Real-time pain detection and pain management system

Abstract

The present invention provides a real-time pain detection system. The system includes: a device for acquiring biomedical signals related to pain of an object in need; a computing device for converting the biomedical signals acquired within a given period of time into signal data for measuring pain, analyzing the data to be divided into two or more models, the two or more models at least including a pain model defined by data showing a peak profile and a pain-free model defined by data showing a flat profile, and then measuring the pain state of the object based on the analysis result; a processing device for generating a pain index using the analysis result according to the needs or feelings of the object; and a display for displaying the pain state of the object.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 62 / 894,532, filed on August 30, 2019, the entire contents of which are incorporated herein by reference if fully set forth herein. Technical Field

[0003] The present invention relates to a system and method for real-time pain detection by analyzing physiological signals, and a pain management system using the system or method for real-time pain detection. Background Art

[0004] Pain is considered an unpleasant emotional and sensory experience that may be associated with real or potential tissue damage. Today, pain is one of the most important clinical symptoms that can be used to detect the severity of a patient's injury. However, pain is always subjective, and each patient learns the use of the term through previous experiences related to injury. Therefore, it is difficult to objectively measure the degree of pain by methods that detect a physiological condition or use a parameter related to a physiological condition.

[0005] Some prior art references disclose pain monitoring / detection by measuring various physiological signals. For example, U.S. Patent No. 6,117,075 discloses a method and apparatus for determining the depth of anesthesia (DOA) by measuring skin temperature or photoplethysmographic pulse pressure to define and analyze oscillation patterns, or correlations between simultaneous oscillation patterns measured at different physical locations, thereby obtaining an index of the depth of anesthesia.

[0006] U.S. Patent No. 6,685,649 provides a method for monitoring the condition of a patient under anesthesia or sedation. The method focuses on and relies on analyzing a single parameter related to the cardiovascular system, specifically using the time interval between the consecutive waveforms, the pressure from the consecutive waveforms, and the time rate from the consecutive waveforms.

[0007] So far, it has been reported that physiological signals such as skin conductance, blood pressure (BP), heart rate (HR), electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmograph (PPG), and temperature have been used to determine the depth of anesthesia (DOA) or pain level. However, medical studies have shown that the use of a combination of parameters from different physiological signals can significantly improve the achieved pain and no pain classification performance compared to using any single signal alone for discrimination (Guignard, Clinical anaesthesiology, Vol. 20, No. 1: pp. 161-180, 2006).

[0008] It is reported that a group of physiological characteristics are used to form a pain index or nociception index to determine the patient's state. For example, U.S. Patent No. 9,498,138 B2 provides an index called "PMD200" and relates to a system and method for monitoring by performing multidimensional analysis on multiple physiological signals to generate an index.

[0009] It is considered unacceptable for a person who is amenable to safe intervention to experience untreated severe pain while under the care of a physician. Pain management should be provided whenever medically indicated. Any pain management techniques for the patient must be considered. Accurate prediction of pain intensity can provide valuable insights that can be effectively used to ultimately determine the location of pain and formulate a rational treatment plan accordingly. Therefore, pain prediction can improve the quality of daily life of patients in health-related fields such as rehabilitation, home health care, and emergency medical services.

[0010] U.S. Patent No. 7,924,818 discloses an obstetric analgesia system for providing short-acting analgesics for pain management during labor, which can effectively predict uterine contractions in real time to coordinate the administration of analgesics so that the peak effectiveness of the analgesics coincides with the intermittent pain of labor. However, in addition to the pain during labor being related to uterine contractions, the pain cannot be effectively managed through automatic analgesic administration because the timing and intensity of the pain cannot be predicted.

[0011] The analgesia nociception index (ANI) based on heart rate variability has been reported to reflect different degrees of acute pain. The aim of this study was to compare the analgesia nociception index (ANI) score with a numeric rating scale (NRS, 0-10) based on self-assessment of pain in the recovery room. A study of the analgesia nociception index (ANI) was published to evaluate a new parameter for acute postoperative pain. However, it was concluded that the analgesia nociception index (ANI) could not reflect different states of acute postoperative pain measured by a numeric rating scale (NRS) after sevoflurane-based general anesthesia in adults (Ledowski et al., Br. J. Anaesth., Vol. 111, No. 4: pp. 627-9, 2013).

[0012] Therefore, there remains a need to develop a system and method for detecting or monitoring or identifying the presence and severity of pain that does not rely on subjective assessments of pain whenever a patient's self-rating of pain is not readily available (e.g., sedated patients, very young children, individuals with learning disabilities), and a system and method for managing pain that is capable of determining the time to administer analgesics before the onset of pain. Summary of the invention

[0013] Accordingly, the present invention provides a system and method for detecting or monitoring a subject's pain in real time using a biomedical signal (eg, heart rate) after analysis and conversion.

[0014] In one aspect, the present invention provides a real-time pain detection system, comprising: a device for acquiring biomedical signals related to pain of a subject in need; a computing device for converting the biomedical signals acquired within a given time period into signal data for measuring pain, analyzing the data to divide into two or more models, the two or more models including at least a pain model defined by data showing a peak profile and a pain-free model defined by data showing a flat profile, and then measuring the pain state of the subject based on the results of the analysis; a processing device for generating a pain index based on the needs or feelings of the subject using the results of the analysis; and a display for displaying the pain state of the subject.

[0015] On the other hand, the present invention provides a method for pain management of an object, comprising: acquiring biomedical signals related to the pain of the object, converting the biomedical signals acquired within a given time period into signal data for measuring pain, analyzing the data to divide into two or more models, the two or more models at least including a pain model defined by data displaying a peak contour and a pain-free model defined by data displaying a flat contour, and then measuring the pain state of the object based on the results of the analysis, and generating a processing means for generating a pain index according to the needs or feelings of the object using the results of the analysis.

[0016] In another aspect, the present invention provides a pain management system comprising: a real-time pain detection system of the present invention; an analgesic system for delivering an analgesic or performing a pain relief method; and means for communicating between the real-time pain detection system and the analgesic system. The system is capable of effectively predicting pain in real time to activate the analgesic system in advance of pain, so that the administration of an analgesic or pain relief method is based on the time and / or intensity of pain.

[0017] In a preferred embodiment of the present invention, the biomedical signal is a signal related to heart rate, including but not limited to heart rate (heart rate, HR), pulse rate (pulse rate, PR), heart rate variability (heart rate variability, HRV), and electrocardiogram (electrocardiogram, ECG).

[0018] In one embodiment of the present invention, an analgesic system is provided for administering a short-acting intravenous, transdermal, transmucosal, or intramuscular analgesic that provides improved pain relief timed to the pain cycle.

[0019] In certain embodiments of the present invention, the analgesic agent may be a drug or pharmaceutical agent that is highly titratable, has a rapid and predictable onset of action, and has a short duration of biological activity.

[0020] In other embodiments, an analgesic system is provided for administering an analgesic, or electrical stimulation (e.g., a transcutaneous electrical nerve stimulation (TENS) unit) or other method of blocking pain sensation, which is delivered / applied to the patient early enough to have an effect during the painful portion of the pain cycle.

[0021] According to the present invention, a method for monitoring pain in a subject includes: obtaining biomedical signals related to pain in the process of predicting pain to establish a pain model for the patient and the optimal time for analgesic administration. In a related embodiment, the system of the present invention monitors the time and intensity based on the collected biomedical signals.

[0022] In the present invention, the biomedical signal used may be any physiological signal related to heart rate. Currently available technologies that can analyze and monitor physiological signals related to heart rate include, but are not limited to, heart rate (HR), pulse, heart rate variability (HRV), blood volume pulse (BVP), or electrocardiogram (ECG). These signals reflect the activity level of the autonomic nervous system, which is related to the secretory activity of the myocardium and visceral organs.

[0023]

[00136] The terms "heart rate" or "HR" or "pulse" as used herein refer to the rate at which the heart beats as measured by the contractions (beats) of the heart per minute (bpm).

[0024] As used herein, the term "heart rate variability" or "HRV" refers to the physiological phenomenon of variation in the time intervals between heart beats, which can be measured by the changes in the heart beat intervals.

[0025] The blood volume pulse (BVP) signal comes from a photoplethysmographic (PPG) sensor, which transmits infrared light through tissue to monitor blood volume in capillaries and arteries. Therefore, changes in the BVP amplitude reflect instantaneous sympathetic nerve activation. Most PPG sensors can be placed anywhere on the body, with the finger being the most common location for recording BVP signals.

[0026] The electrocardiogram (ECG) is an electrophysiological signal related to the electrical activity of the sinoatrial node, reflecting cardiovascular activity. In addition, the ECG response to external stimuli (e.g., pain and stress) produces a large variability in the physiological signals of a given subject. Therefore, we can use ECG signals to extract general information about the state or intensity of pain.

[0027] In one embodiment of the present invention, when ECG data is reliably detected at the onset, the ECG data can serve as an effective precursor to defining a pain and no pain model for coordinated delivery of analgesics, thereby matching the pain relief capabilities of the analgesics to the pain cycle.

[0028] In an embodiment of the present invention, the pain management system further comprises a device for delivering a short-acting analgesic to the subject before the pain, so that the analgesic ability of the analgesic reaches a peak with the pain. For example, the pain management system comprises a device for providing an audible or visual warning signal for notification.

[0029] In the present invention, the subject pain management system further provides means for triggering the delivery of analgesic.

[0030] In another embodiment of the present invention, a pain management system is provided that has an automatic analgesic delivery feature for automatically delivering an analgesic and / or adaptively changing the concentration of the analgesic based on monitored biomedical signals related to heart rate (e.g., via a monitored ECG). In a related embodiment of the present invention, the pain management system can determine the degree of pain and change the concentration of the analgesic based on this data. This "degree of pain" refers to the timing and / or intensity of pain, which can be determined by any of the following situations: (1) the current ECG; (2) the time history of the ECG; or (3) through patient input to the system, and / or by some combination of the above (1)-(3), which depends on the subject's pain level and varies according to the subject's needs or feelings.

[0031] In an embodiment of the present invention, the ECG signal is converted from the time domain to the frequency domain, and then the data is divided into two waveforms, and data analysis is performed to find the characteristics and differences of the two waveforms. Finally, the sign function and value are estimated, which can represent the degree of injury sensation.

[0032] In the present invention, a pain management system is provided that automatically delivers an analgesic prior to pain. The system preferably accepts patient input to titrate the dose of the analgesic and includes a respiratory monitor, such as a pulse oximeter, to monitor the patient's oxygen saturation to ensure safety. In a related embodiment, the pain management system preferably controls the delivery of the analgesic while monitoring the patient's clinical condition using a pulse oximeter. In another related embodiment, the analgesic system preferably controls a transdermal, transmucosal or intramuscular administration system.

[0033] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The foregoing summary, as well as the following detailed description of the invention, will be better understood when read in conjunction with the accompanying drawings.For the purpose of illustrating the invention, there are shown in the drawings embodiments that are presently preferred.

[0035] In the attached picture:

[0036] Figure 1 A flow chart is provided for a pain monitoring system according to the present invention.

[0037] Figure 2 Provides pain module, where the blue signal is FFT Flat (i) Standard deviation distribution, the red signal is FFT Flat (i) The first order of standard deviation distribution.

[0038] Figure 3The Fast Fourier Transform of an ECG in one embodiment of the present invention is shown and labeled FFT Flat (i).

[0039] Figure 4 The results of ECG peak and smooth test data are shown; the upper figure is the ECG during the smooth uterine contraction test data; the lower figure is the ECG during the peak uterine contraction test data.

[0040] Figure 5 The peak and flat original FFT accumulation results are shown; where, in case 1, the red signal is the flat FFT data and the blue signal is the peak FFT data.

[0041] Figure 6 The peaked and flat FFT raw mean distributions in Case 1 are shown.

[0042] Figure 7 A comparison between the peaked and flat data in Case 1 is shown.

[0043] Figure 8 Peak and flat original FFT accumulation results are shown; where, in case 2, the red signal is flat FFT data and the blue signal is peak FFT data.

[0044] Fig. 9 The peaked and flat FFT raw mean distributions in Case 2 are shown.

[0045] Fig.10 A comparison between peaked and flat data in Case 2 is shown.

[0046] Fig.11 The peak and flat original FFT accumulation results are shown; where, in case 3, the red signal is the flat FFT data, and the blue signal is the peak FFT data.

[0047] Fig.12 Peaked and flat FFT raw mean distributions in Case 3 are shown.

[0048] Fig.13 A comparison between peaked and flat data in Case 3 is shown.

[0049] Fig.14 Peak and flat original FFT accumulation results are shown; where, in case 4, the red signal is flat FFT data and the blue signal is peak FFT data.

[0050] Fig.15 The peaked and flat FFT raw mean distributions in Case 4 are shown.

[0051] Fig.16A comparison between peaked and flat data in Case 4 is shown.

[0052] Fig.17 A parturient's uterine contractions are shown.

[0053] Fig.18 : Point pt_4 is the time "12:04:30", and the end point of point pt_13 is the time; wherein ECG data of a period is given, and the pain of the parturient is predicted from 12:04:00 to 12:10:00, with a cycle of 10 seconds; g ratio =1.5,Alpha=1.

[0054] Fig.19 : Anova statistical analysis results of the parturient numbered 0512, p = 6.81633x10 -132 , where point pt_12 is at time "12:06:00", point pt_26 is at time "12:08:10", and the end point of point pt_33 is at time "12:09:20".

[0055] Fig. 20 : Comparison of peak and flat data of parturient No. 0512.

[0056] Fig.21 : Anova statistical analysis results of the parturient No. 0714, p=0.

[0057] Fig. 22 : Comparison of peak and flat data of parturient No. 0714.

[0058] Fig.23 An analysis architecture diagram of the present invention is provided. DETAILED DESCRIPTION

[0059] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0060] The present invention provides a novel analgesic system and method for managing pain. According to the system and method, biomedical data is monitored for use in coordinating the delivery of pain management devices and methods to have an effect consistent with the pain cycle.

[0061] The present invention provides a real-time pain detection system, which includes: a device for acquiring biomedical signals related to pain of a subject in need; a computing device, which is used to convert the biomedical signals acquired within a given time period into signal data for measuring pain, analyze the data to divide it into two or more models (at least including a pain model defined by data showing a peak profile and a pain-free model defined by data showing a flat profile), and then measure the pain state of the subject based on the results of the analysis; a processing device, which is used to generate a pain index based on the needs or feelings of the subject using the results of the analysis; and a display, which displays the pain state of the subject.

[0062] On the other hand, the present invention provides a method for pain management of an object, comprising acquiring biomedical signals related to the object's pain, converting the biomedical signals acquired within a given time period into signal data for measuring pain, analyzing the data to divide them into two or more models (including at least a pain model defined by data displaying a peak profile and a pain-free model defined by data displaying a flat profile), and then measuring the object's pain state based on the results of the analysis, and a processing device for generating a pain index based on the needs or feelings of the object using the results of the analysis.

[0063] In another aspect, the present invention provides a pain management system comprising: a real-time pain detection system of the present invention; an analgesic system for delivering an analgesic or performing a pain relief method; and means for communicating between the real-time pain detection system and the analgesic system. The system is capable of effectively predicting pain in real time to activate the analgesic system in advance of pain, so that the administration of an analgesic or pain relief method is based on the time and / or intensity of pain.

[0064] In operation, a monitoring device is used to collect biomedical data related to pain, which is clinically relevant data related to pain. A computing device is provided to analyze the collected biomedical signals and then transmit the biomedical data to establish a pain model. The pain model can be defined by an algorithm for determining data such as pain onset, pain frequency, pain duration, pain intensity, time history of pain cycles, etc. The biomedical data related to pain is then used to determine the time required to deliver an analgesic so that the effectiveness of the analgesic is consistent with the pain. Based on the determined analgesic delivery time, the analgesic delivery device is activated to deliver the analgesic to the patient.

[0065] In the present invention, an analysis architecture diagram is provided, such as Fig.23 As shown:

[0066] Extraction of clinical biomedical data

[0067] Clinicians currently use a variety of techniques to extract biomedical data related to pain, which can be used in accordance with the present invention to build pain models (including, for example, pain onset, pain frequency, pain duration, etc.).

[0068] In one embodiment, the detection of pain-related biomedical data used with the analgesic system of the present invention can be performed using conventional methods or measurements. In the present invention, any available technology that can analyze and monitor physiological signals related to pain includes, but is not limited to, blood volume pulse (BVP), electrocardiogram (ECG), and skin conductivity level (SCL). These signals reflect the activity level of the autonomic nervous system, which is related to the secretory activity of the myocardium and visceral organs.

[0069] In the present invention, the biomedical data related to pain may be any clinical data, including data used to determine the absence, presence, and intensity of pain (Cruccu et al., 2010; et al., 2011), such as Numeric Pain Rating Scales (NPRS), Verbal Rating Scales (VRS), and Visual Analog Scales (VAS), (Frampton and Hughes-Webb, 2011). These self-report scales have been well used and validated in cancer patients (Caraceni et al., 2002). In addition, the McGill Pain Questionnaire (MPQ) and the Brief Pain Inventory are also used to assess a wider range of pain perceptions in multidimensional scales (Frampton and Hughes-Webb, 2011). Self-described pain provides an important clinical reference indicator and has been shown to be an effective method for appropriately treating patients suffering from pain in most cases (Brown et al., 2011). In addition, pain assessments that use recognition and prediction of human behavior, including vocalizations (Puntillo et al., 2004), body movements (Young et al., 2006), and facial expressions (Lucey et al., 2011; Kaltwang et al., 2012; Irani et al., 2015) can also be used. Although behavioral methods exist, they may not be applicable to individuals with paralysis or other movement disorders that affect behavior. By observing an individual's face, a large number of features related to affective states, including pain states, can be extracted. Measurements focusing on multiple biophysiological signals can also be used, such as heart rate variability (De Jonckheere et al., 2010, 2012; Faye et al., 2010; Logier et al., 2010), skin conductance or electrodermal activity (Harrison et al., 2006; Treister et al., 2012), electromyography (Oliveira et al., 2012), electroencephalography (Nir et al., 2010; Huang et al., 2013), and functional magnetic resonance imaging (fMRI), (Marquand et al., 2010; Brown et al., 2011). Pain assessment methods implemented through multimodal signals have been confirmed to be very effective, and some are even significantly better than single signal modes (Werner et al., 2014; et al., 2015). Pain intensity is quantitatively measured from multiple physiological signals obtained from wearable sensors. It can also include automatic identification of pain intensity from physiological signals such as electromyography (EMG) and body movement combined with Support Vector Machines (SVM) and Random Forests (RF) as classifiers to identify three pain intensities (Olugbade et al., 2015). Kachele et al. used electromyography (EMG), skin conductance level (SCL), and electrocardiogram (ECG) combined with unsupervised and semi-supervised learning to build a personalized continuous pain intensity recognition system (Kachele et al., 2016).

[0070] Establishment of pain model

[0071] The system of the present invention includes a computing device for analyzing collected biomedical signals to define pain and pain-free models (e.g., ECG data). In a preferred embodiment, the computing device for defining pain and pain-free models includes a device for receiving and analyzing sensor inputs to accurately determine pain onset, pain frequency, pain duration, pain intensity, pain cycle history time, etc. The system of the present invention may include a graphical user interface to display biomedical data related to pain, pain models, and to enable user interaction.

[0072] In one embodiment, the system of the present invention further comprises an intelligent system that can use the biomedical data related to pain generated by the computing device to provide biomedical clinical data to determine the onset of a pain cycle. In addition, the intelligent system can be configured in the analgesic system of the present invention to provide real-time assistance in providing pain management support (i.e., the type of analgesic to be administered, the likelihood of delivery within a period of time, etc.).

[0073] According to the present invention, the computing device is preferably a digital signal processor, which can: (1) automatically, accurately, and in real time extract biomedical signals, such as ECG signals, from sensor inputs; (2) evaluate the quality of biomedical data provided by the processor in view of environmental noise; and (3) determine the onset of pain, pain frequency, pain duration, pain intensity, etc. based on the biomedical data.

[0074] The biomedical signals (i.e., ECG signals, etc.) collected according to the present invention are transmitted from the data extraction device to the computing device for signal processing. The computing device may also be responsible for the maintenance of the collected biomedical data and the maintenance of the analgesia system itself. The computing device may also detect user inputs via user interface devices known to those skilled in the art and operate according to the user inputs.

[0075] In certain embodiments, the computing device includes a memory capacity large enough to perform operations according to the algorithm of the present invention. The memory capacity of the present invention can support the loading of computer program code via a computer readable storage medium, wherein the program includes source code for performing the operation algorithm of the present invention. Alternatively, the memory capacity can support direct programming of the CPU to perform the operation algorithm of the present invention. Standard bus configurations can transfer data between the CPU, memory, ports, and any communication devices.

[0076] Communication devices such as wireless interfaces, cable modems, satellite links, microwave repeaters, and traditional telephone modems can transmit biomedical data from the computing device to the provider via the network. Networks that can be used to transmit biomedical data include, but are not limited to, local area networks, intranets, and the open Internet.

[0077] According to the present invention, a novel obstetric analgesia system is provided which includes a patient controlled analgesia (PCA) function which enables the patient to automatically administer analgesics after signaling the onset of pain.

[0078] In a common form of patient-controlled analgesia (PCA) for the present invention, a mechanical device is provided to the subject, which includes a reservoir and a patient-operable pump. The pump dispenses increasing doses of analgesic from the reservoir into the subject's intravenous (IV) system based on patient demand. The device may also include a lockout interval feature that prevents the patient from re-administering for a period of time to ensure that over-medication is avoided.

[0079] Systems for pain management according to the present invention include analgesic systems, which include, but are not limited to: intravenous, subcutaneous, intramuscular, intra-articular, parenteral, peritoneal, intranasal, inhalation, oral, rectal, intravaginal, topical, nasal, ophthalmic, local, transdermal, sublingual, epidural, intrathecal delivery of analgesics (e.g., analgesics, anesthetics, sedatives, tranquilizers, or anesthetic antagonist combinations) or electrical stimulation of spinal nerves (e.g., transcutaneous electrical nerve stimulation (TENS)).

[0080] According to the present invention, analgesics can be automatically delivered based on established contraction data. In certain embodiments, analgesics that cause sensory loss are automatically delivered by any of the following methods: local block, paracervical block, pudendal block, epidural anesthesia and analgesia, spinal anesthesia and analgesia, and inhalation anesthesia.

[0081] The invention is illustrated in the following embodiments and examples.

[0082] The symbols are defined as follows:

[0083] TPeak(i): time of peak in uterine contraction graph;

[0084] TFlat(j): time of flattening in uterine contraction graph;

[0085] S_pt: 10000 sampling points, total duration is 20 seconds;

[0086] ECG(n): ECG recording of a woman in labor;

[0087] F: frequency = 1000ms / 512 sampling points;

[0088] Fcut: Cutting frequency.

[0089] 1. Implementation of the pain model

[0090] 1.1 Data Extraction

[0091] The uterine contraction of each parturient is compared with the ECG pattern during labor. The peak time in the uterine contraction graph is marked as TPeak(i). The flat time in the uterine contraction graph is marked as TFlat(i). When the parturient's uterine contraction is at peak or flat time T(i), we capture 10,000 ECG signals during T(i). The ECG sampling frequency is 512Hz, and the total 10,000 pts is 20 seconds. We mark the ECG at the peak of uterine contraction as ECG Peak (i) The ECG when the uterus contracts slowly is marked as ECG Flat (i).

[0092] 1.2 Data processing

[0093] First, we collect 10,000 ECG(i) signal points. Second, we perform a Fast Fourier Transform (FFT) and obtain the resulting FFT Peak (i) FFT Flat (i). Third, set the “cutting frequency” F cut We focused on the larger differences between the peaky and flat FFT results and compared their pain statistics. Finally, we divided the EEG data into two groups, “peaky” and “flat,” and calculated the mean and standard deviation for each.

[0094] In the example of four parturients, the mean values ​​of each "peak" and "flat" showed a significant difference. The "peak" and "flat" groups were tested using ANOVA and a strong significant difference was obtained.

[0095] 1.3 Model Implementation

[0096] Based on the following assumptions: the highest peak of the uterine contraction graph is the time of pain, while the lowest flat point of the uterine contraction graph is the time of pain-free. Peak (i) and FFT to define the painless model Flat From the results of (i), it is clear that there is a big difference between the two FFT grouping signals. This means that an effective frequency domain analysis diagram can be obtained by performing FFT transformation on the 20-second ECG signal. We can compare the FFT (i) data with the flat general graph and determine the occurrence of pain.

[0097] We calculated the FFT Peak (i) and FFT Flat (i) The mean and standard deviation of the average value distribution. From the mean value distribution, we use one standard deviation as the observation range of the effective pain model. In order to improve the difference, speed up the calculation and reduce the judgment time, we calculated the first-order differential of the peak (defined as the pain model) and flat (defined as the pain-free model) standard deviation distribution. The variance of each frequency is shown, and the threshold of the time of pain occurrence can be set in the experience data.

[0098] Randomly collect ECG signals from a patient, calculate their FFT(i1) distribution, and then calculate FFT(i1) and FFT Flat Then, the difference is calculated and the FFT Flat (i) The ratio of the standard deviation. If the difference is large, exceeding 200% for the low frequency or 100% for the full frequency accumulation, then this ECG (i) can be determined to be within the pain duration. The results of the pain module analysis are as follows Figure 1 shown.

[0099] 2. Operation function

[0100] 2.1 Extraction of peak uterine contractions

[0101] a. Extract ECG(n) at TPeak(i) and label it as n(TPeak(i))

[0102] b. Get pain data showing a peak profile:

[0103]

[0104] c. Calculate ECG by Fast Fourier Transform (FFT) Peak (i) Frequency domain data, defined as FFT Peak (i) From FFT Peak In the distribution of (i), we can find the difference in pain between pain and no pain.

[0105] d. Keep the valid data from -F_cut to F_cut, and the constant value of the sampling point.

[0106]

[0107] e.FFT Peak (i) The absolute value represents the energy at the time of occurrence

[0108] Figure 2 shows the Fast Fourier Transform of the ECG, labeled FFT Peak (i).

[0109] 2.2 Extraction of gentle uterine contractions

[0110] a. Extract ECG(n) at TFlat(i) and label it as n(TFlat(i))

[0111] b. Obtain pain data during slow time:

[0112]

[0113] c. Calculate ECG by Fast Fourier Transform (FFT) Flat (i) Frequency domain data, defined as FFT Flat (i) From FFT Flat (i) In the distribution, we can find the difference in pain between pain and no pain.

[0114] d. Keep the valid data from -F_cut to F_cut, and the constant value of the sampling point.

[0115]

[0116] e.FFT Flat (i) The absolute value represents the energy at the time of occurrence

[0117] Figure 3 shows the Fast Fourier Transform of the ECG, labeled FFT Flat (i).

[0118] f. Anova1 statistical test

[0119] p=Anova1(|FFT Peak (i) |;|FFT Flat (i)|, category)

[0120] like Figure 3 As shown, the signals were divided into two groups with a p-value less than 0.05.

[0121] 2.3 Establishing the final model and threshold

[0122] FFT(k) represents continuous ECG data, and its periodic sampling is S_pt. In order to enhance the characteristics of FFT(k) distribution, we use the first-order differential as FFT'(k). Flat (k) to obtain the pain index g(k), where α can be set by the subject's personal feeling of pain:

[0123]

[0124] Judgment function:

[0125] Where X and Y are defined as “pain” and “no pain” respectively; and

[0126] Among them, g can be easily set from each birth history record ratio and normal Threshold.

[0127] In one embodiment of the present invention, the values ​​of X and Y are defined as 0 and 1, respectively, representing "most painful" and "no pain". In another embodiment of the present invention, the measurement of pain can use more than two values. For example, the values ​​of X and Y are defined as 5 (maximum pain) and 0 (no pain), respectively, so the pain level can be expressed as 5 (maximum pain), 4 (more painful), 3 (moderate pain), 2 (mild pain), 1 (mild pain), and 0 (no pain).

[0128] In one embodiment of the present invention, Figure 4 2 shows ECG data showing a peak contour (Peak) and a flat contour (Flat), including an ECG in flat uterine contraction test data (upper figure) and an ECG in peak uterine contraction test data (lower figure).

[0129] 2.4 Test results of four pregnant women

[0130] Scenario 1

[0131] The peak and flat original FFT accumulation results are as follows Figure 5 As shown in the figure, it includes the flat FFT data shown in red and the peak FFT data shown in blue. The peak and flat FFT raw average distributions are shown in the figure. Figure 6 shown.

[0132] Anova statistical analysis was performed on case 1, p = 1.7961x10 -47 . Figure 7 A comparison between the peaked data and the flat data in Case 1 is given in FIG.

[0133] Scenario 2

[0134] The peak and flat original FFT accumulation results are as follows Figure 8 As shown in the figure, it includes the flat FFT data shown in red and the peak FFT data shown in blue. The peak and flat FFT raw average distributions are shown in the figure. Fig. 9 shown.

[0135] Anova statistical analysis was performed on case 2, p = 6.81633x10 -132 . Fig.10 A comparison between the peaked data and the flat data in Case 2 is given in FIG.

[0136] Scenario 3

[0137] The peak and flat original FFT accumulation results are as follows Fig.11 As shown in the figure, it includes the flat FFT data shown in red and the peak FFT data shown in blue. The peak and flat FFT raw average distributions are shown in the figure. Fig.12 shown.

[0138] Anova statistical analysis was performed on case 3, p = 3.86697x10 -31 . Fig.13 A comparison between the peaked data and the flat data in Case 3 is given in FIG.

[0139] Scenario 4

[0140] The peak and flat original FFT accumulation results are as follows Fig.14 As shown in the figure, it includes the flat FFT data shown in red and the peak FFT data shown in blue. The peak and flat FFT raw average distributions are shown in the figure. Fig.15 shown.

[0141] Anova statistical analysis was performed on case 4, p=0. Fig.16 A comparison between the peaked data and the flat data in Case 4 is given in FIG.

[0142] Real-time detection of patients’ pain signals through continuous ECG data

[0143] We continuously collect ECG data from the machine. Each S_pt cycle data can transfer ECG data from time domain to frequency domain. We can get the pain index to estimate the pain level of the patient. In practical applications, we can set S_pt / 2 as the period of monitoring rate.

[0144] For example, we set S_pt = 10240 as periodic data in each calculation sample, and the sampling rate of the heart rate monitor is 512Hz. Therefore, we can set the period to S_pt / 2 = 5120, which means calculating the pain trend within 10 seconds. This parameter needs to meet enough heart rate ECG samples and the observation time is not too long in the same duration. By comparing the peak and flat results in g(k), we can easily set g ratio As well as the alpha value, we can usually set g ratio =1.5,alpha=1.

[0145] Although this specification contains many details, these details should not be interpreted as limitations on the scope of the invention or the scope of protection that can be claimed, but should be interpreted as descriptions of features specific to specific embodiments or examples of the invention. Certain features described in the context of separate embodiments or examples in this specification may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment or example may also be implemented separately in multiple embodiments or examples or in any appropriate suitable subcombination.

Claims

1. A real-time pain detection system, comprising: Means for acquiring pain-related biomedical signals in a subject; A computing device, which is used to convert the time-domain biomedical signal related to pain acquired in each given time period into frequency-domain signal data for measuring pain through frequency domain conversion, and analyze the relationship between the frequency-domain signal data and predetermined model data having the correlation between the frequency-domain biomedical signal and pain, wherein the predetermined model data includes at least a pain model defined by a peak profile in the frequency-domain data and a no-pain model defined by a flat profile in the frequency-domain data, the computing device generates a pain index using the result of the analysis, and determines the pain state of the subject based on the pain index; and a display that displays the pain state of the subject.

2. The real-time pain detection system according to claim 1, wherein: The biomedical signal comes from an electrocardiogram (ECG).

3. A pain management system, comprising: The real-time pain detection system according to claim 1 or 2; and an analgesic system for automatically delivering medicine to the subject according to the pain index and the pain state.

4. The pain management system of claim 3, further comprising: Means for communicating between the real-time pain detection system and the analgesia system.

5. The pain management system according to claim 4, wherein: The analgesic system is used to administer analgesics intravenously, transdermally, transmucosally, or intramuscularly.

6. A method for detecting pain, comprising: Converting a pain-related time-domain biomedical signal of a subject acquired in each given period of time into frequency-domain signal data for measuring pain by frequency domain conversion; Analyzing the frequency domain signal data for a relationship with predetermined model data having a correlation between the frequency domain biomedical signal and pain, the predetermined model data comprising at least a pain model defined by a peak profile in the frequency domain data and a no-pain model defined by a flat profile in the frequency domain data; and A pain index is generated using the results of the analysis, and the pain state of the subject is determined based on the pain index.

7. The method according to claim 6, wherein: The biomedical signal is from an electrocardiogram (ECG).

8. The method according to claim 7, wherein the conversion comprises deriving a first-order differential Fast Fourier Transform (FFT') of the biomedical signal to obtain the frequency domain signal data, wherein the predetermined model data in the analysis includes FFT' model data, and The pain index (g(k)) was generated using the following formula: in, FFT'(k) is the first order differential of the continuous ECG data FFT(k), and α can be set by the subject's pain perception.

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