Fetal Ultrasound Unit

Abstract

An ultrasound processing unit and method for use in ultrasound fetal monitoring are provided. The processing unit is configured to receive Doppler ultrasound data corresponding to one or more initial trial depth windows within a fetal region. An ultrasound signal for each trial depth window is extracted from the Doppler ultrasound data. Unlike the prior art method of seeking an optimal depth range based on signal strength, in the present disclosure, a defined statistical structure metric of the signal is calculated for each depth signal, wherein the statistical structure metric corresponds to an inherent statistical property or characteristic of the signal. A new recording window is then selected for collecting a fetal heart rate signal based on selecting a window that is estimated to maximize the statistical structure metric of the ultrasound signal derived from the new window.

Description

Technical Field

[0001] The present invention provides an ultrasound processing unit, and in particular provides an ultrasound processing unit for identifying individual heart rate signals within Doppler ultrasound data. Background Art

[0002] Electronic fetal monitoring (EFM) includes different methods of recording the vital signs of the fetus (eg, pulse rate) during pregnancy and labor. A common method used by EFM systems is the detection of the fetal heart rate (FHR) via Doppler ultrasound.

[0003] This is typically performed by placing a Doppler ultrasound transducer on the maternal abdomen and adjusting the position of the transducer so that the source of the fetal pulse rate (e.g., the fetal heart or fetal aorta) is within the volume covered by the ultrasound pulses emitted by the transducer. The emitted ultrasound pulses are reflected back from the moving internal structures. By detecting the frequency shift (Doppler shift) in the reflected signal, a fetal pulse signal is generated. The fetal pulse signal can then be analyzed by the EFM system, and the detected fetal pulse rate can be displayed and / or recorded.

[0004] Ultrasound (US) Doppler transducers used for this purpose typically utilize an unfocused, approximately cylindrical ultrasound beam field. The extent of the beam volume is defined by a characteristic reception time window. During this window, the US transducer is set to collect reflected signals from any moving anatomical structures.

[0005] In particular, typically an ultrasound transducer alternates in rapid succession between two modes: a transmit mode, which involves generating ultrasound pulses, and a receive mode, which involves receiving reflections of previously transmitted ultrasound pulses, with possible delay or pause periods in between.

[0006] By modifying the delay between the end of the transmit phase and the start of the receive phase and by modifying the duration of the transmit phase or the receive phase, the reception of reflected ultrasound can be limited to a specific depth range. This can be used, for example, to limit reception to depths that contain one or more pulse signal sources and exclude depth ranges that do not contain any pulse signal sources but contribute to the noise level of the recorded signal.

[0007] The depth range over which a signal is recorded is also referred to as a “window.” A window is typically a subset of the total depth range (“total ultrasound field of view”) from which the transducer is able to receive signals.

[0008] exist Figure 1 The diagram shows the process of adjusting the depth window. Figure 1 US observations of the fetal region at different depths are schematically depicted. Figure 1 (a) schematically depicts the deep observation area, and Figure 1 (b) depicts a shallow observation zone. For Figure (a), the timing and duration of the receive window of the ultrasound transducer 12 are adjusted relative to the timing and duration of the US transmissions so that US reflections from greater depths are detected. As a result, a deeper observation volume 14 is obtained. In contrast, Figure 1 In (b), the receiving window is adjusted so that US reflections from a shallower depth are detected, thereby generating a shallower depth volume 14.

[0009] Typically, algorithms for adjusting the size and depth of an ultrasound window will seek to minimize the size of the window as much as possible, since a smaller receive window results in less noise in the recorded signal. For example, US 4984576 describes one such example depth selection algorithm.

[0010] Prior art EFM units (such as this EFM unit) can use an iterative method to determine the optimal depth range to use. In particular, the iterative method can seek a depth range that contains the greatest amount of signal strength while excluding areas that contribute noise or weaker signal components. For example, in Figure 1 middle, Figure 1 The shallower depth range of (b) provides a stronger signal because it includes the fetal heart, but excludes any noise that may exist at depths below the fetal heart.

[0011] Iterative methods can typically use two separate receive windows with independently adjustable start and end depths. One window is used for actual signal acquisition. The other window is used to explore whether the signal strength can be improved by including a larger depth range and / or whether reducing the depth range will significantly reduce the signal strength. Typically, the receive window range is preferably as small as possible because smaller windows generally result in reduced noise.

[0012] Prior art depth selection algorithms (e.g., these) work best when there is only a single pulse rate source within the total ultrasound field of view. However, depending on the orientation and size of the beam field, there may be more than one pulse signal source in the field of view: one pulse signal source corresponding to the maternal pulse rate, and one pulse signal source corresponding to one (or multiple in the case of multiple pregnancies) fetal pulse rate source.

[0013] This iterative method described above cannot distinguish between different pulse signal sources captured in the field of view of the transducer unit, for example, two fetal hearts; or a maternal heart and a fetal heart. Instead, it typically includes all detectable pulse rate sources in its calculations and attempts to include all of these pulse rate sources within the selected depth window. As a result, the adjustment process causes the acquisition of a Doppler ultrasound signal that is a mixture of two or more pulse rate sources. This mixture is difficult to analyze, especially when two pulse rate sources have similar amplitudes or powers. This may result in the loss of fetal heart rate recording or the recording of an erroneous heart rate.

[0014] Reduced fetal heart rate (FHR) values ​​or erroneous FHR values ​​may lead to delayed diagnosis of compromised fetal health or an incorrect diagnosis of this condition.

[0015] It would therefore be advantageous to provide an improved Doppler ultrasound processing method that is able to distinguish between different sources of heart rate within input data. Summary of the invention

[0016] The invention is defined by the claims.

[0017] According to an example of one aspect of the present invention, there is provided an ultrasound processing unit for isolating individual heart rate sources within received Doppler ultrasound data in fetal monitoring, the unit being configured to:

[0018] receiving input Doppler ultrasound data corresponding to at least one trial depth region within a uterine region of a subject, the region having a defined height and depth;

[0019] extracting an ultrasound signal from the ultrasound data corresponding to the at least one trial depth region and determining a specific statistical structure measure of the signal;

[0020] A selection algorithm is applied to determine the depth and height of a new recording region within the subject from which ultrasound signals are collected for measuring fetal heart rate, the selection algorithm being configured to select a window based on the determined statistical structure metric for the trial region and based on maximizing the statistical structure metric of the signals acquired from the new recording region. The algorithm thereby seeks to select a new recording depth region containing only a single (fetal) heart rate source.

[0021] Embodiments of the invention are based on techniques used in statistical signal processing.Embodiments are based on adjusting a recording depth window from which an ultrasound (US) signal is acquired according to an analysis of one or more statistical properties of the signal.

[0022] The statistical structure measure preferably corresponds to an intrinsic statistical property or characteristic of the ultrasound signal.

[0023] Statistical signal processing is a method that treats signals as random processes and exploits their statistical properties to perform signal processing tasks or derive information. Statistical techniques are widely used in signal processing applications. For example, in different areas of image processing, the probability distribution of noise caused when taking an image can be modeled, and techniques can be built based on this model to reduce the noise in the resulting image. See, for example: Scharf, Louis L. "Statistical signal processing: detection, estimation, and time series analysis" (Boston: Addison–Wesley, 1991).

[0024] Embodiments of the present invention include determining a statistical structure metric. The statistical structure metric is preferably an intrinsic property of the ultrasound signal. Therefore, the statistical structure metric is preferably determined based only on a reference to the ultrasound signal (e.g., based on processing of the ultrasound signal) without reference to any external entity (e.g., any external signal or information or reference).

[0025] There are different options for the specific metric used, which will be explained further below. In general, a statistical structure metric refers to a statistical property or characteristic of a signal. It refers to the statistical structure of a signal (the degree of statistical structure). A statistical structure metric essentially refers to a measure of the non-randomness or statistical patterning of a signal. For example, the terms "statistical structure metric" and "non-randomness metric" of a signal can be used interchangeably.

[0026] By way of additional explanation, by A. The book "Independent component analysis" by J. Karhunen and E. Oja discusses, for example, three related, but not identical, concepts related to the statistical "structure" of a signal.

[0027] The first structural concept involves the degree of deviation of the probability distribution function of the signal from an equivalent normal distribution (or "Gaussian distribution") with the same mean and variance. This type of statistical structure can be called "non-Gaussian".

[0028] The second statistical structure measure applies to time series signals, i.e., samples taken at known points in time and thus ordered. In this case, the statistical structure (or non-randomness) corresponds to the extent to which the value at a certain point can be at least partially predicted from known previous values. This structure measure can be referred to as the "structure given by linear autocovariance". This statistical structure measure is also captured, for example, by the power spectral density.

[0029] The third statistical structure measure is also applicable to signals corresponding to time series, but in particular, these signals exhibit non-stationary variances, i.e., the variance of these signals changes slowly over time. Here, the statistical structure measure corresponds to the extent to which changes in variance can be predicted based on previous groups of samples. Here, although it is not possible to predict the value of a single sample based on previous values, changes in variance can be tracked by considering larger groups of adjacent samples.

[0030] Thus, each of these statistical structure measures relates to the degree of statistical patterning or non-randomness of a signal. However, each statistical structure measure is effectively independent of the others. Each statistical structure measure neither implies nor presupposes the existence of the others. A signal will typically exhibit one, two, or all three types of statistical structure.

[0031] In some examples, the derived signal structure metric may relate to or correspond to one primary type of statistical structure, or may, for example, relate to or represent all types of structure present in the signal.

[0032] The present invention is based on the insight that statistical structure metrics of the signal provide a better method to determine parameters of the ultrasound receive window (depth region) than signal amplitude / intensity / power and allows an electronic fetal monitoring (EFM) system to adjust the depth window height and depth parameters so that only one source of pulse signal is included in the ultrasound receive window, even when there are multiple independent sources of pulse rates in the possible ultrasound field of view.

[0033] The statistical structure of a signal can be expressed in terms of different specific attributes or characteristics of the signal or in terms of different representations of the characteristics. For example, the statistical structure of a signal can be expressed in terms of a probability density function, a linear autocovariance, a power spectral density function, or the instability of the variance of the signal. These are merely examples, and further examples will be outlined below.

[0034] The use of a criterion to analyze the trial ultrasound receive window signal that is not based on signal strength but rather on a statistical measure of signal structure solves the above-mentioned problem in the selection algorithms of the prior art, in which the algorithms tend to select depth windows that contain multiple independent pulse rate sources. As discussed above, this problem arises because the known algorithms use signal strength as a key parameter, which results in windows with high signal strength, but among other things, this may be due to the presence of multiple pulse sources.

[0035] Using other options instead of the (preferably inherent) statistical properties (particularly the statistical structure or degree of non-randomness of the acquired signal) avoids this problem. It allows the electronic fetal monitoring system to set the ultrasound reception window parameters so that only one pulse signal source is included in the window (even if there are several sources in the total field of view) and to record the Doppler ultrasound signal from which the pulse rate can be unambiguously derived.

[0036] There are different options for the process performed by the selection algorithm.

[0037] In one set of embodiments, the processing unit may be configured to collect ultrasound signals from a plurality of trial depth regions, and wherein the selection algorithm determines a statistical structure metric for each ultrasound signal.

[0038] The processing unit may be configured to perform a comparison process to arrive at the height and depth of the new recording window.The same statistical structure measures are calculated for each signal so that these measures can be directly compared.

[0039] The comparison process may include comparing the statistical structural metrics of different ultrasound signals to each other. The depth region of the signal having the largest metric value may then be selected as a new recording region or window.

[0040] Alternatively, the comparison process may include comparing each of the metrics with a metric derived for a reference window signal. Again, based on the comparison, a window whose signal achieves a maximum value of the statistical structure metric compared to the reference may be selected as a new recording region or window.

[0041] Additionally or alternatively, the selection algorithm may be configured to perform an iterative process comprising iteratively collecting signals from successive trial depth regions, deriving a statistical structure metric for each signal, and comparing the derived metric with a metric derived for one or more previously trialed windows. This approach differs from the above approach in that signals are collected from different depth regions one at a time, and analysis is performed after the collection of each signal. In the above approach, multiple signals are collected from different depth regions and then the comparison process is performed using, for example, the entire group.

[0042] The selection algorithm may include selecting the new recording region from one or more trial depth regions where ultrasound data was collected.

[0043] Although the examples mentioned above collect ultrasound signals from multiple trial depth zones, in some cases, the selection of a new depth zone can be based on data from only one trial depth zone.

[0044] For example, predefined thresholds for the statistical structure metric may be pre-stored. The statistical structure metric derived from the ultrasound signal extracted for at least one trial depth region may be compared to the predefined threshold. The selection algorithm may be configured to select the trial depth region depending on whether the statistical structure metric for the trial depth region reaches, exceeds, or falls within a defined proximity region of a predetermined threshold.

[0045] The predefined threshold may be pre-calculated as a threshold estimated to maximize the statistical structure metric of the ultrasound signal. In other words, if the statistical structure metric for the trial depth region reaches or falls within a defined proximity region of the threshold, then the statistical structure metric is highly likely to be or be close to the maximum statistical structure metric. This may be based on, for example, empirical testing.

[0046] The predefined threshold may be based on a statistical structure metric pre-collected for a reference depth region. For example, the reference depth region mentioned above may be a depth region for which a statistical structure metric is pre-determined, and wherein the statistical structure metric derived for at least one trial depth region is compared with this pre-stored metric for selecting the depth and height of the new recording region.

[0047] The above represent only a non-limiting selection of possible examples.

[0048] There are different options for the statistical structure measures calculated for each signal.

[0049] The statistical structure measure of a given signal may include: one or more characteristics of a probability density function of the signal; one or more characteristics of the linear cross-covariance of the signal, one or more characteristics of the power spectral density function of the signal, and / or a measure of the instability of the variance of the signal.

[0050] Each of the above options is described in more detail in the next section.

[0051] The statistical structure measure may include an approximation of one or more of the following: kurtosis or its derivatives, negentropy, autocovariance, autocorrelation kurtosis. These options will be described in more detail in the next section.

[0052] The ultrasound processing unit can be operatively coupled to the ultrasound transducer unit when in use for acquiring the Doppler ultrasound data and for adjusting acquisition settings of the transducer unit to adjust the depth region from which ultrasound signals are acquired.

[0053] The acquisition settings may include start timing and stop timing and duration of a transmit pulse, and a receiving window of an ultrasonic transducer included in the ultrasonic transducer unit.

[0054] According to an example of another aspect of the present invention, an ultrasound device is provided, comprising: an ultrasound processing unit according to any example or embodiment summarized above or described below, or according to any claim of the present application; and one or more ultrasound transducers operatively coupled to the ultrasound processing unit for providing the Doppler ultrasound data to the ultrasound processing unit, and optionally having an adjustable acquisition setting for fixing a depth window of the acquired ultrasound data.

[0055] Alternatively, gating the data to select a particular depth window may be performed by the processing unit rather than locally at the ultrasound transducer unit.

[0056] The device may comprise an ultrasound probe unit, the probe unit comprising the ultrasound processing unit and the one or more ultrasound transducers. The probe unit may be a handheld ultrasound probe. The processing unit is integrated in the probe, and the signal processing is thus performed locally in the probe.

[0057] The probe unit may, for example, have a housing in which the ultrasound processing unit and the one or more ultrasound transducers are included.

[0058] According to an example of another aspect of the present invention, a patient monitoring system is provided, which comprises: an ultrasound processing unit according to any example or embodiment summarized above or described below, or according to any claim of the present application; and a connection interface for connecting to the ultrasound transducer unit when in use.

[0059] The connection interface may be a wired connector, or may be a wireless connection interface for connecting to a wireless ultrasound probe.

[0060] The patient monitoring system may further comprise an ultrasound transducer unit coupled to the connection interface.

[0061] For example, the transducer unit may be an ultrasound probe.

[0062] The ultrasonic transducer unit may be a transmitting / receiving unit only, i.e., comprising one or more ultrasonic transducers for transmitting and sensing ultrasonic signals. Here, the ultrasonic processing unit comprises all signal processing components (including components for digitizing and demodulating the analog signals output by the transducer unit) to separate the signals for one or more different depth regions. In this case, the analog signals are transmitted from the transducer unit to the ultrasonic processing unit via the communication interface.

[0063] In other examples, the ultrasonic transducer unit may additionally include local or on-site signal processing components that are used to perform digitization and demodulation of the signal and achieve separation of the depth channel. In this case, the resulting digital data representing the separated signal channels is transmitted from the ultrasonic transducer unit to the ultrasonic processing unit.

[0064] The patient monitoring system may comprise a base station or base unit to which at least an ultrasound transducer unit (eg an ultrasound probe) can be connected. The base station may comprise a display for displaying the result of the processing performed by the ultrasound processing unit.

[0065] The patient monitoring system may further comprise a controller adapted to control the acquisition of ultrasound data by the connected transducer unit when in use.

[0066] The controller can control the transmit and receive circuits of the ultrasonic transducer unit to collect ultrasonic signals representing different depths. The controller can control the duration of the transmit pulse and the receive window and the timing between the transmit pulse and the receive window. The controller can control the gating of the input Doppler signal data over a defined time window to separate signals corresponding to one or more different depth regions within the tissue of the object.

[0067] An example according to a further aspect of the present invention provides an ultrasound processing method for isolating individual heart rate sources within received Doppler ultrasound data in fetal monitoring, the method comprising:

[0068] receiving input Doppler ultrasound data corresponding to at least one trial depth region within a uterine region of a subject, the region having a defined height and depth;

[0069] extracting an ultrasound signal from the ultrasound data corresponding to the at least one trial depth region and determining a statistical structure measure of the signal;

[0070] A selection algorithm is applied to determine a depth and height of a new recording area within the object from which ultrasound signals are collected for measuring fetal heart rate, the selection algorithm being configured to select a window based on a determined statistical structure metric for the trial area and based on maximizing the statistical structure metric of signals acquired from the new recording area.

[0071] The method may be a computer-implemented method, such as a method for implementation by a suitable processor, controller or computer.

[0072] The statistical structure measure preferably corresponds to an intrinsic statistical property or characteristic of the signal.

[0073] The implementation options and details for each of the above steps can be understood and interpreted based on the illustration and description of the apparatus aspect (ie, the ultrasonic processing unit aspect) of the present invention provided above.

[0074] Any examples, options or embodiment features or details described in relation to the apparatus aspect of the invention (regarding the ultrasound processing unit) may be applied or combined or incorporated into the method aspect of the invention.

[0075] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] For a better understanding of the invention and in order to more clearly show how the invention may be put into practice, reference will now be made, by way of example only, to the accompanying drawings, in which:

[0077] Figure 1 illustrates a known method for acquiring fetal heart rate data based on iteratively adjusting the depth of the acquired signal;

[0078] Figure 2 is a block diagram of steps performed by an example processing unit according to one or more embodiments of the present invention; and

[0079] Figure 3 An example ultrasound system is shown in accordance with one or more embodiments. DETAILED DESCRIPTION

[0080] The present invention will be described with reference to the accompanying drawings.

[0081] It should be understood that the detailed description and specific examples, although indicating exemplary embodiments of the apparatus, system and method, are for illustrative purposes only and are not intended to limit the scope of the invention. These and other features, aspects and advantages of the apparatus, system and method of the invention will be better understood from the following description, claims and drawings. It should be understood that these drawings are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the drawings to indicate the same or similar components.

[0082] The present invention provides an ultrasound processing unit and method for use in ultrasound fetal monitoring. The processing unit is configured to receive Doppler ultrasound data corresponding to one or more initial trial depth regions (depth windows) within a fetal region. Ultrasound signals for each trial depth region are extracted from the Doppler ultrasound data. At least one specific statistical structure metric of the signal is calculated for each depth signal. A new recording window is then selected for collecting a fetal heart rate signal based on selecting a window that is estimated to maximize the statistical structure metric of the ultrasound signal derived from the new window.

[0083] The purpose of the present invention is to identify a depth window for recording an ultrasound signal that contains only a single fetal heart rate source. This avoids capturing mixed signals from multiple heart rate sources (which can lead to inaccuracies).

[0084] Existing methods known in the art are based on seeking a depth window that maximizes the signal amplitude or power. However, this is not suitable for isolating a single heart rate source, as areas with multiple sources will tend to generate higher intensity signals and will therefore tend to be favored by these known selection algorithms.

[0085] The present invention is different in that new depth windows are selected not based on signal amplitude but rather on the degree of statistical structure of the signal (in effect the degree of statistical non-randomness attributable to the signal, or the strength of statistical patterning that can be identified in the signal). A single heart rate source will result in a more strongly statistically structured signal than a mixture of multiple heart rate signals. Thus, the criterion of statistical structure or patterning or non-randomness provides a more effective and reliable criterion for selecting depth windows with isolated heart rate sources than methods using signal amplitude or power.

[0086] According to an example of the first aspect of the present invention, an ultrasound processing unit is provided for isolating individual heart rate sources within received Doppler ultrasound data in fetal monitoring. The processing unit is configured to perform a plurality of steps when in use.

[0087] Figure 2 The steps performed by the ultrasound processing unit are shown in block diagram form. These steps will now be briefly outlined and then explained in more detail.

[0088] The ultrasound processing unit is configured to receive 32 input Doppler ultrasound data corresponding to at least one trial depth region within a uterine region of a subject, the region having a defined height and depth.

[0089] The ultrasound processing unit is further configured to extract 34 ultrasound signals from the ultrasound data corresponding to the at least one trial depth region and to determine 36 a defined statistical structure measure of the signal from the ultrasound signals.

[0090] The processing unit is further configured to apply 38 a selection algorithm to determine a depth and height of a new recording area within the subject from which ultrasound signals are collected for measuring the fetal heart rate. The new recording area forms part of the trial area. The selection algorithm is configured to select a new depth window (which is a sub-portion of a previous window corresponding to the trial area) based on the derived statistical structure metric for the trial window and based on seeking a window in which the collected ultrasound signals are estimated to have the maximized statistical structure metric.

[0091] Thus, the selection algorithm can perform a maximization process to identify height and depth parameters of a new recorded depth region (which is typically smaller than the trial depth region) that maximize the statistical structure metric of the signal to be derived from the window. This is done based on the calculated statistical structure metric for one or more trial regions.

[0092] The method performed by the processing unit is based on deriving at least one predefined statistical structure measure for one or more trial depth regions. It is also based on then selecting a recording depth region that maximizes this same statistical structure measure for the US signal derived therefrom.

[0093] As briefly discussed above, there are different options for the specific metric used. In general, a statistical structure metric refers to a statistical property or characteristic of a signal. It refers to the degree of statistical structure (the degree of statistical structure) of a signal. A statistical structure metric essentially refers to a measure of the non-randomness or degree of statistical patterning of a signal. For example, the terms "statistical structure metric" and "non-randomness metric" of a signal may be used interchangeably.

[0094] By way of a set of non-limiting examples, statistical measures of signal structure may be higher order cumulative quantities (e.g., skew or kurtosis or a combination thereof), or may be non-polynomial quantities (e.g., negentropy or an approximation thereof), or they may be related to the temporal structure of the signal (e.g., autocovariance or autocorrelated kurtosis). Statistical structure measures can be used to distinguish unstructured signals (e.g., Gaussian noise or noise without temporal structure) from less structured signals (e.g., a mixture of multiple pulse rate sources), and in turn distinguish unstructured signals (e.g., Gaussian noise or noise without temporal structure) from highly structured signals (a single pulse rate source).

[0095] According to one or more additional non-limiting examples, a statistical structure metric of a given signal may include: one or more characteristics of a probability density function for the signal, one or more characteristics of a power spectral density function for the signal, and / or a metric of instability of a variance of the signal.

[0096] Some of these example statistical structure measures will now be explained in more detail.

[0097] Kurtosis is a property derived from the probability density function of the input signal. An estimate of the kurtosis of a signal x of length N can be obtained by calculating the following formula:

[0098]

[0099] Where m is the mean of x and σ is the standard deviation of x. For a signal x with a Gaussian distribution, this estimate is close to the value 3, while a signal with a non-Gaussian distribution has a value greater than 3 or less than 3.

[0100] Thus, for signals with a Gaussian distribution, subtracting 3 from the estimate and then squaring it will produce a quantity that is zero, whereas for most signals with a non-Gaussian distribution, subtracting 3 from the estimate and then squaring it will produce a non-zero quantity. This quantity can therefore be used as a measure of the statistical structure of a signal: the higher the value, the more the signal deviates from a normal distribution, and therefore the more non-random or statistically structured the signal is.

[0101] The term [kurtosis(x)-3] is sometimes referred to in literature sources as "excess kurtosis". However, the term may also be referred to simply as "kurtosis" in some other sources.

[0102] This criterion (kurtosis) is related to the probability density function of the signal x.

[0103] Another example of a statistical structure metric is negative entropy. Negative entropy is also related to the probability density function of the signal. The estimated result of the negative entropy J of the signal x can be obtained by calculating the following formula:

[0104]

[0105] For example, in A. This approximate result is given in the book "Independent component analysis" by J. Karhunen and E. Oja.

[0106] It should be noted that this approximation, as well as the estimate of kurtosis outlined above, may be slightly sensitive to outliers in the signal, since the third or fourth power of the signal x is used in the calculation.

[0107] In the above formula, optionally, the first term can be omitted, since it is zero for any symmetric probability distribution. This leaves a single term based on the squared value of the excess kurtosis. This can be considered the simplest estimator of negentropy and can be used as a statistical structure measure in example embodiments.

[0108] Negentropy has the following properties: Negentropy is zero for a signal with a Gaussian distribution, and is greater than zero for any signal with a non-Gaussian distribution. Furthermore, the growth of non-zero values ​​of negentropy is directly related to the degree to which the probability distribution for the signal x deviates from a Gaussian distribution. Thus, the metric directly quantifies non-Gaussianity. Thus, the metric provides a direct measure of the statistical structure of a signal.

[0109] However, in general, the negentropy can only be estimated for a signal and often cannot be directly calculated.

[0110] It is possible to construct estimators of negentropy that are more robust to outliers. For example, in A. An example method for constructing such an estimator is given in the book "Independent component analysis" by J.Karhunen and E.Oja.

[0111] For example, a simple estimator for the negative entropy J(x) is:

[0112]

[0113] Negentropy estimators can be made to achieve arbitrarily high accuracy, but at the expense of increased complexity as accuracy increases. Therefore, choosing an estimator for an application is usually a trade-off between the required accuracy and the available computational power.

[0114] Another example of a measure of the statistical structure of a signal is the linear autocovariance.

[0115] A simple example measure based on linear autocovariance is as follows:

[0116]

[0117] This formula can also be found in the book "Independent component analysis" by Oja et al. (see Chapter 18 "Methods using time structure").

[0118] For this example, the value of metric C(x) is zero if two adjacent samples of the signal are uncorrelated, and non-zero if two adjacent samples of the signal are correlated to some extent. To compare two input signal channels, the two signal channel vectors are first normalized by subtracting their respective means and dividing by their respective standard deviations (this eliminates any effect of the power of each channel on C(x) in the result).

[0119] For an input signal channel containing a mixture of heart rate signals from multiple sources, the absolute value of C(x) will be lower than the highest absolute value of C(x) for an individual heart rate signal source.

[0120] In the context of the present invention, the selection algorithm may be configured to search for the recorded region within the object having the highest absolute value of C(x).

[0121] However, it should be noted that this approach may not be completely effective in the case where there are several heart rate sources with exactly the same corresponding C(x) (since these several heart rate sources cannot be distinguished from each other). However, this situation rarely occurs.

[0122] Another example of a statistical structure measure is the instability of the variance of a signal.

[0123] The variance instability of a signal x can be quantified using the fourth-order cumulant, which is part of the autocorrelated kurtosis matrix:

[0124]

[0125] The value of this expression is large for a signal x with a large variation in variance over the interval N, and is small when the variance is closer to a constant.

[0126] Thus, this expression quantifies this instability of the signal. Typically, a mixture of heart rate signal sources is more unstable than a single individual heart rate signal source. Thus, variance instability provides a direct measure of the statistical structure of the signal. In an example, the selection algorithm can search for recording regions within a subject from which the acquired signal exhibits the greatest amount of variance instability.

[0127] In order to compare different input signal channels, this again requires that each signal x is first normalized to zero mean and unit variance over the total sample range.

[0128] Usually, the first term of the formula dominates the following two terms, so the last two terms can be omitted to simplify the calculation.

[0129] This formula can also be used in A. It can be found in the book "Independent component analysis" by J.Karhunen and E.Oja (see Chapter 18 "Methods using time structure").

[0130] By way of explanation, a random process is said to be stable if its finite-dimensional distribution is invariant under time translation. This type of random process can be used to describe physical systems that are in a stable state but still experience random fluctuations. The concept of stability is that the distribution of a stable random process remains unchanged over time.

[0131] The exact computation of these functions requires an infinite number of signal samples. Therefore, in practice one can use approximate results or compute the quantity using multiple available samples that contain some information about the underlying probability distribution.

[0132] By way of further non-limiting example provided by way of illustration, for a signal x that has been pre-processed to have zero mean (i.e., zero baseline) and unit variance (i.e., amplitude of 1, i.e., a normalized signal), examples of statistical measures of the signal include:

[0133] Kurtosis: kurt(x) = E(x 4 )-3(E(x 2 )) 2

[0134] Squared kurtosis: (kurt(x)) 2 =[E(x 4 )-3(E(x 2 )) 2 ] 2

[0135] Approximate result of negative entropy:

[0136] Alternative approximation of negative entropy:

[0137] Another alternative approximation of negative entropy: [E(log(cosh(x))-c2] 2

[0138] Here, E() represents an expected value operator, and c1, c2 represent constants corresponding to the value of the first term when x is Gaussian distributed.

[0139] Preferably, the selection algorithm comprises collecting ultrasound signals from a plurality of trial depth regions and determining at least one statistical structure metric of the signals for each trial region.

[0140] In some embodiments, the selection algorithm may include comparing the statistical structure metrics derived for each test region signal. The metrics for different test regions may be compared to each other, or the metrics for different test regions may all be compared to one or more metrics derived for one or more reference depth regions.

[0141] By comparing the statistical structure metric(s) of the test depth window with the statistical structure metric(s) of the reference depth window, the ultrasound processing unit is able to determine to what extent the height and depth parameters of the test area should be adjusted in order to maximize the statistical structure of the signal in the recording window.

[0142] Where multiple depth regions are tested and their signal structure metrics are compared to each other, a spectrum of different statistical structure values ​​and corresponding depth parameters that produce these different statistical structure values ​​is provided. This allows the relationship between the statistical structure metric and each of the height and depth parameters to be derived. From this relationship, a set of parameters that may produce an ultrasound signal with maximum signal structure can be derived (e.g., extrapolated or interpolated).

[0143] Alternatively, the selection algorithm may include an iterative process in which ultrasound signals are collected from successive trial depth regions one at a time. For each new trial depth region, a statistical structure metric is derived for the ultrasound signal. The statistical structure metric may then be compared with the statistical structure metric of the signal for one or more previously trial depth regions, and based on the results, the algorithm determines whether and in what manner the height and depth parameters for the next trial region should be adjusted in order to improve the statistical structure metric of the signal derived therefrom.

[0144] The selection algorithm may determine for each new trial region whether the current region has maximized the statistical structure metric based on comparison with one or more previous regions. This may be based, for example, on plotting or otherwise calculating the ongoing change in the statistical structure metric as a function of increasing trial region index. When it is detected that the statistical structure metric has reached a turning point (e.g., the second derivative of the calculated function is zero or approximately zero), the selection algorithm may simply use the height and depth parameters of the current trial depth region for the new recording region.

[0145] Alternatively, the selection algorithm may detect for each new trial region whether the statistical structure metric for that region is within a certain threshold proximity region of the statistical structure metric for the previous trial region, in which case height and depth parameters for the new region are selected for the final recorded depth region.

[0146] Embodiments of the present invention relate to controlling the depth region within a subject from which ultrasound signals are extracted. The general process for performing this operation will now be briefly outlined.

[0147] The ultrasound transducer unit is used to obtain Doppler ultrasound data. This can be controlled by the ultrasound processing unit, or can be performed separately. In a preferred example, the ultrasound processing unit of the present invention can be operatively coupled to the ultrasound processing unit when in use, and is suitable for controlling the transducer unit to collect ultrasound data. The processing unit is preferably operable to adjust the collection or other acoustic settings of the ultrasound transducer unit, thereby adjusting the depth region from which the ultrasound signal is collected.

[0148] The transducer unit may include multiple ultrasound transducers or a single transducer. The received ultrasound data typically represents multiple different depths within the target body. From the data, one or more signal channels corresponding to signals for one or more specific depths in the scanned body may be extracted.

[0149] In more detail, in operation, ultrasound pulses may be transmitted by an ultrasound receive / transmit unit into the body being investigated (i.e., the uterine region of the subject). Pulses are transmitted at a defined frequency over a defined transmit window or a set of recurring transmit windows. The receive / transmit unit includes one or more ultrasound transducers for generating and sensing ultrasound signals. It is a form of an ultrasound transducer unit, but does not include signal processing components (in this example, the signal processing components are external to the unit).

[0150] The reflected ultrasound signal is then received at the ultrasound receive-transmit unit. Depending on at which depth the signal is reflected, the reflection will be received at the receive-transmit unit at different points in time. Since the propagation speed of ultrasound in tissue is known (about 1000 m / s), the time delay between transmission and reception can be mapped to the distance the ultrasound pulse has traveled. This distance is then proportional to the depth.

[0151] In some examples, signals are transmitted in a single direction, and ultrasound signals corresponding to different depths within the resulting single cylindrical beam field are received and gated.

[0152] In a further example, the ultrasound transducer unit may comprise an array of individual ultrasound transmitters, and wherein the control unit is configured to apply beamforming using the array to control the directionality of the generated ultrasound beam.

[0153] In either case, the resulting ultrasound data may be amplified by an amplifier and then separated into a plurality of signal channels corresponding to different depth regions within the object. Alternatively, if only a single depth region is sought, the channel corresponding to the single depth region may be extracted from the set of channels.

[0154] For example, signals corresponding to different depths may be separated by gating the incoming signal over different temporal receive windows, each gated signal then providing a different input signal channel corresponding to a different depth.

[0155] In some examples, the duration of and timing between transmit pulses and receive windows can be adjusted to obtain signals from a specific desired depth and then gated over appropriate time windows to provide different depth signals on each depth channel.

[0156] Optionally, a strobe pulse for a channel may be generated by a digital logic unit (e.g., a microcontroller, FPGA, etc.) included in a receive-transmit (or ultrasonic transducer) unit. The strobe signal may then be sampled using an analog-to-digital converter. A relatively low sampling rate (e.g., hundreds to thousands of Hz) may be used.

[0157] Those skilled in the art will be aware of numerous methods for gating the signals to extract channels corresponding to different depths within the body being probed.

[0158] The one or more gated depth signals each provide a corresponding input signal channel (or "depth channel").

[0159] A pre-processing step is applied to each of the one or more depth signal channels. The pre-processing steps may be applied after or before separation of the different input signal channels.

[0160] In particular, demodulation and signal integration may be applied to the input signal for each input signal channel.The demodulation generates a signal having a frequency equal to the Doppler (frequency) shift of the measured Doppler signal compared to the original transmitted signal.

[0161] Bandpass filtering can be applied to each depth signal channel. The filtering is configured to select frequency components of the incoming signal that are within the frequency range desired for fetal heartbeat measurement. This ensures that only relevant frequency components of the data are retained, thereby reducing overall noise.

[0162] In some examples (not shown), an envelope demodulator may additionally be applied. For each depth signal channel, this operation provides an envelope signal corresponding to the variation (as a function of time) of the signal strength (e.g., intensity or variance) for the selected (filtered) frequency range.

[0163] In some examples, the demodulation functionality may be incorporated into a digital microprocessor included in the ultrasound transducer unit, in which case the reflections of the transmitted pulses may be sampled at a high sampling rate (several times the frequency of the transmitted ultrasound pulses (e.g., several MHz) using an analog-to-digital converter, and digital logic or software in the transducer unit may demodulate the received signals and calculate signal values ​​for each depth channel. This may eliminate the need for gate pulses and analog demodulation circuitry.

[0164] Providing a digital microprocessor capable of analog-to-digital conversion at the required resolution and speed to provide processing synchronously with signal acquisition can be challenging with current microprocessor technology. In an alternative example, a dedicated analog-to-digital (A / D) converter included in the ultrasound transducer unit can be used instead to perform the division into depth channels. The A / D converter should operate synchronously in this case, meaning that the ultrasound frequency matches the A / D conversion frequency (preferably, is exactly the same as the A / D conversion frequency).

[0165] An example embodiment of an ultrasonic processing unit will now be briefly outlined.

[0166] An ultrasound processing unit as described above is provided and is operatively coupled to an ultrasound transducer unit when in use. The ultrasound processing unit in combination with the transducer unit is operable to record Doppler ultrasound signals from at least two depth regions within a given object. The depth regions may have adjustable height and depth parameters, or may have fixed height and depth parameters.

[0167] The ultrasound processing unit records Doppler ultrasound signals from each depth region and calculates a statistical structure metric for each Doppler ultrasound signal. Based on a comparison of the value(s) of each signal, the processing unit either adjusts the size and depth parameters of the recorded depth region (or correspondingly adjusts the start depth and end depth) to increase the degree of structure for the final recording window from which the fetal heart rate (FHR) will be collected. Alternatively, the unit may simply select one of the available depth window channels with the largest statistical structure metric to be used as a new recording window for collecting signals for FHR calculation.

[0168] The ultrasound processor can use signal structure criteria such as normalized kurtosis or negative entropy (or any of the other example criteria outlined above) to compare different depth signal channels. These metrics are not affected by the (average) amplitude of the signal and can therefore be used to distinguish channels that contain only or predominantly one independent pulse rate signal from channels that contain a mixture of pulse rate signals or no pulse rate signal at all.

[0169] As one example, by seeking to maximize the approximate negative entropy of the depth channel signal, the selection algorithm will have the effect of adjusting the window size and depth position until only one independent pulse rate signal is recorded.

[0170] According to an example of another aspect of the present invention, an ultrasound device is provided, which includes: an ultrasound processing unit according to any example or embodiment summarized above or described below, or according to any claim of the present application; and one or more ultrasound transducers 76, which are operatively coupled to the ultrasound processing unit to provide Doppler ultrasound data to the ultrasound processing unit, and optionally have adjustable acquisition settings for fixing the depth window of the acquired ultrasound data.

[0171] The device may for example comprise an ultrasound probe unit, the probe unit comprising an ultrasound processing unit and one or more ultrasound transducers.For example, the probe may comprise a housing comprising one or more ultrasound transducers and an ultrasound processing unit.

[0172] According to an example of a further aspect of the invention, a patient monitoring system is provided. Figure 3 An example patient monitoring system 70 is shown in accordance with one or more embodiments.

[0173] The patient monitoring system 70 includes an ultrasound processing unit according to any of the examples or embodiments outlined above or described below or according to any of the claims of the present application. In the example shown, the ultrasound processing unit is incorporated inside the base unit 72. However, in other examples, the ultrasound processing unit may be incorporated locally within the ultrasound transducer unit 76, with which the base unit is (able to) be connected.

[0174] The patient monitoring system 70 further comprises a connection interface in the form of an input connector port 74 for connection, in use, to an ultrasound transducer unit 76 for receiving input Doppler ultrasound data or data derived therefrom. Figure 3 An example of an ultrasound transducer unit 76 is shown for connection to a base station in use. The transducer unit comprises an output connector 78 shaped to engage with an input connector 74 of the base station.

[0175] In the event that an ultrasound processing unit is included in the base station 72 , the input connector 74 may be coupled to the ultrasound processing unit to transmit received ultrasound data.

[0176] exist Figure 3 , connector 74 is shown as a wired connector port. In other examples, the connector may include a wireless connection interface for connecting to a wireless ultrasound probe.

[0177] The patient monitoring system 70 may further comprise an ultrasound transducer unit 76 coupled to the input connector. For example, the transducer units may each be an ultrasound probe.

[0178] The patient monitoring system in this example also includes a display 80 operatively coupled to the ultrasound processing unit of the base station 72 for displaying the results of the performed analysis procedure, such as displaying a visual representation of the one or more second output signals.

[0179] The patient monitoring system 70 may further comprise a controller adapted to control the acquisition of ultrasound data by the connected transducer units when in use.

[0180] The controller can control the transmit and receive circuits of the ultrasonic transducer unit to collect ultrasonic signals representing different depths. The controller can control the duration of the transmit pulse and the receive window and the timing between the transmit pulse and the receive window. The controller can control the gating of the input Doppler signal data over a defined time window to separate or extract different input signal channels corresponding to one or more specific depths within the tissue of the object.

[0181] In some examples, the controller may be included locally within the ultrasound transducer unit, or the control steps performed by the controller may be performed locally at the ultrasound transducer unit.

[0182] As mentioned above, the ultrasound transducer unit may include an ultrasound processing unit. It may be an ultrasound probe unit, for example comprising one or more ultrasound transducers and an ultrasound processing unit operatively coupled to a processing unit. The ultrasound transducer unit may locally perform at least a subset of the above-mentioned ultrasound data preprocessing steps and / or control steps.

[0183] The patient monitoring system may take a different form than that described above. For example, the patient monitoring system may include a monitoring station (eg, a cart-type monitoring station) that includes a display and can be connected to the ultrasound transducer unit.

[0184] In any example, the patient monitoring system can be connected to any number of other sensors or data sources for monitoring the same patient or a different patient.

[0185] An example according to a further aspect of the invention provides an ultrasound processing method for isolating individual heart rate sources within received Doppler ultrasound data in fetal monitoring, the method comprising:

[0186] receiving 32 input Doppler ultrasound data corresponding to at least one trial depth zone within a uterine region of a subject, the zone having a defined height and depth;

[0187] extracting 34 an ultrasound signal from said ultrasound data corresponding to said at least one trial depth region and determining 36 a defined statistical structure measure of said signal;

[0188] A selection algorithm is applied 38 to determine the depth and height of a new recording area within the object from which ultrasound signals are collected for measuring fetal heart rate, the selection algorithm being configured to select the new recording area based on a determined statistical structure metric for the trial area and based on maximizing the statistical structure metric of signals acquired from the new recording area.

[0189] Those skilled in the art can understand and implement variations of the disclosed embodiments when practicing the claimed invention by studying the drawings, the disclosure and the claims. In the claims, the word "comprising" does not exclude other elements or steps, and the words "one" or "an" do not exclude multiple. A single processor or other unit can implement the functions of several items recorded in the claims. Although certain measures are recorded in mutually different dependent claims, this does not indicate that the combination of these measures cannot be used advantageously. If the computer program is discussed above, the computer program can be stored / distributed on a suitable medium, such as an optical storage medium or solid-state medium supplied together with other hardware or as part of other hardware, but can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems. If the term "suitable for" is used in the claims or description, it should be noted that the term "suitable for" is intended to be equivalent to the term "configured to". Any figure mark in the claims should not be interpreted as limiting the scope.

Claims

1. An ultrasound processing unit for isolating individual heart rate sources within received Doppler ultrasound data in fetal monitoring, the unit being configured to: receiving input Doppler ultrasound data corresponding to at least one trial depth region within a uterine region of a subject, the region having a defined height and depth; extracting an ultrasound signal from the ultrasound data corresponding to the at least one trial depth region and determining a defined statistical structure metric of the signal, the statistical structure metric corresponding to an inherent statistical property or characteristic of the signal; and applying a selection algorithm to determine a depth and height of a new recording region within the subject from which to collect ultrasound signals for measuring fetal heart rate, the selection algorithm being configured to select the new recording region based on the determined statistical structure metric for the trial depth region and based on maximizing the statistical structure metric of signals acquired from the new recording region, in, The statistical structure measure of the signal includes one or more of the following: one or more characteristics of a probability density function of the signal; one or more characteristics of a power spectral density function of the signal; a measure of instability of the variance of the signal; Approximations for kurtosis or its derivatives; approximations for negentropy; approximations for autocovariance; and approximations for autocorrelated kurtosis.

2. The processing unit according to claim 1, wherein: The selection algorithm includes collecting ultrasound signals from a plurality of trial depth regions and determining the defined statistical structure metric for each trial region signal.

3. The processing unit according to claim 2, wherein: The selection algorithm comprises a comparison of the statistical structure measures derived for each trial region signal.

4. The processing unit according to claim 3, wherein: The comparison comprises comparing the statistical structure measures for ultrasound signals that are different from each other and / or comprises comparing the statistical structure measure for each trial depth region with a further measure derived for a reference depth region signal.

5. The processing unit according to claim 2, wherein: The selection algorithm comprises an iterative process comprising iteratively collecting signals from successive trial depth regions, deriving a statistical structure metric for each signal, and comparing the derived metric with a metric derived for one or more previously trial windows.

6. The processing unit according to any one of claims 1 to 5, wherein: The ultrasound processing unit can be operatively coupled to the ultrasound transducer unit when in use for acquiring the Doppler ultrasound data and for adjusting acquisition settings of the transducer unit to adjust the depth region from which ultrasound signals are acquired.

7. An ultrasonic device (70), comprising: An ultrasonic processing unit as claimed in any one of claims 1 to 6; as well as One or more ultrasound transducers (76) operatively coupled to the ultrasound processing unit for providing the Doppler ultrasound data to the ultrasound processing unit and optionally having adjustable acquisition settings for fixing a depth window of the acquired ultrasound data.

8. The ultrasonic device according to claim 7, wherein: The apparatus comprises an ultrasound probe unit, which contains the ultrasound processing unit and the one or more ultrasound transducers.

9. A patient monitoring system comprising: An ultrasonic processing unit as claimed in any one of claims 1 to 6; as well as A connection interface is used to connect to the ultrasound transducer unit when in use.

10. The patient monitoring system of claim 9, further comprising an ultrasound transducer unit coupled to the connection interface.

11. A patient monitoring system as claimed in claim 9 or 10, further comprising a controller adapted to control the acquisition of ultrasound data by the connected transducer unit when in use.

12. An ultrasound processing method for isolating individual heart rate sources within received Doppler ultrasound data in fetal monitoring, the method comprising: receiving input Doppler ultrasound data corresponding to at least one trial depth region within a uterine region of a subject, the region having a defined height and depth; extracting an ultrasound signal from the ultrasound data corresponding to the at least one trial depth region and determining a defined statistical structure metric of the signal, the statistical structure metric corresponding to an inherent statistical property or characteristic of the signal; applying a selection algorithm to determine a depth and height of a new recording region within the subject from which to collect ultrasound signals for measuring fetal heart rate, the selection algorithm being configured to select the new recording region based on the determined statistical structure metric for the trial depth region and based on maximizing the statistical structure metric of signals acquired from the new recording region, Wherein, the statistical structure metric of the signal includes one or more of the following: one or more characteristics of a probability density function of the signal; one or more characteristics of a power spectrum density function of the signal; a metric of instability of the variance of the signal; Approximations for kurtosis or its derivatives; approximations for negentropy; approximations for autocovariance; and approximations for autocorrelated kurtosis.

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