WO2017096291A1

WO2017096291A1 - Pain behavior analysis system

Info

Publication number: WO2017096291A1
Application number: PCT/US2016/064804
Authority: WO
Inventors: Mohd W. KHALAF; Bassam Masri
Original assignee: Wyllness Llc
Priority date: 2015-12-02
Filing date: 2016-12-02
Publication date: 2017-06-08

Abstract

An apparatus for collecting and automatically processing pain data to present useful information to patients and practitioners regarding pain includes a sensing circuit for wearing by a patient, a wireless transmitter coupled to the sensing circuit, and a processor coupled to the sensing circuit. The processor receives pain data from the sensing circuit. A memory is coupled to the processor, holding instructions, that when executed cause the apparatus to perform certain actions. The actions include receiving pain data from the sensing circuit, automatically processing the pain data, thereby generating information that identifies pain episodes experienced by the patient, and recording the information in a computer memory. Other actions for analyzing and using pain data are also performed.

Description

PAIN BEHAVIOR ANALYSIS SYSTEM

FIELD

[001 ] The present disclosure relates to methods and apparatus for automated and semi-automated analysis of pain behavior using a sensor and wireless communication device worn by a patient.

BACKGROUND

[002] "Palliative care" sometimes refers to health care methods directed towards improving quality of life of patients facing the problems associated with life-threatening illness, through the prevention and relief of suffering. More generally, the term may refer to any care that includes assessment and treatment of pain, whatever its cause.

[003] Chronic pain often occurs in episodes. Understanding the triggers and profile of pain episodes helps health care practitioners develop therapies for reducing suffering with minimal impact on patient health. Currently, patients often have no alternative to manually recording their own pains and feelings on reporting forms, and submitting forms to health care practitioners. Practitioners often have no alternative to managing patient's pain based on the patients subjective impressions, without objective data or data analysis.

[004] Episodes of pain can be characterized by tangible measurement data, for example, measurements of respiration rate and pattern, pulse, blood pressure, and/or other vital signs. Measurements of vital signs that are correlated to pain episodes are referred to herein as "pain data." However, outside of intensive hospital care, pain data is not generally available for practitioners during the patient's pain episodes, while hospitalization is infeasible for most patients. Moreover, merely collecting pain data continuously for all patients would drown practitioners in pain data, making use of the data economically infeasible in many cases. Thus,

[005] It would be desirable, therefore, to develop new methods, apparatus and systems, that overcome these and other limitations of the prior art, and that collect and automatically process pain data to present useful information to patients and practitioners, whether or not the patient is hospitalized. SUMMARY

[006] This summary and the following detailed description should be interpreted as complementary parts of an integrated disclosure, which parts may include redundant subject matter and/or supplemental subject matter. An omission in either section does not indicate priority or relative importance of any element described in the integrated application. Differences between the sections may include supplemental disclosures of alternative embodiments, additional details, or alternative descriptions of identical embodiments using different terminology, as should be apparent from the respective disclosures.

[007] In an aspect of the disclosure, an apparatus for collecting and automatically processing pain data to present useful information to patients and practitioners regarding pain includes a sensing circuit for wearing by a patient, a wireless transmitter coupled to the sensing circuit, and a processor coupled to the sensing circuit. The processor receives pain data from the sensing circuit. A memory is coupled to the processor, holding instructions, that when executed cause the apparatus to perform a method including certain actions. The actions include receiving pain data from the sensing circuit, automatically processing the pain data, thereby generating information that identifies pain episodes experienced by the patient, and recording the information in a computer memory. Other actions for analyzing and using pain data are also performed.

[008] The apparatus may include a computer or set of connected computers that is integrated with a wearable sensor for wear by a patient. Other elements of the apparatus may include, for example, a sensor or sensors as described more fully herein below, a display screen, an audio output device, and a user input device, which participate in the execution of the method.

[009] To the accomplishment of the foregoing and related ends, one or more examples comprise the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative aspects and are indicative of but a few of the various ways in which the principles of the examples may be employed. Other advantages and novel features will become apparent from the following detailed description when considered in conjunction with the drawings and the disclosed examples, which encompass all such aspects and their equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[010] The features, nature, and advantages of the present disclosure will become more apparent from the detailed description set forth below when taken in conjunction with the drawings in which like reference characters identify like elements correspondingly throughout the specification and drawings.

[01 1 ] Fig. 1 is a sequence diagram illustrating aspects of a use case for calibration of a sensor or sensing device for pain data correlated to patient feedback by a pain management device.

[012] Fig. 2 is a schematic diagram illustrating aspects of behavioral analysis by a pain management device.

[013] Fig. 3 is a schematic diagram illustrating further aspects of behavioral analysis by a pain management device.

[014] Fig. 4 is a flow chart illustrating elements of a pain management prediction process that may be executed by a pain management device.

[015] Fig. 5 is a block diagram illustrating aspects of a pain management device or similar apparatus for automatically processing the pain data, thereby generating information that identifies pain episodes experienced by the patient.

DETAILED DESCRIPTION

[016] Various aspects are now described with reference to the drawings. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. It may be evident, however, that the various aspects may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing these aspects.

[017] The present disclosure concerns apparatus and systems for collecting and automatically processing pain data thereby generating useful information for patients and practitioners identifying onset and duration of pain episodes. The useful information may include, for example, a time-correlated record of current pain intensity level for the patient that is automatically inferred based on the pain data. The apparatus and systems may include at least a pain analysis computing device that detects pain level fluctuations based on pain data collected before, during and after episodes of pain, analyzes the pain data, determines therapeutic conclusions based on the analysis, and outputs a compact representation of the pain data (a "pain profile") and conclusions (if any) in a format suitable for a practitioner to make treatment recommendations based on the patient's pain profile.

[018] In an aspect, the pain behavior system includes a patient-worn sensing circuit equipped with a wireless transmitter or transceiver, that communicates via a wireless LAN and/or cellular network to a host application on a networked computer(s) configured as a client and/or server. The system assists with analyzing chronic pain by providing data and feedback, with the goal of assisting palliative care and if possible, reducing the frequency or severity of problematic behaviors that contribute to pain.

[019] System components and features may include:

[020] (1 ) One or more sensors wirelessly connected to a computer having a user interface and wireless communication transceiver (e.g., iPhone, Android or Windows phone, notepad computer, laptop computer, personal computer, with or without participation by a remote computer server) through Bluetooth™, Wi-Fi (IEEE 802.91 1 ), or other wireless technology; and

(2) An apparatus and/or method for configuring the one or more sensors relative to a patient to detect pain data.

[021 ] Software that configures the computer to analyze data collected from the one or more sensors to determine the state of the patient and his response to pain, including at least making the following determinations:

• Determining the onset and duration of a pain episode;

• Determining a pain severity level, e.g., mild, moderate, or severe, based on a patient profile;

• Determining when changes in pain severity level occur;

• Analyze and report patient data during pain episodes;

• Provide analyzed data to patient^'s provider or patient to share with their provider to help in patient^'s care process; and

• Provide programmable actions or/and automatic notification when pain severity level change to keep patient and provider informed of pain fluctuation, pain attacks and factors impacting their pain and care process. [022] In an aspect of the pain behavior system, an algorithm is executed by a computer to create and adapt a model or profile unique to every patient for optimizing treatment decision making for management of chronic pain. The system helps providers to accurately detect and review patient's pain episodes during or shortly thereafter, by transmitting data and/or analysis results to a receiving device used by the practitioner. The system may also rapidly provide pain management recommendations to the patient during pain episodes.

[023] The algorithm may include multiple processes, for example the processes summarized below:

• Heuristic Training Process: Used to process patient's pain episodes and flare ups with captured vital signs from sensors, by correlating user feedback regarding pain severity to changes in values of the pain data.

• Behavior Analysis Process: Using mathematical algorithms and analytical processes, calculate the patient vitals: Heart rate, Blood Pressure and respiratory rate during pain episode or flare ups. This process is calculation and learning process.

• Prediction Process: Used to monitor vitals and apply mathematical algorithm to predict future pain episodes and flare ups based on patient individual model.

[024] Aspects of the algorithm may be based on empirically observed correlations between pain data (vital signs) and pain severity levels. For example, a pain management device may analyze the patient's vitals: heart rate (HR), blood pressure (BP), respiratory rate and activities in order to determine the patient state during pain and the severity of the pain and the action required during this period.

[025] The empirically observed correlations used to analyze pain data may include, for at least two levels of pain severity. For example, for moderate and superficial pain the correlations may include the following observed data: increase in blood pressure, increase in pulse rate, and increase in respiratory rate. For further example, for severe and deep pain the correlations may include the following observed data: decrease in blood pressure, decrease in pulse rate, and rapid and irregular breathing.

[026] Figure 1 illustrates aspects of a use case 100 for calibration of a sensor or sensing device 106 used to collect pain data, correlated to patient feedback via a pain management device 104. The sensor device 106 may include any one or more sensors, for example an electrode(s) positioned for pulse detection, a microphone positioned to listen to respiration or to determine blood pressure using an inflatable cuff, one or more strain gauges positioned for sensing respiration, an oxygen sensor positioned for respiration monitoring, any other vital sign sensor. In addition, the sensor device 106 may include a processor, a memory, a transceiver and a structure housing these components in a form factor suitable for wearing on a patient's body.

[027] Initially, the sensor device 106 is activated by the user, for example, starting heart rate and activity sensors at 108. In response to a patient input indicating pain at 1 10, the pain management device 104 may instruct the sensor device to initiate active sensing (e.g., of blood pressure or oxygen), at 1 12. The pain management device 104 may include at least processor coupled to a memory, a user interface device (e.g., touchscreen and audio output transducer) and a transmitter or transceiver, the memory holding instructions for the described device operations. The sensor device 106 captures pain data in real time 1 14. The pain management device 104 may store the captured data and output a user interface on the user interface device, that includes instructions for the patient to provide pain severity indications (e.g., mild, moderate, severe) at intervals at 1 16. This data is collected by the pain management device and time stamped at 1 18 until the patient indicates that the pain episode is over at 120. The pain management device 104 may collect pain data from the sensor device during (at 1 14) and/or after (at 122) the pain episode, and correlate the pain data to the patient pain intensity feedback based on time. Once the episode is over, the pain management device develops a patient profile based on comparing the pain data to the patient feedback data at 126. The patient profile identifies characteristics of the pain data (e.g., blood pressure, pulse rate, respiration rate, and O2 blood level) that correlate to pain intensity feedback, within a statistically-derived confidence level. The profile is saved and may be used to assess pain in future episodes. Training may be repeated and refined during any future pain episode.

[028] In another aspect, the same system may be used for patient behavioral analysis in relation to pain data at 124. Behavior data may be useful for improving the accuracy of the patient profile by adjusting the profile to account for other influences on pain data, and may provide valuable information to practitioners and patients for pain management. Behavioral analysis may use certain data from the sensor device, for example, pulse data from a heart rate sensor and motion data from one or more accelerometers, to determine current patient behavior. The pain management device may correlate patient behavior to pain data and/or patient feedback, based on time of collection. Periods of sleep may be detected using sleep sensors, for example by monitoring human rest/activity cycles using a motion sensor worn on the wrist or other body part.

[029] An example of behavior analysis as may be performed by a pain management device is illustrated by FIGURE 2. The use case 200 illustrated by FIGURE 2 may begin immediately after the case 100 illustrated by FIGURE 1 , for example, or at any other desired time. The smart pain management device may send a ready indication to the user at any time, for example by outputting an audible, visible and/or tactile signal or display from the pain management device. Based on the sensor data 202 received from the one or more sensors worn by the patient (e.g., a heart rate sensor and an accelerometer), the pain analysis device may identify periods of sleep 204, non-sleep rest 208, activity 212, physical activity for training , pain 220, or other characteristic behavior. For each behavior, the pain analysis device may compute vital statistics such as minimum, maximum, and average pulse rate, respiration rate, 206, 210, 214, 218, 222, and so forth. The vital statistics may be added to a patient record and/or profile maintained in a system memory.

[030] Further details of behavioral analysis are illustrated by FIGURE 3. For example, processes and parameters 300 for defining illustrated patient states may as indicated under the various process streams for "sleep state" 310, "resting state", 320," "maximum" heart rate or blood pressure 330 using a table of data thresholds 301 to determine alarm states, or "pain state" 340. Sleep state may be detected by absence of movement for a period of time and drop in O2 level. The column 310 for sleep state diagrams an algorithm for calculating the sleep state heart rate and blood pressure using sensor information, for example, pulse, O2, accelerometer and gyroscopic sensor data. The algorithm is executed by a pain management device, which scans through the sensor data and identifies when the patient is laying down and not moving, based on motion and position sensors (e.g., accelerometer or gyroscopic sensor). The device then plots the heart rate (HR), O2, and bloof pressure (BP) for this period which identify the sleep state. The device may calculate a sleep heart rate and blood pressure 309 for the times in which no patient movement is detected.

[031 ] Resting state may be calculated after exiting sleeping state with normal O2 levels and during slight movement. The column 320 diagrams an algorithm for calculating the resting state heart rate and blood pressure using sensor information, for example, for example, information from pulse, accelerometer and gyroscopic sensors. When sensor data indicates a patient laying down and moving, the pain management device may detect a resting state.

[032] The third column 330 diagrams an algorithm for calculating a maximum heart rate based on the patients age. A data table 301 may be used, that defines a different maximum heart rate for each age range, alone or in combination with one or more other patient status values (e.g., gender, cardiac profile). The pain management device may look up or calculate a maximum heart rate using a data table 301 , and use the maximum heart rate in other algorithms such as for pain detection. For example, a "high" heart rate may be set as a percentage of a calculated maximum heart rate for the patient. It should be appreciated that the illustrated table 301 is merely an example, and different table values or configurations may also be useful.

[033] Pain state at certain pain levels may be calculated using current patient heart rate, O2 and blood pressure during pain episode. The column 340 diagrams an algorithm for detecting the heart rate and blood pressure patterns that indicate a level of pain the patient is experiencing during pain episodes. This pattern is detected based on sensor information in real time to identify patient's pain state when and if it occurs, without requiring patient intervention. The patient is not required to take any intentional action to signal a pain level, once the pain management device is calibrated. Initially, a calibration step utilizes the start and stop of pain events indicated by the patient to start calculating a minimum, maximum, and average of heart rate and blood pressure during the pain episode period, and identifies patterns in these or similar metrics during this period, which it correlates to the indicated pain level. The correlated patterns are then used for pain level detection, post calibration.

[034] FIGURE 4 shows a pain management prediction process 400 that may be executed by a pain management device as described herein. The process 400 may include capturing 420 samples of pain data from sensors at intervals of 'X' seconds. For example, samples may be taken for duration of ten seconds at intervals of thirty seconds, or for duration of thirty seconds at intervals of 120 seconds. The device may run a behavioral analytics process 404 as described in connection with Figs. 2-3 after each sample set and determine 406, 408 whether or not the patient is experiencing change in pain. If no change in pain episode is detected, the device may wait for the remaining interval 'X' 420 and cycle back to the next sampling operation. If a change in pain is detected 410, the device may determine whether or not the patient is currently performing physical activities. If the determination is positive, the device may cycle back to the next sampling operation (not shown). If the determination is negative, the device may determine an action such as medication dispensing, physical therapy, relaxation, patient and/or provider notification 412, for example, advising the patient to administer medication or automatically supplying a dose of medication. Once receiving a confirmation signal that the dose is administered 414, the software may determine whether or not there has been a change in heart rate or other vital sign(s) since the last sampling period 416, indicating that medication is taking effect. If the effect is nonexistent or less than a specified threshold, the device may take further action 418. The cycle 400 then repeats.

[035] FIGURE 5 shows a pain management device or similar apparatus 500 for collecting and automatically processing pain data to present useful information to patients and practitioners regarding pain experienced by a patient. The apparatus 500 may include a sensing circuit 512, configured for wearing by a patient, for example using a pressure-sensitive adhesive or elastic cuff to hold the sensor or sensor against the patient's skin. The apparatus 500 may further include a wireless transmitter 514, coupled to the sensing circuit 512. The apparatus 500 may further include at least one processor 510 coupled to the sensing circuit, configured for receiving pain data from the sensing circuit 512. The processor 510 may be coupled to the sensing circuit via the wireless transmitter 514, or via a local bus 519. For example, if the processor 510 and sensing circuit 512 are housed together, or connected to a common substrate (e.g., a circuit board), a local bus 519 may be most suitable. Conversely, if the processor 510 is in a separate device (e.g., in a smart phone or other portable computer) from the sensing circuit 512, a wireless transmitter 514 may be more convenient than a local bus.

[036] The apparatus 500 may further include a memory 516 coupled to the at least one processor 510. The apparatus 500 may include a component 502 for receiving pain data from the sensing circuit, implemented as software, hardware, firmware or a combination of the foregoing. The apparatus 500 may include any component 504 for automatically processing the pain data, thereby generating information that identifies pain episodes experienced by the patient, likewise implemented as software, hardware, firmware or any combination of the foregoing. The apparatus 500 may include a component 506 for recording the information in the computer memory 506 or in another memory or storage device. In an aspect, components 502, 504, 506 may be embodied as encoded instructions in the memory 516, that when executed by the processor 510, cause the apparatus 500 to perform: receiving pain data from the sensing circuit, automatically processing the pain data, thereby generating information that identifies pain episodes experienced by the patient, and recording the information in the computer memory 516 or in another memory or storage device. It should be appreciated that the foregoing instructions or components may be augmented by further instructions or components as described in the detailed description above. Some additional non-limiting examples of further instructions or components are provided below.

[037] The apparatus may include further instructions in the memory 516, or other component, for automatically processing the pain data are further configured for determining onset and duration of a pain episode, a pain severity level based the pain data and on a patient profile, and times at which changes in pain severity level occur. These instructions may be further configured for generating a notification signal indicating changes in the pain severity level, and providing the notification signal to a user interface component.

[038] The apparatus may include further instructions in the memory 516, or other component, for heuristic recognition of pain severity based on the pain data, by correlating user feedback regarding pain severity to changes in values of the pain data.

[039] The apparatus may include further instructions in the memory 516, or other component, for calculating at least one patient vital sign based on the pain data, wherein the at least one patient vital sign includes one or more of a heart rate, blood pressure, blood oxygen level, or respiratory rate. These instructions may be further configured for correlating changes in the at least one patient vital sign to a pain severity level. For example, the instructions may be further configured for correlating one or more of an increase in blood pressure, an increase in pulse rate, and an increase in respiratory rate to a moderate level of pain severity. For further example, the instructions may be further configured for correlating one or more of an decrease in blood pressure, a decrease in pulse rate, a rapid respiratory rate, or an irregular respiratory rate, to a severe level of pain severity. [040] The apparatus may include further instructions in the memory 516, or other component, for detecting a patient activity level comprising sleeping, resting, or active using a motion sensor worn by the patient, for example using analysis algorithms as diagrammed in Figure 3.

[041 ] The apparatus 500 may further include a user interface device coupled to the at least one processor, the user interface device comprising a display screen and an audio output transducer (e.g., a speaker).

[042] The sensing circuit 512 may include one or more electrodes coupled to the sensing circuit, positioned for detecting the patient's pulse. In addition, or in an alternative, the sensing circuit 512 may include one or more microphones coupled to the sensing circuit, positioned for detecting at least one of the patient's respiration rate or blood pressure. In addition, or in an alternative, the sensing circuit 512 may include one or more strain gauges coupled to the sensing circuit, positioned for detecting at least one of the patient's respiration rate or activity level. In addition, or in an alternative, the sensing circuit 512 may include one or more oxygen sensors coupled to the sensing circuit, positioned for detecting the patient's blood oxygen level.

[043] In view of the exemplary systems described supra, methodologies that may be implemented in accordance with the disclosed subject matter have been described with reference to several flow diagrams. While for purposes of simplicity of explanation, the methodologies are shown and described as a series of blocks, it is to be understood and appreciated that the claimed subject matter is not limited by the order of the blocks, as some blocks may occur in different orders and/or concurrently with other blocks from what is depicted and described herein. Moreover, all illustrated blocks are not necessarily required to implement the methodologies described herein.

[044] Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[045] As used in this application, the terms "component", "module", "system", and the like are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component or a module may be, but are not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component or a module. One or more components or modules may reside within a process and/or thread of execution and a component or module may be localized on one computer and/or distributed between two or more computers.

[046] Various aspects will be presented in terms of systems that may include a number of components, modules, and the like. It is to be understood and appreciated that the various systems may include additional components, modules, etc. and/or may not include all of the components, modules, etc. discussed in connection with the figures. A combination of these approaches may also be used. The various aspects disclosed herein can be performed on electrical devices including devices that utilize touch screen display technologies, heads-up user interfaces, wearable interfaces, and/or mouse-and-keyboard type interfaces. Examples of such devices include virtual or augmented reality output devices (e.g., headsets), computers (desktop and mobile), smart phones, personal digital assistants (PDAs), and other electronic devices both wired and wireless.

[047] In addition, the various illustrative logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[048] Operational aspects disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, digital versatile disk (DVD), Blu-ray™, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a client device or server. In the alternative, the processor and the storage medium may reside as discrete components in a client device or server.

[049] Furthermore, the one or more versions may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed aspects. Non-transitory computer readable media can include but are not limited to magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips, or other format), optical disks (e.g., compact disk (CD), DVD, Blu-ray™ or other format), smart cards, and flash memory devices (e.g., card, stick, or other format). Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope of the disclosed aspects.

[050] The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1 . An apparatus for collecting and automatically processing pain data to present useful information to patients and practitioners regarding pain, the apparatus comprising: a sensing circuit, configured for wearing by a patient;

a wireless transmitter, coupled to the sensing circuit;

at least one processor coupled to the sensing circuit, the processor configured for receiving pain data from the sensing circuit; and

a memory coupled to the at least one processor, the memory holding encoded instructions, that when executed by the at least one processor, cause the apparatus to perform:

receiving pain data from the sensing circuit,

automatically processing the pain data, thereby generating information that identifies pain episodes experienced by the patient, and

recording the information in a computer memory.

2. The apparatus of claim 1 , wherein one or more of the at least one processor is coupled to the sensing circuit via the wireless transmitter.

3. The apparatus of claim 1 , wherein one or more of the at least one processor is coupled to the sensing circuit via a local bus.

4. The apparatus of claim 1 , wherein the instructions for automatically processing the pain data are further configured for determining onset and duration of a pain episode, a pain severity level based the pain data and on a patient profile, and times at which changes in pain severity level occur.

5. The apparatus of claim 4, wherein the instructions are further configured for generating a notification signal indicating changes in the pain severity level, and providing the notification signal to a user interface component.

6. The apparatus of claim 1 , wherein the instructions are further configured for heuristic recognition of pain severity based on the pain data, by correlating user feedback regarding pain severity to changes in values of the pain data.

7. The apparatus of claim 1 , wherein the instructions are further configured for calculating at least one patient vital sign based on the pain data, wherein the at least one patient vital sign includes one or more of a heart rate, blood pressure, blood oxygen level, or respiratory rate.

8. The apparatus of claim 7, wherein the instructions are further configured for correlating changes in the at least one patient vital sign to a pain severity level.

9. The apparatus of claim 8, wherein the instructions are further configured for correlating one or more of an increase in blood pressure, an increase in pulse rate, and an increase in respiratory rate to a moderate level of pain severity.

10. The apparatus of claim 8, wherein the instructions are further configured for correlating one or more of an decrease in blood pressure, a decrease in pulse rate, a rapid respiratory rate, or an irregular respiratory rate, to a severe level of pain severity.

1 1 . The apparatus of claim 1 , wherein the instructions are further configured for detecting a patient activity level comprising sleeping, resting, or active using a motion sensor worn by the patient.

12. The apparatus of claim 1 , further comprising a user interface device coupled to the at least one processor, the user interface device comprising a display screen and an audio output transducer.

13. The apparatus of claim 1 , further comprising one or more electrodes coupled to the sensing circuit, positioned for detecting the patient's pulse.

14. The apparatus of claim 1 , further comprising one or more microphones coupled to the sensing circuit, positioned for detecting at least one of the patient's respiration rate or blood pressure.

15. The apparatus of claim 1 , further comprising one or more strain gauges coupled to the sensing circuit, positioned for detecting at least one of the patient's respiration rate or activity level.

16. The apparatus of claim 1 , further comprising one or more oxygen sensors coupled to the sensing circuit, positioned for detecting the patient's blood oxygen level.

17. A method for collecting and automatically processing pain data to present useful information to patients and practitioners regarding pain, the method comprising:

receiving, by a processor, pain data from a sensing circuit worn by a patient; automatically processing the pain data, by the processor, thereby generating information that identifies pain episodes experienced by the patient, and

recording the information in a computer memory.

18. The method of claim 17, further comprising determining onset and duration of a pain episode, a pain severity level based the pain data and on a patient profile, and times at which changes in pain severity level occur.

19. The method of claim 17, further comprising recognizing a pain severity level based on the pain data, by correlating user feedback regarding pain severity to changes in values of the pain data.

20. The method of claim 19, further comprising correlating changes in at least one patient vital sign to the pain severity level.