Methods of quantifying autonomic nervous system activity and identifying potential responders to autonomously regulated therapy

Abstract

A system for performing a diagnostic procedure, the system comprising: a diagnostic device configured to be placed proximate tissue, the diagnostic device having a light emitter and a light detector; a computing device that delivers light from the light emitter to the tissue for a period of time, detects a dynamic property of light scattering particles within the tissue using the light detector, generates a peripheral blood flow waveform of a fluid flowing within the tissue using the detected dynamic property, and transmits the peripheral blood flow waveform to the tissue. Calculating a power spectral density of the generated peripheral blood flow waveform, quantizing a relative level of global sympathetic activity using the calculated power spectral density, identifying a relative change in global sympathetic activity within the quantized relative level of global sympathetic activity, and determining whether the identified relative change in global sympathetic activity falls outside a predetermined range of global sympathetic activity, the predetermined range of global sympathetic activity indicating candidates for therapy.

Description

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 427,574, filed on November 23, 2022, the entire content of which is incorporated herein by reference. Technical Field

[0002] The present disclosure generally relates to quantifying sympathetic nerve activity. In particular, the present disclosure relates to diagnostic methods and systems for quantifying sympathetic nerve activity or changes in sympathetic nerve activity to identify diseases associated with the function of the autonomic nervous system. Background Art

[0003] The sympathetic branch of the autonomic nervous system, or the sympathetic nervous system (SNS), is essential for controlling multiple organs and physiological systems, including the kidneys. The SNS is the primary involuntary body control system that is typically associated with the stress response. Chronic overactivation of the SNS is an adverse adaptation that can drive the progression of many disease states. For example, overactivation of the renal SNS has been experimentally and in humans identified as a possible cause of the complex pathophysiology of arrhythmias, hypertension, volume overload states (e.g., heart failure), obstructive sleep apnea, and progressive kidney disease. Quantifying the relative level of global sympathetic activation in the human body, or the relative changes in sympathetic nerve activity due to various stimuli, has a variety of useful clinical applications, including diagnosing and monitoring diseases associated with the function of the autonomic nervous system. Summary of the Invention

[0004] According to the present disclosure, a system for performing a diagnostic procedure includes: a diagnostic device configured to be placed proximate to tissue, wherein a light emitter and a light detector are disposed on the diagnostic device; a computing device including a processor and a memory storing instructions that, when executed by the processor, cause the computing device to: deliver light from the light emitter into the tissue for a period of time; use the light detector to detect the dynamic properties of light-scattering particles within the tissue during the period of time; use the detected dynamic properties of the light-scattering particles within the tissue during the period of time to generate a peripheral blood flow waveform of the fluid flowing within the tissue; calculate a power spectral density of the generated peripheral blood flow waveform during the period of time; use the calculated power spectral density of the generated peripheral blood flow waveform to quantify the relative level of global sympathetic nerve activity during the period of time; identify relative changes in global sympathetic nerve activity within the quantified relative level of global sympathetic nerve activity during the period of time; and determine whether the identified relative changes in global sympathetic nerve activity fall outside a predetermined range of global sympathetic nerve activity, wherein one or more of the identified relative changes in global sympathetic nerve activity that fall outside the predetermined range indicate candidates for therapy.

[0005] In various aspects, when executed by the processor, these instructions may cause the computing device to use a Fourier transform to calculate the power spectral density of the peripheral blood flow waveform generated during the time period.

[0006] In some aspects, calculating the power spectral density may include using a fast Fourier transform to calculate the power spectral density of the peripheral blood flow waveform generated during the time period.

[0007] In other aspects, when executed by the processor, these instructions may cause the computing device to use the calculated power spectral density of the generated peripheral blood flow waveform to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period.

[0008] In some aspects, when executed by the processor, these instructions may cause the computing device to use either the calculated power spectral density or the calculated aortic impedance spectrum to quantify the relative level of global sympathetic activity during the time period.

[0009] In other aspects, when executed by the processor, these instructions may cause the computing device to use a Windkessel model to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period.

[0010] In various aspects, when executed by the processor, these instructions may cause the computing device to use a three-element Windkessel model to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period.

[0011] In other aspects, when executed by the processor, these instructions may cause the computing device to calculate the amount of light received by the light detector during the time period.

[0012] In some aspects, when executed by the processor, these instructions may cause the computing device to use the calculated amount of light received by the light detector during the time period to generate an arterial blood pressure waveform of the fluid flowing in the target tissue.

[0013] In various aspects, when executed by the processor, these instructions may cause the computing device to calculate the aortic input impedance spectrum of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0014] In some aspects, when executed by the processor, these instructions may cause the computing device to combine the generated peripheral blood flow waveform and the generated arterial blood pressure waveform using one or more of the determined resistance, compliance, amplitude, or phase derived from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0015] In other aspects, when executed by the processor, these instructions may cause the computing device to compute the power spectral density of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0016] In certain aspects, when executed by the processor, these instructions may cause the computing device to combine the generated peripheral blood flow waveform and the generated arterial blood pressure waveform using the mean arterial blood pressure amplitude derived from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform and the derived mean peripheral blood flow waveform during the time period.

[0017] In various aspects, the method may include intentionally applying a sympathetic stimulus to a candidate for the therapy, wherein identifying a relative change in global sympathetic nerve activity includes identifying a relative change in global sympathetic nerve activity during the intentionally applied sympathetic stimulus.

[0018] In other aspects, the intentionally applied sympathetic stimulus may be selected from the group consisting of cold pressor, handgrip exercise, mental stress, Valsalva or Mueller maneuvers, acute catecholamine injection, static equilibrium position, and variable rate pacing.

[0019] In certain aspects, when executed by the processor, these instructions may cause the computing device to identify a relative change in global sympathetic nerve activity due to a naturally occurring sympathetic stimulus.

[0020] In various aspects, the naturally occurring sympathetic stimulus may be selected from the group consisting of circadian rhythm changes, sleep apnea, static equilibrium position, intense physical activity, and respiration.

[0021] In certain aspects, the diagnostic device may be a portable wearable device that is wirelessly coupled to the computing device.

[0022] In other aspects, the diagnostic device may be operably coupled to the computing device using a wired connection.

[0023] In various aspects, when executed by the processor, these instructions may cause the computing device to issue one or more of a clinical action, an alert, or a recommendation based on whether the identified relative change in global sympathetic nerve activity falls outside the predetermined range.

[0024] According to another aspect of the present disclosure, a method for evaluating a candidate for therapy includes: emitting light from a light emitter into a target tissue by a computing device over a period of time; receiving, by the computing device from a light detector, a signal indicative of the dynamic properties of light-scattering particles within the target tissue during the period of time; generating, by the computing device, a peripheral blood flow waveform of a fluid flowing within the target tissue using the detected dynamic properties of the light-scattering particles during the period of time; calculating, by the computing device, a power spectral density of the generated peripheral blood flow waveform during the period of time; quantifying, by the computing device, a relative level of global sympathetic nerve activity during the period of time using the calculated power spectral density of the generated peripheral blood flow waveform; identifying, by the computing device, a relative change in global sympathetic nerve activity within the quantified relative level of global sympathetic nerve activity during the period of time; and determining, by the computing device, whether the identified relative change in global sympathetic nerve activity falls outside a predetermined range of global sympathetic nerve activity, wherein one or more identified relative changes in global sympathetic nerve activity that fall outside the predetermined range indicate a candidate for therapy.

[0025] In various aspects, calculating the power spectral density may include using a Fourier transform to calculate the power spectral density of the generated peripheral blood flow waveform during the period of time.

[0026] In other aspects, calculating the power spectral density may include using a fast Fourier transform to calculate the power spectral density of the generated peripheral blood flow waveform during the period of time.

[0027] In certain aspects, the method may include calculating, by the computing device, an aortic input impedance spectrum of the generated peripheral blood flow waveform during the period of time using the calculated power spectral density of the generated peripheral blood flow waveform.

[0028] In other aspects, quantifying the relative level of global sympathetic nerve activity may include using either the calculated power spectral density or the calculated aortic impedance spectrum to quantify the relative level of global sympathetic nerve activity during the period of time.

[0029] In certain aspects, calculating the aortic input impedance spectrum may include using a Windkessel model to calculate the aortic input impedance spectrum of the generated peripheral blood flow waveform during the period of time.

[0030] In other aspects, calculating the aortic input impedance spectrum may include using a three-element Windkessel model to calculate the aortic input impedance spectrum of the generated peripheral blood flow waveform during the period of time.

[0031] In various aspects, the method may include calculating, by the computing device, the amount of light received by the light detector during the period of time.

[0032] In some aspects, the method can include the computing device using the amount of light received by the light detector during the time period to generate an arterial blood pressure waveform of the fluid flowing within the target tissue.

[0033] In various aspects, computing the aortic input impedance spectrum can include computing the aortic input impedance spectrum of a combined generated peripheral blood flow waveform and a generated arterial blood pressure waveform.

[0034] In other aspects, combining the generated peripheral blood flow waveform with the generated arterial blood pressure waveform can include deriving one or more of determined resistance, compliance, amplitude, or phase from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0035] In some aspects, computing the power spectral density can include computing the power spectral density of a combined generated peripheral blood flow waveform and a generated arterial blood pressure waveform.

[0036] In other aspects, combining the generated peripheral blood flow waveform with the generated arterial blood pressure waveform can include deriving a mean arterial blood pressure amplitude and a mean peripheral blood flow waveform amplitude from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform during the time period.

[0037] In various aspects, the method can include intentionally applying sympathetic stimulation to a candidate for the therapy, wherein identifying a relative change in global sympathetic nerve activity includes identifying a relative change in global sympathetic nerve activity during the intentionally applied sympathetic stimulation.

[0038] In other aspects, the intentionally applied sympathetic stimulation can optionally be selected from the group consisting of cold pressor, handgrip exercise, mental stress, Valsalva or Mueller maneuvers, acute catecholamine injection, static equilibrium position, and variable rate pacing.

[0039] In some aspects, identifying a relative change in global sympathetic nerve activity can include identifying a relative change in global sympathetic nerve activity due to naturally occurring sympathetic stimulation.

[0040] In various aspects, the naturally occurring sympathetic stimulation can optionally be selected from the group consisting of circadian rhythm changes, sleep apnea, static equilibrium position, intense physical activity, and respiration.

[0041] In some aspects, the method can include issuing one or more of a clinical action, an alert, or a recommendation based on whether the identified relative change in global sympathetic nerve activity falls outside the predetermined range.

[0042] According to another aspect of the present disclosure, a method of evaluating and performing a treatment protocol includes: navigating a treatment device to a target tissue, the treatment device being configured to: apply a therapy to the target tissue; during applying the therapy to the target tissue, apply the therapy to the target tissue by the treatment device; monitor global sympathetic nerve activity by a computing device, wherein monitoring global sympathetic nerve activity includes emitting light from a light emitter into tissue over a period of time; detect, by the computing device, dynamic properties of light-scattering particles within the tissue over the period of time by a light detector; generate, by the computing device, a peripheral blood flow waveform of a fluid flowing within the tissue using the detected dynamic properties of the light-scattering particles over the period of time; calculate, by the computing device, a power spectral density of the generated peripheral blood flow waveform over the period of time; quantify, by the computing device, a relative level of global sympathetic nerve activity using the calculated power spectral density of the generated peripheral blood flow waveform; identify, by the computing device, a relative change in global sympathetic nerve activity within the quantified relative level of global sympathetic nerve activity over the period of time; and determine, by the computing device, whether the identified relative change in global sympathetic nerve activity falls outside a predetermined range of global sympathetic nerve activity; and if the determined relative change in global sympathetic nerve activity falls outside the predetermined range, terminate the application of the therapy.

[0043] In various aspects, the method may include adjusting the applied therapy by the computing device in response to the monitored global sympathetic nerve activity.

[0044] In other aspects, the method may include, after applying the therapy to the target tissue, secondarily monitoring global sympathetic nerve activity by the computing device to determine, by the computing device, the efficacy of applying the therapy to the target tissue.

[0045] In certain aspects, calculating the power spectral density may include calculating the power spectral density of the generated peripheral blood flow waveform over the period of time using a Fourier transform.

[0046] In various aspects, calculating the power spectral density may include calculating the power spectral density of the generated peripheral blood flow waveform over the period of time using a fast Fourier transform.

[0047] In other aspects, the method may include calculating, by the computing device, an aortic input impedance spectrum of the generated peripheral blood flow waveform over the period of time using the calculated power spectral density of the generated peripheral blood flow waveform.

[0048] In various aspects, quantifying the relative level of the global sympathetic nerve activity may include quantifying the relative level of the global sympathetic nerve activity over the period of time using one of the calculated power spectral density or the calculated aortic input impedance spectrum.

[0049] In some aspects, calculating the aortic input impedance spectrum may include using a Windkessel model to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period.

[0050] In other aspects, calculating the aortic input impedance spectrum may include using a three-element Windkessel model to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period.

[0051] In various aspects, the method may include the computing device calculating the amount of light received by the light detector during the time period.

[0052] In some aspects, the method may include the computing device using the calculated amount of light received by the light detector during the time period to generate an arterial blood pressure waveform of the fluid flowing in the target tissue.

[0053] In other aspects, calculating the aortic input impedance spectrum may include calculating the aortic input impedance spectrum of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0054] In some aspects, combining the generated peripheral blood flow waveform with the generated arterial blood pressure waveform may include deriving one or more of determined resistance, compliance, amplitude, or phase from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0055] In various aspects, calculating the power spectral density may include calculating the power spectral density of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0056] In other aspects, combining the generated peripheral blood flow waveform with the generated arterial blood pressure waveform may include deriving a mean arterial blood pressure amplitude and a mean peripheral blood flow waveform amplitude from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform during the time period.

[0057] In some aspects, emitting light from a light emitter into tissue and detecting dynamic properties of light-scattering particles within the tissue by a light detector may include disposing the light emitter and the light detector on a diagnostic device.

[0058] In various aspects, the diagnostic device may be a portable wearable device.

[0059] In other aspects, the diagnostic device may be disposed on a patient's finger.

[0060] The present disclosure further discloses a system for performing a diagnostic procedure, the system comprising: a diagnostic device configured to be placed close to tissue, the diagnostic device having a light emitter and a light detector; a computing device that: delivers light from the light emitter to the tissue for a period of time, uses the light detector to detect dynamic properties of light-scattering particles within the tissue, uses the detected dynamic properties to generate a peripheral blood flow waveform of a fluid flowing within the tissue, calculates a power spectral density of the generated peripheral blood flow waveform, uses the calculated power spectral density to quantify a relative level of global sympathetic nerve activity, identifies a relative change in global sympathetic nerve activity within the quantified relative level of global sympathetic nerve activity, and determines whether the identified relative change in global sympathetic nerve activity falls outside a predetermined range of global sympathetic nerve activity, the predetermined range of global sympathetic nerve activity indicating candidates for therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Aspects and embodiments of the present disclosure are described below with reference to the accompanying drawings, in which:

[0062] Figure 1 is a schematic diagram of a system provided in accordance with the present disclosure;

[0063] Figure 2 is Figure 1 a schematic diagram of a workstation of a treatment system of

[0064] Figure 3 is Figure 1 a perspective view of a treatment device of a therapy system of

[0065] Figure 4 is Figure 1 a perspective view of a diagnostic device of a system of , the diagnostic device being shown disposed on a portion of a patient's anatomy;

[0066] Figure 5 is Figure 1 a graphical representation of a user interface of a system of , showing a flow waveform;

[0067] Figure 6 is Figure 5 a graphical representation of a user interface of , showing a photoplethysmogram (PPG) signal;

[0068] Figure 7A is Figure 5 and Figure 6 a graphical representation of preprocessing of a flow waveform and a PPG signal of ;

[0069] Figure 7B is Figure 7A a graphical representation of a power spectral density (PSD) calculated for each of a flow waveform and a PPG signal of ;

[0070] Figure 7C is a graphical representation of a power spectral analysis of arterial mechanical properties using aortic input impedance spectra;

[0071] Figure 8A is for performing Figure 7C a schematic representation of an algorithm for power spectral analysis;

[0072] Figure 8B is for performing Figure 7C another schematic representation of an algorithm for power spectral analysis;

[0073] Figure 9 is a flowchart showing a method of performing a diagnostic procedure according to the present disclosure;

[0074] Figure 10 is a flowchart showing another embodiment of a method of performing a diagnostic procedure according to the present disclosure; and

[0075] Figure 11 is a flowchart showing a method of evaluating and performing a treatment procedure provided according to the present disclosure. DETAILED DESCRIPTION

[0076] The present disclosure relates to diagnostic systems and methods for quantifying sympathetic nerve activity or changes in sympathetic nerve activity to identify diseases associated with autonomic nervous system function. As can be appreciated, the diagnostic systems and methods described herein can be used to evaluate the candidacy of patients for renal denervation (RDN) therapy or other similar therapies for treating diseases caused by overactivation of the SNS. The following description focuses on navigating to the renal arteries, evaluating the renal arteries, and applying a therapy to the renal arteries to identify candidate tissue and denervate sympathetic nerves in, around, and near these renal arteries or, in some embodiments, parasympathetic nerves. However, the present disclosure is not limited thereto and can be used to denervate nerves accessible via any blood vessel or other luminal tissue (e.g., bile duct) described herein. Additionally, in some examples, the devices, systems, and techniques described herein can be used to denervate nerves at multiple target locations within a patient (e.g., at two target locations accessible via different blood vessels). As an example, the devices, systems, and techniques described herein can be used to denervate nerves innervating the liver via the artery supplying the liver and to denervate nerves innervating the kidney via one or more renal arteries or renal blood vessels.

[0077] The diagnostic system uses laser speckle imaging to non-invasively quantify peripheral blood flow and photoplethysmography (PPG) to quantify changes in blood volume and the accompanying waveforms. Models related to flow, volume, and pressure can be used to combine such signals to non-invasively generate arterial blood pressure waveforms over a period of time. The system calculates the power spectral density (PSD) of both the peripheral blood flow measurements and the arterial blood pressure waveforms over a period of time. PSD is a measure of the signal power content relative to frequency and can be calculated in the frequency domain using the Fourier transform (FT), which in an embodiment can be the fast Fourier transform (FFT).

[0078] The diagnostic system performs power spectral analysis of arterial mechanical properties using the aortic input impedance spectrum. The aortic input impedance spectrum quantifies the frequency-independent and -dependent components of left ventricular afterload, such as peripheral arterial resistance and arterial compliance. The aortic input impedance spectrum is calculated using the quantified arterial blood flow measurements and arterial blood pressure waveforms. As can be appreciated, arterial mechanical properties, such as peripheral arteriole size (e.g., resistance), aortic wall stiffness (e.g., compliance), wave reflectivity (e.g., reflectance), can be derived from the amplitude or phase of the aortic input impedance spectrum using mechanical or electrical models of the arterial circulation. In a non-limiting embodiment, the aortic input impedance spectrum can be interpreted using a Windkessel model, such as a three-element Windkessel model.

[0079] The diagnostic system quantifies the relative level and relative changes in activity of global sympathetic nerve activity by analyzing components of the measured peripheral blood flow and PPG signals individually or in combination in the time domain or frequency domain. In this way, the measured peripheral blood flow and PPG signals can be analyzed in various combinations, such as individually in the time domain, individually in the frequency domain, combined in the time domain (e.g., mean PPG signal amplitude and mean measured peripheral blood flow amplitude), or combined in the frequency domain (e.g., resistance, compliance, amplitude, phase, etc.), and so on. If analyzed in combination, it is contemplated that within each combination, various features of the signals can be derived, including time domain parameters (e.g., mean, maximum, minimum, etc.) or frequency domain parameters (e.g., amplitude, phase, etc.). In the frequency domain, the analysis is expected to consist of low-frequency bands and high-frequency bands, multiple bands, or any other frequency bands of interest. In a non-limiting embodiment, a very low-frequency band is analyzed, which is in the range of SNS excitation (e.g., vasomotion).

[0080] Within each combination of the measured peripheral blood flow and PPG signal, the diagnostic system can derive parameters indicating that the SNS activity is in a basal steady state or after an SNS perturbation. The SNS perturbation can be intentionally applied or can occur naturally during long-term monitoring (e.g., during a single night, over several days and nights, etc.) of peripheral blood flow and PPG signal. Intentionally applied SNS perturbations can include cold pressor, handgrip exercise, mental stress (mental arithmetic, Stroop color test, etc.), Valsalva or Mueller maneuvers, acute catecholamine injection (e.g., norepinephrine), static postural changes (e.g., from supine to standing), variable-rate pacing (e.g., modulating heart rate), and so on. Naturally occurring SNS perturbations can include circadian rhythm changes (e.g., morning or evening surges), sleep apnea, static postural changes (e.g., from supine to standing detectable with a 3D accelerometer), intense physical activity (e.g., increased heart rate or activity), respiration, and so on.

[0081] For each of the tracked peripheral blood flow rate and PPG signal, a normal range or normal pattern of variation and one or more thresholds for detecting deviations from the defined normal range or pattern of variation can be defined. As can be appreciated, the defined normal range or normal pattern of variation can be generated based on data obtained from the patient or based on clinical data obtained from an electronic medical record (EMR) system. It is contemplated that, without departing from the scope of the present disclosure, the defined normal range or normal pattern of variation can be expressed as absolute values or proportional changes. In an embodiment, the measurements described herein can be performed acutely (e.g., over a short period of time) or chronically (e.g., over a long period of time) using the same signals obtained from similar portable or wearable devices.

[0082] In an embodiment, the diagnostic system can be used to evaluate the efficacy of a denervation therapy applied to a target tissue. In this way, after applying the denervation therapy to the target tissue, the diagnostic device can again provide parameters indicating that the SNS activity is in a basal steady state or after an SNS perturbation and provide an evaluation of the efficacy of the applied denervation therapy. If the tracked peripheral blood flow rate or PPG signal deviates from the defined normal range or pattern of variation or otherwise exceeds / drops below a predetermined threshold, the application of the denervation therapy has not been successful and further application of the denervation therapy may be required. Conversely, if the tracked peripheral blood flow rate or PPG signal does not deviate from the defined normal range or pattern of variation or otherwise exceeds / drops below a predetermined threshold, the application of the denervation therapy has been successful and further application of the denervation therapy is not required.

[0083] It is envisioned that a diagnostic system device can be utilized while applying a denervation therapy to a target tissue. In this manner, during the application of the denervation therapy, the diagnostic device tracks the peripheral blood flow or PPG signal during the application of the denervation therapy, and when the tracked peripheral blood flow or PPG signal no longer deviates from the defined normal range or pattern of change or otherwise exceeds / drops below a predetermined threshold, the application of the denervation therapy has been successful and the application of the denervation therapy can be terminated. It is envisioned that the therapy system can regulate or otherwise alter the intensity or duration of the application of the denervation therapy based on the tracked peripheral blood flow or PPG signal.

[0084] Turning now to the drawings, the diagnostic systems and methods described herein can be used to evaluate the candidacy of patients for renal denervation (RDN) therapy or other similar therapies for treating diseases caused by overactivation of the SNS. Figure 1 Shown is a guidance and therapy system provided in accordance with the present disclosure and generally identified by reference numeral 10. As will be described in further detail hereinafter, the guidance and therapy system 10 enables navigation of a treatment device 50 to a desired location within a patient's anatomy (e.g., the patient's renal artery), evaluation of the candidacy of tissue within the renal artery for denervation, and application of a denervation therapy to the tissue within the renal artery to denervate the sympathetic nerves within the tissue.

[0085] The therapy system 10 includes a workstation 20, a treatment device 50 operatively coupled to the workstation 20, and in an embodiment, an imaging device 14 that can be operatively coupled to the workstation 20. A patient "P" is shown lying on an operating table 12, with the treatment device 50 inserted through a portion of the patient's femoral artery, although it is contemplated that the treatment device 50 can be inserted into any suitable portion of the patient's vasculature that is in fluid communication with the desired vessel for the therapy (e.g., renal, hepatic, mesenteric, visceral, or other arteries attenuated by the SNS). Although generally described as having one treatment device 50, it is envisioned that the therapy system 10 can employ any suitable number of treatment devices 50. The treatment devices 50 can employ the same or different modalities and can be operatively coupled to the workstation 20. Further, without departing from the scope of the present disclosure, the treatment device 50 can employ a guidewire 64 or a guiding catheter 62 ( Figure 3 ).

[0086] Continuing Figure 1 and additionally referring Figure 2 , the workstation includes a computer 22 and a therapy source 24 (e.g., an RF generator, a microwave generator, an ultrasonic generator, a cryogenic medium source, a chemical source, etc.) operatively coupled to the computer 22. The computer 22 is coupled to a display 26 that is configured to display one or more user interfaces 28 (also inFigure 5 as shown). The computer 22 can be a desktop computer or a tower configuration with a monitor 26, or can include a laptop computer or other computing device. The computer 22 includes a processor 30 that executes software stored in a memory 32. The memory 32 can store one or more application programs 34 and / or algorithms 44 to be executed by the processor 30. A network interface 36 enables the workstation 20 to communicate with various other devices and systems via the Internet. The network interface 36 can connect the workstation 20 to the Internet via a wired or wireless connection. Additionally or alternatively, the communication can be via an ad hoc or wireless network. The network interface 36 can be connected to the Internet via one or more gateways, routers, and network address translation (NAT) devices. The network interface 36 can communicate with a cloud storage system 38, in which additional data, image data, or video can be stored. The cloud storage system 38 can be remote from or within the building of a hospital or clinic, such as in a control or hospital information technology room. It is envisioned that the cloud storage system 38 can also serve as a host for more robust analysis of the acquired image data or images (e.g., additional or enhanced data for analysis and / or comparison, medical images (e.g., fluoroscopy, computed tomography (CT), magnetic resonance imaging (MRI), cone beam computed tomography (CBCT), etc.)). An input module 40 receives input from input devices such as a keyboard, mouse, voice commands, etc. An output module 42 connects the processor 30 and the memory 32 to various output devices such as the monitor 26. In an embodiment, the monitor 26 can be a touchscreen monitor. The workstation 20 can also include a light source 24a that is capable of generating one or more light sources for transmission to a diagnostic device 70 via an optical fiber cable or other means for quantifying sympathetic nerve activity or changes in sympathetic nerve activity in the systems and methods described herein. In an embodiment, the light source 24a can also be included within the diagnostic device 70 itself.

[0087] Figure 3 An embodiment of a treatment device 50 according to the present disclosure is depicted. The treatment device 50 includes an elongate shaft 52 having a handle (not shown) disposed at the proximal end of the elongate shaft 52. The elongate shaft 52 of the treatment device 50 is configured to be advanced within a portion of a patient's vasculature, such as the femoral artery or other suitable portion of the patient's vascular network that is in fluid communication with the patient's renal artery, or any other suitable target vessel. As Figure 3As depicted, the elongate shaft 52 can be configured to be received within a portion of a guiding catheter or sheath (such as a 6F guiding catheter) 62 that is used to navigate the treatment device 50 to a desired location, at which time the guiding catheter 62 is retracted to expose the treatment portion 56 of the treatment device 50, which in the illustrated embodiment includes a plurality of monopolar electrodes 58. The elongate shaft 52 of the treatment device 50 can also include a lumen (not shown) that is configured to slidably receive a guidewire 64, and the treatment device 50 is advanced over the guidewire either alone or in combination with the guiding catheter 62. In this manner, the guidewire 64 is utilized to guide the treatment device 50 to the target tissue using over-the-wire (OTW) or rapid exchange (RX) techniques, at which time the guidewire can be partially or completely removed from the treatment device 50.

[0088] As can be appreciated, navigating a treatment device 50 within a patient's femoral artery or other suitable vasculature to evaluate whether the patient is a candidate for denervation therapy is an invasive procedure. The diagnostic systems and methods described herein allow for a non-invasive assessment of a patient's candidacy for denervation therapy based on physiological properties. Figure 4 An embodiment of a diagnostic device 70 in accordance with the present disclosure is depicted. The diagnostic device 70 includes a light emitter 72 and a light detector 74 that is configured to detect light that is recovered from the light emitter 72 after interacting with target tissue. The light emitter 72 emits partially coherent or coherent light and can be, for example, a diode laser or a vertical-cavity surface-emitting laser (VCSEL) laser. The light emitted by the light emitter 72 can be within the near-infrared spectrum and, in an embodiment, can include wavelengths between approximately 700 nm and approximately 900 nm. The light emitter 72 can be arranged to be in substantially direct contact with the patient's tissue or, in an embodiment, can be arranged to be in a spaced-apart relationship with the patient's tissue. It is contemplated that the light emitter 72 can be operably coupled to a light source 24a via an optical transmission conduit (such as an optical fiber cable, etc.) that is operably coupled to the diagnostic device 70.

[0089] The light detector 74 includes one or more photosensitive elements (not shown) and can be an image sensor in an embodiment. In an embodiment, the light detector 74 can be a silicon-based camera sensor, such as a CMOS or CCD image sensor, etc. As can be understood, the light detector 74 detects the light recovered from the light emitter 72 after interacting with the target tissue. It is expected that the light detector 74 can generate one or more signals related to the detected light and transmit the generated signals to the computer 20. The signals generated by the light detector 74 include quantifiable information (including but not limited to spatial or temporal variations of the intensity) about the intensity of the light detected at one or more photosensitive elements at a point in time or over a time course. In an embodiment, the signals can include information about absorption or scattering events within the tissue and can be analog or digital signals.

[0090] Continue Figure 4 , the diagnostic device 70 can be positioned relative to the target tissue in any suitable position or orientation such that the light emitter 72 and the light detector 74 can be placed in a contact or non-contact geometry or a reflection or transmission geometry. In an embodiment, the light emitter 72 and the light detector 74 can be disposed adjacent to each other on one side of the target tissue or can be disposed on opposite sides of the target tissue. In this way, the diagnostic device 70 can be set on a part of the target tissue or coupled to a part of the target tissue, or can be independent or coupled to a structure independent of the target tissue. In an embodiment, the diagnostic device can be a portable or wearable device, such as a watch, wristband, armband, anklet, belt, skin patch, ear clip, finger clip, etc., and can be remotely (e.g., wirelessly, etc.) or directly (e.g., wired, etc.) coupled to the workstation 20.

[0091] The light emitter 72 emits light to the target tissue for a predetermined period of time. At least a portion of the light scatters inside the target tissue and is detected by the light detector 74. The predetermined amount of time for which the light is emitted from the light emitter 72 is long enough for the light detector 74 to detect the changes occurring within the target tissue. The changes in the target tissue during light emission alter the path of the emitted light or the nature of the detected light. The dynamic properties of the light-scattering particles, such as the rate of movement (e.g., flow rate), can be observed or measured, and a peripheral blood flow waveform 76 can be generated and displayed on the user interface 28. The computer 20 can record the detected signals from the light detector 74 and store the detected signals in the memory 32. The computer 20 analyzes the detected signals received from the light detector 74 and determines information about the target tissue, such as the flow rate of the blood flowing within the target tissue.

[0092] The light detector 74 measures or detects a change in the absorption of light emitted from the light emitter 72. The computer 20 analyzes the measurement results from the light detector 74 and generates a photoplethysmogram (PPG), which can be displayed on the user interface 28 ( Figure 6 ). The PPG and the blood flow signal are closely aligned with the arterial blood pressure, and thus, the PPG signal can form an arterial blood pressure waveform 78 over a period of time. Although typically described as using the same light emitter 72 and light detector 74, it is contemplated that separate light emitters 72 and / or light detectors 74 may be used to generate flow rate measurements (e.g., peripheral blood flow waveform 76) and PPGs (e.g., arterial blood pressure waveform 78) without departing from the scope of the present disclosure.

[0093] Referring Figures 7A to 7C to FIGS. 7 and 8, the computer 20 determines the peripheral blood flow waveform 76 and the arterial blood pressure waveform 78 over a period of time ( Figure 7A ). Thereafter, the computer 20 calculates the power spectral density (PSD) 80, 82 of each of the peripheral blood flow waveform 76 and the arterial blood pressure waveform 78 over the period of time ( Figure 7B ). The PSD is a measure of the power content of a signal with respect to frequency and can be calculated in the frequency domain using a Fourier transform (FT) or a fast Fourier transform (FFT), but it is contemplated that any suitable digital signal processing technique may be used to calculate the PSD of each of the peripheral blood flow waveform 76 and / or the arterial blood pressure waveform 78 without departing from the scope of the present disclosure. Using the PSDs 80, 82 calculated for each of the peripheral blood flow waveform 76 and the arterial blood pressure waveform 78, the computer 20 performs a power spectral analysis of arterial mechanical properties (such as peripheral arteriole size (e.g., resistance), aortic wall stiffness (e.g., compliance), wave reflectivity (e.g., reflectance)) using aortic input impedance spectra 84, 86 ( Figure 7C ). The aortic input impedance spectra 84, 86 quantify the frequency-independent and dependent components of the left ventricular afterload, such as peripheral arterial resistance and arterial compliance. The computer 20 uses the quantified arterial blood flow waveform 76 and arterial blood pressure waveform 78 to calculate the aortic input impedance spectra 84, 86. As can be appreciated, arterial mechanical properties can be derived from the amplitude or phase of the aortic input impedance spectra 84, 86 using a mechanical or electrical model of the arterial circulation (e.g., a simulation model) ( Figure 8A and Figure 8B ). In one non-limiting embodiment, the computer 22 uses a Windkessel model (such as a three-element Windkessel model) to interpret the aortic input impedance spectra 84, 86, but it is contemplated that any suitable model capable of interpreting the aortic input impedance spectra 84, 86 may be utilized without departing from the scope of the present disclosure.

[0094] Reference Figure 9 shows a method for quantifying the relative level of global sympathetic activity and the relative change in activity by analyzing components of a measured peripheral blood flow waveform 76 and a PPG signal (e.g., arterial blood pressure waveform 78) over a period of time, either individually or in combination, in the time domain or the frequency domain, and the method is generally identified by reference numeral 200. The measured peripheral blood flow 202 and the PPG signal 204 can be analyzed in various combinations, such as individually in the time domain 206, individually in the frequency domain (PSD) 208, in combination in the time domain 210 (e.g., average PPG signal amplitude and average measured peripheral blood flow amplitude), in combination in the frequency domain (PSD) 212 (e.g., resistance, compliance, amplitude, phase, etc.), and so on. If analyzed in combination, it is contemplated that within each combination, various characteristics of the signal can be derived, including time domain parameters (e.g., mean, maximum, minimum, etc.) or frequency domain (PSD) parameters (e.g., amplitude, phase, etc.). Within the frequency domain 212, it is expected that the analysis can consist of a low frequency band and a high frequency band, multiple bands, or any other frequency band of interest. In one non-limiting embodiment, a very low frequency band is analyzed, which is within the range of SNS excitation (e.g., vasomotion).

[0095] Within each combination, the computer 20 monitors and derives parameters 214 indicative of SNS activity being in a basal steady state or after an SNS perturbation. It is contemplated that the SNS perturbation can be deliberately applied in a clinical setting, or can occur naturally in the case of long-term monitoring of peripheral blood flow and PPG signals (e.g., over a single night, over several days and nights, etc.) in a clinical setting or outside a clinical setting (e.g., the patient's home, workplace, etc.).

[0096] For each of the tracked peripheral blood flow and PPG signal, a normal range or normal pattern of variation and one or more thresholds for detecting deviation from the defined normal range or pattern of variation are defined, and the normal range or normal pattern of variation and the one or more thresholds may be stored in the memory 32 or uploaded or otherwise set in a memory (not shown) associated with a portable device (such as a wristwatch, etc.). It is contemplated that, without departing from the scope of the present disclosure, the defined normal range or normal pattern of variation may be expressed as an absolute value or a proportional change. If no change is detected or if the tracked peripheral blood flow or PPG signal does not deviate from the defined normal range or pattern of variation or otherwise exceeds / falls below a predetermined threshold 216, the process returns to monitoring the tracked peripheral blood flow and PPG signal 214. If the tracked peripheral blood flow or PPG signal deviates from the defined normal range or pattern of variation or otherwise exceeds / falls below a predetermined threshold 216, the computer 20 may issue a warning (e.g., visual, auditory, tactile, etc.), display clinical actions associated with the deviation, and / or provide recommendations regarding potential diagnostic decisions based on the measured data 218.

[0097] Reference Figure 10 , a method for evaluating a candidate for therapy is shown and the method is generally identified by reference numeral 300. In step 302, light is emitted from a light emitter 72 into a target tissue over a period of time. In step 304, a light detector 74 detects the dynamic properties of light-scattering particles within the target tissue over the period of time. In step 306, the detected dynamic properties of the light-scattering particles over the period of time are used to generate a peripheral blood flow waveform 76 of a fluid flowing within the target tissue. In step 308, the amount of light received by the light detector 74 is measured over the period of time, and in step 310, the calculated amount of light received by the light detector 74 over the period of time is used to generate an arterial blood pressure waveform 78 of the fluid flowing within the target tissue. In step 312, the power spectral density of each of the generated peripheral blood flow waveform 76 and the generated arterial blood pressure waveform 78 is calculated over the period of time. In step 314, the power spectral density calculated for each of the generated peripheral blood flow waveform and the generated arterial blood pressure waveform is used to quantify the relative level of global sympathetic nerve activity over the period of time. In step 316, relative changes in global sympathetic nerve activity within the quantified relative level of global sympathetic nerve activity over the period of time are identified, and in step 318, it is determined whether the identified relative changes in global sympathetic nerve activity fall outside a predetermined range of global sympathetic nerve activity, wherein one or more of the identified relative changes in global sympathetic nerve activity that fall outside the predetermined range indicate a candidate for therapy.

[0098] ReferenceFigure 11 , which shows a method for evaluating and implementing a treatment protocol, and the method is generally identified by reference numeral 400. After navigating a treatment device to a target tissue, a therapy is applied to the target tissue in step 402. During the application of the therapy to the target tissue, in step 404, light is emitted from a light emitter into the tissue. In step 406, the dynamic properties of light-scattering particles within the tissue are detected by a light detector, and in step 408, the detected dynamic properties of the light-scattering particles are used to generate a peripheral blood flow waveform of the fluid flowing within the tissue. In step 410, the amount of light received by the light detector is calculated, and in step 412, the calculated amount of light received by the light detector is used to calculate an arterial blood pressure waveform of the fluid flowing within the tissue. In step 414, the power spectral density of each of the generated peripheral blood flow waveform and the generated arterial blood pressure waveform is calculated. In step 416, the calculated power spectral density for each of the generated peripheral blood flow waveform and the generated arterial blood pressure waveform is used to quantify the relative level of global sympathetic nerve activity. In step 418, the relative change in global sympathetic nerve activity within the quantified relative level of global sympathetic nerve activity is identified, and in step 420, it is determined whether the identified relative change in global sympathetic nerve activity falls outside a predetermined range of global sympathetic nerve activity. If the identified relative change in global sympathetic nerve activity does not fall outside the predetermined range of global sympathetic nerve activity, the method returns to step 404 to continue emitting light into the tissue. If the identified relative change in global sympathetic nerve activity falls outside the predetermined range of global sympathetic nerve activity, then in step 422, the application of the therapy to the target tissue is terminated.

[0099] Although Figure 11 methods have been described in which changes in SNS activity are used to determine the endpoint of therapy application, the present disclosure is not limited thereto. In some embodiments, method 300 may be performed to ensure that a patient is a candidate for denervation. Denervation of a desired blood vessel of the patient may be performed, and then method 300 may be performed again to evaluate the efficacy of the denervation. If sufficient change is detected (e.g., as in step 424), the therapy is terminated. Otherwise, the denervation may continue for a period of time, or the parameters of the denervation (e.g., power, duration, location) may be changed and the denervation performed again. This process may be repeated until denervation has been achieved or a therapy limit has been reached without achieving the desired denervation.

[0100] Return to Figure 3, in an embodiment where the treatment device 50 is an RF ablation catheter, the treatment portion 56 includes one or more electrodes 58 disposed on the shaft 52, and the one or more electrodes are configured to apply a denervation therapy to the target tissue. It is contemplated that the one or more electrodes 58 may be disposed in a spaced-apart relationship from each other and configured to contact the vessel wall in a suitable configuration (such as a helix, expanded configuration), or to contact the vessel wall by manipulating the treatment portion 56 to contact the tissue wall (e.g., in the case of a linear arrangement). In a non-limiting embodiment, the treatment assembly 56 is configured to transition from an initial undeployed state having a generally linear profile to a second deployed or expanded configuration, wherein the treatment assembly 56 forms a generally helical and / or spiral configuration ( Figure 3 ) for delivering therapy at the treatment site and providing therapeutically effective electrical and / or thermally induced renal nerve modulation. In this manner, when in the second expanded configuration, the treatment assembly 56 presses against or otherwise contacts the wall of the patient's vasculature. Although typically described as transitioning to a helical and / or spiral configuration, it is contemplated that the treatment assembly 56 may be deployed into any suitable shape. In an embodiment, depending on the design requirements of the treatment device 50 or the type of treatment protocol being performed, the treatment assembly 56 may be capable of being placed in any suitable number of configurations. As shown herein, the treatment device 50 includes four electrodes 58. However, the present disclosure is not limited thereto, and the treatment device 50 may have more or fewer electrodes 58 without departing from the scope of the present disclosure. Those skilled in the art will recognize that the electrodes 58 may be replaced with ultrasound transducers, microwave antennas, ports for delivering cryoablation media or chemical media, and other implements and / or ablation and denervation modalities without departing from the scope of the present disclosure.

[0101] As shown, the electrodes 58 are disposed in a spaced-apart relationship from each other along the length of the treatment device 50, thereby forming the treatment portion 56. As will be appreciated, these electrodes 58 are in communication with a therapy source 24 that generates, for example, monopolar RF energy to denervate the sympathetic nerves of the associated blood vessel. Additionally or alternatively, the electrodes 58 may deliver RF energy independently of each other (e.g., monopolar), simultaneously, selectively, sequentially, and / or between any desired combination of the electrodes 58 (e.g., bipolar).

[0102] In one embodiment, the treatment device 50 can be a cryotherapy device, where the treatment portion 56 can include a therapy delivery element, such as an occlusive balloon, a non-occlusive balloon, or other balloons that allow blood flow. In this embodiment, the therapy source 24 can include a refrigerant or coolant source or a device that generates a refrigerant. It is contemplated that the treatment device 50 can be a microwave energy device, where the treatment portion 56 can include one or more therapy delivery elements, such as microwave antennas. In this embodiment, the therapy source 24 can be a microwave energy generator operably coupled to the microwave antenna. It is expected that the treatment device can be an ultrasound device, where the treatment portion 56 can include one or more therapy delivery elements, such as ultrasound transducers. In this embodiment, the therapy source 24 can be a radiofrequency energy generator operably coupled to the ultrasound transducer. In an embodiment, the treatment device 50 can be a chemical denervation device, where the treatment portion 56 can include one or more cannulas or needles for administering a chemical denervation agent. In this embodiment, the therapy source 24 can be a chemical denervation agent source operably coupled to the treatment portion 56. Those skilled in the art will recognize that the treatment device 50, the treatment portion 56, and the therapy source 24 can be any suitable combination of devices capable of performing a denervation procedure.

[0103] Although generally described above, it is contemplated that the memory 32 can include any non-transitory computer-readable storage medium for storing data and / or software, the data and / or software including instructions executable by the processor 30 and controlling the operation of the workstation 20 and, in some embodiments, also controlling the operation of the treatment device 50 and / or the imaging device 70. In an embodiment, the memory 32 can include one or more storage devices, such as solid-state storage devices, e.g., flash memory chips. Alternatively, or in addition to the one or more solid-state storage devices, the memory 32 can also include one or more mass storage devices connected to the processor 30 via a mass storage controller (not shown) and a communication bus (not shown).

[0104] Although the description of computer-readable media contained herein refers to solid-state storage devices, those skilled in the art should understand that a computer-readable storage medium can be any available medium accessible to the processor 30. That is, a computer-readable storage medium can include non-transitory, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. For example, a computer-readable storage medium can include RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memory technologies, CD-ROM, DVD, Blu-ray or other optical storage devices, magnetic tape cartridges, tapes, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store the desired information and is accessible to the therapy source 24.

[0105] Although several embodiments of the present disclosure have been shown in the drawings, it is not intended to limit the present disclosure thereto, as it is desired to make the present disclosure as broad as permitted by the art and the specification should be read in the same manner. Therefore, the above description should not be construed as restrictive, but merely as illustrative of embodiments. Those skilled in the art can envision other modifications within the scope and spirit of the appended claims herein.

[0106] The following examples are a non-limiting list of clauses according to one or more technologies of the present disclosure.

[0107] Example 1. A system for performing a diagnostic procedure, the system comprising: a diagnostic device configured to be placed close to tissue, wherein a light emitter and a light detector are disposed on the diagnostic device; a computing device including a processor and a memory storing instructions that, when executed by the processor, cause the computing device to: deliver light from the light emitter into the tissue for a period of time; detect, using the light detector during the period of time, dynamic properties of light-scattering particles within the tissue; generate, using the detected dynamic properties of the light-scattering particles during the period of time, a peripheral blood flow waveform of a fluid flowing within the tissue; calculate a power spectral density of the generated peripheral blood flow waveform during the period of time; quantify, using the calculated power spectral density of the generated peripheral blood flow waveform, a relative level of global sympathetic nerve activity during the period of time; identify a relative change in global sympathetic nerve activity within the quantified relative level of global sympathetic nerve activity during the period of time; and determine whether the identified relative change in global sympathetic nerve activity falls outside a predetermined range of global sympathetic nerve activity, wherein one or more identified relative changes in global sympathetic nerve activity that fall outside the predetermined range indicate candidates for therapy.

[0108] Example 2. The system according to Example 1, wherein when executed by the processor, the instructions cause the computing device to calculate the power spectral density of the peripheral blood flow waveform generated during the time period using a fast Fourier transform.

[0109] Example 3. The system according to Example 2, wherein calculating the power spectral density includes using a fast Fourier transform to calculate the power spectral density of the peripheral blood flow waveform generated during the time period.

[0110] Example 4. The system according to Example 1, wherein when executed by the processor, the instructions cause the computing device to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period using the calculated power spectral density of the generated peripheral blood flow waveform.

[0111] Example 5. The system according to Example 4, wherein when executed by the processor, the instructions cause the computing device to quantify the relative level of global sympathetic activity during the time period using either the calculated power spectral density or the calculated aortic impedance spectrum.

[0112] Example 6. The system according to Example 4, wherein when executed by the processor, the instructions cause the computing device to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period using a Windkessel model.

[0113] Example 7. The system according to Example 5, wherein when executed by the processor, the instructions cause the computing device to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period using the three-element Windkessel model.

[0114] Example 8. The system according to Example 5, wherein when executed by the processor, the instructions cause the computing device to calculate the amount of light received by the light detector during the time period.

[0115] Example 9. The system according to Example 8, wherein when executed by the processor, the instructions cause the computing device to generate an arterial blood pressure waveform of the fluid flowing in the target tissue using the calculated amount of light received by the light detector during the time period.

[0116] Example 10. The system according to Example 9, wherein when executed by the processor, the instructions cause the computing device to calculate the aortic input impedance spectrum of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0117] Example 11. The system according to Example 10, wherein when executed by the processor, the instructions cause the computing device to combine the generated peripheral blood flow waveform and the generated arterial blood pressure waveform using one or more of a determined resistance, compliance, amplitude, or phase derived from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0118] Example 12. The system according to Example 9, wherein when executed by the processor, the instructions cause the computing device to calculate the power spectral density of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0119] Example 13. The system according to Example 12, wherein when executed by the processor, the instructions cause the computing device to combine the generated peripheral blood flow waveform and the generated arterial blood pressure waveform using the mean arterial blood pressure amplitude and the derived mean peripheral blood flow waveform derived from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform during the time period.

[0120] Example 14. The system according to Example 1, the system further comprising intentionally applying sympathetic stimulation to a candidate for the therapy, wherein identifying a relative change in global sympathetic activity includes identifying a relative change in global sympathetic activity during the intentionally applied sympathetic stimulation.

[0121] Example 15. The system according to Example 14, wherein the intentionally applied sympathetic stimulation is selected from the group consisting of cold pressor, handgrip exercise, mental stress, Valsalva or Mueller manoeuvre, acute catecholamine injection, static equilibrium position, and variable rate pacing.

[0122] Example 16. The system according to Example 1, wherein when executed by the processor, the instructions cause the computing device to identify a relative change in global sympathetic activity caused by naturally occurring sympathetic stimulation.

[0123] Example 17. The system according to Example 16, wherein the naturally occurring sympathetic stimulation is selected from the group consisting of circadian rhythm changes, sleep apnoea, static equilibrium position, intense physical activity, and respiration.

[0124] Example 18. The system according to Example 1, wherein the diagnostic device is a portable wearable device, and the portable wearable device is wirelessly couplable to the computing device.

[0125] Example 19. The system according to Example 1, wherein the diagnostic device is operably coupled to the computing device using a wired connection.

[0126] Example 20. The system according to Example 1, wherein when executed by the processor, the instructions cause the computing device to issue one or more of a clinical action, an alert, or a recommendation based on whether a relative change in the identified global sympathetic activity falls outside the predetermined range.

[0127] Example 21. A method for evaluating a candidate for a therapy, the method comprising: causing, by a computing device, a light emitter to emit light into a target tissue of a patient over a period of time; and receiving, by the computing device, from a light detector a signal indicative of dynamic properties of light-scattering particles within the target tissue during the period of time; generating, by the computing device, a peripheral blood flow waveform of a fluid flowing within the target tissue using the dynamic properties of the light-scattering particles during the period of time; calculating, by the computing device, a power spectral density of the generated peripheral blood flow waveform during the period of time; quantifying, by the computing device, a relative level of global sympathetic activity during the period of time using the calculated power spectral density of the generated peripheral blood flow waveform; identifying, by the computing device, a relative change in the global sympathetic activity within the quantified relative level of global sympathetic activity during the period of time; and determining, by the computing device, whether the identified relative change in the global sympathetic activity falls outside a predetermined range of global sympathetic activity, wherein one or more identified relative changes in the global sympathetic activity that fall outside the predetermined range indicate that the patient is a candidate for the therapy.

[0128] Example 22. The method according to Example 21, wherein calculating the power spectral density comprises calculating the power spectral density of the generated peripheral blood flow waveform during the period of time using a Fourier transform.

[0129] Example 23. The method according to Example 22, wherein calculating the power spectral density comprises calculating the power spectral density of the generated peripheral blood flow waveform during the period of time using a fast Fourier transform.

[0130] Example 24. The method according to Example 21, the method further comprising calculating, by the computing device, an aortic input impedance spectrum of the generated peripheral blood flow waveform during the period of time using the calculated power spectral density of the generated peripheral blood flow waveform.

[0131] Example 25. The method according to Example 24, wherein quantifying the relative level of the global sympathetic activity comprises quantifying the relative level of the global sympathetic activity during the period of time using one of the calculated power spectral density or the calculated aortic impedance spectrum.

[0132] Example 26. The method according to Example 24, wherein calculating the aortic input impedance spectrum includes using a Windkessel model to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period.

[0133] Example 27. The method according to Example 25, wherein calculating the aortic input impedance spectrum includes using a three-element Windkessel model to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period.

[0134] Example 28. The method according to Example 25, the method further comprising calculating, by the computing device, the amount of light received by the light detector during the time period.

[0135] Example 29. The method according to Example 28, the method further comprising generating, by the computing device, an arterial blood pressure waveform of the fluid flowing in the target tissue using the calculated amount of light received by the light detector during the time period.

[0136] Example 30. The method according to Example 29, wherein calculating the aortic input impedance spectrum includes calculating the aortic input impedance spectrum of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0137] Example 31. The method according to Example 30, wherein combining the generated peripheral blood flow waveform with the generated arterial blood pressure waveform includes deriving one or more of a determined resistance, compliance, amplitude, or phase from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0138] Example 32. The method according to Example 29, wherein calculating the power spectral density includes calculating the power spectral density of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0139] Example 33. The method according to Example 32, wherein combining the generated peripheral blood flow waveform with the generated arterial blood pressure waveform includes deriving an average arterial blood pressure amplitude and an average peripheral blood flow waveform amplitude from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform during the time period.

[0140] Example 34. The method according to Example 21, the method further comprising intentionally applying a sympathetic stimulus to a candidate for the therapy, wherein identifying a relative change in global sympathetic nerve activity includes identifying a relative change in global sympathetic nerve activity during the intentionally applied sympathetic stimulus.

[0141] Example 35. The method according to Example 34, wherein the deliberately applied sympathetic stimulation is selected from the group consisting of cold pressor, hand tearing exercise, mental stress, Valsalva or Müller manoeuvre, acute catecholamine injection, static equilibrium position, and variable rate pacing.

[0142] Example 36. The method according to Example 21, wherein identifying the relative change in global sympathetic nerve activity includes identifying the relative change in global sympathetic nerve activity caused by naturally occurring sympathetic stimulation.

[0143] Example 37. The method according to Example 36, wherein the naturally occurring sympathetic stimulation is selected from the group consisting of circadian rhythm changes, sleep apnoea, static equilibrium position, intense physical activity, and respiration.

[0144] Example 38. The method according to Example 21, the method further comprising issuing one or more of a clinical action, an alert, or a recommendation based on whether the identified relative change in global sympathetic nerve activity falls outside the predetermined range.

[0145] Example 39. A method of evaluating and implementing a treatment protocol, the method comprising: navigating a treatment device to a target tissue, the treatment device being configured to apply a therapy to the target tissue; applying the therapy to the target tissue by the treatment device;

[0146] During applying the therapy to the target tissue, monitoring global sympathetic nerve activity by a computing device, wherein monitoring global sympathetic nerve activity includes: emitting light from a light emitter into the tissue over a period of time; detecting, by the computing device, the dynamic properties of light-scattering particles within the tissue over the period of time by a light detector; using, by the computing device, the detected dynamic properties of light-scattering particles over the period of time to generate a peripheral blood flow waveform of a fluid flowing within the tissue; calculating, by the computing device, the power spectral density of the generated peripheral blood flow waveform over the period of time; using, by the computing device, the calculated power spectral density of the generated peripheral blood flow waveform to quantify the relative level of global sympathetic nerve activity; identifying, by the computing device, the relative change in global sympathetic nerve activity within the quantified relative level of global sympathetic nerve activity over the period of time; and determining, by the computing device, whether the identified relative change in global sympathetic nerve activity falls outside a predetermined range of global sympathetic nerve activity; and if the determined relative change in global sympathetic nerve activity falls outside the predetermined range, terminating the application of the therapy.

[0147] Example 40. The method according to Example 39, the method further comprising adjusting the applied therapy by the computing device in response to the monitored global sympathetic nerve activity.

[0148] Example 41. The method according to Example 39, the method further comprising, after applying the therapy to the target tissue, the computing device monitoring the global sympathetic nerve activity a second time to determine, by the computing device, the efficacy of applying the therapy to the target tissue.

[0149] Example 42. The method according to Example 39, wherein calculating the power spectral density includes using a Fourier transform to calculate the power spectral density of the peripheral blood flow waveform generated during the time period.

[0150] Example 43. The method according to Example 42, wherein calculating the power spectral density includes using a fast Fourier transform to calculate the power spectral density of the peripheral blood flow waveform generated during the time period.

[0151] Example 44. The method according to Example 39, the method further comprising the computing device calculating an aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period using the calculated power spectral density of the generated peripheral blood flow waveform.

[0152] Example 45. The method according to Example 44, wherein quantifying the relative level of the global sympathetic nerve activity includes using one of the calculated power spectral density or the calculated aortic input impedance spectrum to quantify the relative level of the global sympathetic nerve activity during the time period.

[0153] Example 46. The method according to Example 44, wherein calculating the aortic input impedance spectrum includes using a Windkessel model to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period.

[0154] Example 47. The method according to Example 46, wherein calculating the aortic input impedance spectrum includes using a three-element Windkessel model to calculate the aortic input impedance spectrum of the peripheral blood flow waveform generated during the time period.

[0155] Example 48. The method according to Example 46, the method further comprising the computing device calculating the amount of light received by the light detector during the time period.

[0156] Example 49. The method according to Example 48, the method further comprising the computing device generating an arterial blood pressure waveform of the fluid flowing in the target tissue using the calculated amount of light received by the light detector during the time period.

[0157] Example 50. The method according to Example 49, wherein calculating the aortic input impedance spectrum includes calculating the aortic input impedance spectrum of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0158] Example 51. The method according to Example 50, wherein combining the generated peripheral blood flow waveform and the generated arterial blood pressure waveform includes deriving one or more of determined resistance, compliance, amplitude, or phase from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0159] Example 52. The method according to Example 39, wherein calculating the power spectral density includes calculating the power spectral density of a combination of the generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

[0160] Example 53. The method according to Example 52, wherein combining the generated peripheral blood flow waveform and the generated arterial blood pressure waveform includes deriving a mean arterial blood pressure amplitude and a mean peripheral blood flow waveform amplitude from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform over the period of time.

[0161] Example 54. The method according to Example 39, wherein emitting light from a light emitter into tissue and detecting dynamic properties of light-scattering particles within the tissue by a light detector includes disposing the light emitter and the light detector on a diagnostic device.

[0162] Example 55. The method according to Example 54, wherein the diagnostic device is a portable wearable device.

[0163] Example 56. The method according to Example 54, wherein the diagnostic device is disposed on a finger of a patient.

Claims

1. A system for performing a diagnostic procedure, the system comprising: a diagnostic device configured to be placed proximate to tissue, wherein a light emitter and a light detector are disposed on the diagnostic device; a computing device comprising a processor and a memory, the memory storing instructions that, when executed by the processor, cause the computing device to: deliver light from the light emitter into the tissue for a period of time; detect, using the light detector during the period of time, dynamic properties of light-scattering particles within the tissue; generate, using the detected dynamic properties of the light-scattering particles during the period of time, a peripheral blood flow waveform of a fluid flowing within the tissue; calculate a power spectral density of the generated peripheral blood flow waveform during the period of time; quantify, using the calculated power spectral density of the generated peripheral blood flow waveform, a relative level of global sympathetic nerve activity during the period of time; identify a relative change in global sympathetic nerve activity within the quantified relative level of global sympathetic nerve activity during the period of time; and determine whether the identified relative change in global sympathetic nerve activity falls outside a predetermined range of global sympathetic nerve activity, wherein one or more identified relative changes in global sympathetic nerve activity that fall outside the predetermined range indicate candidates for therapy.

2. The system according to claim 1, wherein the instructions, when executed by the processor, cause the computing device to calculate the power spectral density of the generated peripheral blood flow waveform during the period of time using a fast Fourier transform.

3. The system according to claim 1 or 2, wherein the instructions, when executed by the processor, cause the computing device to calculate an aortic input impedance spectrum of the generated peripheral blood flow waveform during the period of time using the calculated power spectral density of the generated peripheral blood flow waveform.

4. The system according to claim 3, wherein the instructions, when executed by the processor, cause the computing device to quantify the relative level of global sympathetic nerve activity during the period of time using one of the calculated power spectral density or the calculated aortic impedance spectrum.

5. The system according to claim 3 or 4, wherein the instructions, when executed by the processor, cause the computing device to calculate the aortic input impedance spectrum of the generated peripheral blood flow waveform during the period of time using a Windkessel model.

6. The system according to claim 4 or 5, wherein the instructions, when executed by the processor, cause the computing device to calculate an amount of light received by the light detector during the period of time.

7. The system according to claim 6, wherein the instructions, when executed by the processor, cause the computing device to generate an arterial blood pressure waveform of the fluid flowing within the target tissue using the calculated amount of light received by the light detector during the period of time.

8. The system according to claim 7, wherein the instructions, when executed by the processor, cause the computing device to compute the aortic input impedance spectrum of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

9. The system according to claim 8, wherein the instructions, when executed by the processor, cause the computing device to combine the generated peripheral blood flow waveform and the generated arterial blood pressure waveform using one or more of a determined resistance, compliance, amplitude, or phase derived from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

10. The system according to claim 7, wherein the instructions, when executed by the processor, cause the computing device to compute the power spectral density of the combined generated peripheral blood flow waveform and the generated arterial blood pressure waveform.

11. The system according to claim 10, wherein the instructions, when executed by the processor, cause the computing device to combine the generated peripheral blood flow waveform and the generated arterial blood pressure waveform using the mean arterial blood pressure amplitude and the derived mean peripheral blood flow waveform derived from the generated peripheral blood flow waveform and the generated arterial blood pressure waveform during the time period.

12. The system according to any one of claims 1 to 11, the system further comprising intentionally applying sympathetic stimulation to a candidate for the therapy, wherein identifying a relative change in global sympathetic nerve activity includes identifying a relative change in global sympathetic nerve activity during the intentionally applied sympathetic stimulation.

13. The system according to claim 12, wherein the intentionally applied sympathetic stimulation is selected from the group consisting of cold pressor, handgrip exercise, mental stress, Valsalva or Mueller manoeuvre, acute catecholamine injection, static equilibrium position, and variable rate pacing.

14. The system according to any one of claims 1 to 13, wherein the instructions, when executed by the processor, cause the computing device to identify a relative change in global sympathetic nerve activity caused by naturally occurring sympathetic stimulation.

15. The system according to claim 14, wherein the naturally occurring sympathetic stimulation is selected from the group consisting of circadian rhythm changes, sleep apnoea, static equilibrium position, intense physical activity, and respiration.

16. The system according to any one of claims 1 to 15, wherein the diagnostic device is a portable wearable device, the portable wearable device being capable of being wirelessly coupled to the computing device.

17. The system according to any one of claims 1 to 16, wherein the instructions, when executed by the processor, cause the computing device to issue one or more of a clinical action, an alert, or a recommendation based on whether the identified relative change in global sympathetic nerve activity falls outside the predetermined range.

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