Sensitivity analysis for selecting therapy parameter sets

Techniques for controlling delivery of a therapy to a patient by a medical device, such as an implantable medical device (IMD), involve a sensitivity analysis of a performance metric. The performance metric may relate to efficacy or side effects of the therapy. For example, the performance metric may comprise a sleep quality metric, an activity level metric, a movement disorder metric for patients with Parkinson's disease, or the like. The sensitivity analysis identifies values of therapy parameters that defines a substantially maximum or minimum value of the performance metric. The identified therapy parameters are a baseline therapy parameter set, and a medical device may control delivery of the therapy based on the baseline therapy parameter set.

TECHNICAL FIELD

The invention relates to medical devices and, more particularly, to medical devices that deliver a therapy.

BACKGROUND

In some cases, an ailment may affect a patient's sleep quality or physical activity level, or a therapy delivered to the patient to treat the ailment may produce undesirable side effects. For example, chronic pain may cause a patient to have difficulty falling asleep, and may disturb the patient's sleep, e.g., causing the patient to wake. Further, chronic pain may cause the patient to have difficulty achieving deeper sleep states, such as one of the nonrapid eye movement (NREM) sleep states associated with deeper sleep. Other ailments that may negatively affect patient sleep quality include movement disorders, psychological disorders, sleep apnea, congestive heart failure, gastrointestinal disorders and incontinence. As another example, chronic pain may cause a patient to avoid particular physical activities, or activity in general, where such activities increase the pain experienced by the patient. Movement disorders and congestive heart failure may also affect patient activity level.

Furthermore, in some cases, poor sleep quality may increase the symptoms experienced by a patient due to an ailment. For example, poor sleep quality has been linked to increased pain symptoms in chronic pain patients. The link between poor sleep quality and increased symptoms is not limited to ailments that negatively impact sleep quality, such as those listed above. Nonetheless, the condition of a patient with such an ailment may progressively worsen when symptoms disturb sleep quality, which in turn increases the frequency and/or intensity of symptoms.

In some cases, these ailments are treated via a medical device, such as an implantable medical device (IMD). For example, patients may receive an implantable neurostimulator or drug delivery device to treat chronic pain or a movement disorder. Congestive heart failure may be treated by, for example, a cardiac pacemaker.

SUMMARY

In general, the invention is directed to systems, devices and techniques for controlling delivery of a therapy to a patient by a medical device, such as an implantable medical device (IMD), based on a sensitivity analysis of a performance metric. The performance metric may relate to efficacy or side effects associated with a particular therapy. For example, the performance metric may comprise a sleep quality metric, an activity level metric, a posture metric, a movement disorder metric for patients with Parkinson's disease, a side-effects metric, or the like. The sensitivity analysis facilitates generation of a therapy parameter set that defines a substantially maximum or minimum value of the performance metric. A medical device according to an embodiment of the invention may conduct the sensitivity analysis for the performance metric, and identify values for each of a plurality of physiological parameters based on the sensitivity analysis. A system according to an embodiment of the invention may include a monitor, a programmer, and a therapy device to conduct the sensitivity analysis for the performance metric, and determine a baseline therapy parameter set based on the sensitivity analysis. In either case, the medical device or another medical device may control delivery of the therapy based on a baseline therapy parameter set that includes the identified values. The baseline therapy parameter set may be a therapy parameter set found to be most efficacious or to result in the least side effects, as indicated by the performance metric value associated with that therapy parameter set.

For the sensitivity analysis, a medical device may deliver therapy according to a plurality of different therapy parameter sets. Each of the therapy parameter sets comprises a value for each of a plurality of therapy parameters. The plurality of therapy parameter sets for the sensitivity analysis encompass a range of therapy parameter values. The therapy parameter sets may be generated either randomly or non-randomly. The therapy parameter sets may be defined, for example, by the medical device or an external programming device. The medical device, programming device, or another device may monitor performance metric values for each therapy parameter set in order to conduct the sensitivity analysis.

Furthermore, after a baseline therapy parameter set has been identified, the medical device that delivers therapy according to the baseline therapy parameter set may periodically perturb at least one therapy parameter value of the baseline therapy parameter set to determine whether the performance metric value has changed over time. The therapy parameter may be increased or decreased in small increments relative to the range values. If perturbing the therapy parameter improves the performance metric, the therapy parameter value is further increased or decreased to again define a substantially maximum or minimum performance metric value. The baseline therapy parameter set is then updated to correspond to the therapy parameter set with the perturbed therapy parameter value or values. If changing the therapy parameter worsens the performance metric, the baseline therapy parameter set is maintained. The medical device that delivers therapy according to the baseline therapy parameter set, a programming device, or another device may determine the performance metric values for each perturbation, and update the baseline therapy parameter set if indicated by the comparison to the performance metric value for the baseline therapy parameter set.

The medical device or a separate monitor, as examples, may monitor one or more physiological parameters of the patient in order to determine values for the one or more performance metrics. Example physiological parameters that the medical device may monitor include activity level, posture, heart rate, ECG morphology, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity and tone, core temperature, subcutaneous temperature, arterial blood flow, brain electrical activity, eye motion, and galvanic skin response. These parameters may be indicative of sleep quality and activity level, and therefore may be useful in determining the performance metric values for different therapy parameter sets. In some embodiments, the medical device additionally or alternatively monitors the variability of one or more of these parameters. In order to monitor one or more of these parameters, the medical device may include, be coupled to, or be in wireless communication with one or more sensors, each of which outputs a signal as a function of one or more of these physiological parameters.

In one embodiment, the invention is directed to a method comprising delivering a therapy to a patient via a medical device according to each of a plurality of therapy parameter sets, each of the therapy parameter sets including a value for each of a plurality of therapy parameters, and monitoring a value of a performance metric of a patient in response to therapy delivered according to each of a plurality of therapy parameter sets. The method further comprises conducting a sensitivity analysis of the performance metric for each of the plurality of therapy parameter sets, and identifying a baseline value for each of the therapy parameters based on the sensitivity analysis to form a baseline therapy parameter set.

In another embodiment, the invention is directed to a medical device that includes a therapy module and a processor. The therapy module delivers a therapy to a patient according to each of a plurality of therapy parameter sets, each of the therapy parameter sets including a value for each of a plurality of therapy parameters. The processor monitors a value of a performance metric of the patient in response to therapy delivered according to each of a plurality of therapy parameter sets. The processor further conducts a sensitivity analysis of the performance metric for each of the plurality of therapy parameter sets, and identifies a baseline value for each of the therapy parameters based on the sensitivity analysis to form a baseline therapy parameter set.

In another embodiment, the invention is directed to a computer-readable medium containing instructions. The instructions cause a programmable processor to monitor a value of a performance metric of a patient for each of a plurality of therapy parameter sets, wherein a medical device delivers a therapy to the patient according to each of the therapy parameters sets, and each of the parameter sets includes a value for each of a plurality of therapy parameters. The instructions further cause the processor to conduct a sensitivity analysis of the performance metric for each of the plurality of therapy parameter sets, and identify a baseline value for each of the plurality of therapy parameters based on the sensitivity analysis to form a baseline therapy parameter set.

In another embodiment, the invention is directed to a system comprising a therapy device, a monitor, and a computing device. The therapy device delivers therapy to a patient according to each of a plurality of therapy parameter sets, each of the therapy parameter sets including a value for each of a plurality of therapy parameters. The monitor monitors values of at least one physiological parameter of a patient in response to therapy delivered according to each of the plurality of therapy parameter sets. The computing device receives the physiological parameter values from the monitor, identifies values of a performance metric of the patient for each of the plurality of parameter sets based on the physiological parameter values monitored during delivery of therapy according to each of the plurality of therapy parameter sets, conducts a sensitivity analysis of the performance metric for each of the plurality of therapy parameter sets, and identifies a baseline value for each of the therapy parameters based on the sensitivity analysis to form a baseline therapy parameter set.

The invention is capable of providing one or more advantages. For example, through the sensitivity analysis of the performance metric, a baseline therapy parameter set that provides substantially maximum or minimum value of the performance metric may be identified. A medical device may provide therapy according to the baseline therapy parameter set.

Further, the medical device may be able to adjust therapy to produce an improved performance metric value. In particular, the adjustments may address symptoms that cause a poor performance metric value or symptoms that are worsened by a poor performance metric value. Adjusting therapy based on the performance metric value information may significantly improve the patient's performance quality and condition. The ability of a medical device to periodically check performance metric values and adjust therapy parameters based on the performance metric values may reduce the need for the patient to make time consuming and expensive clinic visits when the patient's sleep is disturbed, physical activity level has decreased, or symptoms have worsened.

DETAILED DESCRIPTION

FIG. 1is a conceptual diagram illustrating an example system10that includes an implantable medical device (IMD)14that controls delivery of a therapy to a patient12based on a sensitivity analysis of a performance metric. The performance metric may relate to efficacy or side effects. For example, the performance metric may comprise a sleep quality metric, a physical activity level metric, a posture metric, a movement disorder metric for patients with Parkinson's disease, or the like. The sensitivity analysis determines values of a therapy parameter set that define a substantially maximum or minimum value of the performance metric. In particular, as will be described in greater detail below, IMD14or another device conducts the sensitivity analysis of the performance metric, and determines a baseline therapy parameter set based on the sensitivity analysis. IMD14controls delivery of the therapy based on the baseline therapy parameter set. Furthermore, IMD14or another device may periodically perturb at least one therapy parameter value of the baseline therapy parameter set to determine whether the performance metric value has changed over time.

Feedback entered by patient12, such as comments and/or a pain level value, may also be used as a performance metric to determine the baseline therapy parameter set. In some cases, a clinician or physician may determine a weighting scheme to provide more or less significance to the patient's feedback, i.e., the physician may choose to give the patient feedback zero weight and instead rely completely on other performance metric values, or the physician may judge that the patient has enough perspective to be able to competently gage pain levels and input substantially objective feedback into the sensitivity analysis.

Although the invention may use any performance metric, for purposes of illustration, the invention will be described herein as using a sleep quality metric to control the delivery of therapy to a patient. IMD14may be able to adjust the therapy to address symptoms causing disturbed sleep, or symptoms that are worsened by disturbed sleep. In exemplary embodiments, IMD14delivers a therapy to treat chronic pain, which may both negatively impact the quality of sleep experienced by patient12, and be worsened by inadequate sleep quality.

In the illustrated example system, IMD14takes the form of an implantable neurostimulator that delivers neurostimulation therapy in the form of electrical pulses to patient12. IMD14delivers neurostimulation therapy to patient12via leads16A and16B (collectively “leads16”). Leads16may, as shown inFIG. 1, be implanted proximate to the spinal cord18of patient12, and IMD14may deliver spinal cord stimulation (SCS) therapy to patient12in order to, for example, reduce pain experienced by patient12.

However, the invention is not limited to the configuration of leads16shown inFIG. 1, or to the delivery of SCS therapy. For example, one or more leads16may extend from IMD14to the brain (not shown) of patient12, and IMD14may deliver deep brain stimulation (DBS) therapy to patient12to, for example, treat tremor or epilepsy. As further examples, one or more leads16may be implanted proximate to the pelvic nerves (not shown) or stomach (not shown), and IMD14may deliver neurostimulation therapy to treat incontinence, sexual dysfunction, or gastroparesis.

Moreover, the invention is not limited to implementation via an implantable neurostimulator, or even implementation via an IMD. In other words, any implantable or external medical device that delivers a therapy may control delivery of the therapy based on performance metric information, such as sleep quality information, according to the invention.

Further, the invention is not limited to embodiments in which the therapy-delivering medical device performs the sensitivity analysis. For example, in some embodiments, a computing device, such as a programming device, controls testing of therapy parameter sets by a therapy-delivering medical device, receives performance metric values from the medical device, performs the sensitivity analysis, and provides a baseline therapy parameter set to the therapy-delivering medical device. In some embodiments, multiple computing devices may cooperate to perform these functions. For example, a programming device may control testing of therapy parameter sets by the therapy-delivering medical device and receive performance metric values from the medical device, while another computing device performs the sensitivity analysis on the performance metric values, and identifies the baseline therapy parameter set. The other computing device may provide the baseline therapy parameter set to the programming device, which may in turn provide the baseline therapy parameter set to the medical device. The other computing device may have a greater computing capacity than the programming device, which may allow it to more easily perform the sensitivity analysis, and may, for example, be a server coupled to the programming device by a network, such as a local area network (LAN), wide area network (WAN), or the Internet.

As another example, in some embodiments, the programming device or other computing device may receive values for one or more physiological parameters from the medical device, and may determine values for the performance metric based on the physiological parameter values. Further, in some embodiments of the invention, an implantable or external monitor separate from the therapy-delivering medical device may monitor physiological parameters of the patient instead of, or in addition to the therapy-delivering medical device. The monitor may determine values of the performance metric based on values of the physiological parameters, or transmit the physiological parameter values to a programming device or other computing device that determines the values of the performance metric. In some embodiments, the programming device and the monitor may be embodied within a single device.

Additionally, in some embodiments, a therapy device other than IMD14may deliver therapy during the process of determining the baseline therapy parameter sets. The other therapy device may be an external or implantable trialing device, such as a trial neurostimulator or trial pump. The other therapy delivery device may monitor physiological parameter values of patient12, determine performance metric values, and perform the sensitivity analysis, as described herein with reference to IMD14. In other embodiments, some or all of these functions may be performed by a monitor, programming device, or other computing device, as described above. In such embodiments, IMD14may deliver therapy according to a baseline therapy parameter set determined by a sensitivity analysis during a trialing period, and may perturb the therapy parameters for continued refinement of the baseline therapy parameter set, as will be described in greater detail below.

In the illustrated embodiment, IMD14delivers therapy according to a set of therapy parameters, i.e., a set of values for a number of parameters that define the therapy delivered according to that therapy parameter set. In embodiments where IMD14delivers neurostimulation therapy in the form of electrical pulses, the parameters may include voltage or current pulse amplitudes, pulse widths, pulse rates, duty cycles, durations, and the like. Further, each of leads16includes electrodes (not shown inFIG. 1), and a therapy parameter set may include information identifying which electrodes have been selected for delivery of pulses, and the polarities of the selected electrodes. Therapy parameter sets used by IMD14may include a number of parameter sets programmed by a clinician (not shown), and parameter sets representing adjustments made by patient12to these preprogrammed sets.

In other non-neurostimulator embodiments of the invention, the IMD14may still deliver therapy according to a different type of therapy parameter set. For example, implantable pump IMD embodiments may deliver a therapeutic agent to a patient according to a therapy parameter set that includes, for example, a dosage, an infusion rate, and/or a duty cycle.

System10also includes a clinician programmer20, which is an example of a programming device that may determine values of a performance metric and/or perform a sensitivity analysis, as described above. A clinician (not shown) may use clinician programmer20to program therapy for patient12, e.g., specify a number of therapy parameter sets and provide the parameter sets to IMD14. The clinician may also use clinician programmer20to retrieve information collected by IMD14. The clinician may use clinician programmer20to communicate with IMD14both during initial programming of IMD14, and for collection of information and further programming during follow-up visits.

Clinician programmer20may, as shown inFIG. 1, be a handheld computing device. Clinician programmer20includes a display22, such as a LCD or LED display, to display information to a user. Clinician programmer20may also include keypad24, which may be used by a user to interact with clinician programmer20. In some embodiments, display22may be a touch screen display, and a user may interact with clinician programmer20via display22. A user may also interact with clinician programmer20using peripheral pointing devices, such as a stylus, mouse, or the like. Keypad24may take the form of a complete keyboard, an alphanumeric keypad or a reduced set of keys associated with particular functions.

System10also includes a patient programmer26, which also may, as shown inFIG. 1, be a handheld computing device. Patient12may use patient programmer26to control the delivery of therapy by IMD14. For example, using patient programmer26, patient12may select a current therapy parameter set from among the therapy parameter sets preprogrammed by the clinician, or may adjust one or more parameters of a preprogrammed therapy parameter set to arrive at the current therapy parameter set. As an example, patient12may increase or decrease stimulation pulse amplitude using patient programmer26. Patient programmer26is also an example of a programming device that may determine values of a performance metric and/or perform a sensitivity analysis, as described above.

Patient programmer26may also include a display28and a keypad30to allow patient12to interact with patient programmer26. In some embodiments, display28may be a touch screen display, and patient12may interact with patient programmer26via display28. Patient12may also interact with patient programmer26using peripheral pointing devices, such as a stylus, mouse, or the like.

However, clinician and patient programmers20,26are not limited to the hand-held computer embodiments illustrated inFIG. 1. Programmers20,26according to the invention may be any sort of computing device. For example, a programmer20,26according to the invention may a tablet-based computing device, a desktop computing device, or a workstation.

IMD14, clinician programmer20and patient programmer26may, as shown inFIG. 1, communicate via wireless communication. Clinician programmer20and patient programmer26may, for example, communicate via wireless communication with IMD14using radio frequency (RF) or infrared telemetry techniques known in the art. Clinician programmer20and patient programmer26may communicate with each other using any of a variety of local wireless communication techniques, such as RF communication according to the 802.11 or Bluetooth specification sets, infrared communication according to the IRDA specification set, or other standard or proprietary telemetry protocols.

Clinician programmer20and patient programmer26need not communicate wirelessly, however. For example, programmers20and26may communicate via a wired connection, such as via a serial communication cable, or via exchange of removable media, such as magnetic or optical disks, or memory cards or sticks. Further, clinician programmer20may communicate with one or both of IMD14and patient programmer26via remote telemetry techniques known in the art, communicating via a local area network (LAN), wide area network (WAN), public switched telephone network (PSTN), or cellular telephone network, for example.

As mentioned above, IMD14controls delivery of a therapy, e.g., neurostimulation, to patient12based on a sensitivity analysis of the sleep quality experienced by the patient. In some embodiments, as will be described in greater detail below, IMD14conducts the sensitivity analysis to determine values of a therapy parameter set that defines a substantially maximum value of a sleep quality metric that indicates the quality of sleep experienced by patient12. IMD14determines a baseline therapy parameter set based on the sensitivity analysis and controls delivery of the therapy to patient12, e.g., adjusts the therapy, based on the baseline therapy parameter set. Furthermore, IMD14may periodically perturb at least one therapy parameter value of the baseline therapy parameter set to determine whether the response of the sleep quality metric value to perturbation has changed over time. The perturbation may occur at a preset time, in response to a change in a physiological parameter of a patient, or in response to a signal from a patient or a clinician. The therapy parameter values may be increased or decreased in small increments relative the therapy parameter range.

In some embodiments, IMD14compares the sleep quality metric value defined by the baseline therapy parameter set to a sleep quality metric value defined by the perturbed therapy parameter values. IMD14then adjusts the therapy delivered to patient12based on the comparison. For example, IMD14may maintain the baseline therapy parameter set when the comparison shows no improvement in the value of the sleep quality metric during perturbation. When the comparison shows improvement in the sleep quality metric value during perturbation, IMD14updates the baseline therapy parameter set based on the one or more perturbed therapy parameter values.

In other embodiments, an implantable or external programmer, such as programmers20and26, may perturb at least one therapy parameter value of the baseline therapy parameter set and an implantable or external monitoring device may monitor the sleep quality metric value. The programmer may also conduct the comparison and update the baseline parameter set based on the comparison. An implantable or external therapy device, such as IMD14, may then alter the therapy provided to the patient based on the updated baseline parameter set.

IMD14may monitor one or more physiological parameters of the patient in order to determine values for one or more sleep quality metrics. Example physiological parameters that IMD14may monitor include activity level, posture, heart rate, ECG morphology, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebro spinal fluid, muscular activity and tone, core temperature, subcutaneous temperature, arterial blood flow, brain electrical activity, and eye motion. Some external medical device embodiments of the invention may additionally or alternatively monitor galvanic skin response. Further, in some embodiments, IMD14additionally or alternatively monitors the variability of one or more of these parameters. In order to monitor one or more of these parameters, IMD14may include, be coupled to, or be in wireless communication with one or more sensors (not shown inFIG. 1), each of which outputs a signal as a function of one or more of these physiological parameters.

For example, IMD14may determine sleep efficiency and/or sleep latency values. Sleep efficiency and sleep latency are example sleep quality metrics. IMD14may measure sleep efficiency as the percentage of time while patient12is attempting to sleep that patient12is actually asleep. IMD14may measure sleep latency as the amount of time between a first time when patient12begins attempting to sleep and a second time when patient12falls asleep, e.g., as an indication of how long it takes patient12to fall asleep.

IMD14may identify the time at which patient begins attempting to fall asleep in a variety of ways. For example, IMD14may receive an indication from the patient that the patient is trying to fall asleep via patient programmer26. In other embodiments, IMD14may monitor the activity level of patient12, and identify the time when patient12is attempting to fall asleep by determining whether patient12has remained inactive for a threshold period of time, and identifying the time at which patient12became inactive. In still other embodiments, IMD14may monitor the posture of patient12, and may identify the time when the patient12becomes recumbent, e.g., lies down, as the time when patient12is attempting to fall asleep. In these embodiments, IMD14may also monitor the activity level of patient12, and confirm that patient12is attempting to sleep based on the activity level.

IMD14may identify the time at which patient12has fallen asleep based on the activity level of the patient and/or one or more of the other physiological parameters that may be monitored by IMD14as indicated above. For example, IMD14may identify a discernable change, e.g., a decrease, in one or more physiological parameters, or the variability of one or more physiological parameters, which may indicate that patient12has fallen asleep. In some embodiments, IMD14determines a sleep probability metric value based on a value of a physiological parameter monitored by the medical device. In such embodiments, the sleep probability metric value may be compared to a threshold to identify when the patient has fallen asleep. In some embodiments, a sleep probability metric value is determined based on a value of each of a plurality of physiological parameters, the sleep probability values are averaged or otherwise combined to provide an overall sleep probability metric value, and the overall sleep probability metric value is compared to a threshold to identify the time that the patient falls asleep.

Other sleep quality metrics include total time sleeping per day, and the amount or percentage of time sleeping during nighttime or daytime hours per day. In some embodiments, IMD14may be able to detect arousal events and apneas occurring during sleep based on one or more monitored physiological parameters, and the number of apnea and/or arousal events per night may be determined as a sleep quality metric. Further, in some embodiments, IMD14may be able to determine which sleep state patient12is in based on one or more monitored physiological parameters, e.g., rapid eye movement (REM), S1, S2, S3, or S4, and the amount of time per day spent in these various sleep states may be a sleep quality metric.

FIG. 2is a block diagram further illustrating system10. In particular,FIG. 2illustrates an example configuration of IMD14and leads16A and16B.FIG. 2also illustrates sensors40A and40B (collectively “sensors40”) that output signals as a function of one or more physiological parameters of patient12.

IMD14may deliver neurostimulation therapy via electrodes42A-D of lead16A and electrodes42E-H of lead16B (collectively “electrodes42”). Electrodes42may be ring electrodes. The configuration, type and number of electrodes42illustrated inFIG. 2are exemplary. For example, leads16A and16B may each include eight electrodes42, and the electrodes42need not be arranged linearly on each of leads16A and16B.

Electrodes42are electrically coupled to a therapy delivery module44via leads16A and16B. Therapy delivery module44may, for example, include an output pulse generator coupled to a power source such as a battery. Therapy delivery module44may deliver electrical pulses to patient12via at least some of electrodes42under the control of a processor46, which controls therapy delivery module44to deliver neurostimulation therapy according to one or more neurostimulation therapy programs selected from available programs stored in a memory48. However, the invention is not limited to implantable neurostimulator embodiments or even to IMDs that deliver electrical stimulation. For example, in some embodiments, a therapy delivery module of an IMD may include a pump, circuitry to control the pump, and a reservoir to store a therapeutic agent for delivery via the pump, and a processor of the IMD may control delivery of a therapeutic agent by the pump according to an infusion program selected from among a plurality of infusion programs stored in a memory.

IMD14may also include a telemetry circuit50that enables processor46to communicate with programmers20,26. Via telemetry circuit50, processor46may receive therapy programs specified by a clinician from clinician programmer20for storage in memory48. Processor46may also receive program selections and therapy adjustments made by patient12using patient programmer26via telemetry circuit50. In some embodiments, processor46may provide diagnostic information recorded by processor46and stored in memory48to one of programmers20,26via telemetry circuit50.

Processor46may include a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), discrete logic circuitry, or the like. Memory48may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, and the like. In some embodiments, memory48stores program instructions that, when executed by processor46, cause IMD14and processor46to perform the functions attributed to them herein.

Each of sensors40outputs a signal as a function of one or more physiological parameters of patient12. IMD14may include circuitry (not shown) that conditions the signals output by sensors40such that they may be analyzed by processor46. For example, IMD14may include one or more analog to digital converters to convert analog signals output by sensors40into digital signals usable by processor46, as well as suitable filter and amplifier circuitry. Although shown as including two sensors40, system10may include any number of sensors.

Further, as illustrated inFIG. 2, sensors40may be included as part of IMD14, or coupled to IMD14via leads16. Sensors40may be coupled to IMD14via therapy leads16A and16B, or via other leads16, such as lead16C depicted inFIG. 2. In some embodiments, a sensor located outside of IMD14may be in wireless communication with processor46.

As discussed above, exemplary physiological parameters of patient12that may be monitored by IMD14to determine values of one or more sleep quality metrics include activity level, posture, heart rate, ECG morphology, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity and tone, core temperature, subcutaneous temperature, arterial blood flow, brain electrical activity, and eye motion. Further, as discussed above, external medical device embodiments of the invention may additionally or alternatively monitor galvanic skin response. Sensors40may be of any type known in the art capable of outputting a signal as a function of one or more of these parameters.

In some embodiments, in order to determine one or more sleep quality metric values, processor46determines when patient12is attempting to fall asleep. For example, processor46may identify the time that patient begins attempting to fall asleep based on an indication received from patient12, e.g., via clinician programmer20and a telemetry circuit50. In other embodiments, processor46identifies the time that patient12begins attempting to fall asleep based on the activity level of patient12.

In such embodiments, IMD14may include one or more sensors40that generate a signal as a function of patient activity. For example, sensors40may include one or more accelerometers, gyros, mercury switches, or bonded piezoelectric crystals that generates a signal as a function of patient activity, e.g., body motion, footfalls or other impact events, and the like. Additionally or alternatively, sensors40may include one or more electrodes that generate an electromyogram (EMG) signal as a function of muscle electrical activity, which may indicate the activity level of a patient. The electrodes may be, for example, located in the legs, abdomen, chest, back or buttocks of patient12to detect muscle activity associated with walking, running, or the like. The electrodes may be coupled to IMD14by leads16or wirelessly, or, if IMD14is implanted in these locations, integrated with a housing of IMD14.

However, bonded piezoelectric crystals located in these areas generate signals as a function of muscle contraction in addition to body motion, footfalls or other impact events. Consequently, use of bonded piezoelectric crystals to detect activity of patient12may be preferred in some embodiments in which it is desired to detect muscle activity in addition to body motion, footfalls, or other impact events. Bonded piezoelectric crystals may be coupled to IMD14via leads16or wirelessly, or piezoelectric crystals may be bonded to the can of IMD14when the IMD is implanted in these areas, e.g., in the back, chest, buttocks or abdomen of patient12.

Processor46may identify a time when the activity level of patient12falls below a threshold activity level value stored in memory48, and may determine whether the activity level remains substantially below the threshold activity level value for a threshold amount of time stored in memory48. In other words, patient12remaining inactive for a sufficient period of time may indicate that patient12is attempting to fall asleep. If processor46determines that the threshold amount of time is exceeded, processor46may identify the time at which the activity level fell below the threshold activity level value as the time that patient12began attempting to fall asleep.

In some embodiments, processor46determines whether patient12is attempting to fall asleep based on whether patient12is or is not recumbent, e.g., lying down. In such embodiments, sensors40may include a plurality of accelerometers, gyros, or magnetometers oriented orthogonally that generate signals which indicate the posture of patient12. In addition to being oriented orthogonally with respect to each other, each of sensors40used to detect the posture of patient12may be generally aligned with an axis of the body of patient12. In exemplary embodiments, IMD14includes three orthogonally oriented posture sensors40.

When sensors40include accelerometers, for example, that are aligned in this manner, processor46may monitor the magnitude and polarity of DC components of the signals generated by the accelerometers to determine the orientation of patient12relative to the Earth's gravity, e.g., the posture of patient12. In particular, the processor46may compare the DC components of the signals to respective threshold values stored in memory48to determine whether patient12is or is not recumbent. Further information regarding use of orthogonally aligned accelerometers to determine patient posture may be found in a commonly assigned U.S. Pat. No. 5,593,431, which issued to Todd J. Sheldon.

Other sensors40that may generate a signal that indicates the posture of patient12include electrodes that generate an electromyogram (EMG) signal, or bonded piezoelectric crystals that generate a signal as a function of contraction of muscles. Such sensors40may be implanted in the legs, buttocks, abdomen, or back of patient12, as described above. The signals generated by such sensors when implanted in these locations may vary based on the posture of patient12, e.g., may vary based on whether the patient is standing, sitting, or laying down.

Further, the posture of patient12may affect the thoracic impedance of the patient. Consequently, sensors40may include an electrode pair, such as one electrode integrated with the housing of IMD14and one of electrodes42, that generates a signal as a function of the thoracic impedance of patient12, and processor46may detect the posture or posture changes of patient12based on the signal. The electrodes of the pair may be located on opposite sides of the patient's thorax. For example, the electrode pair may include one of electrodes42located proximate to the spine of a patient for delivery of SCS therapy, and IMD14with an electrode integrated in its housing may be implanted in the abdomen of patient12.

Additionally, changes of the posture of patient12may cause pressure changes with the cerebrospinal fluid (CSF) of the patient. Consequently, sensors40may include pressure sensors coupled to one or more intrathecal or intracerebroventricular catheters, or pressure sensors coupled to IMD14wirelessly or via lead16. CSF pressure changes associated with posture changes may be particularly evident within the brain of the patient, e.g., may be particularly apparent in an intracranial pressure (ICP) waveform.

In some embodiments, processor46considers both the posture and the activity level of patient12when determining whether patient12is attempting to fall asleep. For example, processor46may determine whether patient12is attempting to fall asleep based on a sufficiently long period of sub-threshold activity, as described above, and may identify the time that patient began attempting to fall asleep as the time when patient12became recumbent. Any of a variety of combinations or variations of these techniques may be used to determine when patient12is attempting to fall asleep, and a specific one or more techniques may be selected based on the sleeping and activity habits of a particular patient.

Processor46may also determine when patient12is asleep, e.g., identify the times that patient12falls asleep and wakes up, in order to determine one or more sleep quality metric values. The detected values of physiological parameters of patient12, such as activity level, heart rate, values of ECG morphological features, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity and tone, core temperature, subcutaneous temperature, arterial blood flow, brain electrical activity, eye motion, and galvanic skin response may discernibly change when patient12falls asleep or wakes up. In particular, these physiological parameters may be at low values when patient12is asleep. Further, the variability of at least some of these parameters, such as heart rate and respiration rate, may be at a low value when the patient is asleep.

Consequently, in order to detect when patient12falls asleep and wakes up, processor46may monitor one or more of these physiological parameters, or the variability of these physiological parameters, and detect the discernable changes in their values associated with a transition between a sleeping state and an awake state.

In some embodiments, in order to determine whether patient12is asleep, processor46monitors a plurality of physiological parameters, and determines a value of a metric that indicates the probability that patient12is asleep for each of the parameters based on a value of the parameter. In particular, the processor46may apply a function or look-up table to the current value, and/or the variability of each of a plurality of physiological parameters to determine a sleep probability metric value for each of the plurality of physiological parameters. A sleep probability metric value may be a numeric value, and in some embodiments may be a probability value, e.g., a number within the range from 0 to 1, or a percentage level.

Processor46may average or otherwise combine the plurality of sleep probability metric values to provide an overall sleep probability metric value. In some embodiments, processor46may apply a weighting factor to one or more of the sleep probability metric values prior to combination. Processor46may compare the overall sleep probability metric value to one or more threshold values stored in memory48to detennine when patient12falls asleep or awakes. Use of sleep probability metric values to determine when a patient is asleep based on a plurality of monitored physiological parameters is described in greater detail in a commonly assigned and copending U.S. pat. application Ser. No. 11/081,786, by Ken Heruth and Keith Miesel, entitled “DETECTING SLEEP,” bearing and filed on Mar. 16, 2005, which is incorporated herein by reference in its entirety.

To enable processor46to determine when patient12is asleep or awake, sensors40may include, for example, activity sensors as described above. In some embodiments, the activity sensors may include electrodes or bonded piezoelectric crystals, which may be implanted in the back, buttocks, chest, or abdomen of patient12as described above. In such embodiments, processor46may detect the electrical activation and contractions of muscles associated with gross motor activity of the patient, e.g., walking, running or the like via the signals generated by such sensors. Processor46may also detect spasmodic or pain related muscle activation via the signals generated by such sensors. Spasmodic or pain related muscle activation may indicate that patient12is not sleeping, e.g., unable to sleep, or if patient12is sleeping, may indicate a lower level of sleep quality.

As another example, sensors40may include electrodes located on leads or integrated as part of the housing of IMD14that output an electrogram signal as a function of electrical activity of the heart of patient12, and processor46may monitor the heart rate of patient12based on the electrogram signal. In other embodiments, a sensor may include an acoustic sensor within IMD14, a pressure sensor within the bloodstream or cerebrospinal fluid of patient12, or a temperature sensor located within the bloodstream of patient12. The signals output by such sensors may vary as a function of contraction of the heart of patient12, and can be used by IMD14to monitor the heart rate of patient12.

In some embodiments, processor46may detect, and measure values for one or more ECG morphological features within an electrogram generated by electrodes as described above. ECG morphological features may vary in a manner that indicates whether patient12is asleep or awake. For example, the amplitude of the ST segment of the ECG may decrease when patient12is asleep. Further, the amplitude of QRS complex or T-wave may decrease, and the widths of the QRS complex and T-wave may increase when patient12is asleep. The QT interval and the latency of an evoked response may increase when patient12is asleep, and the amplitude of the evoked response may decrease when patient12is asleep.

In some embodiments, sensors40may include an electrode pair, including one electrode integrated with the housing of IMD14and one of electrodes16, that output a signal as a function of the thoracic impedance of patient12as described above, which varies as a function of respiration by patient12. In other embodiments, sensors40may include a strain gauge, bonded piezoelectric element, or pressure sensor within the blood or CSF that outputs a signal that varies based on patient respiration. An electrogram output by electrodes as discussed above may also be modulated by patient respiration, and may be used as an indirect representation of respiration rate.

Sensors40may include electrodes that output an electromyogram (EMG) signal as a function of muscle electrical activity, as described above, or may include any of a variety of known temperature sensors to output a signal as a function of a core or subcutaneous temperature of patient12. Such electrodes and temperature sensors may be incorporated within the housing of IMD14, or coupled to IMD14wirelessly or via leads. Sensors40may also include a pressure sensor within, or in contact with, a blood vessel. The pressure sensor may output a signal as a function of the blood pressure of patient12, and may, for example, comprise a Chronicle Hemodynamic Monitor™ commercially available from Medtronic, Inc. of Minneapolis, Minn. Further, certain muscles of patient12, such as the muscles of the patient's neck, may discernibly relax when patient12is asleep or within certain sleep states. Consequently, sensors40may include strain gauges or EMG electrodes implanted in such locations that generate a signal as a function of muscle tone.

Sensors40may also include optical pulse oximetry sensors or Clark dissolved oxygen sensors located within, as part of a housing of, or outside of IMD14, which output signals as a function blood oxygen saturation and blood oxygen partial pressure respectively. In some embodiments, system10may include a catheter with a distal portion located within the cerebrospinal fluid of patient12, and the distal end may include a Clark dissolved oxygen sensor to output a signal as a function of the partial pressure of oxygen within the cerebrospinal fluid. Embodiments in which an IMD comprises an implantable pump, for example, may include a catheter with a distal portion located in the cerebrospinal fluid.

In some embodiments, sensors40may include one or more intraluminal, extraluminal, or external flow sensors positioned to output a signal as a function of arterial blood flow. A flow sensor may be, for example, an electromagnetic, thermal convection, ultrasonic-Doppler, or laser-Doppler flow sensor. Further, in some external medical device embodiments of the invention, sensors40may include one or more electrodes positioned on the skin of patient12to output a signal as a function of galvanic skin response.

Additionally, in some embodiments, sensors40may include one or more electrodes positioned within or proximate to the brain of patient, which detect electrical activity of the brain. For example, in embodiments in which IMD14delivers stimulation or other therapy to the brain, processor46may be coupled to electrodes implanted on or within the brain via a lead16. In other embodiments, processor46may be wirelessly coupled to electrodes that detect brain electrical activity.

For example, one or more modules may be implanted beneath the scalp of the patient, each module including a housing, one or more electrodes, and circuitry to wirelessly transmit the signals detected by the one or more electrodes to IMD14. In other embodiments, the electrodes may be applied to the patient's scalp, and electrically coupled to a module that includes circuitry for wirelessly transmitting the signals detected by the electrodes to IMD14. The electrodes may be glued to the scalp, or a headband, hair net, cap, or the like may incorporate the electrodes and the module, and may be worn by patient12to apply the electrodes to the patient's scalp when, for example, the patient is attempting to sleep. The signals detected by the electrodes and transmitted to IMD14may be electroencephalogram (EEG) signals, and processor46may process the EEG signals to detect when patient12is asleep using any of a variety of known techniques, such as techniques that identify whether a patient is asleep based on the amplitude and/or frequency of the EEG signals.

Also, the motion of the eyes of patient12may vary depending on whether the patient is sleeping and which sleep state the patient is in. Consequently, sensors40may include electrodes place proximate to the eyes of patient12to detect electrical activity associated with motion of the eyes, e.g., to generate an electro-oculography (EOG) signal. Such electrodes may be coupled to IMD14via one or more leads16, or may be included within modules that include circuitry to wirelessly transmit detected signals to IMD14. Wirelessly coupled modules incorporating electrodes to detect eye motion may be worn externally by patient12, e.g., attached to the skin of patient12proximate to the eyes by an adhesive when the patient is attempting to sleep.

Processor46may also detect arousals and/or apneas that occur when patient12is asleep based on one or more of the above-identified physiological parameters. For example, processor46may detect an arousal based on an increase or sudden increase in one or more of heart rate, heart rate variability, respiration rate, respiration rate variability, blood pressure, or muscular activity as the occurrence of an arousal. Processor46may detect an apnea based on a disturbance in the respiration rate of patient12, e.g., a period with no respiration.

Processor46may also detect arousals or apneas based on sudden changes in one or more of the ECG morphological features identified above. For example, a sudden elevation of the ST segment within the ECG may indicate an arousal or an apnea. Further, sudden changes in the amplitude or frequency of an EEG signal, EOG signal, or muscle tone signal may indicate an apnea or arousal. Memory48may store thresholds used by processor46to detect arousals and apneas. Processor46may determine, as a sleep quality metric value, the number of apnea events and/or arousals during a night.

Further, in some embodiments, processor46may determine which sleep state patient12is in during sleep, e.g., REM, S1, S2, S3, or S4, based on one or more of the monitored physiological parameters. In some embodiments, memory48may store one or more thresholds for each of sleep states, and processor46may compare physiological parameter or sleep probability metric values to the thresholds to determine which sleep state patient12is currently in. Processor46may determine, as sleep quality metric values, the amounts of time per night spent in the various sleep states. Further, in some embodiments, processor46may use any of a variety of known techniques for determining which sleep state patient is in based on an EEG signal, which processor46may receive via electrodes as described above, such as techniques that identify sleep state based on the amplitude and/or frequency of the EEG signals. In some embodiments, processor46may also determine which sleep state patient is in based on an EOG signal, which processor46may receive via electrodes as described above, either alone or in combination with an EEG signal, using any of a variety of techniques known in the art. Inadequate time spent in deeper sleep states, e.g., S3and S4, is an indicator of poor sleep quality.

FIG. 3further illustrates memory48of IMD14. As illustrated inFIG. 3, memory48stores a plurality of therapy parameter sets60. Therapy parameter sets60may include parameter sets randomly or non-randomly generated by processor46over therapy parameter ranges68set by a clinician using clinician programmer20. Therapy parameter sets60may also include parameter sets specified by a clinician using clinician programmer20and preprogrammed therapy parameter sets.

Memory48may also include parameter data62recorded by processor46, e.g., physiological parameter values, or mean or median physiological parameter values. Memory48stores threshold values64used by processor46in the collection of sleep quality metric values, as discussed above. In some embodiments, memory48also stores one or more functions or look-up tables (not shown) used by processor46to determine sleep probability metric values, or to determine an overall sleep quality metric value.

Further, processor46stores determined sleep quality metric values66for each of the plurality of therapy parameter sets60within memory48. Processor46conducts a sensitivity analysis of the sleep quality metric values for each therapy parameter. The sensitivity analysis determines a value for each therapy parameter that defines a substantially maximum sleep quality metric value. In other words, the sensitivity analysis identifies parameter values that yield the best sleep quality metric values. Processor46then determines a baseline therapy parameter set based on the sensitivity analysis and stores the baseline therapy parameter set with therapy parameter set66or separately within memory48. The baseline therapy parameter set may be identical to a single one of therapy parameter sets60, or may be a new therapy parameter set that includes one or more therapy parameter values from a plurality of therapy parameter sets60. The baseline therapy parameter set includes the values for respective therapy parameters that produced the best sleep quality metric values.

Processor46may collect sleep quality metric values66each time patient12sleeps, or only during selected times that patient12is asleep. Processor46may store each sleep quality metric value determined within memory48as a sleep quality metric value66. Further, processor46may apply a function or look-up table to a plurality of sleep quality metric values to determine overall sleep quality metric value, and may store the overall sleep quality metric values within memory48. The application of a function or look-up table by processor46for this purpose may involve the use of weighting factors for one or more of the individual sleep quality metric values.

In some embodiments, as discussed above, processor46may adjust the therapy delivered by therapy module44based on a change in the sleep quality metric value. In particular, processor46may perturb one or more therapy parameters of the baseline therapy parameter set, such as pulse amplitude, pulse width, pulse rate, duty cycle, and duration to determine if the current sleep quality metric value improves or worsens during perturbation. In some embodiments, processor46may iteratively and incrementally increase or decrease values of the therapy parameters until a substantially maximum value of the sleep quality metric value is again determined.

FIG. 4is a flow diagram illustrating an example method for collecting sleep quality information that may be employed by IMD14alone, or in combination with a computing device and/or a monitor. In some embodiments, as discussed above, a computing device, such as one of programmers20and26, may determine sleep quality metric values based on monitored physiological parameter values, rather than IMD14. Further, in some embodiments, a monitor may monitor physiological parameter values instead of, or in addition to, IMD14.

In the illustrated example, however, IMD14monitors the posture and/or activity level of patient12, or monitors for an indication from patient12, e.g., via patient programmer26(70), and determines whether patient12is attempting to fall asleep based on the posture, activity level, and/or a patient indication, as described above (72). If IMD14determines that patient12is attempting to fall asleep, IMD14identifies the time that patient12began attempting to fall asleep using any of the techniques described above (74), and monitors one or more of the various physiological parameters of patient12discussed above to determine whether patient12is asleep (76,78).

When IMD14determines that patient12is asleep, e.g., by analysis of the various parameters contemplated herein, IMD14will identify the time that patient12fell asleep (80). While patient12is sleeping, IMD14will continue to monitor physiological parameters of patient12(82). As discussed above, IMD14may identify the occurrence of arousals and/or apneas based on the monitored physiological parameters (84). Further, IMD14may identify the time that transitions between sleep states, e.g., REM, S1, S2, S3, and S4, occur based on the monitored physiological parameters (84).

Additionally, while patient12is sleeping, IMD14monitors physiological parameters of patient12(82) to determine whether patient12has woken up (86). When IMD14determines that patient12is awake, IMD14identifies the time that patient12awoke (88), and determines sleep quality metric values based on the information collected while patient12was asleep (90).

For example, one sleep quality metric value that IMD14may calculate is sleep efficiency, which IMD14may calculate as a percentage of time during which patient12is attempting to sleep that patient12is actually asleep. IMD14may determine a first amount of time between the time IMD14identified that patient12fell asleep and the time IMD14identified that patient12awoke. IMD may also determine a second amount of time between the time IMD14identified that patient12began attempting to fall asleep and the time IMD14identified that patient12awoke. To calculate the sleep efficiency, IMD14may divide the first time by the second time.

Another sleep quality metric value that IMD14may calculate is sleep latency, which IMD14may calculate as the amount of time between the time IMD14identified that patient12was attempting to fall asleep and the time IMD14identified that patient12fell asleep. Other sleep quality metrics with values determined by IMD14based on the information collected by IMD14in the illustrated example include: total time sleeping per day, at night, and during daytime hours; number of apnea and arousal events per occurrence of sleep; and amount of time spent in the various sleep states. IMD14may store the determined values as sleep quality metric values66within memory48.

IMD14may perform the example method illustrated inFIG. 4continuously. For example, IMD14may monitor to identify when patient12is attempting to sleep and asleep any time of day, each day. In other embodiments, IMD14may only perform the method during evening hours and/or once every N days to conserve battery and memory resources. Further, in some embodiments, IMD14may only perform the method in response to receiving a command from patient12or a clinician via one of programmers20,26. For example, patient12may direct IMD14to collect sleep quality information at times when the patient believes that his or her sleep quality is low or therapy is ineffective.

FIG. 5is a flow diagram illustrating an example method for identifying and modifying a baseline therapy parameter set based on a sensitivity analysis of a sleep quality metric, which is an example of a performance metric. In the illustrated example, the method is employed by IMD14. However, in other embodiments, a system including one or more of IMD14, a physiological parameter monitor, a trial therapy device, and a programmer and/or other computing device may perform the example method, as described above.

IMD14receives a therapy parameter range68for therapy parameters (100) from a clinician using clinician programmer20via telemetry circuit50. The range68may include minimum and maximum values for each of one or more individual therapy parameters, such as pulse amplitude, pulse width, pulse rate, duty cycle, duration, dosage, infusion rate, electrode placement, and electrode selection. Range68may be stored in memory48, as described in reference toFIG. 3. Processor46then randomly or non-randomly generates a plurality of therapy parameter sets60with individual parameter values selected from the range68(102). The generated therapy parameter sets60may substantially cover range68, but do not necessarily include each and every therapy parameter value within range68, or every possible combination of therapy parameters within range68. Therapy parameter sets60may also be stored in memory48.

IMD14monitors a sleep quality metric of patient12for each of the randomly or non-randomly generated therapy parameter sets60spanning range68(104). The values of the sleep quality metric66corresponding to each of the therapy parameter sets60may be stored in memory48of IMD14. IMD14then conducts a sensitivity analysis of the sleep quality metric for each of the therapy parameters (106). The sensitivity analysis determines a value for each of the therapy parameters that produced a substantially maximum value of the sleep quality metric. A baseline therapy parameter set is then determined based on the therapy parameter values from the sensitivity analysis (108). The baseline therapy parameter set includes a combination of the therapy parameter values individually observed to produce a substantially maximum sleep quality metric. In some embodiments, the patient may enter comments, a pain value from a scale, or other feedback used along with the sensitivity analysis to determine the baseline parameter set. The baseline therapy parameter set may also be stored with therapy parameters sets60in memory48. In some embodiments, the baseline therapy parameter set may be stored separately from the generated therapy parameter sets.

IMD14controls delivery of the therapy based on the baseline therapy parameter set. Periodically during the therapy, IMD14checks to ensure that the baseline therapy parameter continues to define a substantially maximum sleep quality metric value for patient12. IMD14first perturbs at least one of the therapy parameter values of the baseline therapy parameter set (110). The perturbation comprises incrementally increasing and/or decreasing the therapy parameter value. A perturbation period may be preset to occur at a specific time, in response to a physiological parameter monitored by the IMD, or in response to a signal from the patient or clinician. The perturbation may be applied for a single selected parameter or two or more parameters, or all parameters in the baseline therapy parameter set. Hence, numerous parameters may be perturbed in sequence. For example, upon perturbing a first parameter and identifying a value that produces a maximum metric value, a second parameter may be perturbed with the first parameter value fixed at the identified value. This process may continue for each of the parameters in the therapy parameter set.

Upon perturbing a parameter value, IMD14then compares a value of the sleep quality metric defined by the perturbed therapy parameter set to the value of the sleep quality metric defined by the baseline therapy parameter set (112). If the sleep quality metric value does not improve with the perturbation, IMD14maintains the unperturbed baseline therapy parameter set values (114). If the sleep quality metric value does improve with the perturbation, IMD14perturbs the therapy parameter value again (116) in the same direction that defined the previous improvement in the sleep quality metric value. IMD14compares a value of the sleep quality metric defined by the currently perturbed therapy parameter set and the sleep quality metric value defined by the previously perturbed therapy parameter set (118). If the sleep metric value does not improve, IMD14updates the baseline therapy parameter set based on the therapy parameter values from the previous perturbation (120). If the sleep metric value improves again, IMD14continues to perturb the therapy parameter value (116).

Periodically checking the value of the sleep quality metric for the baseline therapy parameter set allows IMD14to consistently deliver a therapy to patient12that defines a substantially maximum sleep quality metric value of patient12. This allows the patient's symptoms to be continually managed even as the patient's physiological parameters change.

In some embodiments, an external computing device, such as clinician programmer20, may generate the plurality of therapy parameter sets over the range. A clinician may then provide the therapy parameter sets to IMD14via clinician programmer20. The computing device may provide individual therapy parameter sets to be tested, and may thus control the testing by IMD14, or may provide a listing of therapy parameter sets to be tested.

Furthermore, an external computing device, such as programmer20, a separate desktop computer, or server, may receive the sleep quality metric values collected by the IMD for the plurality of therapy parameter sets. The external computing device may then conduct the sensitivity analysis to determine the baseline therapy parameter set. The external computing device may also control the subsequent perturbations. In some embodiments, the external computing device may receive physiological parameter values from IMD14, and, rather that IMD14, the external computing device may determine values of the sleep quality or other performance metric based on the physiological parameter values received from IMD14.

In some embodiments, the sensitivity analysis and determination of a baseline therapy parameter set may be performed as part of a trialing process. In such embodiments, an external or implanted trial therapy device, such as a trial neurostimulator, may perform the functions ascribed to IMD14above that are associated with performing the sensitivity analysis and determination of a baseline therapy parameter set. The trial therapy device may include a therapy module44, processor46, and memory48, and may be coupled to sensors40and leads16, as described above with reference to IMD14andFIGS. 2 and 3.

IMD14may then be implanted in patient12, and programmed to deliver therapy according to the baseline therapy parameter set. In such embodiments, IMD14may perform the perturbation and updating functions of the example method illustrated byFIG. 5. In some embodiments, an external computing device may control delivery of a plurality of therapy parameter sets by the trial device, determine performance metric values based on physiological parameter values received from the trial device, and/or perform the sensitivity analysis.

FIG. 6illustrates, a separate monitor130that monitors values of one or more physiological parameters of patient12instead of, or in addition to the trial device or IMD14. Monitor130may include a processor46and memory48, and may be coupled to sensors40, as illustrated above with reference to IMD14andFIGS. 2 and 3. Monitor130may identify performance metric values based on the values of the monitored physiological parameter values, or may transmit the physiological parameter values to a computing device for determination of the performance metric values. In some embodiments, an external computing device, such as a programming device, may incorporate monitor130. In the illustrated embodiment, monitor130is portable, and is configured to be attached to or otherwise carried by a belt132, and may thereby be worn by patient12.

FIG. 6also illustrates various sensors40that may be coupled to monitor130by leads, wires, cables, or wireless connections, such as EEG electrodes134A-C placed on the scalp of patient12, a plurality of EOG electrodes136A and136B placed proximate to the eyes of patient12, and one or more EMG electrodes138placed on the chin or jaw the patient. The number and positions of electrodes134,136and138illustrated inFIG. 6are exemplary. For example, although only three EEG electrodes13are illustrated inFIG. 1, an array of between 16 and 25 EEG electrodes143may be placed on the scalp of patient12, as is known in the art. EEG electrodes134may be individually placed on patient12, or integrated within a cap or hair net worn by the patient.

In the illustrated example, patient12wears an ECG belt140. ECG belt140incorporates a plurality of electrodes for sensing the electrical activity of the heart of patient12. The heart rate and, in some embodiments, ECG morphology of patient12may monitored by monitor130based on the signal provided by ECG belt140. Examples of suitable belts140for sensing the heart rate of patient12are the “M” and “F” heart rate monitor models commercially available from Polar Electro. In some embodiments, instead of belt140, patient12may wear a plurality of ECG electrodes attached, e.g., via adhesive patches, at various locations on the chest of the patient, as is known in the art. An ECG signal derived from the signals sensed by such an array of electrodes may enable both heart rate and ECG morphology monitoring, as is known in the art.

As shown inFIG. 6, patient12may also wear a respiration belt142that outputs a signal that varies as a function of respiration of the patient. Respiration belt142may be a plethysmograpy belt, and the signal output by respiration belt142may vary as a function of the changes in the thoracic or abdominal circumference of patient12that accompany breathing by the patient. An example of a suitable belt142is the TSD201 Respiratory Effort Transducer commercially available from Biopac Systems, Inc. Alternatively, respiration belt142may incorporate or be replaced by a plurality of electrodes that direct an electrical signal through the thorax of the patient, and circuitry to sense the impedance of the thorax, which varies as a function of respiration of the patient, based on the signal. In some embodiments, ECG and respiration belts140and142may be a common belt worn by patient12, and the relative locations of belts140and142depicted inFIG. 6are exemplary.

In the example illustrated byFIG. 1, patient12also wears a transducer144that outputs a signal as a function of the oxygen saturation of the blood of patient12. Transducer144may be an infrared transducer. Transducer144may be located on one of the fingers or earlobes of patient12. Sensors40coupled to monitor130may additionally or alternatively include any of the variety of sensors described above that monitor any one or more of activity level, posture, heart rate, ECG morphology, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity and tone, core temperature, subcutaneous temperature, arterial blood flow, brain electrical activity, eye motion, and galvanic skin response.

FIG. 6also illustrates an external trial therapy device146in conjunction with patient12. In the illustrated example, patient12wears trial therapy device146with monitor130on belt132. The trial therapy device146may be coupled to one or more transcutaneoulsy implanted leads or catheters for delivery of therapy, such as neurostimulation or a drug, to patient12. As described above, trial therapy device146may deliver therapy to patient12during the sensitivity analysis and baseline therapy parameter set determination portion of the method illustrated inFIG. 5and, in some embodiments, may also monitor physiological parameters of patient12, determine performance metric values, and/or perform the sensitivity analysis to determine the baseline therapy parameter set for use by IMD14.

Various embodiments of the invention have been described. However one skilled in the art will appreciate, however, that various modifications may be made to the described embodiments without departing from the scope of the invention. For example, although described herein primarily in the context of treatment of pain with an implantable neurostimulator or implantable pump, the invention is not so limited. Moreover, the invention is not limited to implantable medical devices. The invention may be embodied in any implantable or external medical device that delivers therapy to treat any ailment of symptom of a patient.

As another example, the invention has been primarily described in the context of monitoring a sleep quality metric; however the invention is not so limited. The invention may monitor any performance metric, such as an activity metric, posture metric, a movement disorder metric, or other metrics that indicate the efficacy or degree of side effects associated a therapy delivered to a patient.

In some embodiments, for example, IMD14or any of the other devices described herein may periodically determine an activity level of patient12during delivery of therapy to the patient according to a plurality of parameter sets by monitoring at least one signal that is generated by a sensor40and varies as a function of patient activity, as described above. A value of at least one activity metric for each of a plurality of therapy parameter sets may be determined based on the activity levels associated with that parameter set. An activity metric value may be, for example, a mean or median activity level, such as an average number of activity counts per unit time. In other embodiments, an activity metric value may be chosen from a predetermined scale of activity metric values based on comparison of a mean or median activity level to one or more threshold values. The scale may be numeric, such as activity metric values from 1-10, or qualitative, such as low, medium or high activity.

In some embodiments, each activity level associated with a therapy parameter set is compared with the one or more thresholds, and percentages of time above and/or below the thresholds are determined as one or more activity metric values for that therapy parameter set. In other embodiments, each activity level associated with a therapy parameter set is compared with a threshold, and an average length of time that consecutively determined activity levels remain above the threshold is determined as an activity metric value for that therapy parameter set. One or both of the medical device or a programming device may determine the activity metric values as described herein.

As another example, the device may monitor one or more signals that are generated by respective sensors40and vary as a function of patient posture, as described above. Posture events are identified based on the posture of the patient, e.g., the patient's posture and/or posture transitions are periodically identified, and each identified posture event is associated with the current therapy parameter set.

A value of at least one posture metric is determined for each of the therapy parameter sets based on the posture events associated with that parameter set. A posture metric value may be, for example, an amount or percentage of time spent in a posture while a therapy parameter set is active, e.g., average amount of time over a period of time, such as an hour, that a patient was within a particular posture. In some embodiments, a posture metric value may be an average number of posture transitions over a period of time, e.g., an hour, that a particular therapy parameter sets was active.

In embodiments in which a plurality of posture metrics are determined for each therapy parameter set, an overall posture metric may be determined based on the plurality of posture metrics. The plurality of posture metrics may be used as indices to select an overall posture metric from a look-up table comprising a scale of potential overall posture metrics. The scale may be numeric, such as overall posture metric values from 1-10.

Similarly, a device may sense physiological parameter values of a patient indicative of movement disorders, such as tremor, via one or more sensors40, such as one or more accelerometers. Movement disorder metrics values that may be determined include mean or median values output by the sensors, amounts of time the sensor signal is above or below a threshold, or frequency of episodes above or below a threshold.

Further details regarding activity and posture metric values may be found in U.S.patent application Ser. No. 11/081,785 , by, by Ken Heruth and Keith Miesel, entitled “COLLECTING ACTIVITY INFORMATION TO EVALUATE THERAPY,” and filed on Mar.16, 2005, and U.S. patent application Ser. No.11/081,872, by Ken Heruth and Keith Miesel, entitled “COLLECTING POSTURE INFORMATION TO EVALUATE THERAPY,” and filed on Mar.16 2005. The content of these applications is incorporated herein by reference in its entirety.

Additionally, as discussed above, feedback entered by patient12, may be used as a performance metric instead of, or in addition to, the other performance metrics described herein. One of programming devices20,26may receive the feedback from patient12. In embodiments in which another device, such as a medical device or other computing device, performs the sensitivity analysis, the programming device may provide the feedback or performance metric values derived from the feedback to the other device. As examples, the feedback may include comments, or numeric values for pain, efficacy, or side effect levels.

For example, the programming device20,26may prompt patient12for feedback after a new or modified program is delivered by a therapy-delivering medical device during the sensitivity analysis or perturbation portions of the method illustrated byFIG. 5. Additionally or alternatively, if patient12experiences discomfort, the patient could cause the sensitivity analysis or perturbation to “step backward” to the most recent setting before the setting was changed by the algorithm via the programming device. A perturbation of a therapy parameter may produce results, either related or unrelated to the performance metric, that the patient does not like. For example, a perturbation to a higher drug dosage may result in somnolence, or a perturbation to a higher SCS amplitude may painfully stimulate ribs or abdominal muscles. The patient may cause the sensitivity analysis or perturbation to “step backward” to the most recent setting to rapidly stop the undesirably results.

When the patient causes the algorithm to step backward, the device performing the sensitivity analysis or perturbation may record this as a low performance metric value for the avoided program, or may prevent further program testing, perturbation, or other program selection of the avoided program, or within in a zone of therapy parameters determined based on the avoided program. In embodiments in which feedback is used in addition to one or more other performance metrics, a clinician or physician may determine a weighting scheme to provide more or less significance to the patient's feedback, i.e., the physician may choose to give the patient feedback zero weight and instead rely completely on other performance metric values, or the physician may judge that the patient has enough perspective to be able to competently gage pain levels and input substantially objective feedback into the sensitivity analysis.

These and other embodiments are within the scope of the following claims.