Patent Application
20190343457
The present invention relates to systems and method for pain assessment and continuous monitoring of pain in patients, more specifically in patients who are unable to report pain.
Currently there is no way to objectively assess patients' pain especially from patients that have difficulties to communicate. We developed and tested a method and a smart tool that assesses pain by utilizing physiological parameters monitored by wearable devices. Although pain is believed to be an individual sensation relying on subjective assessment, objective assessment tool is needed for the wellbeing and improved care processes of noncommunicative patients. Such a tool also benefits other patient populations with more accurate medication and clinical-assisted treatment.
Pain is a severe problem for almost all patient groups. It is especially challenging for patients who can't self report their experience. Pain remains poorly managed partly because it is not recognized and assessed properly. In pain assessment, self-report is conventionally considered as the “gold standard”, which needs patients to answer questions verbally, in writing, with finger span or blinking eyes to yes or no questions. In the self-report method, the pain intensity is reported by the patient as numeric scales, which is based on two prerequisites: the patient's cognitive competence and unbiased communication. Although taken as the “gold standard”, this unidimensional model is questioned and debated for its oversimplification and limitation in several vulnerable patient populations. In practice, however, a broader range of non-self-report resources are observed by experienced clinicians to assess pain, for example, grimacing facial expression and body movements as behavioral observation and vital signs as physiologic monitoring. These non-self-report strategies are the theoretical basis and inspiration in developing an automatic pain assessment method to assist and even to replace the subjective self-report method.
In the past several decades, researchers and scientists have been trying to decode pain by monitoring electrical biosignals in different patient populations with a certain type of pain. So far, some correlation is found between electrical biosignals and pain but no individual one is sufficient enough to indicate the presence of pain due to the complexity of automatic nervous system and pain expression. As a consequence, alternative comprehensive model of pain from multiple electrical biosignals are explored. Existing models built in last five years mainly involve physiological pain indicators only from either healthy volunteers with a single type of experimental pain or patients in surgery and few has been applied in a different database for model validation. Furthermore, no model has yet been developed into an automatic pain assessment tool.
Conventional self-report has been taken as the “gold standard” in clinical pain assessment, which need answers from patients to questions or a questionnaire verbally, in writing, with finger span or blinking eyes to yes or no questions. However, there are patients unable to self-report due to cognitive, developmental, or physiologic issues, for example, preverbal toddlers and critically ill patients. The present invention discloses a precise and automatic tool for pain assessment by biosignals acquisition and analysis with wearable sensor device. Through monitoring behavioral and physiological signs, the appearance of pain and pain state are continuously tracked. The present invention additionally discloses the design of a wearable facial expression capture system and a data fusion method.
The present invention further provides automatic and continuous monitoring of pain intensity in patients who are otherwise unable to self-report. The real-time information of the continuous monitoring can be updated to a caregiver nearby or even in a remote location, so as to improve the nursing efficiency and optimize pain management in medication. The present invention includes a multi modal integration of a plurality of physiological and behavioral signals to accurately estimate the pain experienced by the patient. Compared with a single monitoring of physiological signals or behavioral signals, a fusion or integration of the two potential pain indicators contributes a more multidimensional and comprehensive model in automatic pain assessment. In addition, the integration of wearable devices ensures the long term monitoring in patients with lightweight and portable equipment.
In some aspects, the present invention features a facial expression capturing system for measuring pain levels experienced by a human. The system may comprise a flexible mask contoured to at least partially cover one side of the human's face, the mask having an eye recess or opening disposed between an elongated forehead portion of the mask, which is above the eye recess, and a cheek portion of the mask, which is beneath the eye recess; six sensor positions located on the mask such that two sensor positions are located laterally on the elongated forehead portion of the mask and the other four sensor positions located on the cheek portion of the mask and situated in a 2 by 2 arrangement; two or more sensors embedded in the mask, wherein each sensor occupies one of the sensor positions; a sensor node disposed on a lateral flap extending from the cheek portion of the mask, wherein the sensor node comprises a processing module and a transmitter; and connecting leads electrically coupling each of the two or more sensors to the sensor node. When the flexible mask is applied to partially cover one side of the human's face, the sensor positions align with pain-related facial muscles in the human's face and the sensors are configured to detect biosignals from underlying facial muscles such as, for example, the frontalis, corrugator, orbicularis oculi, levator, zygomaticus, and risorius. In some embodiments, the processing module is configured to (i) receive the biosignals from the plurality of sensors, (ii) analyze the biosignals to deduce facial expressions and monitor pain intensity levels experienced by the subject based on the deduced facial expressions, and (iii) transmit the pain intensity levels to a medical care provider, thus allowing the medical care provider to continually monitor the pain intensity levels experienced by the subject thereby providing effective and efficient pain management.
In some embodiments, the flexible mask is composed of polydimethyl silicone elastomer (PDMS). In other embodiments, the sensors (104) comprise Ag/AgCl electrodes. The electrodes may be disposed on an inner surface of the mask (102) such that the electrodes are directly contacting skin when the mask is placed on the human's face.
In one embodiment, the system may include two sensors, where a first sensor occupies a distal-most sensor position located on the forehead portion of the mask, and a second sensor occupies a first row and first column of the 2 by 2 arrangement in the cheek portion of the mask. In a preferred embodiment, the first sensor can detect biosignals from a corrugator facial muscle and the second sensor can detect biosignals from a zygomatic facial muscle.
In another embodiment, the system may comprise five sensors, where a first sensor and a second sensor occupy the two sensor positions on the forehead portion of the mask, a third sensor and a fourth sensor occupy the sensor positions at a first row of the 2 by 2 arrangement in the cheek portion of the mask, and a fifth sensor occupies the sensor position at a second row and second column of the 2 by 2 arrangement. The first sensor can detect biosignals from a corrugator facial muscle, the second sensor can detect biosignals from a frontalis facial muscle, the third sensor can detect biosignals from a levator facial muscle, the fourth sensor can detect biosignals from an orbicularis oculi facial muscle, and the fifth sensor can detect biosignals from a zygomatic facial muscle.
In other aspects, the present invention provides a method for integrating surface electromyogram (sEMG) signals and physiological signals for automatically detecting pain intensity levels experienced by a human. One embodiment of the method may comprise providing a wearable facial expression capturing system for measuring said pain intensity levels. The system includes a flexible mask contoured to at least partially cover one side of the human's face, the mask having an eye recess or opening disposed between an elongated forehead portion of the mask, which is above the eye recess, and a cheek portion of the mask, which is beneath the eye recess; at least two sensors disposed in the mask, wherein a first sensor is disposed in the forehead portion of the mask, and a second sensor is disposed in the cheek portion of the mask; a sensor node disposed on a lateral flap extending from the cheek portion of the mask, the sensor node comprising a processing module and a transmitter; and connecting leads electrically coupling each of the at least two sensors to the sensor node.
The method further comprises applying the flexible mask to partially cover one side of the human's face such that the first sensor aligns with a corrugator facial muscle and the second sensor aligns with a zygomatic facial muscle, detecting sEMG signals from the corrugator facial muscle and the zygomatic facial muscle via the first and second sensors, respectively, filtering the detected sEMG signals via the processing module, transmitting the filtered sEMG signals to a data processing system via the wireless transmitter, and receiving physiological signals transmitted from one or more wearable sensors to the data processing system. In some embodiments, the physiological signals may comprise one or more of a breath rate, a heart rate, a galvanic skin response (GSR), or a photoplethysmogram (PPG) signal. The method continues with extracting features from each of the sEMG signals and the physiological signals, performing feature alignment on features extracted from the sEMG signals and the physiological signals, performing interindividual standardization on each of the sEMG signals and the physiological signals, performing pattern recognition by comparing the sEMG signals and the physiological signals to a database, correlating patterns recognized with pain intensity levels and classifying the pain intensity levels, and displaying the pain intensity levels to a medical care provider, thus allowing for continuous and automatic pain monitoring.
In one embodiment, the step of extracting features from each of the sEMG signals and the physiological signals may comprise a root-mean-square (RMS) feature extraction and a wavelength (WL) feature extraction. In another embodiment, the step of performing feature alignment includes synchronizing the sEMG signals and the physiological signals by using cross-correlation functions. In an additional embodiment, the step of correlating patterns recognized with pain intensity levels and classifying the pain intensity levels are performed using an artificial neural network classifier.
One of the unique and inventive technical features of the present invention includes the wearable mask for facial expression capture and for pain assessment, pain management, and clinical monitoring. Without wishing to limit the invention to any theory or mechanism, it is believed that the technical feature of the present invention advantageously provides for aligning embedded sensors on the mask with facial muscles that are activated when experiencing pain, thereby maximizing the signals detected by the sensors, and further enhancing the sensitivity of the system for measuring pain experienced by the patient.
Another unique and inventive technical feature of the present invention includes analyzing a plurality of physiological signals and comparing the signals with one another and/or a database to correlate the measured physiological signals with pain intensity values. In this way, an accurate measure of the pain levels experienced by the patients may be determined. By continuously monitoring the pain levels and displaying the detected pain levels, a medical provider may be able to make intelligent and effective pain management decisions for the patient, thereby improving quality of life in patients suffering from constant or complex pain, for example.
In addition, the current belief in the prior arts is that the incorporation of the sensors into a mask would interfere with detection. Although the sensors were localized, it was thought that the mask would couple the sensors together such that movement of one sensor would affect other sensors, resulting in noise and inaccurate signal detection. It was also thought that the mask would add significant weight to dislocate the sensors from the desired positions on the human face. Thus the prior art teaches away from the present invention. However, contrary to prior teachings, the embedding of the sensors into the mask of the present invention surprisingly worked and was able to detect signals related to pain expression from the individual facial muscles without exhibiting signal or placement issues. Furthermore, the multimodality resulting from the integration of surface electromyogram (sEMG) signals obtained by the wearable sensor mask and other physiological signals obtained by other sensor devices produced a synergistic effect that enhanced detection of the pain responses and distinguished them from other biological responses in the human. As such, none of the known prior references or work has the unique inventive technical features of the present invention.
Any feature or combination of features described herein are included within the scope of the present invention provided that the features included in any such combination are not mutually inconsistent as will be apparent from the context, this specification, and the knowledge of one of ordinary skill in the art. Additional advantages and aspects of the present invention are apparent in the following detailed description and claims.
The features and advantages of the present invention will become apparent from a consideration of the following detailed description presented in connection with the accompanying drawings in which:
Following is a list of elements corresponding to a particular element referred to herein:
Referring now to
In some embodiments, a thickness of the mask (102) may be selected based on one or more of desired flexibility and overall weight for user comfort, for example. As a non-limiting example, the thickness of the mask (102) may be about 50-150 μm. In one non-limiting example, the thickness of the manufactured mask may be about 100 μm. As a non-limiting example, the overall weight of the mask may be about 7-10 g. In one non-limiting example, the weight of the mask may be about 7.81 g. Other values of thickness and weight may be used without deviating from the scope of the invention.
In some embodiments, the mask (102) is implemented by integrating detecting electrodes into the soft polydimethylsiloxane (PDMS) substrate. As a result, the designed mask is easy-to-apply, and offers a one-step solution, which can largely save the valuable time of the care givers when making setting up for sensing vital bio-signals from patients, in particular in the ICU ward environment. In a non-limiting embodiment, the mask (102) is integrated with a plurality of sensors or electrodes (104) embedded into the mask (102), such that when worn, the plurality of electrodes (104) are in contact with specific detection points on the subject's face (114). In one non-limiting example, the plurality of sensors (104) may include electrodes for detecting surface electromyogram (sEMG) signals from facial muscles. As an example, the electrodes may include six pre-gelled Ag/AgCl electrodes positioned at specific locations (positions 1-6 shown in
In some embodiments, fewer electrodes may be used to detect biosignals from the facial muscles to recognize facial expressions. As a non-limiting example, four electrodes may be positioned to line up with the corrugator, orbicularis oculi, levator, and the zygomatic to study the facial expressions. In other embodiments, as shown in
To recognize facial expressions with sEMG method, three to eight channels of sEMG signals may be used. An example plot showing sEMG signals from eight channels is shown in
Each electrode (104) is aligned with the facial muscles of table 1. Herein, a spacing between individual electrodes is selected such that each electrode overlays on top of a muscle from table 1. Each electrode (104) is integrated on the inner side surface of the mask (102) and closely attached to facial skin for reliable surface electromyogram (sEMG) measurement. The placement of the electrodes is determined by the targeted facial muscles. Due to the soft nature of the implemented mask, the electrode position and the shape of the mask can be slightly adjusted accordingly to accommodate individual facial differences.
Each electrode (104) is electrically coupled to a sensor node (108) via connecting leads (106). As an example, the connecting leads (106) may be snapped or clipped on to the electrode (104) embedded on the mask (102). Herein, the connecting leads may be positioned along a top surface of the mask (102). The sensor node (106) may receive biosignals or sEMG signals detected by the electrodes via the connecting leads (106). The sensor node (108) may include a processing module that is configured for conditioning and digitizing the biosignals. The sensor node (108) may additionally include a wireless transmitter (112) that is configured to wirelessly transmit the biosignals to a receiver end, as shown in
Turning now to
Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. The present invention discloses a continuous pain monitoring systems and methods with the classification of multiple physiological parameters, as shown in
In some embodiments, facial sEMG signals may be gathered when the person is with a neutral expression and facial expressions such, smile, frown, wrinkle nose, and the like. The sEMG signals detected by the plurality of sensors (304) may be sampled as different channels. As an example, when four electrodes are placed on the muscles to detect sEMG signals, four channels may be sampled at 1000 SPS. After the sampling, the signals may be filtered. In a non-limiting example, the sampled signals may be filtered using a 20 Hz high pass Butterworth filter and a 50 Hz notch Butterworth filter. As such, the filtering of the signals reduces the artifacts and power line interference coupled to the connecting leads. The sEMG signals may be segmented into 200 ms slices, for example. In some embodiments, the sEMG signals may be filtered by the processing module (310). In some embodiments, the sEMG signals may be transmitted to a removed server/cloud, as shown in
In some embodiments, the data fusion system (322) may receive raw sEMG signals from the system (302), and the processor (328) may filter and segment the sEMG signals. In some embodiments, the data fusion system (322) may receive filtered and segmented sEMG signals.
Once the sEMG signals and filtered and segmented, a root-mean-square (RMS) feature extraction may be performed on the signals. Mathematically, the RMS features are extracted using the following equation:
The RMS feature extraction may provide insight on sEMG amplitude in order to provide a measure of signal power, for example. Wavelength (WL) feature extraction may be additionally or alternatively performed on the sEMG signals as a measure of signal complexity. WL features are extracted using the following equation:
WL=Σ
i=1
N−1
|x
i+1
−x
i| (2)
A multivariate classifier is trained for expression classification. Parameters of Gaussian distribution for each expression are estimated from training data, i.e. a feature matrix. Herein, the feature matrix may include signals for neutral, smile, frown, wrinkle nose, and the like. In some embodiments, the feature matrix may be stored in the memory (326) of the data fusion system (322).
Then the posterior probability of a given class c in the test data is calculated for pattern recognition. The equation below is Bayes theorem for the univariate Gaussian, where the probability density function of continuous random variable x given class c is represented as a Gaussian with mean μc and variance σc2.
In this way, the sEMG signals may be compared with the feature matrix, and the facial expression may be recognized based on the comparison. As an example, when employing multivariate Gaussian classifier, 10 fold cross-validation is applied and the classification accuracy is about 82.4%. The scatter plot of the RMS features of four expressions from one fold training dataset with three of the four channels sEMG are shown in
In addition to extracting facial expressions based on sEMG signals detected from the facial expression capturing system (302), the pain monitoring system (300) may be configured to receive signals from other wearable sensors/monitors (312). Some non-limiting example of the wearable sensors/monitors include heart rate (HR) sensors, breath rate (BR) sensors, galvanic skin sensors, photoplethymogram (PPG) sensors, and the like. As an example, the wearable sensor (312) may be a watch that is worn on the wrist and monitors the heart rate. As another example, the wearable sensor (312) may be a monitor that is worn on a chest and torso for monitoring the heart rate. As yet another example, the wearable sensor may be the PPG sensor worn on a finger to monitor pulse oxygen in the blood. Other examples of wearable sensor include biopatches and electrodes worn/attached to anywhere on the body.
The pain monitoring system may receive signals from the wearable sensor (312). Herein, the signals received may include one or more of a heart rate (HR), a breath rate (BR), a galvanic skin response (GSR), a PPG signal, and the like. The processor (328) may filter the signals received from one or more of the wearable sensors (312) to remove powerline interference and remove movement artifacts. The processor (312) may additionally perform feature extraction on the signals received from the wearable sensors. Some examples of the feature extraction may include extracting heart rate and heart rate variability features from the ECG, extracting skin conductance level and skin conductance response from the skin sensors, and extracting pulse interval and systolic amplitude from the PPG signal. Other features may be extracted without deviating from the scope of the invention. The processor may combine the sEMG feature extraction and the sensor feature extraction to monitor and manage pain, as described in
Turning to
At 406, the feature extraction may include extracting time domain and frequency domain features of the sEMG signals using RMS and WL features, as described in equations (1) and (2).
Method 400 may simultaneously receive and process physiological signals from other wearable devices as described with reference to
At 414, method 400 includes performing time alignment on the features extracted from sEMG signals, and from the signals such as HR, BR, GSR, PPG, and the like. As such, the sEMG, HR, BT, GSR, and PPG measurements may include signals collected asynchronously by multiple sensors. In order to integrate the signals and study them in tandem, the signals have to be synchronized. In one non-limiting example, the sEMG signals may be aligned with the HR, BR, GSR, PPG signals using cross-correlation functions. Other techniques may be used to synchronize the signals, without deviating from the scope of the invention.
At 416, method 400 includes performing interindividual standardization or normalization. The interindividual standardization includes rescaling the range and distribution of each signal. Rescaling may be used to standardize the range of the sEMG signals and the physiological signals. As such, the standardization of the signals may reduce subject-to-subject and trial to trial variability. In one embodiment, the signals may be standardized by equation (4) shown below:
where X is the feature, p is the mean, and a is the standard deviation. The standardization results in generating a parameter matrix. As an example, the standardization of the sEMG signals may result in a matrix containing one set of RMS features and another set of WL features. For example, for sEMG signals arising from five face muscles, the parameter matrix may include ten standardized values. In addition, the parameter matrix includes standardized physiological signals such as HR, BR, and GSR. Thus, the standardization of the sEMG signals and the physiological signals may generate a 13-dimensional parametric matrix.
At 418, method 400 includes performing pattern recognition. The sEMG signals and the BR, HR, and GSR, signals may be compared with corresponding feature matrices stored in the database (422). Based on the comparison, method 400 may classify the signals into no pain, mild pain, or moderate/severe pain. Herein, the parameters of a built model may be trained by the existing database. The model may then be used to classify the new coming features. The model may also be later updated by retraining with the updated database which involves the labelled new coming features. In one embodiment, the comparison may include performing correlation analysis between the physiological parameters, sEMG, and pain intensity levels. As an example, GSR, HR, and BR in the parameter matrix may be used as predicting. Herein, GSR and HR positively correlated with pain intensity level, indicating that these two parameters were more likely to increase when a healthy subject experiences a high intensity of pain, while BR decreases. Among facial sEMG parameters, ZygRMS includes greater correlation to the pain intensity level than others. GSR, HR, BR and two corrugator superclii parameters in the median matrix showed stronger correlation to the pain intensity level than the parameter matrix. As such, the medians of both corrugator supercilii parameters showed considerable potential for differentiating pain intensity levels. Thus, transient response of facial expressions may correlate to acute pain. In some embodiments, Pearson's linear correlation analysis may be used to compare the sEMG signals and physiological signals with pain intensity levels.
Thus, the present invention discloses automatic pain monitoring by classification of multiple physiological parameters. In addition, by performing parameter matrix classification where the physiological parameter samples are classified every second, it may be possible to continuously monitor pain. The physiological parameters are either clinically accessible or available from wearable devices and are appropriate for continuous and long term monitoring. Besides, this monitoring method may help clinicians and personnel working with patients unable to communicate verbally to detect their acute pain and hence treat it more efficiently.
Examples of Medical Use Cases: Post-Operative Pain Assessment and Patient Behavior Assessment (e.g., Blink and Swallowing)
The automatic pain detecting system and method disclosed herein may be used to detect pain in non-communicative subjects. As an example, in emergency rooms or in ambulances, where patients are sometimes unable to communicate, the present invention may be used to automatically detect the level of pain that the patient is experiencing. As another example, for premature babies or infants or people with cognitive disabilities such as Alzheimer's or dementia, the present invention may be used to automatically detect the level of pain experienced by the subject. Once the pain levels are determined, the medical care provider may be able to administer the proper treatment, or prescribe the correct levels of pain medications, for example.
In some situations, the medical provider may need to assess if the pain is real. For example, in subjects who are opioid/substance users, the medical provider cannot rely on the communication from the subjects. There needs to be an independent and more accurate measure of pain levels, so that the medical provider may be able to corroborate the results with the verbal communication received from the subjects. In this way, the medical provider may be able to selectively prescribe pain medications only when the pain is real.
The present invention may be used in situations to regulate the pain medication dosage. As an example, in postoperative patients who need persistent pain prevention, the present invention may be used to automatically detect the pain levels, thereby providing the medical care provider with an accurate measure of the pain levels experienced by the patients, so that the provider can adjust the dosage of the pain medications based on the measured pain levels. In some examples, the present invention may be used to assess pain in palliative or home care patients. In some more examples, the present invention may be used for detection/prevention of breakthrough pain in cancer. The present invention may also be used to detect work related stress and other unhealthy distress experienced by subjects.
The following is a non-limiting example of the present invention. It is to be understood that said example is not intended to limit the present invention in any way. Equivalents or substitutes are within the scope of the present invention.
To develop a continuous pain monitoring method from multiple physiological parameters with machine learning, HR, BR, GSR and facial surface electromyogram (sEMG) were monitored from healthy volunteers under experimental pain stimulus (
Physiological signals including HR, BR, GSR and five facial sEMG from the right side of the face were continuously recorded throughout the session.
The study subject was seated in an armchair. At the beginning of the study session, the sensors and the device were established and it was ensured that they were able to record and appropriately catch the signals from all devices. The pain was induced by thermal and electrical stimuli in a random fashion, two times for each stimuli. The subjects were tested four times during each session and the tests were 1) electrical stimuli on the right-hand ring finger, 2) electrical stimuli on the left-hand ring finger, 3) thermal stimuli on the right inner forearm, and 4) thermal stimuli on the left inner forearm. The pain exposure starting location was randomized and the change of stimulated skin site helped in avoiding habituation to repeated experimental pain. Each data collection session started by letting the subject settle down and rest for ten minutes, so as to acquaint himself or herself with the study environment. Pain testing was only repeated after the subject's HR and BR had returned (if changed) to their respective baseline level.
The intensity of pain was evaluated using VAS at two time points: t1—when the pain reached an uncomfortable level (VAS 3-4), and t2—when the study subject reported intolerable pain or when stimulus intensity reached the non-harmful maximum. The time points and data definition are illustrated in
Data on sEMG and other physiological data were processed and checked separately, as shown in
To unify the time granularity of sEMG data and other physiological data, sEMG data was split into 1000-sample segments for feature extraction. The root mean square (RMS) in equation (1) and wavelength (WL) in equation (2) were the chosen features, where N was the window length and xi was the ith data point in the window. The RMS feature provided direct insight on sEMG amplitude in order to provide a measure of signal power, while WL was related to both waveform amplitude and frequency [30]. All signal processing was conducted in MATLAB.
For all physiological features, data validation on range and constraint were carried out. After checking, three thermal stimuli tests were excluded from the total of 120 tests due to invalid GSR data in the no pain part and another thermal stimulus test was excluded for invalid sEMG data. All the validated physiological features were standardized with a standard core within each test and constituted the 13-dimensional parameter matrix. This standardization rescaled the range and distribution of each parameter, in which way the within-subject and between-subject difference in value range was suppressed. There were 12,509 samples at one second resolution from 116 tests in the parameter matrix. Each sample with 13 parameters was labelled according to the data division in
To visualize the median matrix in 2-dimensional scatter plots, the dimension of parameters in the median matrix was first reduced from 13 with principal component analysis. The first two principal components of the median matrix were non-normally distributed. Nevertheless, with the ability of multivariate analysis, Gaussian distributions were then estimated for each pain intensity level to observe their approximate distribution boundaries in the first two principal components. To fit Gaussians to the parameters of each group, the mean (μ) and variance (σ2) of Gaussian distribution were estimated in maximum likelihood estimation. In a d-dimensional Gaussian distribution, mean and variance were estimated from
The 95% confidence regions of distributions were marked as approximate boundaries. Tests with different pain stimuli were plotted separately. The significance of each parameter in pain intensity level recognition was observed with correlation analysis. Pearson's linear correlation coefficients between each standardized parameter and labels were calculated, as shown in
Using the classification method in machine learning, a model can be built to predict class labels (i.e. 1—No pain, 2—Mild pain and 3—Moderate/Severe pain) from input features (i.e. parameter matrix or median matrix). The resulting classifier is then used to assign class labels to the testing instance with new input features. One benefit of applying classification is its effectiveness in establishing many-to-many mapping. The classification technique chosen in this study was artificial neural network (ANN), which is a non-linear classifier having generally better performance with continuous and multi-dimensional features. This method emulates the information processing capabilities of human brain neurons and can provide flexible mapping between inputs and outputs.
With 13 parameters as the classifier inputs and 3 pain intensity levels as the outputs, the ANNs classifier was built in three layers: an input layer with 13 units, a hidden layer with 10 units and an output layer with 3 units. The classifier was applied to both the labelled median matrix and the labelled parameter matrix. Before classification, the samples were divided randomly into three proportions, where 70% were training samples being presented initially to the classifier for training the network; 15% were validation samples to improve classifier generalization properly; and the remaining 15% were testing samples, independent from the trained classifier for classifier performance measurement. The classifier in this work was trained and evaluated in MATLAB Neural Network Toolbox®. The receiver operating characteristic (ROC) curve of each classification was presented. Both average accuracy and the area under ROC curve (AUC) were evaluated as the performance of classification. The true positive rate (TPR) was also taken into consideration in the evaluation, indicating the correct recognition rate of each pain intensity level. The distributions of AUC in classification with different number of involved parameters are shown in
Thus, patterns of self-reported acute pain intensity levels from monitored physiological signals were observed, which were categorized into no pain, mild pain and moderate/severe pain based on reported VAS.
As used herein, the term “about” refers to plus or minus 10% of the referenced number.
Various modifications of the invention, in addition to those described herein, will be apparent to those skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims. Each reference cited in the present application is incorporated herein by reference in its entirety.
Although there has been shown and described the preferred embodiment of the present invention, it will be readily apparent to those skilled in the art that modifications may be made thereto which do not exceed the scope of the appended claims. Therefore, the scope of the invention is only to be limited by the following claims. In some embodiments, the figures presented in this patent application are drawn to scale, including the angles, ratios of dimensions, etc. In some embodiments, the figures are representative only and the claims are not limited by the dimensions of the figures. In some embodiments, descriptions of the inventions described herein using the phrase “comprising” includes embodiments that could be described as “consisting of”, and as such the written description requirement for claiming one or more embodiments of the present invention using the phrase “consisting of” is met.
The reference numbers recited in the below claims are solely for ease of examination of this patent application, and are exemplary, and are not intended in any way to limit the scope of the claims to the particular features having the corresponding reference numbers in the drawings.
This application is a non-provisional and claims benefit of U.S. Patent Application No. 62/668,712 filed May 8, 2018, the specification(s) of which is/are incorporated herein in their entirety by reference.
This invention was made with government support under Grant No./Funding Decision No. 286915 awarded by Academy of Finland. The government has certain rights in the invention.
| Number | Date | Country | |
|---|---|---|---|
| 62668712 | May 2018 | US |
