CN114786564A

CN114786564A - Real-time pain detection and pain management system

Info

Publication number: CN114786564A
Application number: CN202080075967.XA
Authority: CN
Inventors: 陈李魁; 沈子贵
Original assignee: Chengxi Technology Co ltd
Current assignee: Chengxi Technology Co ltd
Priority date: 2019-08-30
Filing date: 2020-08-31
Publication date: 2022-07-22
Also published as: TW202133800A; WO2021037260A1; EP4021286A1; EP4021286A4

Abstract

The invention provides a real-time pain detection system. The system comprises: means for acquiring a biomedical signal related to pain of a subject in need thereof; a calculation means for converting biomedical signals acquired within a given period of time into signal data for measuring pain, analyzing the data to separate into two or more models including at least a pain model defined by data showing a peak-like profile and a pain-free model defined by data showing a gentle profile, and further measuring a pain state of the subject based on a result of the analysis; processing means for generating a pain index in dependence on the result of the demand or sensory use analysis of the subject; and a display that displays a pain state of the subject.

Description

Real-time pain detection and pain management system

Cross Reference to Related Applications

Priority of U.S. provisional application No.62/894,532, filed on 30/8/2019, the entire contents of which are incorporated herein by reference if fully set forth herein.

Technical Field

The present invention relates to a system and method for real-time pain detection by analysis of physiological signals, and a pain management system using the system or method for real-time pain detection.

Background

Pain is considered an unpleasant emotional and sensory experience that may be associated with real or potential tissue damage. Pain is today 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 every patient learns the application of the word through previous experience with injuries. Therefore, it is difficult to objectively measure the degree of pain by a method of detecting a physiological condition or using a parameter related to a physiological condition.

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 depth of anesthesia (DOA) by measuring skin temperature or photoplethysmographic pulse pressure to define and analyze oscillatory patterns, or correlations between simultaneous oscillatory patterns measured at different physical locations, to obtain an index of depth of anesthesia.

U.S. patent No.6,685,649 provides a method of monitoring a patient's condition under anesthesia or sedation. The method focuses on and relies on analyzing a single parameter related to the cardiovascular system, in particular using the time interval between the continuous waveforms, the pressure from the continuous waveforms, the time rate from the continuous waveforms.

To date, physiological signals such as skin conductance, Blood Pressure (BP), Heart Rate (HR), Electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmography (PPG), temperature have been reported to be used to determine 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 painless classification performance compared to discrimination using any single signal alone (Guignard, Clinical and aesthetology, Vol. 20, phase 1: page 161-.

A pain index or nociception index is reported to be formed using a set of physiological characteristics to determine the status of a patient. For example, U.S. patent No.9,498,138B2 provides an index referred to as "PMD 200" and relates to a system and method for monitoring by performing a multi-dimensional analysis of a plurality of physiological signals to generate an index.

It is considered unacceptable to have a person who is subjected to safe intervention experience untreated severe pain under the care of a physician. Pain management should be provided for as long as there is a medical indication. Any pain management technique of the patient must be considered. Accurate prediction of pain intensity can provide valuable insight in situations where it can be effectively used to ultimately determine the location of pain and formulate a reasonable treatment accordingly. Thus, pain prediction may improve the quality of daily life of patients in health-related fields such as rehabilitation, home health care and emergency medical services.

Us patent No.7,924,818 discloses an obstetrical analgesic system for providing short-acting analgesics for pain management during labour, which system is capable of predicting contractions effectively in real time to coordinate the administration of the analgesic so that the peak effectiveness of the analgesic coincides with intermittent pain during labour. However, in addition to the fact that pain during labor is associated with contractions, pain cannot be managed effectively by automated analgesic administration because the timing and intensity of pain cannot be predicted.

Analgesic Nociception Index (ANI) based on heart rate variability is reported to reflect varying degrees of acute pain. The objective of this study was to compare the Analgesic Nociceptive Index (ANI) score to a numerical rating scale (NRS, 0-10) based on a self-assessment of pain in the recovery room. A study of the Analgesic Nociceptive Index (ANI) is disclosed to evaluate as a new parameter of acute post-operative pain. However, it was concluded that the Analgesic Nociceptive Index (ANI) does not reflect the different states of acute post-operative pain measured on a numeric scale (NRS) after adult sevoflurane-based general anesthesia (Ledowski et al, Br. J. Anaesth., Vol. 111, 4: pages 627-9, 2013).

Accordingly, 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 assessment of pain whenever a patient's self-score for pain (e.g., sedated patient, very young child, person with learning disability) is not readily available, and a system and method for managing pain that is capable of determining the time of administration of analgesic drugs before pain begins.

Disclosure of Invention

Accordingly, the present invention provides a system and method for detecting or monitoring pain in a subject in real time after analysis and conversion using biomedical signals (e.g., heart rate).

In one aspect, the invention provides a real-time pain detection system comprising: means for acquiring a biomedical signal related to pain of a subject in need thereof; a calculation means for converting biomedical signals acquired within a given period of time into signal data for measuring pain, analyzing the data to divide into two or more models including at least a pain model defined by data showing a peak-like profile and a pain-free model defined by data showing a gentle profile, and further measuring a pain state of the subject based on a result of the analysis; processing means for generating a pain index from the subject's need or sensation, using the results of the analysis; and a display that displays a pain state of the subject.

In another aspect, the invention provides a method of pain management in a subject, comprising: obtaining a biomedical signal related to pain of the subject, converting the biomedical signal obtained in a given period into signal data for measuring pain, analyzing the data to divide into two or more models including at least a pain model defined by data showing a peak-like profile and a pain-free model defined by data showing a gentle profile, and further measuring a pain state of the subject based on the result of the analysis, and processing means for generating a pain index according to the need or feeling of the subject using the result of the analysis.

In another aspect, the invention provides a pain management system comprising: the 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 analgesia system. The system is capable of predicting pain effectively, in real time, to activate the analgesic system prior to pain, thereby basing the analgesic administration or pain relief method on the time and/or intensity of the pain.

In a preferred embodiment of the invention, the biomedical signal is a signal related to heart rate, including but not limited to Heart Rate (HR), Pulse Rate (PR), Heart Rate Variability (HRV), and Electrocardiogram (ECG).

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

In certain embodiments of the invention, the analgesic may be a drug or a highly titratable, rapidly and predictably acting, and biologically active for a short duration.

In other embodiments, an analgesic system is provided for administering an analgesic, or electrical stimulation (e.g., Transcutaneous Electrical Nerve Stimulation (TENS) unit) or other method of blocking pain sensation, which is delivered/applied to a patient early to have an effect in the pain portion of the pain cycle.

According to the invention, a method of monitoring pain in a subject, comprises: biomedical signals related to pain are acquired in the process of predicting pain to build a model of the patient's pain and an optimal time for analgesic administration. In a related embodiment, the system of the present invention monitors time and intensity based on the collected biomedical signals.

In the present invention, the biomedical signal used may be any physiological signal related to heart rate. Currently available techniques 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 heart muscle and internal organs.

The term "heart rate" or "HR" or "pulse" as used herein refers to the heart rate (bpm) measured by the contractions (beats) of the heart per minute.

The term "heart rate variability" or "HRV" as used herein refers to the physiological phenomenon of variation in the time interval between heartbeats, which can be measured by variation in the heart beat interval.

The Blood Volume Pulse (BVP) signal comes from a photoplethysmography (PPG) sensor, which emits infrared light through tissue to monitor blood volume in the microvasculature and arteries. Thus, the change in BVP amplitude reflects the transient sympathetic activation. Most PPG sensors can be placed anywhere on the body, the finger being the most common location to record BVP signals.

An Electrocardiogram (ECG) is an electrophysiological signal associated with electrical activity in the sinoatrial node, reflecting cardiovascular activity. In addition, the response of the ECG to external stimuli (e.g., pain stimuli and stress) can produce large variability in the physiological signal of a given subject. Thus, we can use the ECG signal to extract general information about the pain state or intensity.

In one embodiment of the invention, when ECG data is initially reliably detected, the ECG data can serve as an effective precursor to defining a pain and pain-free model for coordinated delivery of an analgesic to match the pain relief capabilities of the analgesic to the pain cycle.

In an embodiment of the invention, the pain management system further comprises means for delivering a short-acting analgesic to the subject prior to the pain, such that the analgesic potency of the analgesic peaks with the pain. For example, the pain management system includes means for providing an audible or visual warning signal to notify.

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

In another embodiment of the invention, a pain management system is provided having an automatic analgesic delivery feature for automatically delivering an analgesic and/or adaptively changing the concentration of the analgesic based on a monitored biomedical signal related to heart rate (e.g., via a monitored ECG). In a related embodiment of the invention, the pain management system can determine the extent of pain and alter the concentration of the analgesic based on the data. The "degree of pain" refers to the time and/or intensity of pain, and can be determined by either: (1) the current ECG; (2) the time history of the ECG; or (3) by a patient input system, and/or by some combination of (1) - (3) above, depending on the subject's level of pain, as a function of the subject's needs or sensations.

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

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 analgesic and includes a respiratory monitor, such as a pulse oximeter, to monitor the oxygen saturation of the patient to ensure safety. In a related embodiment, the pain management system preferably controls the delivery of the analgesic while monitoring the clinical condition of the patient with a pulse oximeter. In another related embodiment, the analgesic system is preferably a controlled transdermal, transmucosal, or intramuscular administration system.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.

Drawings

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

In the drawings:

fig. 1 provides a flow chart for a pain monitoring system according to the present invention.

FIG. 2 provides a pain module in which the blue signal is FFT_Flat(i) Standard deviation distribution, red signal FFT_Flat(i) First order of the standard deviation distribution.

FIG. 3 shows a fast Fourier transform of an ECG in one embodiment of the invention, labeled FFT_Flat(i)。

FIG. 4 shows the results of ECG spiking and flattening test data; wherein, the upper graph is ECG of the gentle uterine contraction test data; the lower panel is the ECG at peak uterine contraction test data.

FIG. 5 shows the raw FFT accumulation results with peaks and plateaus; here, in case 1, the red signal is the gentle FFT data, and the blue signal is the peak-like FFT data.

Fig. 6 shows the peak-like and flat FFT original mean distribution in case 1.

Fig. 7 shows a comparison between the peak-like and flat data in case 1.

FIG. 8 shows a peak-like and flat raw FFT accumulation result; here, in case 2, the red signal is the gentle FFT data, and the blue signal is the peak-like FFT data.

Fig. 9 shows the peak-like and flat FFT original mean distribution in case 2.

Fig. 10 shows a comparison between the peak-like and flat data in case 2.

FIG. 11 shows the raw FFT accumulation results with peaks and plateaus; here, in case 3, the red signal is the gentle FFT data, and the blue signal is the peak-like FFT data.

Fig. 12 shows the peak-like and flat FFT raw average distribution in case 3.

Fig. 13 shows a comparison between the peak-like and flat data in case 3.

FIG. 14 shows a peak-like and flat raw FFT accumulation result; here, in case 4, the red signal is the gentle FFT data, and the blue signal is the peak-like FFT data.

Fig. 15 shows the peak-like and flat FFT original mean distribution in case 4.

Fig. 16 shows a comparison between the peak-like and flat data in case 4.

Fig. 17 shows uterine contractions of a parturient.

FIG. 18 is a schematic view of: the point pt _4 is time "12: 04: 30", and the end point of the point pt _13 is time; wherein ECG data are given over a period of time and maternal pain is predicted from 12:04:00 to 12:10:00 with a cycle of 10 seconds; g is a radical of formula_ratio＝1.5，Alpha＝1。

FIG. 19 is a schematic view of: anova statistical analysis of maternal woman numbered 0512, p is 6.81633x10^-132Where point pt _12 is time "12: 06: 00", point pt _26 is time "12: 08: 10", and the end point of point pt _33 is time "12: 09: 20".

FIG. 20: peak and flat data for parturient numbered 0512 were compared.

FIG. 21: anova statistical analysis of parturient No. 0714, p ═ 0.

FIG. 22: peak and flat data for parturient woman No. 0714 were compared.

FIG. 23 provides an analytical architecture diagram of the present invention.

Detailed Description

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.

The present invention provides a novel analgesic system and method for managing pain. In accordance with the present system and method, biomedical data is monitored for coordinating the delivery of pain management device methods to have an effect consistent with a pain cycle.

The present invention provides a real-time pain detection system, comprising: means for acquiring a biomedical signal related to pain of a subject in need thereof; a calculation means for converting biomedical signals acquired within a given period of time into signal data for measuring pain, analyzing the data to separate into two or more models (including at least a pain model defined by data showing a peak-like profile and a pain-free model defined by data showing a gentle profile), and further measuring a pain state of the subject based on a result of the analysis; processing means for generating a pain index according to the subject's needs or sensations using the results of the analysis; and a display that displays a pain state of the subject.

In another aspect, the invention provides a method for pain management of a subject, comprising acquiring a biomedical signal related to pain of the subject, converting the biomedical signal acquired within a given period of time into signal data for measuring pain, analyzing the data to separate into two or more models (including at least a pain model defined by data showing a peak-like profile and a pain-free model defined by data showing a gentle profile), and further measuring the pain state of the subject based on the result of the analysis, processing means for generating a pain index according to the need or sensation of the subject using the result of the analysis.

In another aspect, the invention provides a pain management system comprising: the 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 analgesia system. The system is capable of predicting pain effectively in real time to activate the analgesic system prior to pain, thereby basing the analgesic administration or pain relief method on the time and/or intensity of the pain.

In operation, a monitoring device is used to collect biomedical data relating to pain, which is clinically relevant data relating to pain. A computing device is provided to analyze the collected biomedical signals and then transmit the biomedical data to build a pain model. The pain model may be defined by algorithms for determining data such as pain onset, pain frequency, pain duration, pain intensity, time history of pain cycles, etc. The biomedical data relating to the pain is then used to determine the time required to deliver the analgesic such that the effectiveness of the analgesic matches the pain. Based on the determined analgesic delivery time, the analgesic delivery device is activated to deliver the analgesic to the patient.

In the present invention, an analysis architecture diagram is given, as shown in fig. 23:

extraction of clinical biomedical data

Clinicians may currently extract biomedical data related to pain using a variety of techniques that may be used in accordance with the present invention to build pain models (including, for example, pain onset, pain frequency, pain duration, etc.).

In one embodiment, the detection of pain-related biomedical data for use with the analgesic system of the present invention can be performed using conventional methods or measurements. In the present invention, any available technique that can analyze and monitor physiological signals associated with pain includes, but is not limited to, Blood Volume Pulse (BVP), Electrocardiogram (ECG), and Skin Conductance Level (SCL). These signals reflect the activity level of the autonomic nervous system, which is related to the secretory activity of the heart muscle and internal organs.

In the present invention, the biomedical data relating to pain may be any clinical data, including data for determining the absence, presence and intensity of pain (crucu et al, 2010;

et al 2011), such as the numerical Pain Scales (NPRS), language Scales(Verbal Rating Scales, VRS), and Visual Analog Scales (VAS), (Frampton and Hughes-Webb, 2011). These self-reported scales were well applied and validated in cancer patients (Caraceni et al, 2002). In addition, the McGill Pain Questionnaire (MPQ) and the abbreviated Pain scale are also used to assess the more widespread perception of Pain on the multidimensional scale (Frampton and Hughes-Webb, 2011). Self-described pain, in turn, provides an important clinical reference and has proven to be an effective method of adequately treating pain-suffering patients in most cases (Brown et al, 2011). Furthermore, pain assessments that recognize and predict human behavior, including vocalization (Puntilo et al, 2004), physical movement (Young et al, 2006), and facial expression (Lucey et al, 2011; Kaltwang et al, 2012; Irani et al, 2015) may 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 the face of an individual, a number of features related to emotional states, including pain states, can be extracted. Measurements focused on a variety of biophysical signals may 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 (functional magnetic resonance imaging, fMRI), (marquard et al, 2010; Brown et al, 2011). Pain assessment methods implemented by multi-modal signaling have been identified as very effective, some even significantly better than single-signaling modes (Werner et al, 2014;

et al 2015). Pain intensity is quantitatively measured from a variety of physiological signals obtained from wearable sensors. It may also include automatic identification of pain intensity from physiological signals, such as Electromyography (EMG) and body motion and Support vector machine (Support Ve)Vector Machines, SVM) and Random Forest (RF) were combined as classifiers to identify three pain intensities (Olugbade et al, 2015). Kachele et al used Electromyography (EMG), Skin Conductance Level (SCL), and Electrocardiogram (ECG) in combination with unsupervised and semi-supervised learning to build a personalized continuous pain intensity recognition system (Kachele et al, 2016).

Establishment of pain model

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

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

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

The biomedical signals (i.e., ECG signals, etc.) collected in accordance with 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 as well as the maintenance of the analgesia system itself. The computing device may also detect and operate in accordance with user input via user interface devices known to those skilled in the art.

In certain embodiments, the computing device includes a memory capacity large enough to perform the operations of the algorithm according to the present invention. The memory capacity of the present invention may support the loading of computer program code via a computer readable storage medium, where the program comprises source code for executing the operational algorithms of the present invention. Alternatively, the memory capacity may support programming the CPU directly to perform the operational algorithms of the present invention. The standard bus configuration may transfer data between the CPU, memory, ports, and any communication device.

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

In accordance with the present invention, a novel obstetric analgesia system is provided that includes a Patient Controlled Analgesia (PCA) function that enables a patient to automatically administer an analgesic following the communication of a signal relating to the onset of pain.

In a common form of Patient Controlled Analgesia (PCA) for use in the invention, the subject is provided with a mechanical device comprising a reservoir and a patient operable pump. The pump dispenses incremental doses of pain medication from the reservoir into the subject's Intravenous (IV) system according to 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-dosing is avoided.

A system for pain management according to the present invention comprises an analgesia system including, but not limited to: intravenous, subcutaneous, intramuscular, intra-articular, parenteral, peritoneal, intranasal, inhalation, oral, rectal, intravaginal, topical, nasal, ocular, topical, transdermal, sublingual, epidural, intrathecal delivery of analgesics (e.g., analgesics, anesthetics, sedatives, tranquilizers, or combinations of anesthetic antagonists) or electrical stimulation of spinal nerves (e.g., transcutaneous electrical nerve stimulation, TENS)).

In accordance with the present invention, pain medication can be automatically delivered based on established contraction data. In certain embodiments, the analgesic causing the loss of sensation is delivered automatically by any one of the following methods: local block, paracervical block, pudendal block, epidural anesthesia and analgesia, spinal anesthesia and analgesia, and inhalation anesthesia.

The invention is illustrated in the following embodiments and examples.

The symbols are defined as follows:

TPeak (i): time of peak in uterine contraction profile;

tflat (j): time of depression in uterine contractility;

s _ pt: 10000 sampling points, total duration 20 seconds;

ecg (n): maternal ECG recordings;

f: the frequency is 1000ms/512 sampling points;

fcut: the shear frequency.

1. Realization of pain model

1.1 data extraction

Uterine contractions of each parturient were compared to the ECG pattern during their labor. The peak time in the uterine contraction profile is labeled as tpeak (i). The time of flattening in the uterine contraction profile is labeled tflat (i). When parturient uterine contractions were at peak or flat times t (i), we captured 10000 ECG signals during t (i). The ECG sampling frequency was 512Hz for a total of 10000pts of 20 seconds. We label ECG-at peak uterine contractions as ECG_Peak(i) Labeling ECG when uterine contractions are moderate as ECG_Flat(i)。

1.2 data processing

First, we collected 10000 ecg (i) signal points. Second, we perform a Fast Fourier Transform (FFT) and obtain the resulting FFT_Peak(i)、FFT_Flat(i) In that respect Third, setting the "cutting frequency" F_cutTo focus on the larger differences between the peaked and flat FFT results and compare their pain statistics. Finally, we split the EEG data into two groups, peak-like and flat, and calculate each mean and standard deviation.

In four maternal embodiments, each "peaky" and "flat" mean showed a significant difference. Both groups "peak-like" and "flat" were tested using ANOVA and obtained strong significant differences.

1.3 model implementation

Based on the following assumptions: the highest peak of the uterine contraction pattern is the time point of pain, while the lowest plateau of the uterine contraction pattern is the time of no pain. FFT from defined pain model_Peak(i) And FFT defining a pain free model_Flat(i) As a result, the two FFT packet signals are clearly very different. This indicates that an efficient frequency domain analysis plot can be obtained by FFT conversion of the 20 second ECG signal. We can compare the fft (i) data with the flat general graph and judge the onset of pain.

We have calculated the FFT_Peak(i) And FFT_Flat(i) Average and standard deviation of (d). From the mean distribution, we used one standard deviation as the observation for an effective pain model. To improve the variance, speed the calculation and reduce the decision time, we calculated the first differential of the peak (defined as pain model) versus the flat (defined as no pain model) standard deviation distribution. The variance of each frequency is shown and a threshold for the time of pain onset can be set in the experience data.

ECG signals are randomly acquired from a patient, the FFT (i1) distribution is calculated, and then FFT (i1) and FFT are calculated_Flat(i) The difference in average value between. Then, the difference and FFT are calculated_Flat(i) Ratio of standard deviation. This ecg (i) can be determined for the duration of pain if the low frequencies are very different by more than 200% or if the full frequencies accumulate by more than 100%. The results of this pain module analysis are shown in fig. 1.

2. Function of operation

2.1 extraction of Peaked uterine contractions

a. ECG (n) is extracted at TPeak (i) and labeled as n (TPeak (i))

b. Pain data showing peak profile were acquired:

c. computing ECG through Fast Fourier Transform (FFT)_Peak(i) Frequency domain data and defined as FFT_Peak(i) In that respect Slave FFT_Peak(i) In the distribution of (2), we can find out the difference in pain between pain and no pain.

d. And keeping valid data from-F _ cut to F _ cut and sampling the constant value of the sample point.

e.FFT_Peak(i) The absolute value represents the energy of the occurrence time

FIG. 2 shows the fast Fourier transform of ECG, labeled FFT_Peak(i)。

2.2 extraction of gentle uterine contractions

a. ECG (n) is extracted at TFlat (i) and labeled as n (TFlat (i))

b. Pain data were acquired over a flat time:

c. computing ECG by Fast Fourier Transform (FFT)_Flat(i) Frequency domain data and defined as FFT_Flat(i) In that respect Slave FFT_Flat(i) In distribution, we can find the difference in pain between pain and no pain.

e.FFT_Flat(i) The absolute value representing the energy at that time of occurrence

FIG. 3 shows the fast Fourier transform of ECG, labeled FFT_Flat(i)。

Anova1 statistical test

p＝Anova1(|FFT_Peak(i)|；|FFT_Flat(i) I, class)

As shown in fig. 3, the signals are divided into two groups with p values less than 0.05.

2.3 establishing the final model and thresholds

Fft (k) represents the continuous ECG data with periodic samples of S _ pt. To enhance the characteristics of the FFT (k) distribution, we consider the first order differential as FFT' (k). FFT '(k) and FFT'_Flat(k) A comparison is made to obtain a pain index g (k), where a can be set by the subject's personal perception of pain:

judging a function:

wherein X and Y are defined as "pain" and "no pain", respectively; and

wherein g can be easily set from each birth history_ratioAnd g_normalA threshold value.

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 invention, more than two values may be used for the measurement of pain. For example, values X and Y are defined as 5 (max pain) and 0 (no pain), respectively, and thus the degree of pain can be expressed as 5 (max pain), 4 (more pain), 3 (moderate pain), 2 (mild pain), 1 (less pain), and 0 (no pain).

In one embodiment of the present invention, ECG data showing a peak-like profile (peak-like) and a gentle profile (gentle) is shown in fig. 4, including ECG at gentle uterine contraction test data (upper panel) and ECG at peak uterine contraction test data (lower panel).

2.4 test results of four parturients

Case 1

The peak-like and flat raw FFT accumulation results are shown in fig. 5, including flat FFT data displayed in red and peak-like FFT data displayed in blue. The peak-like and flat FFT raw mean distribution is shown in fig. 6.

Anova statistical analysis was performed for case 1, p 1.7961x10^-47. A comparison between the peak-like data and the flat data in case 1 is given in fig. 7.

Case 2

The peak-like and flat raw FFT accumulation results are shown in fig. 8, including flat FFT data displayed in red and peak-like FFT data displayed in blue. The peak-like and flat FFT raw mean distribution is shown in fig. 9.

Anova statistical analysis was performed for case 2, p 6.81633x10^-132. A comparison between the peak-like data and the flat data in case 2 is given in fig. 10.

Case 3

The peak-and-flat raw FFT accumulation results are shown in fig. 11, including flat FFT data displayed in red and peak-shaped FFT data displayed in blue. The peak-like and flat FFT raw mean distribution is shown in fig. 12.

Anova statistical analysis was performed for case 3, p 3.86697x10^-31. A comparison between the peak-like data and the flat data in case 3 is given in fig. 13.

Case 4

The peak-and-flat raw FFT accumulation results are shown in fig. 14, including flat FFT data displayed in red and peak-shaped FFT data displayed in blue. The peak-like and flat FFT raw mean distribution is shown in fig. 15.

Anova statistical analysis was performed for case 4, with p ═ 0. A comparison between the peak-like data and the flat data in case 4 is given in fig. 16.

Real-time detection of pain signals of a patient by means of continuous ECG data

We continuously collect ECG data from the machine. Each S _ pt period data may transfer ECG data from the time domain to the frequency domain. We can obtain a pain index to estimate the pain level of the patient. In practical applications, we can set S _ pt/2 as the period of the monitoring rate.

For example, we set S _ pt — 10240 to periodic data in each calculation sample, and the sampling rate of the heart rate monitor is 512 Hz. Therefore, we can set the period to S _ pt/2 — 5120, which means that the pain trend is calculated within 10 seconds. This parameter needs to satisfy enough heart rate ECG samples and the observation time is not too long within the same duration. By comparing the peaked and flat results in g (k), we can easily set g_ratioAnd alpha value, in general we can set g_ratio＝1.5，alpha＝1。

While this specification contains many specifics, these should not be construed as limitations on the scope of the invention or of what may be claimed, but rather as descriptions of features specific to particular implementations or embodiments of the invention. Certain features that are described in this specification in the context of separate implementations or examples can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation or embodiment can also be implemented in multiple implementations or embodiments, respectively, or in any suitable subcombination.

Claims

1. A real-time pain detection system, comprising: means for acquiring a biomedical signal related to pain of a subject in need thereof; a computing means for converting biomedical signals acquired within a given period of time into signal data for measuring pain, analyzing the data to divide into two or more models including at least a pain model defined by data showing a peak-like profile and a pain-free model defined by data showing a gentle profile, and further measuring a pain state of the subject based on a result of the analysis; processing means for generating a pain index from the subject's needs or sensations, using the results of the analysis; and a display that displays a pain state of the subject.

2. A method of pain management of a subject, comprising: obtaining a biomedical signal related to pain of the subject, converting the biomedical signal obtained within a given period of time into signal data for measuring pain, analyzing the data to divide into two or more models including at least a pain model defined by data showing a peak-like profile and a pain-free model defined by data showing a gentle profile, and further measuring a pain state of the subject based on the result of the analysis, and processing means for generating a pain index according to the need or feeling of the subject using the result of the analysis.

3. A pain management system, comprising: the 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 analgesia system, the system being capable of predicting pain effectively in real-time to activate the analgesia system prior to pain, thereby basing the analgesic administration or pain relief method on the time and/or intensity of pain.

4. The system of any one of claims 1 to 3, wherein the biomedical signal is a signal related to heart rate, including but not limited to Heart Rate (HR), Pulse Rate (PR), Heart Rate Variability (HRV), and Electrocardiogram (ECG).

5. The system of claim 3, wherein the analgesic system is provided for administering a short-acting intravenous, transdermal, transmucosal, or intramuscular analgesic agent that provides improved pain relief timed to the pain cycle.

6. The system of claim 5, wherein the analgesic is a drug or a highly titratable, rapid and predictable onset, and short duration of biological activity.

7. A method of monitoring pain in a subject, comprising: obtaining a biomedical signal related to pain in a process of predicting pain to establish a model of pain in a patient and an optimal time for analgesic administration; in a related embodiment, the system of the present invention monitors time and intensity based on the collected biomedical signals.

8. The method of claim 7, wherein the biomedical signals used are any physiological signals related to heart rate, currently available technologies capable of analyzing and monitoring 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), which reflect the activity level of the autonomic nervous system, which is related to the secretory activity of the cardiac muscle and internal organs.

9. A pain management system, comprising: monitoring means for collecting biomedical signals relating to pain of a patient in need thereof; computing means for analyzing the collected biomedical signals for division into two groups comprising peak-like data and flat data, defining a pain model and a pain-free model, respectively; an analgesic system for delivering an analgesic or performing a pain relief method; and means for communicating between the monitoring system and the analgesic system, wherein the system is capable of predicting pain effectively in real time to activate the analgesic system prior to pain, thereby basing the analgesic administration or pain relief method on the time and/or intensity of the pain.

10. The method of claim 9, wherein the pain index g (k) is defined by the formula

Wherein FFT (k) represents continuous electrocardiogram data, the periodic sampling is S _ pt, and in order to enhance the characteristic of the distribution of FFT (k), the first order differential is defined as FFT ' (k) and FFT ' (k) is divided into two '_Flat(k) The comparison is performed so as to obtain a pain index g (k), wherein a can be set by the pain sensation of the subject.