Systems and methods for adaptive deep brain stimulation

Abstract

In some variations provided herein, a system for deep brain stimulation includes an implantable device that acquires and stores neural activity signal recordings and applies electrical stimulation. The system further includes a personal controller device that establishes a first wireless connection to the implantable device. The personal controller device transmits power to the implantable device, and the implantable device transmits the neural activity signal recordings to the personal controller device via the first wireless connection. The system further includes a clinician programmer device that receives the neural activity signal recordings from the implantable device by establishing a second wireless connection based on activation of the first wireless connection. The clinician programmer device sets one or more stimulation parameters based on the neural activity signal recordings.

Description

[Technical Field]

[0001] The present disclosure relates generally to the field of deep brain stimulation, and more particularly to methods and devices that enable data communication and data storage for adaptive deep brain stimulation systems. [Background technology]

[0002] Deep brain stimulation (DBS) systems have many advantages and are used in a variety of industries, including medical diagnostics and healthcare. For example, DBS delivers electrical impulses to neural structures in a patient's central nervous system, modulating neural activity. Patients' neural activity can also be studied in conjunction with DBS. However, DBS devices implanted in living tissue are typically compact for safety reasons and patient comfort, and therefore have limited power capacity, data processing, data storage, and communication interface capabilities available for operation. Furthermore, conventional DBS devices are often programmed by physicians for predefined stimulation settings. However, individual patients often exhibit unusual symptoms, peaks of neural activity, and frequency bands of neural activity. Such patients may benefit from adaptive, patient-specific calibration. Due to the aforementioned limitations in power capacity, data processing, data storage, and communication interface capabilities of known DBS systems, providing effective and efficient adaptive DBS continues to be sought. Therefore, new and improved systems and methods for DBS are needed. Summary of the Invention

[0003] In general, in some variations, a system for deep brain stimulation may include an implantable device that acquires and stores neural activity signal recordings and applies electrical stimulation. The system may further include a personal controller device that establishes a first wireless connection (e.g., Bluetooth® communication) to the implantable device. The personal controller device may transmit power to the implantable device, and the implantable device may transmit the neural activity signal recordings to the personal controller device via the first wireless connection. The system may further include a clinician programmer device that receives the neural activity signal recordings from the implantable device by establishing a second wireless connection based on activation of the first wireless connection. The clinician programmer device sets stimulation parameters based on the neural activity signal recordings. The clinician programmer device further establishes a second wireless connection (e.g., industrial, scientific, and medical (ISM) communication, short-range device (SRD) communication, etc.) to the implantable device based on activation of the first wireless connection (e.g., upon authentication of a personal identification number entered by a user).

[0004] In some implementations, the power can be inductive power induced in the implanted device. In some implementations, the neural activity signal recordings can be automatically transmitted by the implanted device to the personal controller device during a recharging process and / or on demand.

[0005] The personal controller device generally includes a first unit and a second unit. The first unit may be removably connected to the second unit and may provide power to the second unit when connected to the second unit. The first unit may include memory (e.g., solid-state memory) that stores neural activity signal recordings. The personal controller may further be configured to display an indication of the power status of the implanted device or an indication of the therapy mode of the implanted device. In some variations, the personal controller may receive a signal to change the therapy mode of the implanted device.

[0006] The clinician programmer device may include a custom-designed programmable electronic device, a smartphone, a tablet, and / or a personal computing device. In some cases, the stimulation parameters are first stimulation parameters and the therapy mode is a first therapy mode. The clinician programmer device may be configured to generate a second therapy mode and second stimulation parameters. The clinician programmer device may further transmit the second therapy mode and second stimulation parameters to the implantable device.

[0007] The system may further include a user computing device having an application. The user computing device may be configured to receive patient log data and neural activity signal recordings. In some cases, the patient log data may be recorded and / or received from the implantable device and / or the personal controller device. The user computing device may associate the patient log data and the neural activity signal recordings based on at least a time correlation between the patient log data and the neural activity signal recordings. The user computing device may further determine a medication-on time interval and / or a medication-off time interval based on the neural activity signal recordings and the patient log data. The user computing device may further generate stimulation parameters according to the neural activity signal recordings during the medication-on time interval and the medication-off time interval. In some cases, the medication-on time interval and the medication-off time interval may be determined by a user of the user computing device (e.g., a physician, clinician, etc.). In some cases, the user may determine a threshold value for classifying time intervals into medication-on time intervals and medication-off time intervals. For example, a time interval in which the neural activity signal recordings have an amplitude greater than a threshold value may be classified as a medication-off time interval.

[0008] In some implementations, the user computing device may include a smartphone, a tablet, a personal computing device, etc. The user computing device and / or the clinician programmer device may generate and display plots of the neural activity signal recordings or statistical distributions of the neural activity signal recordings.

[0009] The user computing device may be further configured to extract spectral features within a frequency band of the neural activity signal recording recorded during a predetermined period. The user computing device may determine a medication-on time interval and a medication-off time interval during the predetermined period. The user computing device may further generate a first average value of the spectral features within the medication-on time interval of the frequency band and a second average value of the spectral features within the medication-off time interval of the frequency band. The user computing device may generate stimulation parameters based on the first average value and the second average value. The frequency band may be a low frequency band, an alpha frequency band, or a beta frequency band and a gamma frequency band.

[0010] In some variations, the stimulation parameters may include at least one of a stimulation frequency, a stimulation pulse width, a stimulation amplitude, a superior neural activity signal threshold, and / or an inferior neural activity signal threshold.

[0011] In some implementations, neural activity signal recordings and patient-logged data may be time-recorded. The neural activity signal recordings may include local field potential recordings (e.g., from both hemispheres) in low-frequency, alpha-frequency, beta-frequency, and / or gamma-frequency bands. The local field potentials may include electric field potentials, electromagnetic field potentials, magnetic field potentials, and / or other suitable field potentials. In some variations, the neural activity signal recordings and logged data may be continuously recorded and stored by the implanted device. In some variations, the neural activity signal recordings and logged data are recorded and stored by the implanted device at discrete time intervals.

[0012] The user computing device and / or clinician programmer device may periodically transmit the neural activity signal recordings and / or patient log data or stimulation parameters to the biobank server. In some cases, the user computing device and / or clinician programmer device may delete the neural activity signal recordings and / or patient log data or stimulation parameters from the memory of the user computing device and / or clinician programmer device. Periodically clearing the memory may be advantageous to reduce memory usage of the user computing device and / or clinician programmer device.

[0013] In some variations, the clinician programmer device may establish an authenticated communication channel with the personal controller device. The personal controller device may transmit stimulation parameters (received from the clinician programmer device) to the implanted device. The personal controller device may be further configured to display an indication of the implanted device's remaining power status or an indication of the implanted device's therapy mode. The stimulation parameters include at least one of a stimulation frequency, a stimulation pulse width, a stimulation amplitude, a superior neural activity signal threshold, and / or a inferior neural activity signal threshold.

[0014] The clinician programmer device may receive patient log data from the user computing device and associate the patient log data with the neural activity signal recordings based on a time correlation between at least the patient log data and the neural activity signal recordings. The neural activity signal recordings may include local field potential recordings in the low, alpha, beta, and / or gamma frequency bands. The clinician programmer device may then determine medication-on and medication-off time intervals based on the neural activity signal recordings and the patient log data received from the user computing device. The clinician programmer device may further be configured to generate stimulation parameters according to the neural activity signal recordings during the medication-on and medication-off time intervals.

[0015] In some cases, the stimulation parameters are first stimulation parameters and the therapy mode is the first therapy mode. The clinician programmer device may be configured to generate a second therapy mode and second stimulation parameters and transmit the second therapy mode and second stimulation parameters to the personal controller device. The personal controller device may then transmit the second stimulation parameters and the second therapy to the implantable device.

[0016] The clinician programmer device may be further configured to extract spectral features within frequency bands of the neural activity signal recordings recorded during a predetermined time period. The clinician programmer device may determine medication-on time intervals and medication-off time intervals during the predetermined time period. The clinician programmer device may further generate first average values ​​of the spectral features within the medication-on time intervals of the frequency bands and second average values ​​of the spectral features within the medication-off time intervals of the frequency bands. The clinician programmer device may generate stimulation parameters based on the first average values ​​and the second average values.

[0017] In some implementations, the user computing device and / or clinician programmer device may be configured to train a machine learning model based on past neural activity signal recordings or a set of past stimulation parameters. Once the machine learning model is trained, the user computing device and / or clinician programmer device may identify stimulation parameters by executing the machine learning model based on the neural activity signal recordings.

[0018] In general, in some variations, a method for deep brain stimulation may include receiving neural activity signal recordings acquired over a predetermined period of time. The neural activity signal recordings may be acquired by an implantable device, and the predetermined period of time may be 1 day, 5 days, 10 days, etc. The method may further include mapping the neural activity signal recordings with medication-on and medication-off time intervals determined based on patient log data. The method may further include extracting spectral features within frequency bands of the neural activity signal recordings. The spectral features within the frequency bands may include values ​​of the spectral features for time intervals within the predetermined period of time. The method may further include generating first average values ​​of the spectral features within the medication-on time intervals of the frequency bands over the predetermined period of time and second average values ​​of the spectral features within the medication-off time intervals of the frequency bands over the predetermined period of time. The method may further include generating stimulation parameters based on the first average values ​​and the second average values.

[0019] A method for deep brain stimulation may include measuring a set of impedance values ​​of a first set of electrodes. For example, measuring the set of impedance values ​​is performed during a medication-off period. The method may further include comparing the set of impedance values ​​to an acceptable impedance range to identify a second set of electrodes having impedance values ​​within the acceptable impedance range. The method may further include screening a set of neural activity signal recordings of the patient using the second set of electrodes. The method may further include selecting a third set of electrodes exhibiting the best neural activity signal recordings from the set of neural activity signal recordings.

[0020] In some implementations, the method includes determining a minimum stimulation amplitude A that elicits a detectable clinical benefit in a patient. MIN , and the maximum stimulation amplitude A before inducing side effects in the patient MAX The stimulation parameters may further include defining a minimum stimulation amplitude A MIN and / or maximum stimulation amplitude A MAX The first average value may include the minimum beta frequency band power value P βMINand / or the second average value may include a maximum beta frequency band power value P βMAX may include:

[0021] In some implementations, the stimulation parameters include a DBS amplitude V DBS The DBS amplitude may be generally defined as:

number

[0022] In some implementations, the method may further include determining a peak frequency of the neural activity signal recording within the frequency band and selecting a patient-specific frequency band based on the peak frequency. The method may further include delivering electrical stimuli according to the stimulation parameters to stimulate neural tissue using the implanted device.

[0023] In some implementations, the neural activity signal recording may be generally acquired by an implantable device. The predetermined period may be determined based on an indication of a measured remaining power of the implantable device or may be determined based on an indication of a remaining memory of the implantable device. The method may further include transmitting an indication of the measured remaining power or the remaining memory of the implantable device to the personal controller device, such that the indication of the remaining power or the indication of the remaining memory is displayed to a user of the personal controller device via a user interface. [Brief explanation of the drawings]

[0024] [Figure 1] FIG. 1 is a block diagram of an exemplary deep brain stimulation system. [Figure 2A] 1 is a schematic illustration of an exemplary deep brain stimulation system. [Figure 2B] 1 is a schematic illustration of an exemplary deep brain stimulation system. [Figure 3A]1 is a schematic illustration of an exemplary clinician programmer device. [Figure 3B] 1 is a schematic illustration of an exemplary implantation of an implantable device. [Figure 4A] FIG. 1 is a schematic diagram of one variation of the external housing of the implantable device. [Figure 4B] FIG. 1 is a block diagram of an exemplary implantable device. [Figure 5] FIG. 1 is a block diagram of an exemplary clinician-external device. [Figure 6A] 1 is a schematic illustration of an exemplary patient personal controller device. [Figure 6B] 1 is a schematic illustration of an exemplary patient personal controller device. [Figure 7] FIG. 1 is a schematic diagram of an exemplary method for establishing a wireless connection between an implantable device and a patient personal controller device. [Figure 8] FIG. 1 is a block diagram of an exemplary patient personal controller device. [Figure 9A] 1 is a schematic illustration of an exemplary clinician programmer device. [Figure 9B] 1 is a schematic illustration of an exemplary clinician programmer device. [Figure 10] FIG. 1 is a block diagram of an exemplary clinician programmer device. [Figure 11] 1 is an exemplary method for selecting a set of sensing electrodes, a set of stimulating electrodes, and a power band. [Figure 12] 1 is an exemplary method for adaptive deep brain stimulation. [Figure 13] 1 is an exemplary method for programming a clinician programmer device. [Figure 14] 1 is an exemplary method for programming a clinician programmer device. [Figure 15A] 1 is an exemplary neural activity signal recording stored and analyzed by a deep brain stimulation system. [Figure 15B] 1 is an exemplary neural activity signal recording stored and analyzed by a deep brain stimulation system. [Figure 16] 1 is a flowchart of an exemplary communication method and data flow between supporting components of a deep brain stimulation system. DETAILED DESCRIPTION OF THE INVENTION

[0025] Non-limiting examples of various aspects and variations of the present invention are described herein and illustrated in the accompanying drawings.

[0026] Described herein are exemplary deep brain stimulation systems and methods suitable for reliable and safe deep brain stimulation. The deep brain stimulation systems and methods described herein include implantable devices, patient personal controller devices, user computing devices, and / or clinician programmer devices that can be communicatively coupled to each other to communicate and process data for adaptive or conventional deep brain stimulation.

[0027] One or more deep brain stimulation systems described herein may record, store, communicate, and analyze a patient's neural activity signal recordings for effective and efficient adaptive deep brain stimulation. Furthermore, one or more deep brain stimulation systems may provide adaptive deep brain stimulation (aDBS) by modifying a patient's stimulation parameters in real time based on the neural activity signal recordings. The real-time recording, communication, and / or analysis of a patient's neural activity signal recordings and aDBS may significantly improve clinical outcomes with one or more deep brain stimulation systems described herein compared to conventional deep brain stimulation (cDBS). Using aDBS with one or more deep brain stimulation systems described herein may enable more time-oriented refinement / optimization of stimulation parameters, which may lead to new treatment methods and insights into a patient's condition. Deep Brain Stimulation (DBS) Systems and Data Flow Between Various Devices in a DBS System

[0028] 1 is a block diagram of an exemplary deep brain stimulation system 100. The deep brain stimulation system 100 includes an implantable device 101 (also referred to herein as an “implantable pulse generator (IPG) device”), a patient personal controller device 111, a user computing device 121 (also referred to herein as an “app”), and a clinician programmer device 131 (also referred to herein as a “programmer device”). The implantable device 101 is operably coupled to the patient personal controller device 111 and the clinician programmer device. The patient personal controller device 111 is operably coupled to the user computing device 121 and, in some implementations, may further be operably coupled to the clinician programmer device 131. In some implementations, the user computing device may be operably coupled to the programmer device 131. Brain stimulation system 100 may be used to administer deep brain stimulation to a patient by collecting data from the patient and communicating / analyzing the data between / using implantable device 101, patient personal controller device 111, user computing device 121, and / or clinician programmer device 131, effectively using their respective storage and processing capabilities. User computing device 121 is connected to or operably coupled to biobank server 160 via network 150. Alternatively, or in addition, in some implementations, clinician programmer device 131 may be connected to or operably coupled to biobank server 160 via network 150.

[0029] The patient personal controller device 111, the user computing device 121, and / or the clinician programmer device 131 may each include a hardware-based computing device and / or multimedia device, such as, for example, a smartphone, a tablet, a wearable device, a desktop computer, a laptop, a custom-built computing device, etc. Additionally, each of the patient personal controller device 111, the user computing device 121, and / or the clinician programmer device 131 may be powered by a plug-in and / or include a rechargeable battery. The implantable device 101 may be powered by a rechargeable battery, which may be powered by a direct electrical connection and / or by induction.

[0030] The implantable device 101 described herein is an implantable and rechargeable neurostimulator that can be operably coupled to a patient personal controller device 111 for initialization and / or to a clinician programmer device 131 for programming. The implantable device 101 includes a processor 102, a memory 103, and a communication interface 104 and can be implanted in a patient to record, store, and / or analyze a set of neural activity signal recordings and / or further provide a set of stimulation to the patient based on a set of stimulation parameters. In some variations, the implantable device may further include a battery and / or a set of connectors (e.g., an octapolar connector) for storing power. The implantable device 101 can connect to a deep brain stimulation (DBS) probe extension. The implantable device 101 can provide adaptive DBS (aDBS) and / or conventional DBS (cDBS) to the patient via the probe extension.

[0031] The processor 102 may include, for example, a hardware-based integrated circuit (IC) or any other suitable processing device configured to execute or execute a set of instructions or code. For example, the processor 102 may include a general-purpose processor, a central processing unit (CPU), an accelerated processing unit (APU), an application-specific integrated circuit (ASIC), etc. The processor 102 is operably coupled to the memory 103 via a system bus (e.g., an address bus, a data bus, and / or a control bus, not shown). The processor 102 is operably coupled to the memory 103 via a system bus (e.g., an address bus, a data bus, and / or a control bus, not shown). In some variations, the processor 102 includes and / or is operably coupled to a Vstim generator, a diagnostic device, a current controller, a waveform generator, an impedance measuring device, a signal processing controller, etc.

[0032] The memory 103 of the implantable device 101 may be, for example, a memory buffer, random access memory (RAM), read-only memory (ROM), a flash drive, a secure digital (SD) memory card, an embedded multi-time programmable (MTP) memory, an embedded multimedia card (eMMC), a universal flash storage (UFS) device, etc. The memory 103 may store, for example, one or more codes including instructions for causing the processor 102 to perform one or more processes or functions (e.g., recording a set of neural activity signal recordings, generating a set of pulse signals, etc.).

[0033] The communication interface 104 of the implantable device 101 may be a hardware component of the first computing device 101 operably coupled to the processor 102 and / or the memory 103. The communication interface 104 may be operably coupled to and used by the processor 102. The communication interface 104 may be, for example, a network interface card (NIC), a Wi-Fi® module, a Bluetooth® module, an optical communication module, and / or any other suitable wired and / or wireless communication interface (i.e., Wireless Medical Telemetry Service (WMTS), Medical Device Wireless Communication Service (MedRadio), Medical Implant Communication Service (MICS), Medical Micropower Network (MNN), Medical Body Area Network (MBAN), etc.). The communication interface 104 may be configured to connect the implantable device 101 to the patient personal controller device 111, the user computing device 121, and / or the clinician programmer device 131, as described in further detail herein. In some cases, communication interface 104 may facilitate receipt and / or transmission of sets of neural activity signal recordings and / or sets of stimulation parameters to and from each patient personal controller device 111, user computing device 121, and / or clinician programmer device 131 communicatively coupled to implantable device 101. In some cases, data received via communication interface 104 may be processed by processor 102 or stored in memory 103, as described in further detail herein.

[0034] The patient personal controller device 111 described herein may establish a first wireless connection (RF wireless connection) to the implantable device 101 and / or provide power (inductive power, radio frequency (RF) power harvesting, etc.) to the implantable device 101 for operation. The patient personal controller device 111 may receive / transmit data (e.g., neural activity signal recordings, sets of stimulation parameters, etc.) to the implantable device 101 via the first wireless connection. The patient personal controller device 111 includes a processor 112, a memory 113, and a communication interface 114, which may be structurally and / or functionally similar to the processor 102, the memory 103, and the communication interface 104, respectively. In some examples, the patient personal controller device 111 may receive sets of neural activity signal recordings from the implantable device 101 via the communication interface 114 and store the sets of neural activity signal recordings in the memory 113. In some variations, the patient personal controller device 111 may include a first component and a second component, each having a processor, memory, and communication interface structurally and / or functionally similar to the processor 102, memory 103, and communication interface 104. The first component may be used to recharge the implantable device 101, and the second component may be used to connect to and provide power to the first component.

[0035] User computing device 121 includes processor 122, memory 123, and communication interface 124, which may be structurally and / or functionally similar to processor 102, memory 103, and communication interface 104, respectively. In some cases, user computing device 121 may be a personal device such as a mobile phone, tablet, computing device, watch, virtual reality device, etc. User computing device 121 may include an application (not shown) as software received from communication interface 124, stored in memory 123, and executed by processor 122. For example, code may cause the processor to analyze a set of neural activity data records. Alternatively, the application may be a hardware-based device that may be attached to user computing device 121. For example, an integrated circuit (IC) may cause user computing device 121 to analyze a set of neural activity data records.

[0036] The user computing device 121 described herein may connect and / or be operatively coupled to the patient personal controller device 111 to receive and / or transmit data including neural activity signal recordings. In some cases, the user computing device 121 may receive and store patient log data. The patient log data may be received from the patient personal controller device 111 and / or a user of the user computing device 121 (e.g., the patient, the patient's guardian, the patient's artificial intelligence personal assistant, etc.) and may include, for example, a sequential / chronological description of events (e.g., hourly, daily, weekly, etc.), medication consumption history, etc.

[0037] The application of the user computing device 121 may associate the patient log data and the neural activity signal recordings based on at least a temporal correlation between the patient log data and the neural activity signal recordings. The user computing device 121 may further determine a set of medication-on time intervals and a set of medication-off time intervals based on the neural activity signal recordings and the patient log data. The application may further generate stimulation parameters based on the neural activity signal recordings during the set of medication-on time intervals and medication-off time intervals.

[0038] In some variations, the application may be included / implemented on the patient personal controller device 111 and / or the clinician programmer device 131. For example, the patient personal controller 111 may receive data including a set of neural activity data records from the implantable device 101 and patient log data from a user of the patient personal controller device 111. The patient personal controller 111 may then determine a set of medication-on time intervals and medication-off time intervals based on the set of neural activity data records and the patient log data. In some variations, the application may be implemented on a web service provider and accessed via an application programming interface (API) downloaded and / or installed on the user device 121, the patient personal controller device 111, and / or the clinician programmer device 131.

[0039] The clinician programmer device 131 includes a processor 132, a memory 133, and a communication interface 134, which may be structurally and / or functionally similar to the processor 102, the memory 103, and the communication interface 104, respectively. The clinician programmer device 131 may establish a second wireless connection (RF wireless connection) to the implanted device 101 based on activation of the first wireless connection. The clinician programmer device 131 may store and analyze sets of neural activity signal recordings to provide a stimulation / treatment mode (i.e., cDBS treatment planning mode, aDBS treatment mode) to the implanted device 101. The clinician programmer device 131 may be used to program the implanted device 101. The clinician programmable device 131 may connect or operably couple to the implanted device 101 (e.g., via a 2.5 GHz radio frequency (RF) communication protocol) to receive sets of neural activity signal recordings from the implanted device 101 and / or transmit sets of stimulation parameters to the implanted device 101.

[0040] In one example, the implantable device 101 may initially include a first set of stimulation parameters and a first treatment mode and record a set of neural activity signal recordings (e.g., local field potentials (LFPs)). The first treatment mode may include, for example, a timetable for providing stimulation based on the first stimulation parameters. The implantable device 101 may transmit the set of neural activity signal recordings to the clinician programmer device 131. A clinician (e.g., a doctor, nurse, etc.) using the clinician programmer device 131 may then determine and / or provide a second set of stimulation parameters and / or a second treatment mode based on the set of neural activity signal recordings received from the implantable device 101. The clinician programmer device 131 may transmit the second treatment mode and / or the second set of stimulation parameters to the implantable device 101.

[0041] In some embodiments, the second wireless connection may be an authenticated wireless connection. For example, the second wireless connection may be established only after a user of the clinician programmer device 131 enters a personal identification number (PIN). In some cases, authentication of the second wireless connection occurs after the embedded device 101 and the clinician programmer device 131 exchange keys.

[0042] In some variations, the clinician programmer device 131 may receive neural activity signal recordings from the implantable device 101 based on activation of the first wireless connection without establishing a second wireless connection with the implantable device 101. In such variations, the clinician programmer device 131 may provide therapy modes to the implantable device 101 via the patient personal controller device 111.

[0043] In some variations, the clinician programmer device 131 may be connected and / or operably coupled to a clinician-external device for impedance measurements of externalized probe extensions in the operating room. Impedance measurements may include measuring electrical resistance, capacitance, inductance, etc. The clinician programmer device 131 may further format the impedance values ​​by normalizing the impedance values ​​to a common / standardized scale, parse the impedance values, and / or display the impedance values ​​to a user of the clinician programmer device 131.

[0044] Network 150 may be a digital telecommunications network of servers and / or computing devices. The servers and / or computing devices of the network may be connected via one or more wired or wireless communication networks (not shown) to share resources such as, for example, data storage, connectivity services, and / or computing power. The wired or wireless communication network between the servers and / or computing devices of network 150 may include one or more communication channels, such as radio frequency (RF) communication channels, extremely low frequency (ELF) communication channels, extremely low frequency (ULF) communication channels, low frequency (LF) communication channels, medium frequency (MF) communication channels, ultra high frequency (UHF) communication channels, extremely high frequency (EHF) communication channels, optical fiber communication channels, electronic communication channels, satellite communication channels, etc. Network 150 may include, for example, the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), worldwide interoperability of microwave access networks (WiMAX), a virtual network, any other suitable communication system, and / or a combination of such networks.

[0045] Biobank server 160 may include a server and / or computing device that may be operably coupled to user computing device 121 and / or programmer device 131 via network 150. Biobank server 160 may provide data storage to user computing device 121 and / or programmer device 131. In some embodiments, biobank server 160 may provide connectivity and / or computing services to user computing device 121 and / or programmer device 131 in addition to data storage. In some variations, biobank server 160 may include and / or execute cloud-based services, such as, for example, software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS), etc. In some cases, biobank server 160 receives, processes, and stores neural activity data records, patient log data, and / or stimulation parameters. In some implementations, the biobank server 160 generates a time-stamped version of each set of neural activity data records, patient log data, and / or set of stimulation parameters before storing them in a database (e.g., a Structured Query Language (SQL) database).

[0046] As shown in FIG. 1 , deep brain stimulation system 100 may include an implantable device 101 that acquires and stores neural activity signal recordings and applies electrical stimulation. Deep brain stimulation system 100 further includes a patient personal controller device 111 that establishes a wireless connection to implantable device 101 to receive the neural activity signal recordings and recharge the battery of implantable device 101. The patient personal controller device transmits power to the implantable device, which transmits the neural activity signal recordings to the patient personal controller device via the wireless connection. Patient personal controller device 111 may be operatively coupled (e.g., via a Bluetooth connection, a WiFi connection, etc.) and transmit the neural activity signal recordings and / or patient log data to a user computing device 121. User computing device 121 analyzes the neural activity signal recordings and / or patient log data to determine medication-on time intervals and medication-off time intervals based on the neural activity signal recordings and generate a set of stimulation parameters. Deep brain stimulation system 100 further includes a clinician programmer device 131 that establishes a second wireless connection to implantable device 101 based on activation of the first wireless connection, receives the neural activity signal recordings, and sets stimulation parameters based on the neural activity signal recordings. User computing device 121 may be further configured to connect to network 150 via a network connection (e.g., a WiFi connection, a fifth generation (5G) network connection, etc.) and transmit the neural activity signal recordings, patient log data, and / or stimulation parameters to biobank server 160 via network 150.

[0047] In some cases, the user computing device 121 includes a graphical user interface (GUI) for displaying plots of neural activity signal recordings or statistical distributions of neural activity signal recordings via the GUI of the user computing device 121. The statistical distribution of the neural activity recordings may include, for example, a running average, a daily mean value, a weekly mean value, a variance of the distribution of the neural activity recordings, a local maximum, a local minimum, a global maximum, a global minimum, etc.

[0048] In some cases, to determine the medication-on time intervals and the medication-off time intervals, the user computing device 121 processes (e.g., extracts, displays, etc.) a set of spectral features within a frequency band of the neural activity signal recording recorded during a predetermined period. The frequency band may include a low frequency band, an alpha frequency band, a beta frequency band, a gamma frequency band, etc. The user computing device 121 may further generate a first average value of the set of spectral features within the medication-on time intervals of the frequency bands and a second average value of the set of spectral features within the medication-off time intervals of the frequency bands. The user computing device 121 may generate stimulation parameters based on the first average value and the second average value.

[0049] In some embodiments, a patient identification card is provided to a user of deep brain stimulation system 100. The patient identification card may include information about the user, including the model of the set of devices of deep brain stimulation system 100, a set of names for the set of devices, a set of serial numbers for the set of devices, patient identification information, the date of implantation of implantable device 101 into the user, information about the treating clinician (such as name, phone number, qualifications, authorizations, etc.), information about the manufacturer, a note on whether the patient has implantable device 101 or other implantable devices, a note on whether the patient is eligible for diathermy, a note on whether magnetic resonance imaging (MRI) is contraindicated, general safety information, patient-specific safety information, the patient's medical history, etc. In some cases, the patient identification card may be stored in an application on implantable device 101, patient controller device 111, and / or user computing device 121.

[0050] In some embodiments, the clinician programmer device 131 is not operably coupled to the implantable device 101, and the patient personal controller device 111 may be operably coupled (e.g., via a Bluetooth connection, a WiFi connection, etc.) to transmit the neural activity signal recordings and / or the patient log data to the clinician programmer device 131. In such embodiments, the clinician programmer device 131 analyzes the neural activity signal recordings and / or the patient log data to determine medication-on time intervals and medication-off time intervals based on the neural activity signal recordings and generate a set of stimulation parameters. The clinician programmer device 131 may be further configured to connect to a network 150 via a network connection (e.g., a WiFi connection, etc.) and transmit the neural activity signal recordings, the patient log data, and / or the stimulation parameters to a biobank server 160 via the network 150.

[0051] In some variations, a cDBS treatment mode may be used in addition to an aDBS treatment mode. For example, a month's monitoring / observation of neural activity data records (e.g., local field potential activity stored as a numerical time series) may be stored in the patient controller and / or implanted device 101 and then transmitted to the user computing device 121 and / or clinician programmer device 131 for analysis. The user computing device 121 and / or clinician programmer device 131 may then generate a set of stimulation parameters and an aDBS treatment mode and transmit it to the patient controller and / or implanted device 101 for use.

[0052] 2A and 2B are schematic illustrations of an exemplary deep brain stimulation system according to some variations. As shown in FIG. 2A , the deep brain stimulation system may include an implantable device (also referred to as an IPG), a patient personal controller device (also referred to as a personal controller), a user computing device, and a clinician programmer device (also referred to as a “clinical programmer”). The IPG may acquire and store the patient's neural activity signal recordings and apply electrical stimulation to the patient. The personal controller establishes a wireless connection to the IPG, receives the neural activity signal recordings, and recharges the IPG's battery. The personal controller transmits power to the IPG (e.g., via an induction coil), and the IPG transmits the neural activity signal recordings to the personal controller via the wireless connection. The personal controller may be operably coupled (e.g., via a Bluetooth® connection, a WiFi connection, etc.) to and transmit the neural activity signal recordings and / or patient log data to the user computing device. The user computing device analyzes the neural activity signal recordings and / or the patient log data to determine medication-on time intervals and medication-off time intervals based on the neural activity signal recordings and generate a set of stimulation parameters. The user computing device may be further configured to connect to a network and transmit the neural activity signal recordings, the patient log data, and / or the stimulation parameters to a biobank server. The clinician programmer establishes a second wireless connection to the IPG based on activation of the first wireless connection, receives the neural activity signal recordings, and sets the stimulation parameters based on the neural activity signal recordings.

[0053] As shown in FIG. 2B, the deep brain stimulation system may include an IPG, a personal controller, and a clinician programmer. The IPG may acquire and store the patient's neural activity signal recordings and apply electrical stimulation to the patient. The personal controller establishes a wireless connection to the IPG, receives the neural activity signal recordings, and recharges the IPG's battery. The personal controller may operably couple (e.g., via Bluetooth, WiFi, etc.) and transmit the neural activity signal recordings and / or patient log data to a clinician programmer device. The clinician programmer device may analyze the neural activity signal recordings and / or patient log data to determine medication-on and medication-off time intervals based on the neural activity signal recordings and generate a set of stimulation parameters. The clinician programmer device may connect to a network and transmit the neural activity signal recordings, patient log data, and / or stimulation parameters to a biobank server.

[0054] FIG. 3A is a schematic illustration of one variation of a deep brain stimulation system. The deep brain stimulation system may include an IPG 314, a patient controller device 321, and a clinician programmer device 322. The IPG 314 may be operably connected to the patient 301 via an implantable probe 302, a burr hole cap 303, and a probe extension 304 to record neural activity signals and provide stimulation to the patient 301. The IPG 314 may record and store the patient's 301 neural activity signals and be operably coupled to the patient controller device 321 and the clinician programmer device 322. In some cases, the patient is wearing a T-shirt to help align the patient controller device 321 with the IPG 314 for data communication and power induction. The patient controller device 321 may store neural activity signal recordings from the IPG 314 and provide power to the IPG 314. In some cases, the neural activity signal recordings are deleted from the IPG's memory once they are transmitted to the patient controller device 321. The clinician programmer 322 may also receive neural activity signal recordings from the IPG 314 and set stimulation parameters in the IPG 314 based on the neural activity signal recordings.

[0055] 3B is a schematic illustration of one variation of a deep brain stimulation system that may be used to implant one or more probes in a patient. The deep brain stimulation system may be used by a clinician for open stimulation sessions (e.g., in a hospital, clinic, etc.). During an open stimulation session, the deep brain stimulation system may be configured to include a clinician-external device 323 connected to a probe extension 304 via a probe adapter 305. The clinician-external device 323 may generate a set of stimuli to be transmitted to the patient 301 and store neural activity signal recordings in a memory (not shown) of the clinician-external device 323. The clinician-external device 323 may be operably coupled to and transmit the neural activity signal recordings to a clinician programmer device 322.

[0056] FIG. 4A is a schematic diagram of one variation of the external housing of an implantable device. The implantable device (also referred to herein as an "implantable pulse generator (IPG) device") is compact and has a small volume (e.g., 10 cc, 20 cc, 30 cc, etc.) and / or weight (20 grams, 30 grams, etc.) because it can be implanted in a patient. FIG. 4B is a block diagram of one variation of the implantable device. The implantable device 400B includes a memory 401, an RF chip 402, a battery charger 403, a V stim generator 404, and a main controller 430. The implantable device 400B may further include a diagnostic device 405, a current controller 411, a waveform generator 412, an impedance measuring device 413, a probe 421, a REC electrode selector, a signal processing controller 423, a sensing device power regulator 424, and a local field potential (LFP) sensing device 425.

[0057] The memory 401 may store data including a set of neural activity records, a set of stimulation parameters, etc. The RF chip 402 may process incoming electromagnetic waves and / or process a set of electrical signals received from the main controller 430 to generate outgoing electromagnetic waves. The battery charger 403 may include a set of electrical circuits for providing power and charging the battery of the implantable device 400B. Vstim The generator 404 may generate the stimulation voltage dynamic. The main controller 430 may include, for example, a hardware-based integrated circuit (IC) or any other suitable processing device configured to execute or execute a set of instructions / code. For example, the main controller 430 may include a general-purpose processor, a central processing unit (CPU), an application-specific integrated circuit (ASIC), a microcontroller, etc. The main controller 430 includes the memory 401, the RF chip 402, the battery charger 403, the V stim The generator 404, the diagnostic device 405, the current controller 411, the waveform generator 412, the impedance measuring device 413, the probe 421, the REC electrode selector, the signal processing controller 423, the sensing device power regulator 424, and the LFP sensing device 425 may be operatively coupled to transmit a set of instructions (e.g., via a set of electrical circuits).

[0058] In some embodiments, the implantable device 400B may be initialized by a patient personal controller device. The patient personal controller device may be operably coupled to the implantable device 400B to set an initial set of parameters (e.g., a set of cDBS parameters for initial therapy and / or neural activity signal recording data collection) and start the implantable device 400B. In some embodiments, the implantable device may be programmed by a clinician programmer device. The clinician programmer device may be operably coupled to the implantable device 400B to program the implantable device 400B with a set of stimulation parameters (e.g., a set of aDBS parameters for patient-specific adaptive therapy and / or neural activity signal recording data collection).

[0059] 5 is a block diagram of an exemplary clinician-external device 500 according to some variations. The clinician-external device 500 (such as the clinician-external device shown and described with respect to FIG. 3B) is an external device for impedance measurements in the operating room from an externalized probe extension and can be used by a clinician (e.g., a doctor, nurse, etc.) on the day of implantation to confirm proper electrode placement. The clinician-external device 500 can include an RF antenna 501, a memory 502, an RF chip 503, a power supply device 504, a display 511 (e.g., an LCD monitor), a buzzer 512, an impedance measurement unit 521, a multiplexer 522, a stimulation device 523, a diagnostic device 531, a filtering and amplification device 533, and a control device 540.

[0060] 6A and 6B are schematic diagrams of subcomponents of an exemplary patient personal controller device. A patient personal controller device (such as the patient controller device 111 shown and described with respect to FIG. 1) may include two subcomponents: a recharger unit (FIG. 6B) for recharging the implanted device and a power bank (FIG. 6A) that may connect to and provide power to the recharger unit via a cable. In some embodiments, the recharger unit and power bank may each include a processor, memory, and communication interface structurally and / or functionally similar to the processor 102, memory 103, and communication interface 104, respectively, as shown and described with respect to FIG. 1. The recharger unit may receive sets of neural activity signal recordings from the implanted device via a communication channel between the two and store the received sets of neural activity signal recordings in the recharger unit's memory. The recharger unit may transmit the sets of neural activity signal recordings in the power bank's memory.

[0061] The recharger unit may establish a communication channel with the implanted device (e.g., via a radio frequency (RF) communication channel) to turn the implanted device on or off and / or check the remaining battery level of the implanted device. The power bank may include a user interface including a graphical user interface (GUI) for displaying information to a user of the patient personal controller device and / or a set of buttons for receiving commands from the user. Status updates of the remaining charge (remaining battery level) of the implanted device, the recharger unit, and / or the power bank may be displayed on the power bank's GUI. Status updates of therapy status and malfunction notifications may also be displayed on the GUI. In some cases, the power bank and / or recharger unit may generate warning signs to notify of malfunctions and / or low battery levels. In some cases, the recharger unit may be used to initialize / activate the implanted device. Initialization / activation may include setting an initial set of stimulation parameters and / or charging power for the implanted device. Similarly, the recharger unit may be used to deactivate / shut down the implanted device.

[0062] FIG. 7 is a schematic diagram of an exemplary method for establishing a wireless connection between an implantable device (also referred to herein as an “implantable pulse generator (IPG) device”) and a patient personal controller device. The patient personal controller device may transmit power to charge the IPG via the wireless connection. Alternatively, or additionally, the IPG device may transmit / receive neural activity signal recordings and / or stimulation parameters to / from the patient personal controller device. For example, the IPG may transmit neural activity signal recordings to the patient personal controller device via the wireless connection, and the personal controller device may transmit stimulation parameters or instructions to the IPG via the wireless connection. In some cases, the wireless connection between the implantable device and the patient personal controller device (e.g., a recharger unit of the patient personal controller device) may be established when the patient personal controller device and the implantable device are within a predetermined distance range (e.g., 2 centimeters to 10 centimeters, 1 millimeter to 1 meter, etc.) and direction range. The orientation range may include, for example, aligning the vertical orientation of the patient personal controller device with the vertical orientation of the implanted device within an error margin of 5 rotational degrees, an error margin of 10 rotational degrees, etc.

[0063] 8 is a block diagram of one variation of a patient personal controller device 800 (such as the patient personal controller device shown and described with respect to FIG. 1). The patient personal controller device 800 may be used by the patient to charge an implanted device and / or download neural activity signal data recorded by the implanted device (as shown and described with respect to FIGS. 6 and 7). The patient personal controller device 800 includes an antenna 801 (e.g., a 2.4 GHz RF antenna), memory 802 (e.g., Secure Digital (SD) card memory), an RF chip 803, an induction coil 811, a push button 812, a power regulator 813, a main controller 820, and a Bluetooth controller 814.

[0064] The antenna may transmit and receive input electromagnetic waves representing data that may include a set of neural activity recordings, a set of stimulation parameters, etc. The RF chip 803 may process the input electromagnetic waves received by the antenna and / or process a set of electrical signals received from the main controller 820 to generate output electromagnetic waves. The memory 802 may store data including a set of neural activity recordings, a set of stimulation parameters, etc. The induction coil 811 may generate a magnetic flux to induce power into an implanted device (not shown). The push button 812 may be activated by a user of the patient personal controller device 800 to initiate, activate / deactivate, and / or establish communication with the implanted device. The power regulator 813 may include a set of electrical and / or electronic circuits for adjusting the characteristics of the power induced into the implanted device via the induction coil 811. The Bluetooth® controller 814 may include a set of electrical, electronic, and / or RF circuits for processing and / or generating a set of Bluetooth® signals. The main controller 820 may include, for example, a hardware-based integrated circuit (IC) or any other suitable processing device configured to execute or execute a set of instructions / code. For example, the main controller 820 may include a general-purpose processor, a central processing unit (CPU), an application-specific integrated circuit (ASIC), a microcontroller, etc. The main controller 820 may be operatively coupled to the memory 802, the RF chip 803, the induction coil 811, the push button 812, the power regulator 813, and / or the Bluetooth controller 814, and may generate the set of instructions.

[0065] 9A and 9B are schematic illustrations of an exemplary clinician programmer device (such as the clinician programmer device 131 shown and described with respect to FIG. 1). As shown in FIG. 9A, the clinician programmer device may include a graphical user interface (GUI). In some cases, the GUI may be a touch screen panel so that a user of the clinician programmer device (e.g., a clinician, doctor, nurse, etc.) may interact with the clinician programmer device via the GUI.

[0066] The clinician programmer device may include / implement various software applications, including a connectivity application for checking connectivity with other devices, a stimulation application for delivering stimulation to the patient, and / or a recording application for recording neural activity recordings received from implanted devices and / or the patient personal controller device. The clinician programmer device may further include / implement a therapy application for setting a therapy mode by a user of the clinician programmer device, an impedance application for measuring and setting a set of impedances for electrodes operably coupled to the clinician programmer device, and / or other applications suitable for the clinician programmer device (e.g., a patient information card application, operating system version information, date / time application, memory application, processor application, etc.).

[0067] 9B, the clinician programmer device may include a panel interface (e.g., a back panel interface, a top panel interface, etc.). In some cases, the panel interface may include a power button for turning the clinician programmer device on / off and / or an antenna for receiving and / or transmitting electromagnetic waves representing data from / to an implanted device, a patient personal controller device, a network (e.g., the Internet), etc. The panel interface may further include a power plug port for receiving power from an alternating current (AC) and / or direct current (DC) power source and charging the clinician programmer device's battery. The panel interface may provide a universal serial bus (USB) type port for connecting to an external host.

[0068] 10 is a block diagram of an exemplary clinician programmer device 1000. The clinician programmer device 1000 includes memory 1001 (e.g., electrically erasable programmable read-only memory (EEPROM) memory), an RF chip 1002, an antenna 1003 (e.g., an RF antenna), a touchscreen 1011, a battery charger 1012, a main controller 1030, and a USB-UART converter 1021, and may connect to an external host 1022.

[0069] The antenna 1003 may transmit and receive input electromagnetic waves representing data that may include a set of neural activity recordings, a set of stimulation parameters, etc. The RF chip 1002 may process the input electromagnetic waves received by the antenna and / or a set of electrical signals received from the main controller 1030 to generate output electromagnetic waves. The memory 1001 may store data including a set of neural activity recordings, a set of stimulation parameters, etc. The battery charger 1012 may include a set of electrical circuitry for providing power and charging the battery of the clinician programmer device 1000. The touchscreen 1011 may display a set of images to a user of the clinician programmer device 1000 and receive a set of commands from the user by touching the touchscreen 1011. The USB-UART converter 1021 may convert universal asynchronous receiver-transmitter (UART) port communications to universal serial bus (USB) port communications for interfacing with an external host 1022 (e.g., a laptop computer, a desktop computer, etc.). The main controller 1030 may include, for example, a hardware-based integrated circuit (IC) or any other suitable processing device configured to execute or execute a set of instructions / code. For example, the main controller 1030 may include a general-purpose processor, a central processing unit (CPU), an application-specific integrated circuit (ASIC), a microcontroller, etc. The main controller 1030 may be operatively coupled to the memory 1001, the RF chip 1002, the antenna 1003, the touchscreen 1011, the battery charger 1012, the main controller 1030, the USB-UART converter 1021, and / or the external host 1022, and may generate a set of instructions. Methods for adaptive deep brain stimulation (aDBS) programming

[0070] Described herein is an implantable device (also referred to herein as an "implantable pulse generator (IPG) device") that can provide adaptive deep brain stimulation (aDBS) and / or conventional deep brain stimulation (cDBS). In aDBS mode, a set of stimulation parameters can be altered in real time based on a set of control variables and the patient's neural activity. More specifically, in DBS mode, the IPG device records neural activity signals (e.g., local field potentials (LFPs)) from one or more electrodes of a deep brain stimulation (DBS) probe. The IPG device can then store the neural activity signal recordings as sampled / digitized representations of the neural activity. The IPG device can be further configured to extract the neural activity signal recordings in specific frequency bands (e.g., alpha band, beta band, 12 Hz to 35 Hz, etc.) and adapt a set of stimulation parameters (e.g., stimulation amplitude, stimulation pulse width, etc.) based on a linear relationship. The power of beta oscillations can be linearly correlated with the patient's clinical condition. In other words, a neural activity signal recording with a higher beta power value may indicate a worse clinical condition, and therefore a higher stimulation amplitude may be required. However, when quantitatively implemented in an IPG device, such a linear relationship may need to be calibrated in a patient-specific manner. Therefore, a deep brain stimulation device that controls patient-specific symptoms may be beneficial.

[0071] When the aDBS mode is activated in the IPG device, the amplitude of deep brain stimulation (DBS) is automatically determined / set by the deep brain stimulation system (such as the deep brain stimulation system 100 shown and described with reference to FIG. 1) according to the neural activity signals recorded by the DBS electrodes. More specifically, the aDBS mode modifies the stimulation amplitude according to the power of local field potential (LFP) oscillations in a specific band, such as the beta band (10-35 Hz). Particular patients may have different center frequencies in the beta band and different beta power values. Also, certain patients may respond differently to DBS and require specific stimulation intensities to control their specific symptoms. Therefore, to correctly set the aDBS mode, the treating clinician may define a set of parameters, including: P βMIN : the average minimum power (also referred to herein as the first average value) reached by beta band oscillations for a particular patient (which is usually associated with the on-medication state) P βMAX : the average maximum power (also referred to herein as the second average value) reached by beta band oscillations in a particular patient (which is usually associated with the off-medication state) A min The minimum DBS amplitude that elicits a detectable clinical effect in a given patient A max : The maximum DBS amplitude before inducing side effects in a particular patient V DBS :DBS amplitude output A set of parameters allows the aDBS mode to be calibrated.

number

[0072] 11 is a method 1100 for selecting a set of sensing electrodes, a set of stimulating electrodes, and a power band. Method 1100 includes, at 1101, checking the impedance of a set of electrode pairs and excluding a subset of electrode pairs from the set of electrode pairs that have abnormal impedance values. Method 1100 further includes, at 1102, screening the power spectrum of the set of neural activity (e.g., local field potential (LFP) activity) at the remaining electrode pairs. Method 1100 also includes, at 1103, titrating a treatment window and selecting a power band. max and A min A further includes defining max and A min A may be measured when the patient is not taking medication. Method 1100 further includes, at 1104, selecting an electrode pair for sensing and exhibiting the highest beta activity, excluding the stimulating electrode. In some cases, A may be measured when the patient is taking medication. max and A min can be determined.

[0073] In some embodiments, method 1100 may be implemented using a clinician programmer device (such as the programmer device 131 shown and described with respect to FIG. 1) and / or a user computing device (such as the user computing device 121 shown and described with respect to FIG. 1). The clinician programmer device may be configured to check the impedance of all available electrode pairs. Alternatively, or additionally, the electrode impedance check may be performed using an external clinician device at the time the probe is implanted. Each electrode with an impedance outside an acceptable impedance range, such as an acceptable impedance range of 500 to 2000 ohms, is stored in the memory of the clinician programmer device (stored in the memory 133 of the programmer device 131 shown and described with respect to FIG. 1). Such electrodes with impedance outside the acceptable impedance range are excluded from stimulation and / or recording. The clinician programmer device performs a short-term (e.g., 10-30 second) recording of a set of neural activity (e.g., LFP activity) for each available electrode pair (excluding those with abnormal impedance values) while the patient is in a medication-free state. To ensure appropriate recording conditions, it is recommended that short-term recordings of the neural activity set be performed when the patient with predominant Parkinson's disease symptoms is in a medication-free state (e.g., after overnight stimulation and pharmacological withdrawal). The clinician programmer device can be configured to examine the characteristics of the short-term recording of the neural activity set in a therapeutic window (e.g., a predetermined frequency range). The therapeutic window generally indicates oscillatory activity that characterizes the short-term recording of the neural activity set. In some cases, the oscillatory activity is identified by a peak in the therapeutic window of the short-term recording of the neural activity set. The peak can be characterized by its intensity (also referred to herein as "spectral power") and reported / displayed by the clinician programmer device. The clinician programmer device further stores the peak frequency (i.e., the frequency at which the power spectrum has the highest value) and the power spectrum associated with the short-term recording of the neural activity set. The clinician programmer device performs the above procedure for each available electrode pair (both sides).

[0074] The clinician programmer device may be configured to select a set of electrodes on one or more implanted probes that will enable the best patient-specific symptom control with the lowest side effects (i.e., clinical outcomes) and the lowest energy delivered to the patient's tissue. Each electrode may include broadband spectral characteristics and record neural activity signals over a wide spectral range. The clinician programmer device may use the selected electrodes even if they have already been identified for short-term recording of a set of neural activity. The clinician programmer device may select an A associated with the selected electrodes. min (the minimum amplitude that elicits clinical benefit for the patient) and A max The clinician programmer device may further define A (maximum amplitude before inducing an adverse effect). min and / or A max The clinician programmer device may store the beta frequency band information. The clinician programmer device may further be configured to identify the electrode pair with the strongest beta frequency band component (excluding the electrode pair selected for stimulation and electrodes with abnormal impedance) with the highest power in the beta frequency band (e.g., between 10 Hz and 35 Hz). While the beta frequency band is generally understood to range from 10 to 30 Hz, a patient-specific beta frequency band may vary from patient to patient and may be determined by the deep brain stimulation system to personalize the aDBS mode for the patient. This is because the power peak of the neural activity signal (also referred to as "patient-specific power") may occur at different frequencies for each patient. For example, patient A may have more activity at 15 Hz than 25 Hz, patient B may have a peak activity at 20 Hz with much less activity at other frequencies in the beta band, and patient C may have more activity at 30 Hz than 10 Hz. The clinician programmer device may be configured to determine and / or select the range / boundaries of the patient-specific frequency band (+ / - 2 Hz, + / - 3 Hz, etc.). For example, a patient-specific beta frequency band may be + / - 2 Hz around the beta peak measured for the patient.

[0075] An implantable pulse generator (IPG) device acquires and / or stores neural activity signal recordings, such as local field potentials (LFPs), for a predetermined period of time. The predetermined period of time may be determined by a clinician and, in some variations, may be about 1 week, 2 weeks, 20 weeks, 1 day, 10 days, 15 days, 30 days, 45 days, 60 days, etc. The IPG device acquires neural activity signals over the predetermined period of time, which may be stored in the memory of the IPG device as neural activity signal recordings. The IPG and / or any of the external devices described herein (e.g., patient controller device, programmer device, etc.) calculates a first average value P based on the neural activity signal recordings from the IPG. βMIN and the second mean value P βMAXNeural activity signal recordings are recorded from at least one electrode from the electrode pair selected as described above to sense and indicate the selected highest beta activity. In particular, the IPG device stores spectral features of the neural activity signal recordings for a set of predetermined periods in a non-volatile memory (such as the memory 103 of the implantable device 101 shown and described with reference to FIG. 1). In some cases, the length of each predetermined period T may be determined based on the memory space (e.g., 100 MB, 1 GB, 4 GB, 8 GB, 128 GB, etc.). In such cases, a shorter predetermined period T is allocated to a larger memory space to improve the temporal resolution of the data. In some cases, in addition to the spectral features of the neural activity signal recordings, the IPG device may store patient-specific power from the patient-specific frequency bands selected as described above. A patient-specific power value from the patient-specific frequency band needs to be stored for each patient-specific predetermined period T'. In some cases, the patient-specific predetermined period T' may be set by a physician, determined based on the spectral characteristics of the neural activity signal recordings, and / or predetermined (e.g., based on total and / or remaining data storage capacity, total and / or remaining battery, a trade-off between battery power consumption and temporal resolution of the data, a trade-off between data storage capacity and temporal resolution of the data, etc.). For example, the neural activity signal recordings may, on average, exhibit a spectral peak or a spectral dip every time T1. Therefore, the patient-specific predetermined period T' may be set to a factor of T1 (e.g., multiplied by 0.5, 2, 3, etc.). In some cases, the patient-specific predetermined period T' is equal to the predetermined period T. Thus, the neural activity signal recordings are recorded and processed by the IPG for X days (e.g., X≧1). The neural activity signal recordings may be recorded during both deep brain stimulation (DBS) medication-on time intervals (e.g., when DBS medication is set to on) and / or medication-off time intervals (e.g., when DBS medication is set to off). The medication-on time interval and / or medication-off time interval may be determined based on the patient's specific medical condition.

[0076] In one example, when an IPG device is implanted to replace an old IPG device (e.g., due to battery depletion), the Parkinson's disease patient may be in an advanced stage of Parkinsonism and potentially unable to tolerate the medication-off time interval of the IPG device's stimulator. In another example, when an IPG device is first implanted in a patient, there is typically an adjustment period during which the electrode impedance can be adjusted before the medication-off time interval is set. The implantation of the probe may produce "startling effects," including edema around the electrodes, which may ultimately bias the assessment of the clinical efficacy of the stimulation. Therefore, it is common clinical practice to wait until the end of the adjustment period before turning on DBS during the medication-off time interval. Such an adjustment period is often suitable for collecting data. Preferably, the method for collecting data includes the first number of days X (e.g., X≧1) that DBS is turned on, so that neural activity signal recordings (power spectra) and / or patient-specific power of the neural activity signal recordings (e.g., from at least one electrode) can be acquired and / or stored as data. The data stored in the IPG device may then be downloaded, for example, via a radio frequency communication channel, to the patient personal controller device (such as the patient personal controller device 111 shown and described with respect to FIG. 1) at each recharge cycle. The recharge cycle may occur, for example, once daily, every other day, every three days, every four days, every five days, weekly, etc. Once the data is downloaded to the patient personal controller device, it is deleted from the memory of the IPG device and stored continuously in the memory of the patient personal controller device (such as the memory 113 shown and described with respect to FIG. 1), which may have a larger memory size. In some cases, moving the data from the IPG device to the patient personal controller device allows the IPG device to collect data for an extended period of time (e.g., 1 month, 2 months, 3 months, 6 months, 12 months, etc.).

[0077] Described herein is a method for determining a set of stimulation parameters (which may also be referred to herein as a "set of adaptation rules") for providing adaptive deep brain stimulation (aDBS) in an implantable pulse generator (IPG) device. The set of stimulation parameters includes a minimum stimulation amplitude A MIN , maximum stimulation amplitude A MAX , P βMIN The first mean value, or P, is represented by βMAX The set of stimulation parameters may include a second average value represented by: DBS can be collectively defined as

number

[0078] In some cases, the minimum stimulus amplitude A MIN and / or maximum stimulation amplitude A MAX may be determined for a particular patient's treatment window as described above with respect to FIG. 11. In some cases, the minimum stimulation amplitude A MIN and / or maximum stimulation amplitude A MAX can be determined empirically by incrementally increasing the stimulation amplitude (e.g., starting from a zero value to a clinician-determined stimulation value) and recording the clinical results of the incremental increases.

[0079] 12 is an exemplary method 1200 for adaptive deep brain stimulation. Method 1200 may be performed, for example, by a clinician programmer device (such as clinician programmer device 131 shown and described with respect to FIG. 1) and / or using a user computing device (such as user computing device 121 shown and described with respect to FIG. 1). Method 1200 may include extracting 1201 a set of spectral features of a set of neural activity signal recordings for a predetermined number of days, selecting 1202 a set of spectral features within frequency bands of the set of spectral features, selecting 1204 a set of medication-on time intervals and a set of medication-off time intervals for the predetermined number of days, generating 1205 a minimum average of the set of selected spectral features within the frequency bands and the set of medication-on time intervals, and generating 1206 a maximum average of the set of selected spectral features within the frequency bands and the set of medication-off time intervals. Optionally, method 1200 may include generating 1203 an average of the set of selected spectral features within the frequency bands and over the predetermined number of days.

[0080] 13 and 14 are exemplary methods for programming a clinician programmer device for adaptive deep brain stimulation. The method includes: βMIN and the second mean value P βMAXThe method includes collecting local field spectral features for several days (e.g., 1 day, 2 days, 10 days, etc.). The method further includes selecting a frequency band of the local field potential from a low frequency band, an alpha frequency band, a beta frequency band, or a gamma frequency band. The method further includes averaging the power of the local field spectral features across the selected frequency band. In some cases, a set of moving average values ​​of the power of the local field spectral features across the selected frequency band may be calculated for a set of periods (e.g., 10 minutes, 1 hour, 6 hours, 1 day, 2 days, etc.). Accordingly, a set of medication-on times and a set of medication-off times may be selected from the set of moving average values. In some cases, a threshold value is determined by a user (e.g., a patient, a clinician, a doctor, etc.), and periods where the moving average value is above / below the threshold value may be classified as medication-off times / medication-on times. Finally, the first average value P βMIN can be calculated by averaging the power in the selected band over the medication on time, resulting in a second average value P βMAX can be calculated by averaging the power in the selected bands over the medication off time.

[0081] As described above, a deep brain stimulation system may store and analyze sets of neural activity signal recordings to provide stimulation / treatment modes, including a conventional deep brain stimulation (cDBS) treatment planning mode and / or an adaptive deep brain stimulation (aDBS) treatment mode. In some cases, a user of a user device (such as the user computing device shown and described with respect to FIG. 1 ) may select to use a cDBS treatment mode. The cDBS treatment planning mode may include setting a set of stimulation parameters, including stimulation frequency, pulse width, and / or amplitude. In some cases, a user of a user device may select to use an aDBS treatment mode. The aDBS treatment planning mode may include setting a set of stimulation parameters, including maximum power, minimum power, bandwidth, minimum stimulation amplitude, maximum stimulation amplitude, frequency, pulse width, etc. Further variations of stimulation / treatment modes are provided in U.S. Pat. No. 10,596,379, which is incorporated herein by reference in its entirety.

[0082] In some embodiments, a user of the clinician programming device and / or user computing device may select (via the GUI of the clinician programming device) to use an open stimulation mode (also referred to as an "open stimulation session"). During the open stimulation mode, the user may set / assign parameters for the probes (e.g., each including a pair of electrodes) to determine the stimulation parameters for the open stimulation mode. The parameters may include, for example, the stimulation amplitude, frequency, pulse width, etc. for each electrode. In some embodiments, the user may operate the clinician programming device and / or user computing device in a neural signal recording mode to record neural activity signals (e.g., local field potentials) for a period of time (e.g., 30 seconds) and select to visualize the power spectrum of the neural activity signal recording in a table and / or graph. In some cases, the graph may include a statistical distribution of the neural activity signal recording, such as, for example, a running average, deviation, ensemble mean, mean, etc.

[0083] 15A and 15B show exemplary neural activity signal recordings stored and analyzed by a deep brain stimulation system. As described above, the deep brain stimulation system may acquire and / or store neural activity signal recordings, such as local field potentials (LFPs), for a predetermined period (e.g., 1 day, 10 days, etc.). For example, as shown in FIG. 15A, neural activity signal recordings for one day (e.g., during normal daily activities) may be acquired and recorded separately for the left subthalamic nucleus (STN) and the right STN in a frequency range between 5 Hz and 35 Hz. The deep brain stimulation system may select a frequency band for the neural activity signal recording from the low frequency band, the alpha frequency band, the beta frequency band, and / or the gamma frequency band. For example, as shown in FIG. 15A, a frequency band between 12 Hz and 20 Hz may be selected for analysis. The deep brain stimulation system may further average the power of the neural activity signal recordings across the selected frequency bands. For example, as shown in FIG. 15B, the averaged neural activity signal recordings can be further analyzed by the deep brain stimulation systems and methods described above to determine medication-on and medication-off time intervals.

[0084] FIG. 16 is a flowchart of an exemplary communication method and data flow 1610 between support components (also referred to as “support systems”) 1620 of a deep brain stimulation system (such as the deep brain stimulation system 100 shown and described with respect to FIG. 1 ) in some variations. The deep brain stimulation may implement the data flow 1610 using the support components 1620. The support components may include an IPG 1621 (implantable device 101 of FIG. 1 ), a patient controller 1622 (patient personal controller device 111 of FIG. 1 ), an application 1623 (implemented on the user computing device 121 or clinician programmer device 131 of FIG. 1 ), and a cloud service (biobank server of FIG. 1 ) 1624. The data flow includes daily data collection 1610 at the IPG device. The daily data may include sets of neural activity signal recordings and / or patient log data. The data flow includes downloading 1612 the daily data to the patient controller 1622 for long-term data storage. The data flow includes downloading long-term data in an application 1623. The data flow includes sending and storing 1614 long-term data to / within a cloud service 1624.

[0085] The foregoing description, for purposes of explanation, used specific nomenclature to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that specific details are not required to practice the present invention. Thus, the foregoing descriptions of specific variations of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teachings. The variations were chosen and described in order to explain the principles of the invention and its practical application, so that others skilled in the art may utilize various variations with various changes suited to the invention and the particular use intended. It is intended that the following claims and their equivalents define the scope of the invention.

Claims

1. A system (100) for deep brain stimulation, the system comprising: an implantable device (101, 400B) configured to acquire and store neural activity signal recordings and apply electrical stimuli; a personal controller device (111, 800) configured to establish a first wireless connection to the implantable device (101, 400B), wherein the personal controller device (111, 800) is configured to transmit power to the implantable device (101, 400B), and the implantable device (101, 400B) is configured to transmit neural activity signal recordings to the personal controller device (111, 800) via the first wireless connection; a clinician programmer device (131, 322, 1000) configured to establish a second wireless connection to the implantable device (101, 400B) based on activation of the first wireless connection, and thereby receive neural activity signal recordings from the implantable device (101, 400B) via the second wireless connection; the clinician programmer device (131, 322, 1000) is configured to set a plurality of stimulation parameters based on the neural activity signal recording. system.

2. The system further includes a user computing device (121), the user computing device comprising: Receive patient log data; correlating the patient log data with the neural activity signal recordings based on at least a temporal correlation between the patient log data and the neural activity signal recordings; determining a plurality of medication-on time intervals and a plurality of medication-off time intervals based on the neural activity signal recordings and the patient-logged data; generating the plurality of stimulation parameters according to the neural activity signal recording during the plurality of medication-on and medication-off time intervals. The system (100) of claim 1.

3. The user computing device (121) further comprises: generating a statistical distribution of the neural activity signal recordings, the distribution including a plurality of means and a plurality of variances; and displaying, via a user interface, a plot of the neural activity signal recording, the plurality of mean values, and the plurality of variance values. The system (100) of claim 2.

4. The user computing device (121) further comprises: extracting a plurality of spectral features within a frequency band of the neural activity signal recording recorded during a predetermined time period; determining a plurality of medication-on time intervals and a plurality of medication-off time intervals during said predetermined period of time; generating first average values ​​of the plurality of spectral features within the plurality of medication-on time intervals for the frequency band and second average values ​​of the plurality of spectral features within the plurality of medication-off time intervals for the frequency band. The system (100) of claim 2 or 3.

5. The system of claim 4 , wherein the user computing device is configured to generate the plurality of stimulation parameters based on the first average value and the second average value.

6. The user computing device (121) further comprises: training a machine learning model based on a set of historical neural activity signal recordings that does not include the neural activity signal recordings or a set of historical stimulation parameters that does not include the plurality of stimulation parameters; and configuring the machine learning model to identify the plurality of stimulation parameters based on the neural activity signal recording. The system (100) according to any one of claims 2 to 5.

7. The user computing device (121) further comprises: transmitting the neural activity signal recordings and / or the patient log data or the plurality of stimulation parameters to a biobank server; configured to delete the neural activity signal recording and / or the patient log data or the plurality of stimulation parameters from a memory of the user computing device. The system (100) according to any one of claims 2 to 6.

8. the power being inductive power induced from a patient personal controller device (111, 800) to the implantable device (101, 400B) via an inductive link for recharging; the neural activity signal recording and / or patient log data is automatically transmitted by the implantable device (101, 400B) to a personal controller device (111, 800) during recharging; The system (100) according to any one of claims 1 to 7.

9. The clinician programmer device (131, 322, 1000) is configured to establish an authenticated communication channel with the personal controller device (111, 800), the clinician programmer device (131, 322, 1000) further comprising: receiving patient log data from the personal controller device; correlating the patient log data with the neural activity signal recordings based on at least a temporal correlation between the patient log data and the neural activity signal recordings; determining a plurality of medication-on time intervals and a plurality of medication-off time intervals based on the neural activity signal recordings and the patient-logged data; generating the plurality of stimulation parameters according to neural activity signal recordings during the plurality of medication-on and medication-off time intervals. The system (100) of claim 1.

10. The clinician programmer device (131, 322, 1000) further comprises: generating a statistical distribution of the neural activity signal recordings comprising a plurality of means and a plurality of variances; and displaying, via a user interface, a plot of the neural activity signal recording, the plurality of mean values, and the plurality of variance values. The system (100) of claim 9.

11. The clinician programmer device (131, 322, 1000) further comprises: extracting a plurality of spectral features within a frequency band of the neural activity signal recording recorded during a predetermined time period; determining a plurality of medication-on time intervals and a plurality of medication-off time intervals during said predetermined period of time; generating first average values ​​of the plurality of spectral features within the plurality of medication-on time intervals for the frequency band and second average values ​​of the plurality of spectral features within the plurality of medication-off time intervals for the frequency band. A system (100) according to claim 9 or 10.

12. The clinician programmer device (121) further comprises: transmitting the neural activity signal recordings and / or the patient log data or the plurality of stimulation parameters to a biobank server; configured to delete the neural activity signal recording and / or the patient log data or the plurality of stimulation parameters from the memory of the clinician programmer device. The system (100) according to any one of claims 9 to 11.

13. A system (100) for deep brain stimulation, the system comprising: an implantable device (101, 400B) configured to acquire and store neural activity signal recordings and apply electrical stimuli; a personal controller device (111, 800) configured to establish a first wireless connection to the implantable device (101, 400B), wherein the personal controller device (111, 800) is configured to transmit power to the implantable device (101, 400B), and the implantable device (101, 400B) is configured to transmit neural activity signal recordings to the personal controller device (111, 800) via the first wireless connection; a clinician programmer device (131, 322, 1000), the clinician programmer device (131, 322, 1000) comprising: establishing a second wireless connection to the embedded device, the second wireless connection being based on activation of the first wireless connection; receiving the neural activity signal recording from the implantable device via the second wireless connection; and generating a plurality of stimulation parameters according to the neural activity signal recording. system.

14. The system further comprises a user computing device configured to establish a third wireless connection to the personal controller device (111, 800), the user computing device (111, 800) comprising: receiving the neural activity signal recording from the personal controller device (111, 800) via the third wireless connection; receiving patient log data from the personal controller device (111, 800); determining a plurality of medication-on time intervals and a plurality of medication-off time intervals based on at least one of the neural activity signal recordings and the patient log data. The system (100) of claim 13.

15. The user computing device (121) and / or the clinician programmer device (131, 322, 1000) may further transmitting the neural activity signal recordings and / or patient log data or the plurality of stimulation parameters to a biobank server directly or via a user computing device; configured to delete the neural activity signal recording and / or the patient log data or the plurality of stimulation parameters from a memory of the user computing device. A system (100) according to claim 13 or 14.

16. the power being inductive power induced from a patient personal controller device (111, 800) to the implantable device (101, 400B) via an inductive link for recharging; the neural activity signal recording and / or patient log data is automatically transmitted by the implantable device (101, 400B) to a personal controller device (111, 800) during recharging; A system (100) according to any one of claims 13 to 15.

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