Physiological measurement device, method and program

Abstract

The invention relates to a physiological measuring device having a processor configured to receive at least one digital signal from a front-end circuit of the physiological measuring device, the digital signal representing an analog signal present at the input of the front-end circuit and at least one filtered digital signal, the filtered digital signal being generated from the digital signal by at least one digital filter on the basis of a set of filter coefficients.To enable an efficient implementation of the monitoring regarding alarms in terms of cost and power consumption, such that interference with other measuring devices applied to the same patient is minimized, it is proposed that said measuring device comprises a coefficient calculator configured to perform an adaptive calculation on the digital signal and the filtered digital signal, and a checker configured to determine alarm information from the result of the adaptive calculation.

Description

[Technical field]

[0001] The present invention relates to a physiological measuring device, comprising a processor configured to receive at least one digital signal from a front-end circuit of the physiological measuring device, the digital signal representing an analog signal present at the input of the front-end circuit and at least one filtered digital signal, the filtered digital signal being generated from the digital signal by at least one digital filter based on a set of filter coefficients. [Background technology]

[0002] An example of a measurement device of the above mentioned kind having a front end including a digital filter is described in WO 2018 / 162365 A1, which describes a measurement device that can be calibrated by determining a set of filter coefficients of the digital filter based on an analog test signal applied to an input channel of the measurement device. Summary of the Invention [Problem to be solved by the invention]

[0003] During operation, the physiological measurement device should monitor certain technical aspects of the measurement process and alert the user if certain problems occur, such as a fault in the device, cable or sensor / transducer, the presence of significant interference, or if the connection between the sensor / transducer and the patient is of poor quality or lost.

[0004] Moreover, it is desirable to provide this monitoring for alarm detection and processing in a cost-effective manner, i.e., with low overhead in terms of additional circuitry and computational power required to implement the monitoring.

[0005] Finally, the monitoring should not involve the injection of auxiliary currents into the patient's body (e.g. for active impedance measurements to determine the contact quality of adhesive electrodes), even if the auxiliary currents are limited according to the applicable technical standards. Even if the measurement devices comply with the standards, there is a risk of undesired interactions between different devices if they are used to simultaneously monitor the same patient. The generation of the auxiliary currents also increases the power consumption of the device, which can be a factor in battery-powered measurement devices.

[0006] It is therefore an object of the present invention to provide a measuring device, a method and a computer program that allows alarm-related monitoring to be carried out efficiently in terms of cost and power consumption, while minimizing interference with other measuring devices applied to the same patient. [Means for solving the problem]

[0007] In a first aspect of the present invention, a physiological measurement device is presented, comprising: a processor configured to input at least one digital signal from a front-end circuit of the physiological measurement device, the digital signal representing an analog signal present at an input of the front-end circuit and at least one filtered digital signal, the filtered digital signal being generated from the digital signal by at least one digital filter based on a set of filter coefficients; a coefficient calculator configured to perform an adaptation operation based on the digital signal and the filtered digital signal; and a checker configured to determine alarm information from a result of the adaptation operation.

[0008] The inventors of the present application have realized that the results of the adaptation calculations required to implement an adaptive filter can be exploited for alarm-related monitoring. As a result, only little additional effort is required to provide this alarm-related monitoring. Furthermore, special analog monitoring circuits, e.g. for impedance measurement by injecting auxiliary currents, are omitted. As a result, the cost and power consumption of the measurement device are lower. Because there is no injected auxiliary current, interference with other measurement devices is minimized.

[0009] The result of the adaptation operation includes an updated set of filter coefficients, and determining the alarm information is based on the updated set of filter coefficients. Although any information contained in the result of the adaptation operation can be used, the use of the filter coefficients allows for effective detection of alarm conditions because the adaptive filter coefficients reflect the characteristics of the analog signal applied to the measurement device.

[0010] Preferably, said checker is a constraint checker operating on at least one constraint defined on at least one variable resulting from said adaptation operation, i.e. determining alarm information is based on at least one alarm constraint applicable to at least one filter coefficient in said set of filter coefficients.

[0011] In one example, the adaptation operation is subject to at least one value constraint on filter coefficients, and the alarm constraint is a subset of the value constraint. A constraint defines a set of values ​​that a particular variable, e.g., a filter coefficient, can reach. For simplicity, a constraint and the respective set defined by the constraint are considered synonymous herein. For example, to ensure that a digital filter functions properly, a value constraint is considered in the adaptation operation to ensure that a particular variable, e.g., a filter coefficient, is within a reasonable range. Determining alarm information includes checking at least one alarm constraint against a result of the adaptation operation.

[0012] There are various types of alarms. The types of alarms include "poor electrode quality", "electrode off", "interference", "bad cable" and / or "bad device". In order to be able to detect and handle several different types of alarms, it is proposed that the alarm information comprises several alarm information elements for different alarm types and specific alarm constraints are assigned to each of these alarm information elements.

[0013] The alarm types may be predefined classes. One class corresponds to technical alarms. Technical alarms include alarms or fault conditions related to technical or physical aspects of the measurement process performed by the measurement device, and are activated when problems occur, such as an internal defect of the measurement device, a faulty cable connected to the input, a faulty electrode sensor or transducer, the presence of significant interference, or a faulty or lost connection between the input and the respective electrode sensor / transducer. Another class corresponds to application-specific alarms. Application-specific alarms refer to predefined conditions of a physiological parameter or process measured by the measurement device. Thus, in a particular implementation, the alarm types include technical alarms and / or application-specific alarms.

[0014] In another embodiment, determining the alarm information is based on at least one group of alarm constraints, where the alarm constraints in the group of alarm constraints are associated with different severity levels for a particular type of alarm, and when defining a group of two or more alarm constraints for a particular alarm type, each alarm information element has three or more different discrete values ​​corresponding to the different severity levels for that alarm type.

[0015] In an exemplary implementation, to enable simple and reliable determination of the severity level, the alarm constraints in a group of alarm constraints can be sorted starting with a first alarm constraint in the group, with each subsequent alarm constraint of a particular alarm constraint in the group being a superset of the particular alarm constraint, such that each subsequent alarm constraint corresponds to a higher severity level than the particular alarm constraint.

[0016] Although the principles of the present disclosure can be advantageously applied to single-channel measurement devices configured to process one analog signal, many practical applications, in particular ECG or EEG, are based on multi-channel devices configured to process multiple analog signals (e.g. originating from ECG or EEG electrodes). In such multi-channel applications, the result of the adaptation operation comprises multiple sets of filter coefficients for multiple digital filters and multiple alarm constraints, each of which is applicable to a different set of the multiple sets of filter coefficients. Simultaneous checking of multiple constraints for multiple different results of the adaptation operation is also called joint constraint check.

[0017] Although this disclosure is not directed to the particular type of method used to perform the adaptive computation, preferably performing the adaptive computation comprises solving an optimization problem, where the characteristics of a digital filter are defined based on a metric to be maximized or minimized, as well as value constraints.

[0018] In a further aspect of the present invention, a method for operating a physiological measurement device is presented, the method comprising the steps of inputting at least one digital signal representative of an analog signal present at an input of a front-end circuit of the measurement device, inputting at least one filtered digital signal generated from the digital signal by at least one digital filter based on a set of filter coefficients, performing an adaptive calculation based on the digital signal and the filtered digital signal, and determining alarm information from a result of the adaptive calculation.

[0019] In yet another aspect of the present invention, there is provided a corresponding computer program having program code means for causing a computer to perform the methods disclosed herein, in particular the steps of a method for operating a physiological measurement device, when the computer program is run on a computer, and a non-transitory computer readable recording medium storing a computer program product which, when executed by a processor, performs the methods disclosed herein.

[0020] Preferred embodiments of the invention are defined in the dependent claims. It is understood that the claimed methods, systems, computer programs and media have similar and / or identical preferred embodiments as the claimed systems, in particular as defined in the dependent claims and disclosed herein. [Brief description of the drawings]

[0021] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter. [Figure 1] FIG. 1 shows a block diagram of a physiological measurement device. [Diagram 2] FIG. 2 shows a flow chart of a method for operating the physiological measurement device of FIG. [Diagram 3] FIG. 3 illustrates an example alarm constraint. [Figure 4]FIG. 4 shows a set of alarm constraints for different types of alarms. [Diagram 5] FIG. 5 shows a group of alarm constraints for a given alarm type, but with different severity levels for that alarm type. [Figure 6] FIG. 6 shows a signal flow diagram of a physiological measurement device having multiple digital filters associated with different channels. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] 1 shows a block diagram of a physiological measurement device 11. The present disclosure is not limited to a particular type of physiological or medical measurement device. The device 11 may be a patient monitor, a maternal / fetal monitor, an electroanatomical imaging system, etc. Furthermore, the measurement device may be a consumer device such as a smart watch or a fitness tracker with the capability of monitoring physiological parameters such as an electrocardiogram (ECG) curve.

[0023] Basically, the measuring device 11 has an input 13 for receiving an analog electrical signal s representative of a physiological parameter to be measured by the device 11. When the device 11 is in operation, the input 13 is typically connected to electrodes, sensors or transducers or the like (not shown) that are applied to the human or patient's body. Furthermore, the device 11 includes a front end 15 having an analog front end circuit 17 and a digital section 19. The front end 15 is connected to a back end unit 21 of the device 11.

[0024] The front-end circuit 17 comprises an analog filter 23, the input of which is coupled to said input 13 and the output of which is connected to an analog-to-digital converter (ADC) 25 of the front-end circuit 17. In the example shown, the analog filter 23 is designed to function as an anti-aliasing filter. However, the analog filter may be designed for different and / or further purposes or may be omitted entirely. The analog-to-digital converter 25 is configured to convert an analog signal s applied to the input 13 and filtered by the analog filter 23 into a respective digital input signal x. The front-end circuit 17 is connected to an input of an adaptive digital filter 27 of the digital section 19 of the front-end 15 such that said input signal x is processed by the adaptive digital filter 27.

[0025] The adaptive digital filter 27 processes (e.g., filters) an input signal x, thereby deriving a set of filter coefficients f1,...,f k ,...,f K where K denotes the number of filter coefficients. To this end, the adaptive digital filter 27 comprises a filter block 29 which implements a digital filter according to a Finite Impulse Response (FIR) topology. Different kinds of filter topologies can be applied as well. The digital output signal y thus corresponds to a filtered signal generated by filtering the digital input signal x.

[0026] The output of the adaptive digital filter 27 of the digital section 19 is connected to a back-end unit 21 of the measuring device 11 such that, when the measuring device 11 is in operation, said output signal y is transferred to the back-end unit 21. The back-end unit 21 comprises functions for processing and / or analysing the output signal y representative of a physiological parameter as required by the considered application (e.g. ECG or EEG). Depending on the type of measuring device, the back-end unit 21 comprises a user interface device for interacting with a human user (in particular for visualising the parameter represented by the output signal y) and / or a communication circuit for transferring data relating to the parameter represented by the output signal y to a computer system.

[0027] The adaptive digital filter 27 has a filter coefficient f K In the example shown, the coefficient calculator 31 is configured to calculate the coefficient f during operation of the measuring device 11. K For example, the coefficient calculator 31 may be adapted to repeatedly calculate the set of filter coefficients f K The set of can be recalculated.

[0028] Any suitable algorithm for performing such an adaptive operation may be implemented in the coefficient calculator 31. For example, given an input signal x, an output signal y, and the current filter coefficients f k Based on the set of filter coefficients f uIn the illustrated example, the update algorithm implemented in block 31 applies an optimization method to minimize or maximize a metric of the output signal y, e.g., deviation from a reference signal, time domain or frequency domain characteristics, or a statistical metric of the output signal y, e.g., total variation, skewness, kurtosis or entropy. This metric (or any other kind of target function) may be defined to measure the signal quality of the output signal to be optimized based on various aspects. For example, one aspect is the common mode content of the signal to be minimized. Further aspects of the signal quality relate to the presence or absence of frequency bands in the signal, the DC part of the signal, or numerical range limitations to prevent saturation / overflow.

[0029] Therefore, the calculations performed by the coefficient calculator 31 are to obtain the new filter coefficients f k , in particular the current filter coefficients f k The result of this adaptation operation is the newly computed filter coefficients f k Contains a set of.

[0030] The digital section 19 of the front end 15 further comprises an alarm constraint checker 33 configured to determine alarm information from the result of said adaptive calculation. To this end, the alarm constraint checker 33 applies alarm constraints to the result of said adaptive calculation in order to determine said alarm information. As shown in FIG. 1, the alarm information A is made up of a number of alarm information elements a1,...,a2, which relate to different alarm types. N Each alarm constraint may be predefined. For example, an alarm constraint C1(f1,...,f K ) may be defined to determine the first alarm information element a1. Thus, other constraints C2,...,C N can be defined for other alarm information elements and alarm types. The alarm constraint defines a set of values ​​for at least a part of the result of the adaptation operation. For example, the alarm constraint may be defined for the set of filter coefficients f1,...,kF As explained below, the constraints can be defined as a vector x, e.g., x=(f1,...,f K ) can be defined using the inequality

[0031] The alarm generator 35 of the digital section 9 is configured to receive the alarm information A and to generate at least one alarm based on this alarm information A. The alarm generator 35 is configured to generate a first class of alarms, called technical alarms CT, based on the alarm information A. Technical alarms include alarms or damage conditions related to technical or physical aspects of the measurement process and are activated in case of problems such as an internal defect of the measuring device 11, a faulty cable connected to the input 13, a faulty electrode, sensor or transducer, the presence of significant interference or a poor quality or loss of connection between the input 13 and the respective electrode / sensor, transducer. The alarm generator 35 is adapted to forward the technical alarms CT to the back-end unit 21.

[0032] According to an extension of the example shown in Fig. 1, application-specific alarms CA can be generated by the alarm generator 35 in addition to or instead of the technical alarms CT. A class of application-specific alarms CA refers to a predefined state of a physiological parameter or process measured by the measuring device 11, e.g. "ventricular fibrillation" in the ECG.

[0033] The constraint checker 33 in the illustrated example calculates the filter coefficients f k The filter coefficients f k In addition to this filter coefficient f k Instead of g, it is possible to use other state information of the coefficient calculator 31. This internal state information is g in FIG. land may include at least one slack variable, for example when utilizing the optimization technique in block 31.

[0034] The operations performed by the digital section 19 of the front end 15 are at least partly performed in software. In general, any combination of hardware and software can be used to implement the functions of the digital section 19 described herein. The measurement device 11, preferably the digital section 19, therefore includes a programmable computer, for example a processing unit 37. The processing unit 37 includes at least one processor 39 operable to execute a computer program stored in a memory device 41 of the processing unit 37. The computer program is programmed to execute at least some of the operations performed by the measurement device 11. Furthermore, the program may be programmed to execute a method for operating the measurement device 11, which method is described in more detail below. The processing unit 37 may be a general-purpose computer, the I / O circuitry of which is coupled to the rest of the measurement device such that the operations described herein are performed.

[0035] 2 shows a method 43 for operating the physiological measuring device 11. After a start 45 of said method 43, a step 47 is performed for inputting an input signal x from the front-end circuit 17, said input signal x representing an analog signal s present at the input of the front-end circuit 17.

[0036] In a following step 49 , an output signal y is received, which corresponds to the filtered digital signal produced by the digital filter block 29 of the adaptive digital filter 27 .

[0037] After inputting the signals x and / or y, the method 43 calculates the input signal x, the output signal y and / or the current filter coefficients f kBased on the set of k In another example, if the adaptation computation 51 does not require the input signal x or the output signal y, the respective input operation 47 or 49 can be omitted.

[0038] Calculated filter coefficients f k belongs to the result of the adaptive calculation carried out in step 51. Step 53, executed after step 51, determines alarm information A based on the result of the adaptive calculation 51.

[0039] Finally, step 55 of the method 43 generates at least one alarm based on the alarm information A. As explained above, depending on the specific implementation, technical classes of alarms CT and / or application specific classes of alarms CA are supported.

[0040] Then, a step 57 is performed for waiting for a next iteration of the method 43 by returning to step 47. Step 57 may trigger the next iteration based on a predefined delay and / or based on a predefined event occurring within the measurement device 11. In particular, step 57 may wait until a sufficient number of samples of the input signal x and / or the output signal y have been generated during operation of the measurement device 11 and are ready to be input.

[0041] Steps 47, 49, 51 and 57 are used in conjunction with the measuring device 11 using adaptive filter techniques to improve the quality of the signal y output by the front end 15. Steps 53 and 55, which mainly involve checking the result of the adaptation operation against the constraints, are dedicated to the function of the measuring device 11 with respect to alarm generation and processing. As a result, the overhead due to alarm generation is low compared to other approaches where the entire circuit for detecting and generating alarms is built in parallel with the signal processing circuit. Furthermore, alarms can be detected and generated independently of a specific measurement process, such as an impedance measurement process that requires a test signal to be applied to the input 13. The absence of a test signal not only simplifies the design of the measuring device 11, but also reduces the possibility of undesired interactions with other medical devices (not shown) connected to the same person / patient as the measuring device 11.

[0042] As shown in Fig. 3, the alarm constraints evaluated in steps 53 and 33 can be interpreted as geometrical regions in a space defined by various variables resulting from the adaptation operations, for example on the filter coefficients f1, f2. For example, the digital adaptive filter 27 may constrain parameters, such as the filter coefficients, to a set of value constraints. The alarm constraints are then defined as a subset of each of the value constraints. The value constraints considered by the coefficient calculator 31 are shown as a rectangle 61. Valid solutions for the filter coefficients f1, f2 lie within this rectangle 61. The alarm constraints are bounded by a circle 63. More specifically, the alarm constraints 63 require that the filter coefficients f1, f2 must correspond to points that are outside the circle 63. The dotted area 65 is therefore called a "good area" where the alarm information A does not indicate an alarm. However, the shaded area 67 outside the circle 65 corresponds to alarm constraints and thus corresponds to an "alarm area" where the checking of the constraints performed by block 33 and step 53 generates alarm information A indicative of an alarm that is then processed by the alarm generator 35. Figure 3 shows only one example. In general, the alarm constraints 63 should be selected to be a subset of the value constraints 61 applied by the coefficient calculator 31.

[0043] FIG. 4 shows an example where different alarm constraints 63a, 63b are defined for different types of alarms. A first alarm constraint corresponds to filter coefficients f1, f2 that indicate points that are within the checkered region 63a. Another region, shown in FIG. 4 as a shaded region 63b, corresponds to a second alarm constraint. Filter coefficients f1, f2 that correspond to points that are within this shaded region 63b correspond to the second alarm constraint. A region 65 outside these two alarm constraints 63a, 63b but within the value constraint 61 corresponds to a "good region". In the shown example, the first alarm constraint defines an "electrode off" alarm (poor connection with the electrode) and corresponds to the region 63a. The second alarm constraint corresponds to an "interference alarm" (the measuring device 11 and / or electrodes, transducers, sensors, etc. are experiencing excessive interference) and corresponds to the shaded region 63b.

[0044] Other alarms may include "poor electrode quality", "electrode off", "bad cable" and / or "bad device", etc.

[0045] FIG. 5 shows each alarm information element a i 5 shows an extension of the example of FIG. 3, where the alarm information generated according to the example of FIG. 3 is of Boolean type (two different discrete values), while the alarm information element has three or more different discrete values. As shown in FIG. 5, a group of alarm constraints is used to determine the respective alarm information. Different alarm constraints are associated with different severity levels of a particular type of alarm. In the example shown, two alarm constraints 63 belong to a group of alarm constraints 69. The respective regions correspond to a "yellow region" (dotted ring region) and a "red region" (solid dashed line). The good region 65 corresponds to the white disk in the center of FIG. 5. In this context, the good region 65 is also called the "green region". "Green", "yellow" and "red" are associated with increasing severity levels of the alarm. Alarms with alarm information elements that can have three or more discrete values ​​can be used to indicate electrode quality, so that more than two levels of electrode quality can be determined.

[0046] The example of FIG. 4 showing multiple types of alarms can be expanded in the same way by assigning a group of alarm constraints to at least one alarm type.

[0047] In general, the alarm constraints in a group of alarm constraints should be defined starting with the first alarm constraint in the group (e.g., the yellow region) and each subsequent alarm constraint of a particular alarm constraint in the group is a superset of that alarm constraint, with each subsequent alarm constraint corresponding to a higher severity level. As shown in FIG. 5, the alarm constraint corresponding to the highest severity level is identical to the value constraint 61.

[0048] As shown in FIG. 6, the principles of the disclosure can also be applied in connection with a measurement device 11 with multiple channels. The example shown has two channels. There are therefore two analog signals s1, s2 (not shown), two respective digital input signals x1, x2 and two respective filtered output signals y1, y2. A subtractor 71 in the digital section 19 is configured to subtract the first output signal y1 from the second output signal y2, so as to perform a differential measurement by the subtractor 71 and output a respective vector signal v. Furthermore, a number of digital filters 27 are implemented, each of which is assigned to a specific input signal x1, x2. The results of the adaptation calculations taken into account by the alarm constraint checker 33 include the results of the adaptation calculations performed by the individual adaptive digital filter coefficient calculators 31. The alarm constraint checker 33 is adapted to perform a joint alarm constraint check, i.e. to compare the results of the adaptation calculations (e.g. the filter coefficients f , y ) of the multiple coefficient calculators 31 that are part of the separate adaptive digital filters 27 for the respective alarm constraints. k,1 , f k,2 The method is configured to check for a set of

[0049] Although in the above examples are shown in the form of two-dimensional drawings, in practical implementation the value constraints and alarm constraints can be expressed as inequalities of matrix equations or norms of vectors of matrix equations. Calculations based on these equations (e.g. inequalities of matrix equations) can be performed in the constraint checker 33 and the respective method step 53. The equations may be linear equations, polynomials or other kinds of non-linear equations. If a formulation based on the equations is inappropriate, it is also possible to express the value and / or alarm constraints in an algorithmic formulation and to perform the constraint checker 33 and the respective method step 53 on the basis of this algorithmic formulation.

[0050] When using linear equations, the general form of the vector x containing the adaptive filter parameters, i.e., the set of filter coefficients, is c T x+d≦0 ||Ax+b||+d≦0 ||Ax+b||+d≧0 Depending on the choice of matrix A, vectors b and c, scalar d, and the vector norm ||·|| (e.g., Euclidean (l2) norm, Manhattan (l1) norm, max (l∞) norm), a wide variety of constraint types can be formulated.

[0051] The above form of specification of constraints can be applied both to define the value constraints used to calculate the updated set of filter coefficients, as well as to define the alarm constraints applied by the constraint checker 33 and method step 53. However, each alarm constraint is a subset of the value constraints, in other words the set of values ​​x allowed according to each alarm constraint is a subset of the values ​​x allowed according to the value constraints. This subset relationship is also illustrated in Figures 3 to 5. All alarm constraints are shown as areas that lie within the area bounded by the rectangles 61 corresponding to the respective value constraints.

[0052] Some examples are given below: 1 Txd≦0: Limit the sum of coefficients to less than d (gain at f=0 Hz). -1 T x+d≦0: Limit the gain at f=0Hz above d ||x-x0||-d≦0: Limit the distance x from the reference point, initial point, or default point x0 to a maximum of d

[0053] The last example is particularly useful when the ideal case x value is known and used as the initial value x0 of the filter coefficients. The scalar c is interpreted as the maximum distance from the ideal case that the adaptive filter coefficients can reach without tripping an alarm.

[0054] In summary, the measuring device 11 described herein comprises means for generating technical alarms or means for generating application-specific alarms in said measuring device, which comprises an adaptive filter as part of its signal processing chain. In other words, the parts 33 and 35 dedicated to alarm generation and processing, as well as steps 53 and 55, depend on the functions required to process the signal to be measured in any case, such as for example updating the filter coefficients in block 31 and step 51. As a result, the generation and handling of errors can be implemented simply and efficiently.

[0055] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description is to be considered exemplary or explanatory and not restrictive, and the invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0056] In the claims, the word "comprising" does not exclude other elements or steps, nor does it exclude a plurality of them. A single element or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0057] The computer program may be stored / distributed on a suitable transitory medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless communication systems.

[0058] Any reference signs in the claims should not be construed as limiting the scope.

Claims

1. In a physiological measuring device, a processor configured to receive at least one digital signal from a front-end circuit of the physiological measurement device, the digital signal representing an analog signal present at an input of the front-end circuit and at least one filtered digital signal, the filtered digital signal being generated from the digital signal by at least one digital filter based on a set of filter coefficients; a coefficient calculator configured to perform an adaptive calculation based on the digital signal and the filtered digital signal; and a checker configured to determine alarm information from a result of the adaptive calculation. A physiological measurement device comprising:

2. The device of claim 1 , wherein a result of the adaptive operation includes an updated set of filter coefficients, and wherein determining the alarm information is based on the updated set of filter coefficients.

3. The device of claim 2 , wherein determining the alarm information is based on at least one alarm constraint applicable to at least one filter coefficient in the set of filter coefficients.

4. The device of claim 3 , wherein the adaptive operation is subject to at least one value constraint on filter coefficients, and the alarm constraint is a subset of the value constraints.

5. The device of claim 1 , wherein the alarm information comprises a plurality of alarm information elements for different alarm types, and specific alarm constraints are assigned to each of these alarm information elements.

6. The device of claim 5 , wherein the alarm types include technical alarms and / or application specific alarms.

7. 2. The device of claim 1, wherein determining the alarm information is based on at least one group of alarm constraints, the alarm constraints in the group of alarm constraints being associated with different severity levels of a particular type of alarm.

8. 8. The device of claim 7, wherein the alarm constraints in the group of alarm constraints are sorted starting with a first alarm constraint in the group and each subsequent alarm constraint of a particular alarm constraint in the group is a superset of the particular alarm constraint, the subsequent alarm constraints corresponding to a higher severity level than the particular alarm constraint.

9. 2. The device of claim 1, wherein a result of the adaptive operation comprises a plurality of sets of filter coefficients for a plurality of digital filters and a plurality of alarm constraints, each of the alarm constraints being applicable to a different set of the plurality of sets of filter coefficients.

10. The device of claim 1 , wherein performing the adaptive calculation comprises solving an optimization problem.

11. 1. A method for operating a physiological measurement device, the method comprising: receiving from a front-end circuit of the physiological measurement device at least one digital signal representative of an analog signal present at an input of the front-end circuit; inputting at least one filtered digital signal generated by at least one digital filter from said digital signal based on a set of filter coefficients; performing an adaptive operation based on the digital signal and the filtered digital signal; and determining alarm information from the result of said adaptive calculation; The method comprising:

12. A computer program comprising program code means for causing a computer to carry out the steps of the method according to claim 11 when said computer program is run on said computer.

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