] In a twenty-first embodiment, the difference between the high-resolution average in the nineteenth embodiment and the input physiological waveform represents a signal deviation.

[0104] In a twenty-second embodiment, the difference between the high-resolution average and the preliminary baseline estimate in the nineteenth embodiment represents the average deviation.

[0105] In a twenty-third embodiment, the weighted preliminary baseline estimate and the weighted high-resolution average in the nineteenth embodiment are summed to produce a final baseline estimate.

[0106] In a twenty-fourth embodiment, a physiological waveform monitor includes: a deviation estimation module, which generates a high-resolution average from an input physiological waveform in operation; a preliminary baseline estimation module, which generates a preliminary baseline estimate in operation; a weight generation module, which generates a plurality of weights in operation based on the signal amplitude of the deviation from the input physiological waveform and the average deviation in the preliminary baseline estimate; a signal correction module; and a display, which displays an automatically blocked physiological waveform in operation. The signal correction module generates the automatically blocked physiological waveform from the difference between the input physiological waveform and a final estimated baseline, the final estimated baseline being a combination of the high-resolution average weighted by a first weight of a plurality of weights and the preliminary baseline weighted by 1 minus the first weight of the plurality of weights; and generates a baseline-corrected signal from a combination of the automatically blocked physiological waveform weighted by a second weight of the plurality of weights and the final estimated baseline; and generates feedback of the baseline-corrected signal to the preliminary baseline estimation module; and.

[0107] In a twenty-fifth embodiment, the input physiological waveform in the twenty-fourth embodiment is a raw acquired waveform or a baseline-corrected signal.

[0108] In a twenty-sixth embodiment, the deviation estimation module in the twenty-fourth embodiment further includes a high resolution moving average finite impulse response ("FIR") filter with a small window size.

[0109] In the twenty-seventh embodiment, the small window size in the twenty-fourth embodiment is 0.2 seconds.

[0110] In a twenty-eighth embodiment, generating a high-resolution average from an input physiological waveform in the twenty-fourth embodiment includes applying a high-resolution moving average finite impulse response filter with a small window size to the input physiological waveform.

[0111] In the twenty-ninth embodiment, the small window size in the twenty-fourth embodiment is 0.2 seconds.

[0112] In a thirtieth embodiment, the deviation in the input physiological waveform in the twenty-fourth embodiment is a difference between the input physiological waveform and a high-resolution average of the input physiological waveform.

[0113] In a thirty-first embodiment, the preliminary baseline estimation module in the twenty-fourth embodiment further comprises a moving average finite impulse response filter with a large window size.

[0114] In the thirty-second embodiment, the large window size in the thirty-first embodiment is 1 to 2 seconds.

[0115] In a thirty-third embodiment, generating the preliminary baseline estimate in the twenty-fourth embodiment includes applying a moving average finite impulse response filter with a large window size to the correction signal.

[0116] In the thirty-fourth embodiment, the large window size in the thirty-third embodiment is 1 to 2 seconds.

[0117] In a thirty-fifth embodiment, a method for automatically blocking a physiological waveform display to mitigate baseline drift in a physiological waveform is substantially as shown and described.

[0118] In a thirty-sixth embodiment, a physiological monitoring device is configured to mitigate baseline drift in a physiological waveform substantially as shown and described.

[0119] In a thirty-seventh embodiment, a local physiological system is configured to mitigate baseline drift in a physiological waveform substantially as shown and described.

[0120] In a thirty-eighth embodiment, a larger physiological system configured to mitigate baseline drift in a physiological waveform is substantially as shown and described.

[0121] Expressions such as "including" and "may include" that may be used in the present disclosure indicate the existence of the disclosed functions, operations, and constituent elements, and do not limit the existence of one or more additional functions, operations, and constituent elements. In the present disclosure, terms such as "including" and / or "having" may be interpreted as indicating a certain characteristic, number, operation, constituent element, component, or combination thereof, but should not be interpreted as excluding the existence or possibility of adding one or more other characteristics, numbers, operations, constituent elements, components, or combinations thereof.

[0122] As used herein, the article "a" is intended to have its ordinary meaning in the patent field, i.e., "one or more". In this article, when applied to a value, the term "approximately" generally means within the tolerance range of the equipment used to produce the value, or in some examples, means plus or minus 10%, or plus or minus 5%, or plus or minus 1%, unless otherwise explicitly specified. In addition, in this article, the term "substantially" as used herein means, for example, most of, or almost all, or all, or having an amount ranging from about 51% to about 100%. In addition, the examples in this article are intended to be illustrative only, and are presented for the purpose of discussion and not as a limitation.

[0123] As used herein, "providing" an item means having possession of and / or control over the item. This may include, for example, forming (or assembling) some or all of the item from its constituent materials, and / or obtaining possession and / or control of an already formed item.

[0124] Unless otherwise defined, all terms used herein (including technical and / or scientific terms) have the same meanings as those of ordinary skill in the art to which the present disclosure belongs. In addition, unless otherwise defined, all terms defined in generally used dictionaries may not be over-interpreted. Hereinafter, details are set forth to provide a more thorough explanation of the embodiments. However, it will be apparent to those skilled in the art that the embodiments may be implemented without these specific details. In other examples, in order to avoid making the embodiments obscure, known structures and devices are shown in block diagram form or with schematic diagrams, rather than being shown in detail. In addition, the features of the different embodiments described below may be combined with each other, unless otherwise specifically noted. For example, changes or modifications described in one embodiment may also be applicable to other embodiments, unless otherwise noted.

[0125] In addition, equivalent or similar elements or elements having equivalent or similar functions are indicated by equivalent or similar reference numerals in the following description. Since identical or functionally equivalent elements are given the same reference numerals in the drawings, repeated descriptions of elements provided with the same reference numerals may be omitted. Therefore, descriptions provided for elements having the same or similar reference numerals are mutually interchangeable.

[0126] It will be understood that when an element is referred to as being "connected" or "coupled" to another element, the element may be directly connected or coupled to the other element, or there may be intervening elements. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements. Other words used to describe the relationship between elements should be interpreted in a similar manner (e.g., "between" versus "directly between," "adjacent" versus "directly adjacent," etc.).

[0127] In the present disclosure, expressions including ordinal numbers (such as "first", "second", etc.) can modify various elements. However, such elements are not limited by the above expressions. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are only used for the purpose of distinguishing an element from other elements. For example, the first box and the second box indicate different boxes, but both are boxes. As a further example, without departing from the scope of the present disclosure, the first element may be referred to as the second element, and similarly, the second element may also be referred to as the first element.

[0128] A sensor refers to a component that converts a physical quantity to be measured into an electrical signal (e.g., a current signal or a voltage signal). The physical quantity may include, for example, electromagnetic radiation (e.g., infrared or visible light photons), magnetic fields, electric fields, pressure, force, temperature, current, or voltage, but is not limited thereto.

[0129] ECG signal processing as used herein refers, without limitation, to manipulating an analog signal in such a way that the signal meets the requirements of the next stage for further processing. ECG signal processing may include conversion between the analog and digital domains (e.g., via analog-to-digital or digital-to-analog converters), amplification, filtering, conversion, biasing, range matching, isolation, and any other process required to make the sensor output suitable for processing.

[0130] The term "module" is used herein in accordance with its ordinary and customary meaning in the art - hardware, software, or a combination of hardware and software that implements certain desired functions. The interchangeability between software and hardware is well known in the art and finds applicability in technical contexts such as those disclosed herein. Thus, a module may be implemented, for example, in an electronic circuit composed of electronic components such as transistors, resistors, inductors, capacitors, etc. Alternatively, a module may be implemented in software in the form of executable instructions stored by a processor-based resource. Alternatively, a module may be implemented in an integrated circuit such as an ASIC, EPROM, or EEPROM. Or, perhaps in some combination of these options.

[0131] In one or more embodiments, use of the phrases "can," "capable of," "operable for," or "configured to" refers to some device, logic, hardware, and / or element that is designed in such a manner that enables the device, logic, hardware, and / or element to be used in a specified manner.

[0132] In one or more embodiments, the use of the phrase "exceeds" indicates that the measured value may be above a predetermined threshold value (e.g., an upper threshold value), or below a predetermined threshold value (e.g., a lower threshold value). When a predetermined threshold range (defined by an upper threshold value and a lower threshold value) is used, the use of the phrase "exceeds" in one or more embodiments may also indicate that the measured value is outside the predetermined threshold range (e.g., above the upper threshold value or below the lower threshold value).

[0133] The subject matter of the present disclosure is provided as an example of the apparatus, system, method, circuit and program for performing the features described in the present disclosure. However, in addition to the above features, further features or variations are also contemplated. It is contemplated that the implementation of the components and functions of the present disclosure can be accomplished with any newly generated technology, which can replace any of the above-implemented technologies.

[0134] The detailed description is made with respect to the accompanying drawings and is provided to help fully understand the various example embodiments of the present disclosure. Without departing from the spirit and scope of the present disclosure, the functions and arrangements of the elements discussed may be changed. Various embodiments may omit, replace or add various programs or components as appropriate. For example, features described with respect to certain embodiments may be combined in other embodiments. In addition, for the sake of clarity and brevity, descriptions of well-known functions and configurations may be omitted. Therefore, those of ordinary skill in the art will recognize that various changes and modifications may be made to the examples described herein without departing from the spirit and scope of the present disclosure.

[0135] Therefore, various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Throughout the disclosure, the terms "example," "examples," or "exemplary" indicate examples or instances, and do not imply or require any preference for the examples mentioned. Therefore, the disclosure is not limited to the examples and designs described herein, but is to be consistent with the widest scope consistent with the principles and novel features disclosed.

Claims

1. A method for automatically blocking a physiological waveform display to mitigate baseline drift, comprising: Extracting a low-resolution preliminary baseline estimate from the input physiological waveform; extracting a high-resolution deviation average from the input physiological waveform; subtracting the low-resolution preliminary baseline estimate from the high-resolution deviation average to extract a mean deviation of the input physiological waveform; determining a weight factor positively correlated with the mean deviation; applying the weighting factor to a mean deviation of the input physiological waveform; applying the difference between 1 and the weight factor to the preliminary baseline estimate; combining the weighted mean deviation of the input physiological waveform with the weighted preliminary baseline estimate to extract an adjusted baseline estimate; as well as The adjusted baseline estimate is subtracted from the input physiological waveform to obtain an automatically blocked physiological waveform for display.

2. The method according to claim 1, wherein: The weight factor is between 0 and 1.

3. The method according to claim 1, wherein: Determining a weighting factor that is positively correlated with the mean deviation includes obtaining the mean deviation divided by the square of the sum of the mean deviation and a constant.

4. The method according to claim 1, wherein: The constant is 1±10%.

5. The method according to claim 1, wherein: Combining the weighted average deviation of the input physiological waveform with the weighted preliminary baseline estimate suppresses the deviation in the preliminary baseline estimate.

6. The method according to claim 1, further comprising: suppressing deviations in the physiological waveform of the automatic block; adding a bias-suppressed, automatically blocked physiological waveform to the adjusted baseline estimate; as well as The adjusted baseline estimate is fed back into the low resolution preliminary baseline estimate generation.

7. The method according to claim 6, wherein: Suppressing deviations in the physiological waveform of the automatic block further comprises: subtracting the input physiological waveform from the high-resolution deviation average to extract a high-resolution deviation; determining a deviation suppression weighting factor by using an average weighting factor and the high-resolution deviation having a suppression coefficient; and The deviation suppression weighting factor is applied to the automatically blocked physiological waveform.

8. The method according to claim 6, wherein: The deviation suppression weight factor is between 0 and 1.

9. The method according to claim 6, wherein: The inhibition coefficient was 10±10%.

10. A physiological waveform monitor, comprising: a deviation estimation module that, in operation, generates a high-resolution average from an input physiological waveform; a preliminary baseline estimate module, operable to generate a preliminary baseline estimate; a weight generation module operable to generate a plurality of weights based on signal amplitudes from deviations in the input physiological waveform and average deviations in the preliminary baseline estimate; The signal correction module, in operation: generating an automatically blocked physiological waveform from a difference between the input physiological waveform and a final estimated baseline, the final estimated baseline being a combination of a high-resolution average weighted by a first weight of the plurality of weights and a preliminary baseline weighted by 1 minus the first weight of the plurality of weights; generating a baseline-corrected signal from a combination of the automatically blocked physiological waveform and the final estimated baseline weighted with a second weight of the plurality of weights; as well as generating feedback of the baseline-corrected signal to the preliminary baseline estimation module; as well as A display is provided for displaying the physiological waveform of the automatic blocking in operation.

11. The physiological waveform monitor according to claim 10, wherein: The deviation estimation module further includes a high resolution moving average finite impulse response ("FIR") filter with a small window size.

12. The physiological waveform monitor according to claim 10, wherein: Generating a high-resolution average from an input physiological waveform includes applying a high-resolution moving average finite impulse response filter having a small window size to the input physiological waveform.

13. The physiological waveform monitor according to claim 10, wherein: The deviation in the input physiological waveform is the difference between the input physiological waveform and a high-resolution average of the input physiological waveform.

14. The physiological waveform monitor according to claim 10, wherein: The preliminary baseline estimation module further includes a moving average finite impulse response filter with a large window size.

15. The physiological waveform monitor according to claim 10, wherein: Generating a preliminary baseline estimate includes applying a moving average finite impulse response filter with a large window size to the correction signal.

Images