Systems and methods for biosignal-based neural monitoring

Abstract

The present disclosure provides a method for neural monitoring based on a biological signal, the method including monitoring and analyzing the biological signal to identify the location of an autonomic nerve associated with a stimulus-evoked muscle response of smooth muscle of a target organ, wherein the analyzing includes signal analysis of the biological signal to obtain time-domain signal characteristics and time-frequency-domain signal characteristics, and determining whether the biological signal is representative of the stimulus-evoked muscle response based thereon.

Description

[Technical Field]

[0001] FIELD OF THE INVENTION The present invention relates to a method, a medical system, a computer program product, and a computer-readable medium for biosignal-based neuromonitoring. [Background technology]

[0002] Technical background Surgical interventions in the pelvic region (e.g., low anterior resection in the area of ​​the rectum, bladder, or internal genitalia) carry a high risk of injury to the autonomic and somatic pelvic nerves. The consequences of pelvic nerve injury, particularly to the autonomic nerves, can include postoperative fecal and urinary disorders (bowel and bladder disorders) and sexual dysfunction. This can occur, for example, after low anterior resection (LAR) with total mesorectal excision (TME) for rectal cancer. Thus, because the pelvic nerves provide function to innervated organs, including the bladder, rectum, and genitalia, nerve preservation is important for patients' functional outcomes and quality of life.

[0003] Therefore, to avoid nerve damage, it is important to accurately locate nerves, including autonomic nerves, which are particularly difficult to identify.

[0004] The complexity of the pelvic nerve structure is brought about, inter alia, by very thin nerve fibers, the largely autonomous part of the plexus, and individual variations in nerve position.

[0005] Distinguishing nerves from surrounding tissues, particularly adipose, connective, and scar tissue, can be extremely difficult without technical assistance, even with extensive knowledge of neuroanatomy and neurophysiology.

[0006] The goal of intraoperative neuromonitoring of the pelvic nerves is to provide a tool that offers technical assistance in identifying the pelvic autonomic nerves.

[0007] Currently, the physical principle of neural discrimination in neuromonitoring is based on direct stimulation of nerves and electrophysiological recording of the target organ's response. By evaluating the stimulation response, conclusions can be made about the nerve's pathways and function.

[0008] However, neuromonitoring of the autonomic nervous system poses challenges because known neuromonitoring techniques for monitoring the motor and sensory nervous systems, such as electromyography (EMG) or stimulus and evoked potential (EP) recording, cannot be directly transferred to the autonomic nervous system due to differences in excitability and stimulus response.

[0009] Specifically, the striated muscles of a motor unit contract with a latency of several milliseconds after a stimulation pulse is applied to the innervating nerve. The refractory period is several milliseconds. In contrast, excitation of the autonomic nervous system does not result in a stimulation-pulse-synchronized muscle action potential. Therefore, currently known techniques suitable for the motor and sensory nervous systems cannot be easily applied to the autonomic nervous system.

[0010] The present invention aims to provide a method, a medical system, a computer program product and a computer readable medium that make it possible to overcome at least some of the problems identified above.

[0011] In particular, it is an object of the present invention to provide techniques or tools for reliably locating autonomic nerves.

[0012] In the following, aspects, examples and exemplary steps of the present invention, as well as embodiments thereof, are disclosed. Various exemplary features of the present invention can be combined in accordance with the present invention where technically suitable and feasible. Summary of the Invention [Means for solving the problem]

[0013] Illustrative and concise description of the invention Certain features of the present invention are briefly discussed below, but this should not be understood to limit the invention to only the features or combination of features discussed in this section.

[0014] The present invention provides a method, a medical system, a computer program product and a computer readable medium as set out in the independent claims. Preferred embodiments are set out in the dependent claims.

[0015] The present disclosure provides, among other things, a method for neural monitoring based on a biological signal, the method including monitoring and analyzing the biological signal to identify the location of an autonomic nerve associated with a stimulus-evoked muscle response of smooth muscle of a target organ, the method including signal analysis of the biological signal to obtain time-domain signal characteristics and time-frequency-domain signal characteristics, and determining based thereon whether the biological signal is representative of a stimulus-evoked muscle response of smooth muscle of the target organ.

[0016] Overview of the Invention In this section, general features of the invention are described by way of example with reference to possible embodiments of the invention.

[0017] The present disclosure provides a method for neural monitoring based on biological signals, the method including monitoring and analyzing biological signals to identify the location of autonomic nerves associated with stimulus-evoked muscle responses of smooth muscle of a target organ.

[0018] More specifically, analyzing the biological signal may involve, for example, analyzing the shape of the biological signal. For example, analyzing the biological signal may detect a shape characteristic of a stimulus-evoked muscle response of smooth muscle.

[0019] The method may enable the location of an autonomic nerve through neural stimulation of the autonomic nerve, i.e., by applying stimulation to tissue at a target location. The neural stimulation causes a stimulation-evoked muscle response in smooth muscle. Therefore, analyzing the biosignal to detect the stimulation-evoked muscle response makes it possible to determine whether neural stimulation of the autonomic nerve occurs when stimulation is applied to tissue at the target location, thereby enabling the location of the autonomic nerve to be identified. Specifically, the method may include distinguishing whether a feature of the biosignal represents a stimulation-evoked muscle response or whether it is due to artifact or other muscle activity or movement, for example, caused by organ movement and / or respiration.

[0020] The stimulation may be electrical stimulation, for example having a square wave signal, which may be selectively applied to the tissue by monopolar or bipolar electrodes, and may specifically be applied to the tissue by a handheld probe.

[0021] As will be seen in more detail below, the stimulus-evoked muscle response may be, for example, a stimulus-evoked muscle contraction. The target organ may be, for example, the rectum or bladder.

[0022] The biosignal may be, for example, an impedance signal obtained by measuring muscle impedance, or may be, for example, a bladder pressure signal obtained by measuring bladder pressure.

[0023] It should be understood that the method may be performed simultaneously on several different bio-signals, such as a first and second bio-signal, which may be a bladder pressure signal and an impedance signal, respectively, or may be different impedance signals, such as a rectal impedance and a bladder impedance.

[0024] When performing the method for different biological signals, the same stimulus may optionally be used. Acquisition may be performed separately for each of the different biological signals. Therefore, each of the steps of the method described in this disclosure for a biological signal may be performed for each of the different (e.g., first and second) biological signals. This may improve reliability.

[0025] According to the present disclosure, the method, in particular, the analyzing includes performing a time-domain signal analysis of the biological signal to obtain one or more time-domain signal characteristics, the biological signal being based on measurement data obtained by a neuromonitoring device.

[0026] The biological signal based on the measurement data obtained by the neural monitoring device should be understood broadly. This may involve the biological signal corresponding to the signal output by the neural monitoring device. Alternatively, the biological signal may be obtained by preprocessing the output data from the neural monitoring device, for example, by filtering and / or normalizing the output data from the neural monitoring device. Further details are outlined below. The measurement data output by the neural monitoring device may be impedance measurement data from a smooth muscle impedance measurement or cystometry data.

[0027] Time-domain signal analysis should be broadly understood as the analysis of a biological signal in the time domain. Time-domain signal characteristics should be broadly understood and may include any characteristics that can be derived from a biological signal in the time domain. They may include characteristics derived from the biological signal itself, and / or from the derivative of the biological signal, and / or from the integral of the biological signal. These are described in more detail below.

[0028] According to the present disclosure, the method, in particular, analyzing includes performing a time-frequency domain signal analysis of the biological signal to obtain one or more time-frequency domain signal characteristics.

[0029] Time-frequency domain signal analysis should be broadly understood as the analysis of a biological signal in the time-frequency domain. Time-frequency domain signal characteristics should be broadly understood and may include any characteristics that can be derived from a biological signal in the time-frequency domain. They may include characteristics derived from a time-frequency transform of the biological signal itself and / or a time-frequency transform of a derivative of the biological signal. For example, the transform coefficients of the time-frequency transform, particularly their magnitudes, may be analyzed as part of the time-frequency domain signal analysis. For example, a wavelet transform (WT), particularly a continuous wavelet transform (CWT), a discrete wavelet transform (DWT), or a short-time Fourier transform (STFT), may be used for the time-frequency transform, and their respective transform coefficients may be analyzed as part of the time-frequency domain signal analysis.

[0030] According to the present disclosure, the method, in particular, analyzing includes determining whether the biological signal represents a stimulus-evoked muscle response based on time-domain signal characteristics and time-frequency-domain signal characteristics.

[0031] For example, the criteria may be related to the shape of the biosignal in the time domain, such as its change from a base level, the onset of the change, the maximum amplitude of the change, the time from the onset of the change to reaching the maximum amplitude of the change, etc.

[0032] The changes in the biosignal may represent biosignal features that represent a stimulus-evoked muscle response of smooth muscle, or may be due to artifact or other muscle activity.

[0033] Analyzing the biological signal may include determining candidate features, e.g., in the time domain and / or the time-frequency domain, and determining which of the candidate features are representative of a stimulus-evoked muscle response of the smooth muscle, thereby distinguishing artifacts and non-stimulus-evoked muscle activity from the stimulus-evoked muscle response of the smooth muscle, as described in more detail below.

[0034] According to the present disclosure, the method includes outputting an indication that a stimulus-evoked muscle response has been detected if the biological signal is determined to be representative of a stimulus-evoked muscle response.

[0035] In other words, an output may be provided indicating that a nerve, the stimulation of which produces a stimulus-evoked muscle response in the smooth muscle of the target organ, has been detected at the location where the stimulus was applied.

[0036] As can be seen from the above, the location of autonomic nerves can be identified by the methods of the present disclosure.

[0037] Thus, in summary, the disclosed method allows for reliable localization of autonomic nerves.

[0038] In contrast to the motor nervous system, stimulation of the autonomic nervous system can generally result in modulation of smooth muscle activity by triggering action potential spikes. Smooth muscle activity is characterized by rhythmic, spontaneous changes in membrane potential, called slow waves, with a frequency of, for example, 3-15 per minute. Spikes are triggered only when the slow waves exceed a threshold potential, resulting in a sluggish smooth muscle response to stimulation, particularly contraction. This slow smooth muscle response, e.g., contraction, typically occurs several seconds after a stimulation pulse is applied to the tissue. Therefore, the timing of stimulation alone is not sufficient to reliably correlate biosignal changes with the stimulation, making the disclosed method even more advantageous.

[0039] Obtaining measurement data underlying the biosignal may include impedance measurements, and thus the biosignal may be an impedance signal. More specifically, for example, measuring the impedance of pelvic smooth muscle during direct stimulation of tissue that may include pelvic autonomic nerves may enable localization of the pelvic autonomic nerves.

[0040] For example, the neuromonitoring device may comprise an impedance measuring device or a cystometry device.

[0041] As will be seen below, the methods of the present disclosure may use a direct nerve stimulator configured to apply the stimulation. Alternatively, or in addition, the methods of the present disclosure may use a display device configured to output an indication that a stimulation-evoked muscle response has been detected.

[0042] As an example, according to the present disclosure, at least two electrodes for impedance measurement may be placed per organ (e.g., bladder and / or rectum), and direct nerve stimulation may be performed using a handheld probe. This configuration and method of the present disclosure allows for the measurement of (slow) smooth muscle responses, e.g., contractions, induced by the stimulation.

[0043] It should be noted that the present disclosure provides a method that may also enable automatic differentiation of biosignals representing stimulus-evoked muscle responses from signal features caused by organ movement or respiration. While these differ in signal shape and frequency, reliably distinguishing between them is extremely difficult, and visually distinguishing between them, especially by a human operator, requires skill, suffers from review delays, and is highly error-prone. The signal analysis of the present disclosure makes it possible to overcome these challenges.

[0044] Thus, the present disclosure provides methods that address at least some of the above-mentioned problems.

[0045] According to the present disclosure, the method may include distinguishing between bio-signal features representing artifacts and bio-signal features representing stimulus-evoked muscle responses based on time-domain signal characteristics and time-frequency-domain signal characteristics. Optionally, the distinguishing may further include distinguishing bio-signal features representing stimulus-evoked muscle responses from bio-signal features that do not represent a significant bio-signal response, specifically a significant impedance change or a significant bladder pressure change. Tissue impedance may refer to tissue impedance.

[0046] That is, the method may include identifying features in the biological signal and classifying the features into multiple classes, one class being features representative of stimulus-evoked muscle responses and another class being features representative of artifacts.

[0047] According to the present disclosure, the time-domain signal characteristics may include the maximum amplitude within the biosignal waveform, specifically the impedance change or bladder pressure change, relative to a base level of the biosignal, specifically the impedance base level or bladder pressure base level.

[0048] The base level of the biosignal may be the initial state of the biosignal waveform before any transient signal characteristics or signal changes, for example, before any muscle response.

[0049] Alternatively, or in addition, the time-domain signal characteristics may include an onset latency of a temporal signal change within the biosignal waveform. The onset latency may be the time at which the biosignal change begins after application of a stimulus. To determine the onset latency, data regarding the applied stimulus may be used as input, e.g., data retrieved from a data storage device or data received, e.g., in real time, from a device applying the stimulus.

[0050] Alternatively or additionally, the time-domain signal characteristics may include a slope of a transient signal change in the biosignal and / or a time to reach a maximum slope of the transient signal change in the biosignal. Alternatively or additionally, the time-domain signal characteristics may include a duration of a transient signal change in the biosignal from the onset of the transient signal change and / or regression coefficients, e.g., linear, polynomial, or cubic spline.

[0051] According to the present disclosure, the time-frequency domain signal characteristics may include the magnitude of the transform coefficients, specifically the localization of the magnitude extrema of the transform coefficients, and / or the absolute or relative values ​​of the magnitude extrema. The time-frequency domain features described above may include magnitude extrema. As explained in the context of the foregoing features, they may be differentiated, i.e., classified, as representing a stimulus-evoked muscle response or as representing an artifact or an insignificant biosignal change.

[0052] As an example, only extrema within a particular time-frequency region, and / or extrema that have at least a predetermined minimum distance from neighboring extrema, and / or extrema that have at least a predetermined minimum magnitude, may be classified as representing a stimulus-evoked muscle response.

[0053] According to the present disclosure, time-frequency domain signal analysis may include transforming a biological signal or a first derivative of a biological signal into the time-frequency domain and analyzing one or more transform coefficients, in particular the limits of the magnitude extrema of the transform coefficients and / or the absolute and / or relative values ​​of the magnitude extrema of the transform coefficients.

[0054] According to the present disclosure, the time-frequency domain signal analysis may include analysis of the transform coefficients of a wavelet transform WT, for example, CWT or DWT, or analysis of the transform coefficients of an STFT.

[0055] The time-frequency domain signal analysis may specifically include selecting a window function and scaling the window function, e.g., stretching and / or compressing the window function, and / or shifting the window function. The time-frequency domain signal analysis may further include obtaining one or more transform coefficients for each of the plurality of samples. The time-frequency domain signal analysis may further include analyzing at least a subset of the one or more coefficients, specifically analyzing the magnitude of one or more coefficients as a function of the scaling and / or shifting coefficients. As an example, the time-frequency domain signal analysis may include analyzing the coefficients as described above, e.g., by analyzing extrema in the magnitude of the coefficients.

[0056] In the present disclosure, the samples may be analog-to-digital converted instantaneous measurements obtained by a neuromonitoring device configured to obtain measurement data, specifically an impedance measuring device and / or a pressure measuring device.

[0057] According to the present disclosure, analyzing one or more transform coefficients may include representing the magnitude of one or more of the transform coefficients as a function of time and frequency, specifically representing CWT, DWT, or STFT coefficients as a function of shifting and / or scaling factors.

[0058] Analyzing the one or more transform coefficients may further include identifying candidate features in the function, specifically identifying local maxima and / or minima of the function as candidate features.

[0059] Analyzing the one or more transform coefficients may further include determining, for at least one of the candidate features, whether the candidate feature is representative of a stimulus-evoked muscle response, which determining is based on one or more of the time-domain signal characteristics and / or one or more of the time-frequency-domain signal characteristics, in particular the location, i.e., time and frequency / wavelet scale, and / or magnitude, of the candidate feature.

[0060] For example, candidate features having a magnitude below a predetermined threshold (a magnitude-based threshold) may be discarded. Alternatively or additionally, candidate features, particularly duplicate candidate features, having a time distance below a predetermined threshold (a time-based threshold) may be discarded, and the candidate feature having the largest magnitude among the duplicate candidate features may be retained as the resulting candidate feature.

[0061] Alternatively, or in addition, the wavelet scale corresponding to the resulting candidate features may be the primary criterion for distinguishing between stimulus-evoked muscle responses and artifacts. Candidate features representing artifacts may be separated from candidate features representing stimulus-evoked muscle responses based on a predetermined threshold wavelet scale. Resulting candidate features whose corresponding wavelet scale falls within a predetermined time interval and whose corresponding wavelet scale exceeds the predetermined threshold wavelet scale may be compared in magnitude. Resulting candidate features whose corresponding wavelet scale falls below the predetermined threshold wavelet scale and whose corresponding magnitude is less than the magnitude of the resulting candidate features whose corresponding wavelet scale exceeds the predetermined threshold wavelet scale may be discarded.

[0062] According to the present disclosure, the time-frequency domain signal analysis may include analyzing one or more transform coefficients taking into account time-domain signal characteristics and / or information derived from the time-domain signal characteristics. Specifically, determining whether a candidate feature is representative of a stimulus-evoked muscle response may be performed taking into account time-domain signal characteristics and / or information derived from the time-domain signal characteristics.

[0063] For example, candidate features obtained by time-frequency domain signal analysis may be discarded based on time-domain signal characteristics, e.g., if it is determined that the time-domain signal characteristics exclude the presence of features representing stimulus-evoked muscle responses where the candidate features were identified in the time-frequency domain.

[0064] Thus, reliability may be improved with respect to certain false positives.

[0065] According to the present disclosure, the time-frequency domain signal analysis may include determining a time window corresponding to a selected portion of the time-domain bio-signal, specifically a portion after the onset of the bio-signal change, which may be selected based on the slope of the time-domain bio-signal.

[0066] The time-frequency domain signal analysis may include determining whether a candidate feature represents a stimulus-evoked muscle response based at least on the determined time window. Specifically, the candidate feature being within the time window may be one of one or more criteria for determining whether the candidate feature represents a stimulus-evoked muscle response. For example, the candidate feature being outside the time window may lead to discarding the candidate feature. This allows for the rapid discarding of candidate features that cannot clearly represent a stimulus-evoked muscle response, for example, because they are too early to expect a stimulus-evoked muscle response.

[0067] The disclosed method may include performing a continuous wavelet transform (CWT) of the biosignal or of a first derivative of the biosignal, particularly the first derivative of the impedance signal. Alternatively, the disclosed method may include performing a discrete wavelet transform (DWT) of the biosignal or of a first derivative of the biosignal, particularly the first derivative of the impedance signal. Alternatively, the disclosed method may include performing a short-time Fourier transform (STFT) of the biosignal or of a first derivative of the biosignal, particularly the first derivative of the impedance signal.

[0068] Each of these different transformations results in transformation coefficients suitable for the above analysis and provides a solution that allows a particularly reliable determination of whether a biosignal is representative of a stimulus-evoked muscle response.

[0069] According to the present disclosure, biosignal monitoring and / or analysis may be triggered by receiving an indication that stimulation is being applied to tissue. By way of example, biosignal monitoring may be performed intermittently, e.g., only when a stimulation-evoked muscle response is expected. By way of example, monitoring may be triggered by data indicating that stimulation has been applied or is about to be applied. Such data may be received, for example, from a device used to apply the stimulation.

[0070] According to the present disclosure, the time-domain signal analysis and / or the time-frequency-domain signal analysis may be performed taking into account one or more characteristics of the stimulus, particularly the onset time, duration, and / or amplitude of the stimulus. The characteristics of the stimulus may be determined from data received from a device used to apply the stimulus and / or may be stored data. The characteristics of the stimulus may include and / or be derived from, for example, operating parameter settings of the device used to apply the stimulus.

[0071] The disclosed method may include preprocessing the output signal of the neuromonitoring device to obtain a biosignal, which preprocessing may include normalizing the output signal to a base level of the biosignal, particularly to a base level impedance or a base level bladder pressure, and / or applying a low pass filter, particularly after normalizing the output signal and / or after performing sweep extraction. Impedance may refer to tissue impedance.

[0072] As described above, the base level of a biosignal may be an initial state of the biosignal waveform before any transient signal characteristics or signal changes, e.g., before any muscle response. For example, if the biosignal is an impedance signal, the base level impedance may be the level of the impedance signal at an initial state of the impedance signal waveform before any transient signal characteristics or signal changes, e.g., before any muscle response. The impedance signal may represent not only muscle impedance, but also device, lead, electrode, other tissue, etc.

[0073] Using the example of impedance, which may equally apply to bladder pressure signals, normalization may be performed so that the normalized signal is U(t) / U(0), where U(t) is the output signal at time t, e.g., the measured impedance or bladder pressure, and U(0) is the measured base level of the output signal, e.g., the base level impedance or base level bladder pressure. Thus, a dimensionless signal proportional to the actual change, e.g., the impedance change or bladder pressure change in the tissue, may be obtained.

[0074] As mentioned above, preprocessing may include filtering. As an example, a low-pass filter may be applied to the output signal or normalized output signal of the neuromonitoring device. Such a filter may remove or reduce artifacts, for example, in the case of impedance measurement artifacts from changes in membrane potential, noise, or respiration. This may result in a smoother signal.

[0075] The filtering may be performed using a shape-preserving filter, which preserves the characteristic shape of the biological signal caused by the stimulus-evoked muscle response, such as changes in impedance or bladder pressure, allowing for improved signal analysis. A digital filter, such as an IIR filter, may be used.

[0076] The methods of the present disclosure may include determining an integral and / or derivative, e.g., a first derivative, of a biological signal, and the results of the integral and / or derivative may be used as input for time-domain signal analysis and / or time-frequency domain signal analysis.

[0077] For example, the first derivative may be transformed into the time-frequency domain for time-frequency signal analysis, which allows the signal to be specifically analyzed based on its shape, for example, to identify whether a distinctive shape is present in the stimulus-evoked muscle response.

[0078] Alternatively, the (original) biosignal may be transformed into the time-frequency domain for time-frequency signal analysis using the expected temporal signal change waveform as a window function, which allows the signal to be specifically analyzed based on its shape, for example, to identify whether there is a characteristic shape in the stimulus-evoked muscle response.

[0079] According to the present disclosure, the time domain signal analysis and the time-frequency domain signal analysis, and optionally the integration and / or differentiation of the biological signal, may be performed continuously or for multiple sweeps of the biological signal, in particular for each of consecutive sweeps of the biological signal.

[0080] When the time-domain signal analysis and the time-frequency domain signal analysis, and optionally the integration and / or differentiation of the biological signal, are performed for each of multiple sweeps of the biological signal, in particular for each of consecutive sweeps of the biological signal, the time-domain signal characteristics and the time-frequency domain signal characteristics may be determined separately and optionally independently for each sweep, in which case some or all of the pre-processing steps may optionally be performed separately and optionally independently for each sweep.

[0081] A sweep should be understood broadly and may be a portion of a signal, for example, each sweep having a predetermined duration.

[0082] According to the present disclosure, outputting an indication that a stimulus-evoked muscle response has been detected may include providing an audio and / or visual and / or tactile output to the user. For example, a warning may be displayed on a display device, e.g., as part of a GUI, and / or a visual, tactile and / or audio warning signal may be output. Any suitable means may be used for this purpose, e.g., known indicator devices.

[0083] According to the present disclosure, as described above, the biosignal may be an impedance signal or a bladder pressure signal.

[0084] According to the present disclosure, as described above, the stimulus-evoked muscle response may be a muscle contraction.

[0085] The method of the present disclosure may include obtaining measurement data, particularly continuously, by a neuromonitoring device. Specifically, the method of the present disclosure may include measuring, particularly continuously, the impedance of smooth muscle of a target organ by an impedance measuring device. Alternatively or additionally, the method of the present disclosure may include measuring, particularly continuously, bladder pressure by a pressure measuring device.

[0086] The disclosed methods may include applying a stimulus to a portion of tissue, particularly by a handheld device. The stimulus may be, for example, a square wave signal. The method may also optionally include providing information regarding characteristics of the stimulus, such as pulse length or amplitude, as input for analysis of the biosignal. Alternatively, or in addition, the method may include outputting a trigger signal to trigger monitoring of the biosignal, for example, before or when applying the stimulus.

[0087] The disclosed method may include, in response to determining that the biological signal represents a stimulus-evoked muscle response caused by a stimulus applied to the portion of the tissue, outputting an indication that the portion of the tissue includes a nerve associated with smooth muscle.

[0088] Outputting an indication that a stimulus-evoked muscle response has been detected and / or outputting an indication that the portion of tissue contains nerves associated with smooth muscle may be performed immediately upon determining that the biological signal is indicative of a stimulus-evoked muscle response.

[0089] That is, the output may inform the user in near real time when a stimulus-evoked muscle response occurred, and therefore when the applied stimulus stimulated the autonomic nerve.

[0090] According to the present disclosure, the method steps are performed on at least two biosignals, the biosignals being obtained for different target organs to identify the location of autonomic nerves associated with stimulation-evoked muscle responses of smooth muscles of each of the different target organs.

[0091] For example, according to the present disclosure, the method may include obtaining measurement data, e.g., impedance data, for at least two different organs. For example, the impedance of the bladder and the impedance of the rectum may be obtained. Thus, at least two biosignals may be obtained, e.g., one based on the measured impedance of the bladder and another based on the measured impedance of the rectum. The steps of the method of the present disclosure performed after obtaining the measurement data for the different target organs may be performed for each of these biosignals.

[0092] This may not require the application of separate stimuli to the tissue: specifically, a common stimulus may be applied and each of the two or more biosignals may be analyzed to determine whether it represents a stimulus-evoked muscle response.

[0093] Some or all of the steps of monitoring and analyzing biological signals to identify the location of autonomic nerves associated with stimulation-induced muscle responses of smooth muscles of a target organ according to the present disclosure, particularly all steps of the method according to the present disclosure, may be performed automatically.

[0094] The present disclosure also provides a medical system configured to perform the method of the present disclosure, i.e., one or more, particularly all, of the steps of the method of the present disclosure.

[0095] In particular, the medical system may comprise a computing system configured to perform and / or control one or more, in particular all, of the steps of the methods of the present disclosure. The computing system may comprise processing means configured to perform monitoring and analysis of the biological signals.

[0096] The computing system may also include one or more output devices, such as a display device or other visual output device and / or an audio output device and / or a tactile output device, such as a vibration generating device, configured to perform the step of outputting an indication that a stimulus-evoked muscle response has been detected. By way of example, a suitable signaling device may be used to perform the step of outputting the indication.

[0097] The medical system of the present disclosure may include a device, particularly a handheld device, such as a handheld probe, configured to apply stimulation to a portion of tissue. For example, the device may include an instrument consisting of a handpiece and an anode and / or cathode for delivering stimulation from a neurostimulation device to the portion of tissue.

[0098] Alternatively or additionally, the medical system of the present disclosure may include a neuromonitoring device configured to acquire measurement data, in particular an impedance measuring device configured to measure, in particular continuously, the impedance of the smooth muscle of the target organ, or a pressure measuring device configured to measure, in particular continuously, the bladder pressure.

[0099] As described above, the present disclosure may include acquiring measurement data, e.g., impedance data, for at least two different organs. Accordingly, the medical system may be configured to acquire measurement data for at least two target organs, e.g., by separate neuromonitoring devices or by a common neuromonitoring device. In particular, the medical system may be configured to acquire bladder impedance and rectal impedance. Thus, at least two biosignals may be obtained. The steps of the method of the present disclosure, which are performed after acquiring measurement data for different target organs, may be performed for each of the biosignals.

[0100] As an example, the neuromonitoring device may include at least a pair of electrodes configured to be attached to each target organ to measure impedance.

[0101] The present disclosure also provides a computer program product comprising instructions that, when executed by a computer, cause the computer to perform and / or control the methods of the present disclosure, i.e., one or more, particularly all, of the steps of the methods of the present disclosure.

[0102] The present disclosure also provides a computer-readable medium comprising instructions that, when executed by a computer, cause the computer to perform and / or control the methods of the present disclosure, i.e., one or more, particularly all, of the steps of the methods of the present disclosure.

[0103] For the sake of completeness, it should be noted that the present invention does not require, or specifically does not include, or involve invasive steps amounting to substantial physical interference with the body, which require professional medical expertise to perform and which, even if performed with the requisite professional care and expertise, entail substantial health risks. For example, the present invention does not include steps of positioning a medical implant to secure the medical implant to an anatomical structure, or steps of securing a medical implant to an anatomical structure, or steps of preparing an anatomical structure to secure a medical implant to an anatomical structure. More specifically, the present invention does not require, or specifically does not include, or involve any surgical or therapeutic activity. Instead, the present invention is directed to locating nerves, if applicable, in a given tissue portion. For this reason alone, no surgical or therapeutic activity, or specifically no surgical or therapeutic step, is required or suggested by performing the present invention.

[0104] The features and advantages discussed above in the context of the methods apply equally to the medical systems, computer program products, and computer-readable media of the present disclosure.

[0105] The present disclosure also relates to the use of the medical system or any embodiment thereof for localizing autonomic nerves, including, for example, determining whether a selected portion of tissue contains an autonomic nerve.

[0106] definition This section provides definitions of certain terms used in this disclosure and also forms part of this disclosure.

[0107] Computer-Implemented Method A method according to the invention is, for example, a computer-implemented method. For example, all steps of a method according to the invention, or only a portion of the steps (i.e., less than the total number of steps), may be performed by a computer (e.g., at least one computer). One embodiment of a computer-implemented method is the use of a computer to perform a data processing method. One embodiment of a computer-implemented method is a method relating to computer operation, in which a computer is operated to perform one, several, or all steps of the method.

[0108] A computer comprises, for example, at least one processor and, for example, at least one memory for (technical) processing of data, for example, electronically and / or optically. The processor is, for example, made of a substance or composition that is a semiconductor, for example, at least partially n- and / or p-doped semiconductor, for example, II, III, IV, V, VI semiconductor material, for example, (doped) silicon and / or gallium arsenide. The described calculating or determining step is, for example, performed by a computer. The determining or calculating step is, for example, a step of determining data within the framework of a technical method, for example, within the framework of a program. A computer is, for example, any kind of data processing device, for example, an electronic data processing device. A computer may be a device generally considered as such, for example, a desktop PC, a notebook, a netbook, etc., but may also be any programmable device, for example, a mobile phone or an embedded processor. A computer may, for example, comprise a system (network) of "sub-computers," each of which corresponds to a computer in itself. The term "computer" also includes cloud computers, for example, cloud servers. The term "cloud computer" includes, for example, a system of at least one cloud computer and a cloud computer system comprising a plurality of operatively interconnected cloud computers, such as a server farm. Such cloud computers are preferably connected to a wide area network, such as the World Wide Web (WWW), and are located within a so-called cloud of computers all connected to the WWW. Such an infrastructure is used in "cloud computing" to describe computation, software, data access, and storage services without the end user needing to know the physical location and / or configuration of the computers delivering a particular service. For example, the term "cloud" is used in this regard as a metaphor for the Internet (World Wide Web).For example, a cloud provides a computing infrastructure as a service (IaaS). A cloud computer can function as a virtual host for an operating system and / or a data processing application used to execute the method of the present invention. A cloud computer is, for example, the Elastic Compute Cloud (EC2) provided by Amazon Web Services™. The computer is equipped with an interface, for example, to receive or output data and / or perform analog-to-digital conversion. The data can be, for example, data representing physical properties and / or data generated from technical signals. The technical signals can be, for example, generated by (technical) detection devices (such as, for example, devices for detecting marker devices) and / or (technical) analysis devices (such as, for example, devices for performing (medical) imaging methods), and can be, for example, electrical or optical signals. The technical signals can represent, for example, data received or output by a computer. The computer is preferably operably coupled to a display device that allows information output by the computer to be displayed, for example, to a user. One example of a display device is a virtual or augmented reality device (also called virtual or augmented reality glasses) that can be used as "goggles" for navigation. A specific example of such augmented reality glasses is Google® Glass (a trademark of Google, Inc.). An augmented or virtual reality device can be used both to input information into a computer through user interaction and to display information output by the computer. Another example of a display device is a standard computer monitor with a liquid crystal display operably coupled to a computer to receive display control data from the computer, for example, to generate signals used to display image information content on the display device. A specific embodiment of such a computer monitor is a digital light box. One example of such a digital light box is Buzz®, a product of Brainlab AG.The monitor may also be the monitor of a portable, eg handheld, device such as a smartphone, personal digital assistant, or digital media player.

[0109] The invention also relates to a program, which when executed on a computer causes the computer to perform one or more or all of the steps of the methods described herein, and / or a program storage medium on which said program is stored (in particular in non-transitory form), and / or a computer comprising said program storage medium, and / or a (e.g. technically generated, physical, e.g. electrical) signal wave, e.g. a digital signal wave, carrying information representing a program, e.g. an said program, comprising code means adapted to perform, e.g. any or all of the steps of the methods described herein.

[0110] Within the framework of the present invention, computer program elements may be embodied by hardware and / or software (which includes firmware, resident software, microcode, etc.). Within the framework of the present invention, computer program elements may take the form of a computer program product, which may be embodied by a computer-usable, e.g., computer-readable, data storage medium, comprising computer-usable, e.g., computer-readable, program instructions, "code," or "computer program" embodied in said data storage medium, for use on or in connection with an instruction execution system. Such a system may be a computer, which may be a data processing device comprising means for executing computer program elements and / or programs according to the present invention, for example a digital processor (central processing unit or CPU) for executing the computer program elements, and optionally a volatile memory (e.g., random access memory or RAM) for storing data used by and / or generated by executing the computer program elements. Within the framework of the present invention, a computer usable, e.g., computer readable, data storage medium may be any data storage medium that can contain, store, communicate, propagate, or transport a program for use on or in connection with an instruction execution system, apparatus, or device. A computer usable, e.g., computer readable, data storage medium may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a propagation medium, such as the Internet.The computer-usable or computer-readable data storage medium may be, for example, paper or other suitable medium on which the program is printed, since the program can be captured electronically, for example, by optically scanning paper or other suitable medium, and then compiled, interpreted, or processed in an appropriate manner. The data storage medium is preferably a non-volatile data storage medium. The computer program product and any software and / or hardware described herein form various means for performing the functions of the present invention in exemplary embodiments. The computer and / or data processing device may include, for example, a guidance information device including means for outputting guidance information. The guidance information can be output to the user visually, for example, by visual indicating means (e.g., a monitor and / or lamp), acoustically, by acoustic indicating means (e.g., a speaker and / or digital audio output device), and / or tactilely, by tactile indicating means (e.g., a vibrating or vibration element incorporated in the appliance). For purposes of this document, a computer is, for example, a technical computer comprising technical, e.g., tangible, components, for example, mechanical and / or electronic components. Any device so referred to herein is a technological, eg, tangible, device.

[0111] Data Acquisition The expression "data acquisition" encompasses, for example, scenarios in which data is determined by a computer-implemented method or program (within the framework of a computer-implemented method). Determining data encompasses, for example, measuring physical quantities, converting the measurements into data, e.g., digital data, and / or calculating (and, for example, outputting) data by a computer, e.g., within the framework of a method according to the invention. The meaning of "data acquisition" also encompasses scenarios in which data is received or retrieved by a computer-implemented method or program (e.g., input to a computer-implemented method or program), e.g., from another program, a step of a previous method, or a data storage medium, for further processing, e.g., by the computer-implemented method or program. The generation of data to be acquired may, but need not, be part of a method according to the invention. Thus, the expression "data acquisition" can also mean, for example, waiting for the reception of data and / or receiving data. The received data may, for example, be input via an interface. The expression "data acquisition" may also mean that a computer-implemented method or program performs a step to (actively) receive or retrieve data from a data source, for example, from a data storage medium (e.g., ROM, RAM, database, hard drive, etc.) or via an interface (e.g., from another computer or network). The data acquired by the disclosed method or device may be acquired from a database located on a data storage device operable to the computer for data transfer between the database and the computer, for example, from the database to the computer. The computer acquires the data to use as input for the step of determining the data. The determined data may be output again to the same or another database and stored for later use.The database or the database used to implement the disclosed method can be located on a network data storage device or server (e.g., a cloud data storage device or server), or on a local data storage device (such as a mass storage device operatively connected to at least one computer executing the disclosed method). The data can be made "ready for use" by performing an additional step before the acquiring step, according to which the data is generated to be acquired. The data is, for example, detected or captured (e.g., by an analysis device). Alternatively, or in addition, the data is input according to an additional step, for example, via an interface. The generated data can be, for example, input (e.g., to a computer). The data can also be provided according to an additional step (preceding the acquiring step) by performing an additional step of storing the data in a data storage medium (e.g., ROM, RAM, CD, and / or hard drive), so that the data is ready for use within the framework of the method or program according to the present invention. Thus, the step of "acquiring data" can also include instructing a device to obtain and / or provide the data to be acquired. In particular, the obtaining step does not include an invasive step that amounts to a substantial physical interference with the body, requiring professional medical expertise to perform and that carries substantial health risks even when performed with the requisite professional care and expertise. In particular, the step of obtaining data, e.g., the step of determining data, does not include a surgical step, and in particular does not include a step of treating the human or animal body with surgery or therapy. In order to distinguish between the various data used by the present method, the data are denoted (i.e., referred to) as "XY data" or the like, and are defined in terms of the information they describe, and are therefore preferably referred to as "XY information" or the like.

[0112] Medical Workflow A medical workflow includes multiple workflow steps that are performed during a medical procedure and / or medical diagnosis. Workflow steps are typically, but not necessarily, performed in a predetermined order. Each workflow step, for example, represents a specific task, which can be a single action or a set of actions. Examples of workflow steps are capturing a medical image, positioning the patient, attaching a marker, performing a resection, moving a joint, placing an implant, etc.

[0113] BRIEF DESCRIPTION OF THE DRAWINGS The present invention will now be described with reference to the accompanying drawings, which provide background information and depict specific embodiments of the invention, although the scope of the invention is not limited to the specific features disclosed in the context of the drawings. [Brief explanation of the drawings]

[0114] [Figure 1] 1 illustrates a schematic diagram of a method according to the present disclosure. [Figure 2] 1 illustrates a schematic diagram of a medical system in which methods according to the present disclosure may be employed; [Figure 3] Illustrates biosignals from three different sweeps. [Figure 4] 10 illustrates another example of a biological signal. [Figure 5] 1 illustrates an example of a normalized biosignal. [Figure 6] Denotes a Ricker wavelet. [Figure 7] 1 illustrates the first derivative of a biosignal. [Figure 8a] 1 illustrates an impedance signal, a CWT scalogram in the time-frequency domain, and a 3D plot of the CWT scalogram. [Figure 8b] 1 illustrates an impedance signal, a CWT scalogram in the time-frequency domain, and a 3D plot of the CWT scalogram. [Figure 8c]1 illustrates an impedance signal, a CWT scalogram in the time-frequency domain, and a 3D plot of the CWT scalogram. [Figure 9] 1 shows a flow chart illustrating steps for determining the presence or absence of a biosignal representing a stimulus-evoked muscle response. [Figure 10] This is a flowchart following the flowchart of FIG. [Figure 11] 1 is a schematic diagram of a system according to the present disclosure. [Figure 12a] 1 illustrates a flow chart of an exemplary method. [Figure 12b] 1 illustrates a flow chart of an exemplary method. [Figure 12c] 1 illustrates a flow chart of an exemplary method. [Figure 12d] 1 illustrates a flow chart of an exemplary method. [Figure 12e] 1 illustrates a flow chart of an exemplary method. [Figure 12f] 1 illustrates a flow chart of an exemplary method. DETAILED DESCRIPTION OF THE INVENTION

[0115] Description of the embodiment FIG. 1 illustrates steps of a method for neural monitoring based on a biosignal, such as an impedance signal or a bladder pressure signal, according to the present disclosure.

[0116] The method includes monitoring and analyzing biological signals based on measurement data obtained by a neural monitoring device, such as an impedance measuring device or a cystometry device.

[0117] A portion of the biological signal may represent a stimulus-induced muscle response, in particular a contraction, of smooth muscle. Optionally, the method may include applying a stimulus to the tissue and determining whether a stimulus-induced muscle response is detected, which indicates that a nerve associated with the muscle response was stimulated when the stimulus was applied to the tissue.

[0118] Monitoring and analysis are performed to identify the location of autonomic nerves associated with stimulation-induced muscle responses in the smooth muscle of the target organ, i.e., by applying stimulation as described above, it can be determined whether or not there are autonomic nerves at the location of the stimulation.

[0119] The method, in particular analyzing the biological signal, may include step S11 of performing a time-domain signal analysis of the biological signal to obtain one or more time-domain signal characteristics, examples of which are provided in more detail below.

[0120] In particular, analyzing the biological signal may include a step S12 of performing a time-frequency domain signal analysis of the biological signal to obtain one or more time-frequency domain signal characteristics. The analysis may be performed on the biological signal itself and / or a derivative of the biological signal.

[0121] For example, a Continuous Wavelet Transform (CWT) may be performed on the biosignal or the first derivative of the biosignal. Alternatively, a DWT, an STFT, etc. may be used. The time-frequency domain signal analysis may include analyzing the transform coefficients, in particular their magnitudes, as will be explained in more detail in the context of Figures 8a to 8c.

[0122] Each of steps S11 and S12 may include sub-steps. Steps S11 and S12 or their sub-steps do not need to be performed in any particular order and may, for example, be performed sequentially or simultaneously.

[0123] In particular, analyzing the biological signal may include step S13 of determining whether the biological signal represents a stimulus-evoked muscle response based on time-domain signal characteristics and time-frequency domain signal characteristics.

[0124] Criteria may be associated with the shape of the biosignal. Determining whether the biosignal is representative of a stimulus-evoked muscle response may involve distinguishing between biosignal features that are representative of a stimulus-evoked muscle response, artifacts, or signal features that are not representative of a significant biosignal change.

[0125] The method of the present disclosure includes step S14 of outputting an indication that a stimulation-evoked muscle response has been detected if the biosignal is determined to be indicative of the stimulation-evoked muscle response. For example, a visual and / or audio output may be provided to the user. This output serves as an indication that the portion of tissue to which stimulation is being / has been applied contains nerves associated with the smooth muscle of the target organ. Therefore, the location of the autonomic nerves can be identified.

[0126] Optionally, if in step S15 a stimulation-evoked muscle response is not detected, the method may include outputting an indication that a stimulation-evoked muscle response was not detected, which serves as an indication that the stimulated portion of tissue does not contain nerves associated with smooth muscle of the target organ.

[0127] In optional step S21, measurement data is obtained by a neuromonitoring device. The measurements may include, for example, measuring the impedance of the smooth muscle of the target organ by an impedance measuring device or measuring bladder pressure by a pressure measuring device.

[0128] The measurements may be performed continuously. The measurement data may be used as a biological signal and therefore as input data for analyzing the biological signal. Alternatively, the measurement data may be pre-processed to obtain a biological signal, which is then used as input data for analyzing the biological signal.

[0129] In optional step S22, stimulation is applied to a portion of the tissue. Optionally, this may trigger monitoring and / or analysis of biosignals. Alternatively, monitoring and / or analysis may be performed regardless of whether stimulation is applied.

[0130] In optional step S23, the measurement data obtained by step S21 is preprocessed to obtain a biosignal, e.g., an impedance signal or a bladder pressure signal. Step S23 is shown to include a preprocessing step S23a of normalizing the signal, e.g., with reference to a base level or value of the biosignal, and a preprocessing step S23b of applying a filter, e.g., a low-pass filter.

[0131] For completeness, it should be noted that the steps outlined above may be performed for one target organ or for two or more target organs, each having a corresponding biosignal, in which case signal processing and analysis may be performed separately for each of the biosignals in the manner described above.

[0132] This does not require the application of separate stimuli to the tissue, and in particular, in preferred embodiments, a common stimulus may be used, and each of the two or more biosignals may be analyzed to determine whether it represents a stimulus-evoked muscle response.

[0133] FIG. 2 illustrates an exemplary medical system and exemplary organs and nerves that may be used to perform methods according to the present disclosure. A pelvic nerve and a hand probe are shown schematically. Additionally, the bladder and rectum are shown with impedance measurement electrodes of an impedance measurement device connected to each organ. In this example, the impedance measurement device is referred to as an impedance module and communicates with a computing system referred to as a main unit. The main unit may include a data processing component and a graphical user interface. Additionally, a direct nerve stimulator that applies stimulation to the nerve is connected to the main unit, and may include a hand probe that contacts tissue to apply stimulation.

[0134] As an example, the pelvic nerve is shown in the figure. As the schematic suggests, the nerve is very small and difficult to identify optically, making impedance-based identification of the presence of the nerve advantageous.

[0135] In the following, some examples of the steps of the above method are presented in more detail. Note that the following steps are described using an example in which the biosignal is an impedance signal. However, the biosignal may alternatively be a bladder pressure signal or some other signal representing a stimulus-evoked muscle response of the smooth muscle of the target organ. The steps described below may also be applied to these alternative biosignals in the described manner.

[0136] Furthermore, the following discussion is primarily based on the use of the CWT to perform time-frequency domain signal analysis. However, it should be understood that the DWT or STFT may be used instead, and that if the DWT or STFT is used, the analysis can be performed in a similar manner by analyzing the respective transform coefficients in a manner similar to that described below for the CWT transform coefficients.

[0137] Figure 3 shows biosignals, in this example impedance signals, from three different sweeps. Each biosignal represents a stimulus-evoked muscle response. In this figure, normalization has already been performed on the impedance measurement data. As can be seen, the impedance is normalized relative to the base level impedance. The figure also shows the respective stimuli applied to the nerve. In this example, the stimuli are square waves applied at different times with different amplitudes.

[0138] 3, it can be seen that the shape of the impedance curve for each of the stimuli is similar, and therefore the shape of the curve is generally a good predictor for determining whether neural stimulation has occurred. However, as can also be seen from the figure, there are various factors that make it difficult, especially for a human, to visually inspect and recognize the shape of the impedance signal. Therefore, the automated signal analysis method of the present disclosure improves reliability.

[0139] 4 shows another example of an impedance signal that includes both artifact and impedance changes due to nerve stimulation. Clearly, artifact can be a significant problem in properly analyzing impedance signals, making the signal analysis step of the present disclosure particularly advantageous in that it can distinguish between artifact and biosignals representing stimulation-evoked muscle responses.

[0140] Figure 5 shows an example of a normalized impedance signal, in this example the bladder impedance, and a filtered impedance signal. In this example, a third-order IIR Bessel low-pass filter with an exemplary frequency of 0.15 Hz was used. However, different filters and parameters may be used instead.

[0141] Regarding time-frequency signal analysis, as explained previously, the STFT is one possibility for transforming the impedance signal into the time-frequency domain, however, to improve frequency and time resolution, the Continuous Wavelet Transform (CWT) may be used instead.

[0142] The continuous wavelet transform of a function is determined by the dot product of the function and the wavelet family multiplied by a normalization factor and can be expressed as:

[0143]

number

[0144] where k represents the shifting coefficient. Shifting the mother wavelet and its scaled version results in a coefficient that indicates the similarity of the wavelet to the signal being analyzed, depending on frequency (scaling coefficient) and time (shifting coefficient).

[0145] There is a correlation between the wavelet scale and the equivalent frequency with a proportionality constant: equivalent / pseudo frequency F pseudo is expressed by the following formula:

[0146]

number

[0147] F C represents the center frequency of the mother wavelet (proportionality constant), and F S represents the sampling frequency, and Δt represents the sampling interval.

[0148] Figure 6 shows a so-called Ricker wavelet, also known as a Mexican hat wavelet, which has a signal shape similar to the first derivative of the impedance signal shown in Figure 7, which represents the stimulus-evoked muscle response. Figure 7 shows the normalized signal (labeled "raw") and the normalized and filtered signal (labeled "filtered") and their first derivatives.

[0149] 8a to 8c show an impedance signal, a CWT scalogram reflecting the magnitude of the CWT coefficients corresponding to the time-frequency representation of the impedance signal, and a 3D plot of the CWT scalogram, respectively.

[0150] In Figure 8a, an impedance change occurs, as can be seen in the impedance signal. The scalogram of the time-frequency representation of the impedance signal shows that two regions contain maximum values ​​(hereafter referred to as "peaks") in the magnitude of the CWT coefficients. This can also be seen in the form of two peaks in the 3D plot. Both peaks may be candidate features that could represent a stimulus-evoked muscle response and the resulting impedance change.

[0151] 8b shows similar candidate features to those shown in FIG. 8a, so that stimulus-evoked muscle responses can be identified in the biosignal based on the peaks. Additionally, lower magnitude maxima (smaller peaks) are visible in the 3D plot. These smaller peaks may represent artifacts and may be discarded from the set of candidate features that may represent stimulus-evoked muscle responses.

[0152] In general, larger scaling factors may indicate features that represent stimulus-evoked muscle responses, and lower scaling factors may indicate features that represent artifacts.

[0153] Figure 8c shows various maxima in the magnitude of the CWT coefficients at lower wavelet scaling factors, particularly the peaks, which may represent artifacts. Therefore, this impedance signal in Figure 8c is identified as not representing a stimulus-evoked muscle response.

[0154] As can be seen from the above, extreme values ​​(peaks) in the magnitude of the CWT coefficients at lower wavelet scales may represent artifacts, while peaks at larger wavelet scales may represent stimulus-evoked muscle responses. A predetermined threshold value of the wavelet scale (corresponding to a maximum value of the magnitude of the CWT coefficients) may be the primary criterion for distinguishing between stimulus-evoked muscle responses and artifacts. The scale threshold value that separates candidate features representing artifacts from candidate features representing stimulus-evoked muscle responses may be determined, for example, based on multiple control data.

[0155] Alternatively, or in addition, if the magnitude of the feature is below a predetermined threshold, for example the maximum magnitude of the CWT coefficients, the candidate feature may be discarded as an artifact or as an insignificant impedance change.

[0156] FIG. 9 shows a flowchart for determining the presence or absence of a biosignal representing a stimulation-evoked muscle response. First, raw time-domain data is input and preprocessing is performed. In a first step, it may be determined whether nerve stimulation (DNS) is active. If not, no determination is necessary. The method may return to preprocessing. If direct nerve stimulation (DNS) is active, an optional signal sweep extraction is performed. For example, signal analysis is performed in the time and time-frequency domains, as described above. For wavelet scales that exceed a predetermined threshold (threshold 1, e.g., 37) and remain below a predetermined threshold (threshold 2, e.g., 300), maximum magnitudes (peaks) of the CWT coefficients are detected. Thus, candidate peaks, including magnitude, wavelet scale, and time relationships, that may represent stimulation-evoked muscle responses and corresponding impedance changes are obtained. However, some of the candidate peaks may not represent stimulation-evoked muscle responses but rather artifacts or insignificant impedance changes.

[0157] 10 shows a flowchart that follows the previous one to illustrate such classification. Thus, the maximum magnitude (peak) of the CWT coefficients for wavelet scales below a predetermined threshold (threshold 1) is detected. Thus, candidate peaks, including magnitude, wavelet scale, and time relationship, that may represent artifacts are obtained.

[0158] The resulting candidate peaks whose corresponding wavelet scales fall within a predetermined time interval below a predetermined threshold wavelet scale (threshold 1) and the resulting candidate peaks whose corresponding wavelet scales exceed a predetermined threshold wavelet scale (threshold 1), particularly overlapping candidate peaks, may be compared in magnitude. Candidate peaks whose corresponding wavelet scales are below the predetermined threshold wavelet scale and whose corresponding magnitudes are less than the magnitudes of candidate peaks whose corresponding wavelet scales exceed the predetermined threshold wavelet scale are discarded, indicating candidate peaks representing stimulus-evoked muscle responses.

[0159] Candidate peaks whose corresponding wavelet scale is below the predetermined threshold wavelet scale and whose corresponding magnitude is greater than the magnitude of candidate peaks whose corresponding wavelet scale is above the predetermined threshold wavelet scale are indicative of candidate peaks that represent artifacts.

[0160] Additionally, it is determined whether the maximum amplitude in the time domain biosignal waveform is less than or equal to a threshold value of 3 (for example, 0.9%), and if so, no significant impedance change has occurred.

[0161] Thresholds 1 to 3 may be predetermined thresholds obtained empirically or semi-empirically.

[0162] For completeness, it should be noted that this method has already been successfully tested in animal studies and clinical studies with humans. Studies have shown that signal analysis in the time and time-frequency domains by this closure, using for example the discretized CWT and Ricker wavelets of the first derivative of the biosignal, can yield signal features that allow distinguishing between biosignals representing stimulus-evoked muscle responses and artifacts.

[0163] 11 shows a schematic diagram of a medical system 1 according to the present disclosure. The medical system may, for example, comprise a computing system 2 comprising processing means 2a and storage means 2b, which may comprise temporary memory, e.g., RAM, and / or permanent memory, e.g., ROM. The computing system may comprise or be connected to a display device 3. Furthermore, optionally, the computing system may comprise one or more communication interfaces 4 for receiving and transmitting data via one or more data connections 5. For example, the computing system may comprise one or more computers.

[0164] The medical system may further comprise a neuromonitoring device 6 configured to acquire measurement data. In particular, the neuromonitoring device may be configured to measure, in particular continuously, the impedance of the smooth muscles of a target organ, for example the bladder or rectum, or the bladder pressure. The neuromonitoring device, for example an impedance measuring device, may be connected to the computing system via one of the communication interfaces 4. Alternatively, the processing means of the computing system may be at least partly comprised in the neuromonitoring device. The impedance measuring device may comprise two electrodes 6a and 6b that can be attached to tissue, in particular to the smooth muscles of the target organ.

[0165] The medical system may further comprise a device 7, particularly a handheld device, configured to apply stimulation to a portion of tissue. For example, the device may comprise an electrode 7a and a power source 7b. The device 7 may be connected to a computing system via one of the communication interfaces 4 and may be configured to provide information to the computing system indicative of the timing and / or characteristics of the stimulation being applied.

[0166] A more detailed example of the method of the present disclosure is provided below and illustrated in the flowcharts shown in Figures 12a through 12f.

[0167] example The change in impedance signal triggered by slow contraction of smooth muscle during direct stimulation of the innervated nerve is characterized by a characteristic signal shape. After stimulating the nerve tissue for a few seconds, a positive or negative change in impedance is observed, which reaches a peak a few seconds later. After reaching the maximum value, i.e., after reaching the peak, and after discontinuing the nerve stimulation, a relaxation period of a few seconds occurs until the initial level is restored. Nerve stimulation of different strengths, and therefore contraction strengths, result in differences in the maximum signal amplitude and the slope of the change, i.e., the frequency of the biosignal. The characteristic signal shape is preserved.

[0168] According to the present disclosure, software-based automated impedance signal analysis can be performed to detect characteristic impedance changes and resulting smooth muscle contractions induced by autonomic nerve stimulation, particularly to discriminate against artifacts. Signal analysis is subsequently performed for direct nerve stimulation. Signal analysis includes classification with information regarding at least the presence of characteristic stimulation-induced impedance changes, the presence of artifacts, or the absence of significant impedance changes. Based on signal characteristics in the time and time-frequency domains, classification of the response to stimulation and therefore distinction of artifacts from physiologically induced positive stimulation responses or the absence of significant impedance changes is performed.

[0169] In the following, exemplary steps of the method according to the present disclosure are presented in detail.

[0170] Data Preprocessing In the following, an example of data pre-processing is presented in detail, which may be used, for example, in step S23 of Figure 1. Note that this merely serves as an illustrative example, and other pre-processing steps may also be performed.

[0171] Signal analysis of the impedance signal at the target organ, e.g., the bladder and / or rectum, is performed from at least the time of direct neural stimulation application, also referred to as the current confirmation phase. This may be the time during which the stimulation pulse is applied to the tissue. A sampling frequency of 10 Hz may be used. Specifically, 50 data points corresponding to 5 seconds of data acquisition are collected for each impedance measurement channel prior to signal analysis.

[0172] Changes in tissue impedance during muscle contraction are evaluated relative to pre-contraction conditions. To do so, the signal portion extracted for analysis, also called a sweep, is normalized to the pre-contraction impedance level, also called the initial or base level impedance or base impedance. The base impedance may correspond to the impedance of the main device, current leads, electrodes, and tissue connections. This may be determined by calculating the average U(0) of a predetermined number of samples, e.g., 15 samples, at the start of stimulation. The extracted sweep is normalized by dividing the value of each sample in the sweep by the determined average U(0) of, e.g., the first 15 samples. After subtracting 100% (value 1), a dimensionless signal shape (U(t) / U(0)-1) is obtained, which is proportional to the change in tissue impedance during muscle contraction.

[0173] The normalized sweep is then subjected to a low-pass filter to suppress signal overlaps, such as spontaneous changes in membrane potential, noise, or artifacts caused by breathing. The low-pass filter also smooths the signal. Low-pass filtering, for example, can be performed using a digital IIR filter, leaving essentially unchanged the characteristic shape of the impedance change caused by smooth muscle contraction.

[0174] Subsequently, the integral and first derivative of the normalized and low-pass filtered sweep, i.e., the shape of the gradient, can be determined, which can be input parameters for signal analysis in the time and time-frequency domains.

[0175] Signal analysis in the time domain In the following, we present in detail an example of time-domain signal analysis, which may be used, for example, in step S11 of Fig. 1. Note that this merely serves as an illustrative example, and other analysis steps may also be performed.

[0176] The signal analysis in the time domain may include determining at least one of the following:

[0177] Maximum amplitude of impedance change, e.g., in percent of the initial level, i.e., base impedance Signal onset latency Duration of the impedance change, e.g. from the start of the impedance change to the maximum impedance change Depending on the results of the previously determined integrals, local extrema of the signal, for example negative integral minima or positive integral maxima, are detected.

[0178] For example, the threshold for determining a local extremum may be 25% of the maximum value within the sampling interval of the sweep being analyzed. Thus, multiple local extrema are possible within this sampling interval. The largest extremum, e.g., maximum or minimum, after initiating direct neural stimulation (current confirmation phase) corresponds to the maximum amplitude of the impedance change in percent of the initial level. Impedance changes whose amplitude is below a certain threshold, e.g., 0.9%, are not characterized as significant impedance changes.

[0179] The onset latency corresponds to the time from the start of application of direct nerve stimulation (current confirmation phase) to the onset of impedance change. To calculate the onset latency, the local extremum of the first derivative of the signal is determined. The first local extremum in terms of time of the first derivative, i.e., the first maximum slope, is determined. A backward search starting from the previously determined time point determines the zero transition in the normalized, unfiltered raw signal. This value corresponds to the onset latency of the impedance change. The onset latency may be output as a parameter for monitoring by the user.

[0180] The duration of the impedance change may be determined by subtracting the onset latency from the time to reach the maximum amplitude of the impedance change.

[0181] Signal analysis in the time-frequency domain In the following, we present in detail an example of time-frequency domain signal analysis, which may be used, for example, in step S12 of Fig. 1. Note that this merely serves as an illustrative example, and other analysis steps may also be performed.

[0182] The data is transformed into the time-frequency domain to determine signal features that can be used to distinguish between a positive stimulus response induced by the stimulus and artifacts caused by organ movement that are not induced by the stimulus. The time-frequency transformation determines the frequency information of the signal at each time point, which allows the determination of transient signal changes characteristic of non-stationary signals. Such signal changes may include the onset of impedance changes in the target muscle during stimulation of the innervated nerve.

[0183] The first derivative of the normalized and filtered sweep can be transformed into the time-frequency domain using the discretized continuous wavelet transform (CWT). This corresponds to the convolution of the sweep with a time-limited window function of oscillatory nature (the wavelet), allowing for signal decomposition in both the time and frequency domains. The convolution is repeated iteratively with time-shifted wavelets (using a shifting coefficient) and frequency-modified wavelets (using a scaling coefficient). This results in the function of the transform coefficients as a function of time and equivalent frequency. The compression and expansion of the mother wavelet (initial function / prototype of the full window function), summarized as wavelet scaling, is inversely proportional to the equivalent frequency, so the detected equivalent frequency decreases with increasing scaling coefficients (wavelet stretching) and increases with decreasing scaling coefficients (wavelet compression). Time resolution is proportional to the wavelet shifting (also called translation) and improves with smaller shifting coefficients. The higher the absolute magnitude of the coefficient obtained at a given time point, the higher the correlation between the wavelet and the analyzed signal at that time point.

[0184] To enable signal analysis with as few data points as possible at the earliest possible time point, the characteristic impedance change is detected immediately after its onset, preferably before it reaches its maximum value. In practice, the earliest possible time point may mean as soon as possible after the onset of direct nerve stimulation. Due to the similarity of the signal shape of the first derivative of the biosignal representing the stimulus-evoked muscle response and the corresponding impedance change, the Ricker wavelet (Mexican hat wavelet) is preferably used. The scaling and shifting parameters are preferably set to 300 scaling and stepwise shifting of the data points. The transformation yields coefficients (proportional to frequency) that depend on time and wavelet scaling. These coefficients can be represented as an intensity graph (scalogram).

[0185] Then, extrema in the three-dimensional data space of the coefficient magnitudes resulting from the transform are detected. This detection is preferably performed in a window of 10 data points that is iteratively moved along the time axis, starting from zero and gradually / stepping up to the maximum number of data points. The threshold for detecting extrema is adjusted after shifting the window and may be, for example, 25% of the maximum magnitude of the coefficients within the window.

[0186] Extrema within the window are preferably detected within a scaling interval above a threshold, e.g., 37, with a scan rate of 10 Hz and a mother wavelet frequency of 0.25 Hz. Extrema at the left and right boundaries of the window and those corresponding to extrema already detected may be discarded. As a result, extrema are obtained depending on the corresponding scaling and shifting coordinates. The magnitude of the coefficients of the transformation can be visualized for the detected extrema in two dimensions and time-dependent.

[0187] Then, a time-dependent smoothing of the detected extrema is performed, which in this case can correspond to summarizing extrema that are close to each other on the time axis. For example, a threshold value can be used that is one-third of the average expected duration of a characteristic impedance change at a data point, e.g., a duration of 61 data points. If several extrema are detected with a time distance of less than the threshold, the maximum extrema of the local extrema located adjacent to each other within this time period is determined, and all other extrema are discarded. This results in smoothed extrema that depend on the corresponding scaling and shifting coordinates for a scaling range above a threshold value, e.g., 37.

[0188] Then, the detection of extrema in the three-dimensional data space of the magnitude of the coefficients obtained from the transform is repeated in the scaling region below a threshold, e.g., 37, using the same parameters and steps as above, for a sampling frequency of 10 Hz and a mother wavelet frequency of 0.25 Hz. This results in smoothed extrema in the dependence of the corresponding scaling and shifting coordinates for the scaling region below a threshold, e.g., 37.

[0189] If there are extrema in both scaling regions within a window region of a previously determined threshold (e.g., 61 data points), the magnitudes of these extrema, corresponding to the magnitudes of the coefficients of the transform, are compared. This allows for differentiation between characteristic impedance changes induced by the stimulus and non-stimulation-induced impedance changes and / or artifacts. If the magnitude of the extrema in the scaling region above a threshold, e.g., 37, is greater than the magnitude of the extrema in the scaling region below a threshold, e.g., 37, then there is likely a characteristic impedance change induced by the stimulus. Otherwise, there is likely an artifact. If the amplitude determined during a period within the sweep is below a threshold, e.g., 0.9%, then the signal analysis may conclude that there is no significant impedance change.

[0190] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative and not restrictive. The invention is not limited to the disclosed embodiments. It will be apparent to those skilled in the art in view of the foregoing description and drawings that various modifications may be made within the scope of the invention as determined by the claims.

Claims

1. 1. A method for biosignal-based neural monitoring, the method comprising monitoring and analyzing biosignals to identify the location of autonomic nerves associated with a stimulus-evoked muscle response of smooth muscle of a target organ, the method comprising: performing (S11) a time-domain signal analysis of the biological signal to obtain one or more time-domain signal characteristics, the biological signal being based on measurement data obtained by a neuromonitoring device; performing a time-frequency domain signal analysis of the biological signal to obtain one or more time-frequency domain signal characteristics (S12); determining whether the biological signal represents a stimulus-evoked muscle response based on the time domain signal characteristics and the time-frequency domain signal characteristics (S13); and if the biological signal is determined to be representative of a stimulus-evoked muscle response, outputting an indication that a stimulus-evoked muscle response has been detected (S14).

2. 2. The method of claim 1, wherein the method comprises distinguishing between features of the biosignal that represent artifacts, features of the signal that represent stimulus-evoked muscle responses, and optionally features of the biosignal that do not represent significant biosignal responses, in particular significant tissue impedance changes and / or significant bladder pressure changes, based on the time-domain signal characteristics and time-frequency domain signal characteristics.

3. The time domain signal characteristics are a maximum amplitude in the waveform of the biosignal, specifically a tissue impedance change and / or a bladder pressure change, relative to a base level of the biosignal, specifically an impedance base level and / or a bladder pressure base level; the onset latency of a transient signal change in the biological signal; the gradient of a temporal signal change in said biological signal; the time to reach a maximum slope of a transient signal change in the biosignal; a duration of the transient signal change in the biological signal from the onset of the transient signal change; at least one of the regression coefficients 3. The method according to claim 1 or 2.

4. 10. A method according to any of the preceding claims, wherein the time-frequency domain signal characteristics comprise magnitudes of the transform coefficients, in particular limits of magnitude extrema of the transform coefficients.

5. the time-frequency domain signal analysis includes transforming the biological signal or the first derivative of the biological signal into the time-frequency domain and analyzing one or more transform coefficients; 10. A method according to any preceding claim.

6. 10. The method according to any of the preceding claims, wherein the time-frequency domain signal analysis comprises an analysis of transform coefficients of a Wavelet Transform (WT) or an analysis of transform coefficients of a Short-Time Fourier Transform (STFT).

7. The time-frequency domain signal analysis includes: - selecting a window function and scaling said window function, e.g. stretching and / or compressing said window function and / or shifting said window function; obtaining one or more transform coefficients for each of the plurality of samples; analyzing at least a subset of said one or more coefficients, in particular analyzing the magnitude of said one or more coefficients as a function of said scaling factor and / or said shifting factor, The method of claim 6.

8. Analyzing the one or more transform coefficients includes: - expressing the magnitude of one or more of the transform coefficients as a function of time and frequency, in particular expressing WT or STFT coefficients as a function of shifting and / or scaling factors; identifying candidate features in the function, specifically identifying local maxima and / or minima of the function as candidate features; and for at least one of the candidate features, determining whether the candidate feature is representative of a stimulus-evoked muscle response, said determining being based on one or more of the time-domain signal characteristics and / or one or more of the time-frequency domain signal characteristics, in particular a position and / or a magnitude of the candidate feature.

8. The method according to any one of claims 4 to 7.

9. 10. The method of claim 1, wherein the time-frequency domain signal analysis comprises analyzing the one or more transform coefficients taking into account the time-domain signal characteristics and / or information derived from the time-domain signal characteristics, and in particular determining whether a candidate feature is representative of a stimulus-evoked muscle response is performed taking into account the time-domain signal characteristics and / or the information derived from the time-domain signal characteristics.

10. the time-frequency domain signal analysis includes determining a time window corresponding to a selected portion of the time-domain biosignal, particularly a portion selected based on a gradient, particularly a portion after the onset of the biosignal change; The time-frequency domain signal analysis includes determining whether a candidate feature is representative of a stimulus-evoked muscle response based on at least the determined time window, and specifically, the candidate feature being within the time window is one of one or more criteria for determining whether a candidate feature is representative of a stimulus-evoked muscle response.

10. The method according to claim 8 or 9.

11. The method comprises: performing a Continuous Wavelet Transform (CWT) of the biosignal or of the first derivative of the biosignal, in particular of the first derivative of the impedance signal, or performing a Discrete Wavelet Transform (DWT) of the biosignal or of the first derivative of the biosignal, in particular of the first derivative of the impedance signal, or performing a Short-Time Fourier Transform (STFT) of the biosignal or the first derivative of the biosignal, in particular the first derivative of the impedance signal, 10. A method according to any preceding claim.

12. the monitoring and / or analysis of the biological signal is triggered by receiving an indication that a stimulation is being applied to tissue; and / or the time domain signal analysis and / or the time-frequency domain signal analysis is based on one or more characteristics of the stimulus, in particular onset time, length, and / or amplitude of the stimulus applied to the tissue; 10. A method according to any preceding claim.

13. 10. The method of any preceding claim, wherein the method comprises pre-processing an output signal of the neuromonitoring device to obtain the biosignal, and wherein the pre-processing comprises normalizing the output signal to a base level of the biosignal, in particular a base level tissue impedance or a base level bladder pressure, and / or applying a low pass filter, in particular after normalizing the output signal and / or after performing a sweep extraction.

14. 10. The method according to any of the preceding claims, wherein the method comprises integration and / or differentiation of the biological signal, the results of the integration and / or differentiation being used as input for the time domain signal analysis and / or the time-frequency domain signal analysis.

15. 10. The method according to any of the preceding claims, wherein the time domain signal analysis and time-frequency domain signal analysis, and optionally integration and / or differentiation of the biological signal, are performed continuously or for multiple sweeps of the biological signal, in particular for each of consecutive sweeps of the biological signal.

16. 10. The method of any preceding claim, wherein outputting an indication that a stimulus-evoked muscle response has been detected comprises providing an audio and / or visual and / or tactile output to the user.

17. 10. The method of any preceding claim, wherein the biosignal is an impedance signal or a bladder pressure signal.

18. 10. The method of any preceding claim, wherein the stimulus-evoked muscle response is a muscle contraction.

19. Acquiring measurement data, in particular continuously, by means of a neuromonitoring device, in particular measuring the impedance of the smooth muscle of the target organ, in particular continuously, by means of an impedance measuring device or measuring the bladder pressure, in particular continuously, by means of a pressure measuring device; applying stimulation to a portion of tissue, particularly by a handheld device; and in response to determining that the biological signal represents a stimulus-evoked muscle response caused by the stimulus applied to the portion of the tissue, outputting an indication that the portion of the tissue includes a nerve associated with the smooth muscle.

10. A method according to any preceding claim.

20. 10. A method according to any preceding claim, wherein the steps of the method are performed on at least two biological signals, the biological signals being obtained for different target organs in order to identify the location of autonomic nerves associated with stimulation-evoked muscle responses of smooth muscles of each of the different target organs.

21. A medical system (1) configured to perform the steps of any of the preceding claims, comprising a computing system (2) configured to perform the steps of any one of claims 1 to 20.

22. a device (7), in particular a handheld device, configured to apply stimulation to a portion of tissue, and / or a neuromonitoring device (6) configured to acquire said measurement data, in particular an impedance measuring device configured to measure, in particular continuously, the impedance of the smooth muscles of the target organ and / or a pressure measuring device configured to measure, in particular continuously, the bladder pressure, A medical system (1) according to claim 21.

23. A computer program product comprising instructions, which when executed by a computer, cause the computer to perform the method of any one of claims 1 to 20.

24. A computer readable medium comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 20.

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