Systems and methods for signal stabilization for stimulus application accounting for lead migration

Abstract

Disclosed herein are systems and methods for applying a stimulus to a target object in a human body based on sensed physiological data relating to the target object. The physiological data may be sensed by a detection lead that may vary in distance from the target object based on a position of the human body. The sensed physiological data can be processed to account of this lead migration such that the processed data does not vary based on a distance between the detection lead and the target object.

Description

[0001]SYSTEMS AND METHODS FOR SIGNAL STABILIZATION FOR STIMULUS APPLICATION ACCOUNTING FOR LEAD MIGRATION Priority Claim The present application claims priority to and is a continuation of PCT Application No. PCT / IB2024 / 059174 filed September 20, 2024, which is hereby incorporated by reference herein in its entirety. Technical Field The present disclosure relates to a stimulus application system for applying electrical stimulation to a location in a patient’s body. Background U.S. Patent Application No.2017 / 0216607 discloses a neural implant device which comprises a circuit configured to receive an input signal and to generate an electrical signal based on the received input signal. However, this conventional neural implant device has drawbacks that control based on the internal physiological signals is not performed, and thus, stimulus corresponding to the status of the object person may not be applied. PCT Publication No. WO 2023 / 1888437 provides a stimulus application system, an implant device, a controller device, a method for controlling a controller device, and a program, capable of applying stimulus corresponding to the status of the object. In particular, the publication discloses an implant device implanted in an object body which is an animal including a human being that can be wirelessly communicably connected to a controller device arranged outside of the object body. The implant device can include circuitry for detection which detects an electrical signal representing a physiological signal at a predetermined part in the object body, circuitry for transmission / reception which transmits detection information representing time variation of the detected electrical signal, and receives, from the controller device, a stimulation instruction representing a stimulus to be applied to the object body, and circuitry for application which applies an electrical stimulus to a predetermined part in the object body, on the basis of the stimulation instruction received by the circuitry for transmission / reception. In some applications, systems such as the above have been used for stimulating a nerve. However, it has been found that the detected electrical signal representing the physiological signal from the nerve (e.g., nerve activity signal) can vary based on a position of the user’s body. In particular, the amplitude of the nerve activity data can vary whether a user is standing or sitting such that the nerve activity will appear to be changing rapidly, but the difference is due to the position of the user’s body. As such, providing stimulation based on this signal without accounting for this change may lead to inaccurate and / or inefficient treatment. Summary Disclosed herein are systems and methods for applying a stimulus to a target object in a human body based on sensed physiological data relating to the target object. The physiological data may be sensed by a detection lead that may vary in distance (i.e., lead migration) from the target object based on a position of the human body. The sensed physiological data can be processed to stabilize the signal to account of this lead migration such that the processed data does not vary based on a distance between the detection lead and the target object. In an embodiment, a stimulus application system can include an implant device configured to be implanted in a body of a patient including a sensor unit including a detection lead configured to detect physiological signals at a predetermined detection location in the body at predetermined time intervals and a stimulus circuit unit configured to apply a stimulus to a predetermined stimulation location in the body upon receiving a stimulation instruction. At least one controller can be configured to receive raw detection information pertaining to the physiological signals detected by the sensor unit at each predetermined time interval, the raw detection information varying based on a distance between the detection lead and the predetermined detection location. The raw detection information can be decoded such that the decoded detection information is substantially independent of the distance between the detection lead and the predetermined detection location. For the purpose of this disclosure, this “decoding” can be achieved through any suitable mathematical or algorithmic operation, including but not limited to division, subtraction, logarithmic scaling, or by applying a trained machine learning model. The decoded detection information can be analyzed to determine if a stimulation instruction should be generated. In some embodiments, the at least one controller decoding the raw detection information includes filtering the detection information to obtain filtered detection information. In some embodiments, the at least one controller decoding the raw detection information includes processing both the raw detection information and the filtered detection information. In some embodiments, the at least one controller processing both the raw detection information and the filtered detection information includes dividing a result of processing the filtered detection information by a result of processing the raw detection information. In some embodiments, the at least one controller decoding the raw detection information includes filtering the raw detection information in a first frequency range of interest to obtain the filtered detection information representing a physiological signal of interest, filtering the raw detection information in a second frequency range different from the first frequency range to obtain a lead position reference signal, and dividing a value derived from the filtered detection information by a value derived from the lead position reference signal. In some embodiments, the first frequency range of interest is predetermined by a process comprising correlating a power spectral density of the raw detection information with a corresponding physiological marker and selecting the frequency range with a high statistical correlation. In some embodiments, the second frequency range is predetermined by a process comprising analyzing the raw detection information during patient postural changes and selecting the frequency range that exhibits high signal stability during a constant posture and high signal sensitivity between different postures. In some embodiments, the system further includes an auxiliary sensor configured to generate a reference signal indicative of a physical state of the patient, wherein the at least one controller is configured to decode the raw detection information by normalizing a value derived from the raw detection information with a value derived from the reference signal generated by the auxiliary sensor. In some embodiments, the auxiliary sensor is configured to generate a reference signal indicative of a physical state of the patient and may include, but is not limited to , at least one of an accelerometer, an Inertial Measurement Unit (IMU), a gyroscope, a magnetometer, or a pressure sensor. In some embodiments, the at least one controller is further configured to periodically and autonomously initiate a re-calibration procedure to re-identify an optimal parameter for a reference signal used for decoding the raw detection information, and to update a system parameter based on the re-identified optimal parameter. In some embodiments, the controller is configured to initiate the re-calibration procedure at predetermined time intervals or in response to a detected degradation in signal quality. In some embodiments, the at least one controller, when analyzing the decoded detection information, is further configured to prevent the generation of the stimulation instruction if a current value of the decoded detection information is less than a previous value of the decoded detection information, even if the current value is above a stimulation threshold. In some embodiments, the predetermined detection location and the predetermined stimulation location is a nerve. In some embodiments, wherein the nerve is the sacral nerve. In some embodiments, the system further includes an external programmer configured to wirelessly communicate with the implant device. In some embodiments, the at least one controller is part of the implant device. In an embodiment, a method for stimulus application can include detecting, with a sensor unit of an implantable device, physiological signals at a predetermined detection location in the body, thereby generating raw detection information that varies based on a distance between a detection lead of the sensor unit and the predetermined detection location. The at least one controller can decode the raw detection information to generate decoded detection information that is substantially independent of the distance between the detection lead and the predetermined detection location. The decoded detection information can be analyzed to determine if a stimulation instruction should be generated. A stimulus circuit unit of the implantable device can apply a stimulus to a predetermined stimulation location in response to the generation of the stimulation instruction. The above summary is not intended to describe each illustrated embodiment or every implementation of the subject matter hereof. The figures and the detailed description that follow more particularly exemplify various embodiments. Brief Description of Drawings Subject matter hereof may be more completely understood in consideration of the following detailed description of various embodiments in connection with the accompanying figures, in which: FIG. 1 is a structural block diagram showing an example of a stimulus application system according to an aspect of the present disclosure. FIG. 2 is a functional block diagram showing an example of an implant device according to an aspect of the present disclosure. FIG. 3 is a functional block diagram showing an example of a control unit of an implant device according to an aspect of the present disclosure. FIG.4 is a flowchart showing an operation example of a stimulus application system according to an aspect of the present disclosure. FIG.5 schematically depicts the effect of body position on nerve sensing with a lead according to an aspect of the present disclosure. FIG.6 graphically depicts the effect of body position on nerve sensing with a lead according to an aspect of the present disclosure. FIGS.7A-7D graphically depict a process for decoding nerve activity data according to an aspect of the disclosure. FIG. 8 depicts a decoded nerve signal in which the amplitude of the signal is not affected by lead migration according to an aspect of the disclosure. FIG. 9 depicts a flowchart of method steps in a method of decoding nerve activity data to minimize the effects of lead migration. FIGS.10A-10B depict decoded nerve activity over time. FIG.11 depicts a flowchart of method steps in a method of decoding nerve activity data to minimize the effects of lead migration. FIGS. 12A-12C graphically depict a process for decoding nerve activity data according to an aspect of the disclosure While various embodiments are amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail. It should be understood, however, that the intention is not to limit the disclosed inventions to the particular embodiments described. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the subject matter described herein. Detailed Description The following detailed description should be read with reference to the drawings in which similar elements in different drawings are numbered the same. The drawings, which are not necessarily to scale, depict illustrative embodiments and are not intended to limit the scope of the invention. As exemplified in FIG.1, a stimulus application system 1 according to an aspect of the present disclosure includes an implant device 10 implanted in an object body which is an animal including a human being and a programmer 20 and charging device 30 which can be arranged outside of the object body. Stimulus application system 1 may further communicate with a cloud platform 40. Implant device 10, depicted in FIG.2, can include a transmission / reception unit 11, a power supply unit 12, a stimulus circuit unit 13, a sensor unit 14, and a control unit 15. The transmission / reception unit 11 of the implant device 10 can transmit data to the programmer 20 arranged outside of the object body, in accordance with instructions input from the control unit 15. Further, the transmission / reception unit 11 can receive data from the programmer 20 and output the data to the control unit 15. In various embodiments, a widely known data transmission / reception method can be adopted, such as NFC, Wi-Fi, Bluetooth (registered trademark), RFID wireless communication standard, and the like. According to an embodiment, the transmission / reception unit 11 can receive wireless power transfer from the recharging device 30 and output the received power to the power supply unit 12. The power supply unit 12 can be provided with a battery B and supply power to each unit of the implant device 10. Battery of power supply unit 12 can be wirelessly recharged, as will be described in more detail below. The stimulus circuit unit 13 is under control of the control unit 15 and can apply a stimulus to the object body through one or more electrodes arranged on a lead at predetermined parts (hereinbelow, referred to as stimulation parts) in the object body. Herein, the stimulation part where the electrode lead is arranged is a part to which stimulus to a nerve etc., can be applied, as used in Spinal Cord Stimulation, Sacral Neuro Modulation, Vagus Nerve Stimulation, Deep Brain Stimulation, and the like. The stimulation parts can be selected in accordance with the type of stimulus to be applied to the object body. For the arrangement of the electrodes of the stimulus circuit unit 13, arrangements widely known as the arrangements used for the above-mentioned various stimulation methods can be adopted. Therefore, detailed explanation therefor is omitted here. Further, the stimulus may be a periodical electric signal, a single pulse signal, etc., and an amplitude, a frequency, a duration, a pulse width, and the like of the signal can be controlled by the control unit 15. The sensor unit 14 can detect electric signals representing physiological signals of the object body (for example, signals representing the physiological signals at the detection parts of the object body, by the magnitude of potential thereof), through one or more electrodes arranged at predetermined parts (hereinbelow, referred to as detection parts) in the object body. The physiological signal can include one or more of membrane potential, Nerve action potential, Organ pressure, Tissue impedance, temperature, and other signals acting as biomarkers, in the object body, and the physiological signal can be selected in accordance with a rule predetermined depending on the type of stimulus to be applied. Further, the stimulation part and the detection part may be different parts, adjacent (comparatively close) parts, or the same part. The sensor unit 14 can also be or include other sensor to detect non-electrical signals (such as pressure, temperature, etc.) that represent physiological signals of the object body. The control unit 15 can include a program controller device such as a CPU, and a storage device such as a memory. The control unit 15 can process detection information representing the electrical signal detected by the sensor unit 14 (for example, in case that the electrical signal represents the physiological signal at the detection part in the object body by the magnitude of its potential, the detection information is information representing the magnitude of the potential). The control unit 15 can analyze the detection information representing the detected electrical signal every time that the electrical signal is detected, or can store the information representing the electrical signals detected for a plurality of times in a memory, etc., and then process the information representing the detection information stored in the memory, at a predetermined time. Namely, for example, the detection information may include information representing an electrical signal as a result of one-time detection, or may include information representing a plurality of electrical signals as a result of a plurality of times of detection (representing detection information representing the time variation of the detected electrical signal). The control unit 15 can also process detection information representing non-electrical signals detected by the sensor unit 14 (for example, pressure or temperature, etc.) to perform analysis, detection and store the information in a memory, etc. Further, the control unit 15 can provide instructions (stimulation instruction) to the stimulation circuit 13. The control unit 15 can determine parameters such as the frequency and the intensity (amplitude) of the electrical signal to be applied to the object body as a stimulus, the pulse width (in case that the electrical signal is a pulse signal), and in addition, the time when the stimulus is to be applied, the duration of the stimulus, and the like. Then, the control unit 15 can control the stimulus circuit unit 13 so that the stimulus of the electrical signal defined by the determined parameters is applied. The stimulus circuit unit 13 is capable of delivering various forms of therapeutic energy, not limited to electrical stimulation. It can be configured to produce other types of stimuli, including but not limited to, thermal (such as heat or cold), mechanical (such as ultrasound), or electromagnetic (such as radio waves) energy, thereby expanding the potential therapeutic applications of the system. The programmer 20 can be arranged at a position outside of the object body and wirelessly communicable with the implant device 10. In some embodiments, the programmer 20 can provide initial or updated programming of parameters for the implant 10 to follow and the processor of the implant 20 then carries out detection, analysis and stimulation functions independently. For example, a physician may program the implant 10 with the programmer 20 during an office visit, with the implant 10 then operating without need for further external instruction. The programmer 20 can also receive historical therapy data stored by the implant. Such data can include, for example, sensor data detected by sensor unit 14, stimulation data provided by stimulation circuit 13, etc. The programmer 20 can also transmit data to and receive data from artificial intelligence system 40, as will be described in more detail below. Recharging device 30 can be a wearable recharger configured to transmit power to recharge the battery in the power supply unit 12 of the implant. Wearable recharger 30 can be selectively worn by the user when the implant needs to be charged and then removed from the user as desired. In embodiments, recharging device 30 can provide an indication of a charging status and / or battery level of the power supply unit 12 of the implant. Wearable recharger 30 can itself be recharged by connection to a power source. Further details regarding wireless power technology that can be used with aspects of the disclosure can be found in U.S. Patent Publication No. 2023 / 0344273, which is hereby incorporated by reference herein in its entirety. Cloud platform 40 can employ, for example, any available cloud computing service. Programmer 20 can communicate information to and receive information from cloud platform 40 over WiFi communications or other known communication modalities. Cloud platform 40 can include data storage that stores data relating to system 1. In embodiments, programmer 20 can transmit historical data received from implant 10 for a plurality of patients to cloud platform 40. Cloud platform 40 may further include an Artificial Intelligence (AI) system that can analyze the historical data and provide suggestion for improvements to therapy provided by implant 10. For example, cloud platform 40 may store a separate file for each patient using an implant 10. The AI system may analyze the data across all patients, a group of patients, etc. and provide suggestions for modifications to improve therapy for one or more individual patients based on the analysis. Referring now to Figure 3, an operation example of the control unit 15 of the implant device 10 will be explained. According to an embodiment, the control unit 15 executes the programming received from the programmer 20 and stored in a storage device. To carry out the programming, the control unit 15 functionally comprises a reception unit 151, a stimulus determination unit 152, and an instruction transmission unit 153. The reception unit 151 can receive detection information detected by the sensor unit 14, the detection information being a detection result of the signals detected by the sensor unit 14 as a physiological signal of the object body. On the basis of the detection information received by the reception unit 151, the stimulus determination unit 152 can determine the details of the stimulus to be applied to the object body by the stimulation circuit 13. As mentioned above, the detection information represents an electrical signal at a predetermined detection part in the object body, detected by the sensor unit 14. The stimulus determination unit 152 can use this detection information to acquire information regarding the time variation of the physiological signal at the predetermined part in the object body. For example, when the detection information includes information representing one electrical signal, as a result of detection for one time, the stimulus determination unit 152 can accumulate and store the detection information for a plurality of times to acquire information representing the time variation of the electrical signal (physiological signal at a predetermined part in the object body). For example, the stimulus determination unit 152 determines the type of stimulus with reference to stimulus setting information in which a plurality of mutually different stimulus applying conditions are associated with information representing the details of the stimulus corresponding to each stimulus applying condition. The stimulus setting information can be set in advance and transmitted to implant 10 by programmer 20 and stored in the storage device. The stimulus determination unit 152 can output the information representing the details of the stimulus to the instruction transmission unit 153. The instruction transmission unit 153 can transmit the stimulation instruction representing the details of the stimulus determined by the stimulus determination unit 152, to be carried out by the stimulation circuit 13. The stimulus setting information used here may be defined on the basis of the detection result of the electrical signal representing the physiological signal, which is acquired by the sensor unit 14. Figure 4 depicts a flowchart showing an operation example of a stimulus application system according to an aspect of the present disclosure. In the following example, the implant device 10 is implanted in the object body, i.e., the body of a human being, and leads including one or more electrodes for providing stimuli are arranged at the stimulation parts and the detection parts used for Sacral Neuro Modulation. The sensor unit 14 of the implant device 10 senses nerve activity of the sacral nerve. In other examples, stimulation application system can be employed in a similar manner to stimulate other nerves or areas of the body. In this process, first, the sensor unit 14 of the implant device 10 detects the electrical signals representing the physiological signals in the human body by the electrodes arranged in the detection parts, and generates detection information representing the detected electrical signals(S11). The sensor unit 14 transmits the generated detection information to the control unit 15 of the implant device 10, at a predetermined time (for example, every time that the detection is performed) (S12). Then, the implant device 10 examines whether or not an instruction is received from the control unit 14 within a predetermined time (S13). If no instruction is received (S13: No), the process returns to Step S11, and is continued. For a sensor unit 14 using non-electrical signals to detect a physiological signal in the human body, the detected signals (S11) would be non-electrical signals. In Step S12, the control unit 15 receives and stores the detection information transmitted by the sensor unit 14 (S21). The detection information stored in Step S21 is processed to decode the information to obtain data of interest (S22), such as a frequency range of interest and reference frequency range (described in more detail below), after a predetermined number of data points are stored. In one embodiment, the information is decoded after digital data points are stored. Further details on one example of this decoding process are described in more detail below. For a sensor unit 14 using non-electrical signals to detect physiological signals in the human body, the decoded information to obtain signal of interest(S22) will be non-electrical signals. In embodiments, once the decoded physiological data of interest is obtained at Step S22, a baseline level is obtained from the decoded data (S23). The baseline is set as a statistically derived metric of the signal intensity from each data gathering cycle, representative of a low-activity state. For instance, this metric could be the minimum value, a specific low percentile, or an average of the lowest recorded values. The stimulation threshold can then be determined from this baseline data (S24). Subsequently, it is determined if the current data is above the stimulation threshold (S25), and if so, details of a stimulation to be applied are determined (S26). Further details regarding determining the stimulus to be applied can be found in PCT Publication No. WO 2023 / 1888437, which is hereby incorporated herein by reference in its entirety. If the data is not above the baseline by a threshold amount at Step S25, the system reverts to Step S21. Further details regarding calculation of and comparison with the baseline can be found in copending PCT Patent Application No. PCT / IB24 / 059172 entitled DYNAMIC BASELINE ADJUSTMENT FOR STIMULUS APPLICATION SYSTEMS, filed on the same day as the present application, which is hereby incorporated by reference in its entirety. The controller unit 15 transmits an instruction representing the details of the stimulus determined in Step S26 to the stimulation circuit 13 (S27). If the stimulation circuit 13 receives the instruction from the control unit 15 in Step S13 (S13: Yes), it then generates and applies the specified stimulus to the human body. This involves setting the appropriate stimulus parameters—such as the type of energy (e.g., electrical, thermal, mechanical), intensity, frequency, and duration—and controlling the delivery of this energy to the stimulation part through a suitable output component so that the stimulus is applied as determined (S14). The stimulus application system 1 according to the present aspect repeats the operations from Step S11 to Step S14, and the operations from Step S21 to Step S27. Accordingly, according to an example of the present aspect, the stimulus to be applied is varied in accordance with the status of the object human body, etc., and thus, a stimulus suitable for the status of the object can be applied. In one example, this stimulus application system can be used for stimulating a nerve. For example, the system could be used for suppressing the symptoms of overactive bladder (OAB) by stimulating the sacral nerve. As noted above, stimulus can be applied to a nerve based on a comparison of decoded nerve activity data to a baseline. However, Applicant has been found that an amplitude of the decoded nerve activity data varies based on a position of a user’s body. In particular, the amplitude of the nerve activity data varies whether a user is standing or sitting such that the nerve activity will appear to be different based on the decoded data, but the difference is solely due to the position of the user’s body. Applicant has determined that the reason for this variance is that the detection lead is a different distance from the nerve when the user is sitting than when the user is standing (i.e., lead migration). Figure 5 schematically depicts this difference. As can be seen in the figure, the lead 50 is further from the nerve 52 when the user is in the standing position than in the sitting position. Although the particular percentages depicted in Figure 5 are exemplary only, these percentages represent the fact that the strength of the signal detected from the nerve diminishes as the lead 50 moves further from the nerve 52. The resulting decoded nerve activity data over time in a situation where a user repeatedly transitions between sitting and standing is depicted in Figure 6. Each time the user transitions from sitting to standing, the corresponding nerve activity 54 decreases sharply. Conversely, when the user goes back to sitting, the corresponding nerve activity 56 rapidly increases. The resulting graphical depiction gives the appearance that the nerve activity is rapidly decreasing and increasing in alternating fashion, when in fact these large fluctuations are due to the body position of the user, and corresponding distance between the lead and the nerve, rather than any variation in nerve activity. Embodiments disclosed herein provide for a signal decoding process that computationally normalizes the raw nerve activity data to remove artifacts arising from lead migration caused by body position change. This process stabilizes the signal by rendering it substantially independent of the changing distance between the detection lead and the nerve. In particular, the present disclosure provides a five-step process for decoding which is graphically depicted in Figures 7A-7D. Initially, raw data of the sensed nerve activity is recorded as Process 1. Process 2 filters this raw data based on a decoding frequency range to provide a filtered data set for the sampling period. The identification of the optimal frequency range for the physiological signal of interest is a quantitative process designed to isolate the specific signal components that are most representative of the target physiological state. In an embodiment, a method for quantitatively identifying this frequency range comprises the following steps: 1. Synchronized Data Acquisition: A segment of the raw physiological signal from the detection lead is acquired simultaneously with a corresponding ground-truth physiological marker or a clinically relevant event. This marker provides a definitive indication of the patient's state. Examples of such markers include, but are not limited to, urodynamic measurements of bladder pressure or volume, or a patient-initiated signal (e.g., via the external programmer) indicating a sensation of urgency to urinate. 2. Signal Decomposition and Feature Extraction: The acquired raw physiological signal is computationally decomposed into a plurality of constituent signal components, typically corresponding to distinct frequency bands. Various signal processing techniques, such as a Short-Time Fourier Transform (STFT) or wavelet analysis, are applied to extract one or more relevant signal features (e.g., power, amplitude, or phase) from each of these frequency bands over time. 3. Statistical Correlation Analysis: A statistical analysis is performed to quantify the relationship between the extracted signal features from Step 2 and the synchronized ground- truth marker from Step 1. This analysis, which may involve calculating a Pearson correlation coefficient or mutual information, generates a correlation score for each frequency band, indicating how well the signal in that band tracks with the actual physiological event. 4. Selection of Optimal Frequency Range: Based on the results of the statistical analysis, the frequency band (or plurality of bands) that is determined to be the most indicative of the target physiological state is selected. This selection is typically based on identifying the band with the highest and most statistically significant correlation score. This selected band is then implemented as the "frequency range of interest" for the band- pass filter used in the decoding process. The results of Process 1 and Process 2, showing both the raw data signal and the filtered data signal overlaid on each other, or physiological signal of interest and the detection lead reference position overlaid on each other, are depicted in Figure 7A. In further embodiments, the identification of this high-correlation frequency range can be facilitated by data from one or more auxiliary sensors configured to detect the patient's physical state. For instance, an accelerometer or a multi-axis Inertial Measurement Unit (IMU), either integrated within the implant device or included in a communicatively coupled external device, can provide data indicative of the patient's posture (e.g., sitting, standing, lying down, etc.) or activity level (e.g., walking, resting, etc.). This sensor data can be used as a clinically relevant event marker to automatically label or segment the raw physiological signal. A subsequent statistical analysis can then identify the frequency band within the physiological signal that most strongly and consistently correlates with a specific, posture-dependent physiological state, thereby providing a robust, data-driven method for selecting the frequency range of interest. The quantification and selection of this physiological signal of interest can be established during a characterization or calibration phase. In such a phase, the raw sensed signal is analyzed in relation to a corresponding physiological marker or clinically relevant event (e.g., urodynamic measurements of bladder pressure, or a patient-initiated signal indicating a sensation of urgency to urinate). Various signal processing techniques, including but not limited to time-frequency analyses such as Short-Time Fourier Transform (STFT) or wavelet analysis, can be applied to the raw signal to extract one or more signal features (such as power, amplitude, or phase) from various spectral components. A statistical relationship (e.g., by calculating a Pearson correlation coefficient or mutual information) is then established between the extracted signal features in various frequency bands and the corresponding marker or event. Based on this analysis, the frequency band or bands determined to be most indicative of the target physiological state are selected as the "frequency range of interest" for the filter. In an embodiment, a method for identifying the reference frequency as detection lead reference position comprises: 1. Acquisition of a characterization signal: A raw signal is acquired from the sensor unit over a period encompassing one or more changes in the physical state of the implant, such as those induced by patient postural transition (e.g., from a seated to a standing position). This acquisition may be performed during a predetermined calibration procedure by analyzing signals recorded during ambulatory use. 2. Signal decomposition: The acquired characterization signal is computationally decomposed into a plurality of constituent signal components, wherein each component corresponds to a distinct frequency band. This decomposition can be affected by various signal processing means, including but not limited to: Time-Frequency Domain Analysis, band-pass filter, etc. 3. Quantitative evaluation of signal components: Each constituent signal component is subsequently evaluated against a set of predetermined selection criteria to ascertain its suitability as a reference signal. Said criteria are configured to assess: Static-state signal stability and Transitional-state signal sensitivity. The Static-state signal stability indicates the temporal stability of the signal component during periods of minimal or no lead migration (e.g., within a single posture). A suitable component exhibits a smooth and stable waveform amplitude, indicative of low contamination from transient physiological events. The Transitional-state signal sensitivity indicates the magnitude of the response of the signal component to a state transition known to induce lead migration. 4. Selection of the reference frequency: The frequency band corresponding to the signal component that demonstrates an optimal balance between high static-state and high transitional-state sensitivity is selected as the detection lead reference frequency for use in the signal normalization process. Next, the same data processing steps are carried out in Process 3 and 4 separately on both the raw data signal and the filtered data signal. Referring to Figure 7B, in graph 60A the initial raw data signal 62A is depicted. As can be seen in the figure, the value of the amplitude fluctuates above and below zero. To process the signal, first the absolute value of each data point is obtained as shown in the signal 62B in graph 60B, in which the value of the amplitude is always positive. Next, the mean of the absolute value of each data point is obtained to arrive at the single value 64 for the sampling period depicted in graph 60C. Figure 7C depicts the same process carried out on the original filtered data signal 72A depicted in graph 70A. First the absolute value of the signal is obtained in signal 72B depicted in graph 70B and then the mean of this signal is obtained to arrive at data point 74 in signal 70C representing the value of the signal for the sampling period. Finally, in process 5 the data point 74 from the filtered data signal is divided by the data point 64 from the raw data signal to achieve a final decoded data point 84 for the sampling period as depicted in Figure 7D. An example of the end result over time is depicted in Figure 8, with a decoded nerve signal 90 in which the amplitude of the signal is not affected by lead migration. The manner in which the above data processing procedure eliminates the effect of distance between the lead and the nerve on the amplitude of the decoded signal can further be explained mathematically. Because the signal (S) changes based on distance (X), the relationship can be expressed S = f (X). The function can further be expressed as: change ௗௌof amplitude =ௗ^. Therefore, to achieve stable nerve activity, the effect of distance (dX) needs to be eliminated. To do this, first the filtered data set is obtained as described above with respect to process 2. Next, processes 3 and 4 are carried out to obtain the mean of the absolute value of each data point for each of the raw and filtered data sets with the end result being a single data point 64, 74 for each data set for each sampling period. The data point end result of each of these calculations can be expressed asௗோ^ ௗோଶௗ^ and ௗ^ , respectively.The final step in calculating the data point for the in process 5 in which the data point 74 for the filtered data is divided by the data point 64 for the raw data will then eliminate the effect of distance on the value, because^^^^2 ^^^^1 ^^^^2. As such, following these calculations any change in distance X be^t^w^^een÷data^^p^o^in=ts d^o^^e^s1not affect the results of the decoding process. This calculation is just one example embodiment of Figure 5, and the raw data and filtered data can be combined in other ways to eliminate the effect of distance on the results of the decoding process. The function of decoding or normalizing the signal can be achieved through any suitable mathematical or algorithmic operation, including but not limited to division, subtraction, logarithmic scaling, or by applying a trained machine learning model to remove the distance-based effect. A flowchart of steps in method of decoding nerve activity data 200 to minimize the effects of lead migration is depicted in Figure 9. At step 202 data points are recorded for the sampling period. At step 204 the raw data can be filtered based on a decoding frequency range as discussed above. The raw data can be processed at step 206 and the filtered data processed at step 208 to arrive at a single data point for each data set as set forth above. For example, each data set can be processed to provide the absolute value of the data point followed by a mean value for each data set based on the absolute values. At step 210, a final decoded data point can be calculated by dividing the data point calculated for the filtered data set by the data point calculated for the raw data set. The decoded values for each sampling period are then processed to determine if stimulation should be applied as discussed above with respect to Figure 4. In addition, PCT Publication No. WO 2023 / 1888437 discloses a decoding process that can also be employed to minimize the effects of lead migration using the divisional mathematical calculation mentioned above. In embodiments, an additional analysis step can be undertaken prior to delivering stimulation to the patient. In particular, in the context of sacral nerve stimulation for treatment of overactive bladder, providing stimulation during the voiding stage of a voiding cycle (i.e., when the bladder is emptying) can inhibit voiding efficacy. As such, it is desirable to avoid stimulating the nerve during voiding even if the analysis above would otherwise indicate a need for stimulation. This can be done by preventing stimulation any when a new / current decoded nerve activity value is lower than the previous decoded nerve activity value. For example, referring to Figure 10A, nerve activity data 92 is depicted over time. At times when the nerve activity 92 is over the stimulation threshold 94 and a difference between a current value and a previous value is positive, stimulation can be applied, such as the portion 96 of the nerve activity data. However, if the difference between the current value and the previous value is negative, such as in the portion 98 of the nerve activity data, stimulation is not applied even though the nerve activity data is above the stimulation threshold 94. Figure 10B further depicts this analysis. The nerve activity data 92 value of 445 is above the stimulation threshold 94, but the value of the data point 445 minus the previous data point value of 458 is negative. As such, a stimulation instruction is not provided to the system at the time of the data point 445. In some circumstances, the accuracy of the above-described decoding process can be affected when the raw data clearly indicates the urinary cycle. To address this issue, another embodiment of a decoding process disclosed herein adds a secondary analysis using an additional signal as a reference that has a smooth and stable waveform. This reference signal can be obtained from raw data and filtered to a specific frequency range and further processed to obtain a smooth and stable amplitude level of the waveform. In an alternative and complementary embodiment, the reference signal used to normalize the data is not derived from a frequency band of the raw physiological signal. Instead, the reference signal may be generated by at least one separate auxiliary sensor configured to directly or indirectly measure the patient's posture, motion, or physical orientation. Such auxiliary sensors may include, but are not limited to, one or more accelerometers, gyroscopes, or a complete Inertial Measurement Unit (IMU) or other types of sensors capable of detecting the physical state of the patient, such as pressure sensors or strain gauges configured to measure muscle tension. These sensors can be co-located with the implant device or housed in an external wearable device that is in communication with the controller. In this configuration, the output of the posture sensor (e.g., a vector representing the orientation relative to gravity from an accelerometer) can serve as the direct reference signal. The controller is then configured to normalize the physiological signal of interest (e.g., the filtered nerve activity data) using a value derived from this posture sensor data. This method provides a highly accurate and direct measurement of the physical state causing lead migration, potentially offering a more robust signal stabilization than a reference derived from the physiological signal itself. This approach can also be used in combination, for instance by using the posture sensor data to validate or refine the reference signal derived from a frequency band of the physiological signal. Figure 11 depicts an example of such a decoding process 300. Similar to the process 200 described above, data points are recorded for a sampling period at step 302 and that raw data is filtered in a frequency range of interest (e.g., with a 300-400 Hz bandpass filter) at step 304. This filtered data is then processed by obtaining the absolute value of each data point at step 306 and calculating the mean of the absolute value of each data point at step 308. Smoothing can then be performed on the mean value data at step 310 by calculating a moving average of the data to reduce data fluctuation. Decoding process 300 also filters the raw data acquired at step 302 in a reference frequency band, such as, for example, 10-100 Hz at step 314. The reference data is processed in the same manner by calculating an absolute value of the reference data at step 316, calculating a mean value of the absolute value at step 318 and smoothing the mean value data by calculating a moving average of the data at step 320 to reduce data fluctuation. The purpose of this reference frequency is to determine the detection lead’s reference position. Therefore, when the lead position remains unchanged, this frequency must be kept as stable and smooth as possible. For instance, signals in the low-frequency band (10–100 Hz) exhibit slower variations in amplitude because they change gradually over time. As a result, short-term disturbances, such as small noise spikes or rapid fluctuations, have little effect on them. To determine this reference frequency, a different frequency band-pass filtered is performed and compared to determine which frequency range can achieve stable and smooth signal level. This approach can also use Time-Frequency Domain Analysis to identify the reference frequency to use. To ensure the long-term efficacy and robustness of the signal stabilization, the stimulus application system may be further configured to perform a periodic and adaptive re-identification of the optimal reference frequency. The initial reference frequency, while optimal at the time of calibration, may become suboptimal over time due to factors such as lead micro-migration, tissue encapsulation, or other physiological changes in the patient. Accordingly, in an embodiment, the at least one controller is configured to autonomously and periodically initiate a re-calibration procedure. This procedure may be executed at predetermined intervals (e.g., daily or weekly) or may be triggered by the detection of a predefined event, such as a degradation in signal quality or a detected change in therapy efficacy. During this re-calibration, the controller can repeat the reference frequency identification process and frequency range of interest identification process by analyzing recently acquired signal data and opportunistically identifying signal segments corresponding to postural changes. The controller then re-evaluates the signal components against the selection criteria of static-state stability and transitional-state sensitivity. If this analysis reveals a new frequency band that serves as a more robust proxy for lead migration than the currently stored reference frequency, the controller is configured to update the system parameters and utilize this new, more optimal reference frequency for subsequent signal decoding and normalization. This adaptive capability ensures the therapy remains personalized and effective for the specific patient across the entire lifetime of the implant. In some embodiments, this re-calibration analysis may be performed by an external device, such as the programmer or the cloud platform, which then transmits the updated reference frequency parameters to the implant device. After processing the data, the decoding result can be obtained by dividing the smoothed mean value of the data in the frequency range of interest by the smoothed mean value data in the reference frequency band at step 322. This decoded result can further be smooth with by calculating a moving average to reduce data fluctuation at step 324. The decoding result can then be output at step 326. Figures 12A-12C graphically depict an example of this process 300. Figure 12A depicts the decoded data in the frequency range of interest and Figure 12B depicts the reference signal. When the decoded data is divided by the reference data, the resulting decoded signal is depicted in Figure 12C. As can be seen in this figure, the signal has been stabilized such that lead migration caused by the user, e.g., sitting, laying, standing, etc. does not cause fluctuations in the signal. With regard to the above detailed description, like reference numerals used therein may refer to like elements that may have the same or similar dimensions, materials, and configurations. While particular forms of embodiments have been illustrated and described, it will be apparent that various modifications can be made without departing from the spirit and scope of the embodiments herein. Accordingly, it is not intended that the invention be limited by the forgoing detailed description. In an embodiment, a stimulus application system can include an implant device configured to be implanted in a body of a patient including a sensor unit including a detection lead configured to detect physiological signals at a predetermined detection location in the body at predetermined time interval and a stimulus circuit unit configured to apply an electrical stimulus to a predetermined stimulation location in the body upon receiving a stimulation instruction. At least one controller can be configured to receive raw detection information pertaining to the physiological signals detected by the sensor unit at each predetermined time interval, the raw detection information varying based on a distance between the detection lead and the predetermined detection location, decode the raw detection information such that the decoded detection information does not vary based on the distance between the detection lead and the predetermined detection location and analyze the decoded detection information to determination if a stimulation instruction should be generated. . In embodiments, the at least one controller decoding the raw detection information includes filtering and / or performing Time-Frequency Domain Analysis of the detection information to obtain filtered detection information, wherein this filtered detection information can be a physiological signal of interest and detection lead reference position. In embodiments, the at least one controller filtering the raw detection information includes performing a band-pass filter on the raw detection information in a frequency range of interest to obtain a physiological signal of interest and detection lead reference position. In embodiments, the at least one controller decoding the raw detection information includes processing both the raw detection information and the filtered detection information or physiological signal of interest and the detection lead reference position. In embodiments, the at least one controller processing the raw detection information and the filtered detection information includes obtaining a single value representing the raw detection information and a single value representing the filtered detection information for each predetermined time interval. In embodiments, the at least one controller processing the physiological signal of interest and the detection lead reference position includes obtaining a single value representing the physiological signal of interest and a single value representing the detection lead reference position for each predetermined time interval. In embodiments, the at least one controller processing both the raw detection information and the filtered detection information includes dividing a result of processing the filtered detection information by a result of processing the raw detection information. In embodiments, the at least one controller processing both the physiological signal of interest and the detection lead reference position includes dividing a result of processing the physiological signal of interest by a result of processing the detection lead reference position. In embodiments, the detection information comprises a plurality of data points. In embodiments, the at least one controller decoding the detection information includes obtaining an absolute value of each of the plurality of data points and a mean of the absolute value of each of the plurality of data points. In embodiments, the at least one controller performs data smoothing for the raw detection information, filtered detection information, physiological signal of interest, detection lead reference position and / or absolute value of each of the plurality of data points. In embodiments, the predetermined detection location is a nerve. In embodiments, the predetermined stimulation location is a nerve. In embodiments, the nerve is the sacral nerve. In embodiments, the raw detection information comprises nerve activity data. In embodiments, the at least one controller is part of the implant device. In an embodiment, the system can further include a wearable recharging device configured to recharge a battery of the implant device. In embodiments the system can further include an external programmer configured to wirelessly communicate with the implant device and the at least one controller is part of the external programmer. In an embodiment a method includes applying electrical stimulation to a predetermined stimulation location in a body of a patient using the implant device and / or the system as described above. The entirety of each patent, patent application, publication, and document referenced herein is hereby incorporated by reference. Citation of the above patents, patent applications, publications and documents is not an admission that any of the foregoing is pertinent prior art, nor does it constitute any admission as to the contents or date of these documents. Modifications may be made to the foregoing embodiments without departing from the basic aspects of the technology. Although the technology may have been described in substantial detail with reference to one or more specific embodiments, changes may be made to the embodiments specifically disclosed in this application, yet these modifications and improvements are within the scope and spirit of the technology. The technology illustratively described herein may suitably be practiced in the absence of any element(s) not specifically disclosed herein. The terms and expressions which have been employed are used as terms of description and not of limitation and use of such terms and expressions do not exclude any equivalents of the features shown and described or portions thereof and various modifications are possible within the scope of the technology claimed. Although the present technology has been specifically disclosed by representative embodiments and optional features, modification and variation of the concepts herein disclosed may be made, and such modifications and variations may be considered within the scope of this technology.

Claims

What is claimed is:

1. A stimulus application system, comprising: an implant device configured to be implanted in a body of a patient including a sensor unit including a detection lead configured to detect physiological signals at a predetermined detection location in the body at predetermined time intervals and a stimulus circuit unit configured to apply a stimulus to a predetermined stimulation location in the body upon receiving a stimulation instruction; and at least one controller configured to: receive raw detection information pertaining to the physiological signals detected by the sensor unit at each predetermined time interval, the raw detection information varying based on a distance between the detection lead and the predetermined detection location; decode the raw detection information such that the decoded detection information is substantially independent of the distance between the detection lead and the predetermined detection location; and analyze the decoded detection information to determine if a stimulation instruction should be generated.

2. The system of claim 1, wherein the at least one controller decoding the raw detection information includes filtering the detection information to obtain filtered detection information.

3. The system of claim 2, wherein the at least one controller decoding the raw detection information includes processing both the raw detection information and the filtered detection information.

4. The system of claim 3, wherein the at least one controller processing both the raw detection information and the filtered detection information includes dividing a result of processing the filtered detection information by a result of processing the raw detection information.

5. The system of claim 2, wherein the at least one controller decoding the raw detection information comprises:filtering the raw detection information in a first frequency range of interest to obtain the filtered detection information, the filtered detection information representing a physiological signal of interest; filtering the raw detection information in a second frequency range to obtain a lead position reference signal, wherein the second frequency range is different from the first frequency range; and dividing a value derived from the filtered detection information by a value derived from the lead position reference signal.

6. The system of claim 5, wherein the first frequency range of interest is predetermined by a process comprising correlating a power spectral density of the raw detection information with a corresponding physiological marker and selecting the frequency range with a high statistical correlation.

7. The system of claim 5 or claim 6, wherein the second frequency range is predetermined by a process comprising analyzing the raw detection information during patient postural changes and selecting the frequency range that exhibits high signal stability during a constant posture and high signal sensitivity between different postures.

8. The system of any preceding claim, further comprising an auxiliary sensor configured to generate a reference signal indicative of a physical state of the patient, wherein the at least one controller is configured to decode the raw detection information by normalizing a value derived from the raw detection information with a value derived from the reference signal generated by the auxiliary sensor.

9. The system of claim 8, wherein the auxiliary sensor comprises at least one of an accelerometer, an Inertial Measurement Unit (IMU), a gyroscope, a magnetometer, or a pressure sensor.

10. The system of any preceding claim, wherein the at least one controller is further configured to periodically and autonomously initiate a re-calibration procedure to re- identify an optimal parameter for a reference signal used for decoding the raw detection information, and to update a system parameter based on the re-identified optimal parameter.

11. The system of claim 10, wherein the controller is configured to initiate the re- calibration procedure at predetermined time intervals or in response to a detected degradation in signal quality.

12. The system of any preceding claim, wherein the at least one controller, when analyzing the decoded detection information, is further configured to: prevent the generation of the stimulation instruction if a current value of the decoded detection information is less than a previous value of the decoded detection information, even if the current value is above a stimulation threshold.

13. The system of any preceding claim, wherein the predetermined detection location and the predetermined stimulation location is a nerve.

14. The system of claim 13, wherein the nerve is the sacral nerve.

15. The system of any of claims 1-14, further comprising an external programmer configured to wirelessly communicate with the implant device.

16. The system of any of claims 1-15, wherein the at least one controller is part of the implant device.

17. A method for stimulus application, the method comprising: detecting, with a sensor unit of an implantable device, physiological signals at a predetermined detection location in the body, thereby generating raw detection information that varies based on a distance between a detection lead of the sensor unit and the predetermined detection location; decoding, with at least one controller, the raw detection information to generate decoded detection information that is substantially independent of the distance between the detection lead and the predetermined detection location; analyzing the decoded detection information to determine if a stimulation instruction should be generated; and applying, with a stimulus circuit unit of the implantable device, a stimulus to a predetermined stimulation location in response to the generation of thestimulation instruction.

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