1. A method for categorizing placebo responders using EEG data, comprising the steps of: collecting a first EEG data from a sample of participants before and after placebo administration; providing an external stimuli to participants; collecting a second EEG data from participants before and after providing stimuli; analysing the collected first and second EEG data using machine learning algorithms; and using the analysed EEG data to categorize participants as placebo responders or non-responders.

2. The method of claim 1, wherein the EEG data is collected using a portable or stationary EEG recording device.

3. The method of claim 1, wherein the machine learning algorithms use feature selection and extraction techniques to identify the most important EEG features associated with placebo response.

4. The method of claim 1, wherein the machine learning algorithms use supervised learning techniques to train a model for predicting placebo response.

5. The method of claim 1, wherein the externa stimuli is selected from electric, chemical, light, aroma, audio, video, mechanical and combination thereof.

6. A system for categorizing placebo responders using EEG data, comprising: a first EEG recording device for collecting a first EEG data from participants; an external stimulation providing device is arranged to provide an external stimuli to participants; a second EEG recording device for collecting a second EEG data from participants; a data analysis module for analyzing the collected first and second EEG data using machine learning algorithms; and a categorization module for using the analyzed EEG data to categorize participants as placebo responders or non-responders.

7. The system of claim 6, wherein the data analysis module includes a cloud-based platform that enables the use of scalable machine learning algorithms.

8. The system of claim 6, wherein the categorization module provides personalized feedback and recommendations to participants based on their placebo response category.  PSYCHOLOGICAL TEST TO DETERMINE PLACEBO RESPONSE IN PATIENT Abstract Possible implementations of the disclosed methodology for classifying placebo responders using EEG data include obtaining a first EEG data from a sample of subjects before and after placebo treatment. Participants may additionally be given an external stimulus in certain embodiments. Other embodiments may additionally include obtaining a second set of EEG readings from participants before and after they are shown stimuli. In certain embodiments, the recorded first and second-generation EEG data is analysed using machine learning methods. Certain embodiments may additionally include classifying people as placebo responders or non-responders based on the results of an analysis of their EEG data. , C , Claims:Claims :

1. A method for categorizing placebo responders using EEG data, comprising the steps of: collecting a first EEG data from a sample of participants before and after placebo administration; providing an external stimuli to participants; collecting a second EEG data from participants before and after providing stimuli; analysing the collected first and second EEG data using machine learning algorithms; and using the analysed EEG data to categorize participants as placebo responders or non-responders.

2. The method of claim 1, wherein the EEG data is collected using a portable or stationary EEG recording device.

3. The method of claim 1, wherein the machine learning algorithms use feature selection and extraction techniques to identify the most important EEG features associated with placebo response.

4. The method of claim 1, wherein the machine learning algorithms use supervised learning techniques to train a model for predicting placebo response.

5. The method of claim 1, wherein the externa stimuli is selected from electric, chemical, light, aroma, audio, video, mechanical and combination thereof.

6. A system for categorizing placebo responders using EEG data, comprising: a first EEG recording device for collecting a first EEG data from participants; an external stimulation providing device is arranged to provide an external stimuli to participants; a second EEG recording device for collecting a second EEG data from participants; a data analysis module for analyzing the collected first and second EEG data using machine learning algorithms; and a categorization module for using the analyzed EEG data to categorize participants as placebo responders or non-responders.

7. The system of claim 6, wherein the data analysis module includes a cloud-based platform that enables the use of scalable machine learning algorithms.

8. The system of claim 6, wherein the categorization module provides personalized feedback and recommendations to participants based on their placebo response category.

Description:An AI based technique To Determine Placebo Response In Patient
Field of the Invention
[0001] The present invention relates generally to psychological assessment, more particularly to a system and method to identify a placebo responder in a person.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Placebos have traditionally played a significant role in medical and healing techniques. A placebo is typically accepted as a valid and helpful element of medical therapy. Randomized controlled trials (RCTs) almost always show a placebo response, although these results are frequently written off as the result of unpredictable confounds. Several common medical diseases, such as peptic ulcers, irritable bowel syndrome, hypertension, low back pain, arthritis, anxiety disorders, depression, and ADHD have all been linked to potent placebo effects.
[0004] Yet, there are numerous technical issues with standard methods for figuring out placebo effects in patients. For instance, the placebo effect is consistently noted in nearly all randomised placebo-controlled clinical studies (RCT), especially in trials of pain management. Current scientific dogma presupposes that uncontrollable confounds are predominantly responsible for RCT-related placebo reactions. Instant solutions discourage incorporation of psychological data of patient.
[0005] Various technological solutions (e.g., method for treating neuropathic pain, big data-driven personalized management of chronic pain, etc.) are disclosed in patent literature. Few of the exemplary documents are discussed below.
[0006] US7335474B2 (By: Perlegen Sciences Inc) Methods and systems for identifying biological marker-placebo effect correlations are provided. Clinical trial design and data analysis of clinical trial data is modified to accommodate marker-placebo effect correlations.
[0007] US20150317447A1 (By: Tools 4 Patient SA) relates to a method for predicting a placebo response in an individual, suffering from or at risk of developing a pain disorder is described. Data is collected from the individual by querying the individual on personality and/or health traits, or performing one or more social learning and/or (bio)physical tests on said individual. The data is used in a mathematical model which attributes a Scoring Factor to the individual. The Scoring Factor is a measure of propensity to raise a placebo response and/or a measure of the intensity of the response for the pain disorder. Tools for implementing the method and preferred uses of the method are described.
[0008] WO2015169810A1 (By: Alvaro Pereira et al) The current invention concerns a method for predicting a placebo response in an individual, comprising collecting data via - querying said individual on personality and health traits; and/or - performing one or more social learning and/or (bio)physical tests on said individual; characterized in that said data is used in a mathematical model stored on a computer for computing a correlation between the input data, thereby attributing a Scoring Factor to said individual, whereby said Scoring Factor is a measure of propensity to raise a placebo response and/or a measure of the intensity of said response.
[0009] However, the technological solutions for identifying placebo responder suffers from various limitations such as, lack of proper treatment, inaccuracy etc. Therefore, more advancement in this field of technology is required. More specifically, to a system and method to perform psychological test to identify a placebo responder in a person.
Summary
[00010] The present invention relates generally to medical treatment, more particularly to a system and method to identify a placebo responder in a person.
[00011] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00012] The following paragraphs provide additional support for the claims of the subject application.
[00013] Embodiments of the present disclosure may include a method for categorizing placebo responders using EEG data, including the steps of collecting a first EEG data from a sample of participants before and after placebo administration. Embodiments may also include providing an external stimuli to participants. Embodiments may also include collecting a second EEG data from participants before and after providing stimuli. Embodiments may also include analysing the collected first and second EEG data using machine learning algorithms. Embodiments may also include using the analysed EEG data to categorize participants as placebo responders or non-responders.
[00014] In some embodiments, the EEG data may be collected using a portable or stationary EEG recording device. In some embodiments, the machine learning algorithms use feature selection and extraction techniques to identify the most important EEG features associated with placebo response. In some embodiments, the machine learning algorithms use supervised learning techniques to train a model for predicting placebo response. In some embodiments, the externa stimuli may be selected from electric, chemical, light, aroma, audio, video, mechanical and combination thereof.
[00015] Embodiments of the present disclosure may also include a system for categorizing placebo responders using EEG data, including a first EEG recording device for collecting a first EEG data from participants. Embodiments may also include an external stimulation providing device may be arranged to provide an external stimuli to participants. Embodiments may also include a second EEG recording device for collecting a second EEG data from participants. Embodiments may also include a data analysis module for analyzing the collected first and second EEG data using machine learning algorithms. Embodiments may also include a categorization module for using the analyzed EEG data to categorize participants as placebo responders or non-responders. In some embodiments, the data analysis module includes a cloud-based platform that enables the use of scalable machine learning algorithms. In some embodiments, the categorization module provides personalized feedback and recommendations to participants based on their placebo response category.
[00016] In some embodiments, the. In some embodiments, the artificial intelligence technique may be selected from artificial neural networks (ANNs) or simulation neural networks (SNNs), deep neural network (DNN), Radial Basis Function Neural Network (RBF)Convolutional Neural Networks (CNN),Recurrent Neural Networks (RNN).
[00017] In some embodiments, the method may include estimating the size of a placebo effect based on brain imaging data. In some embodiments, the pain ratings may be evaluated based on physiological parameters of the subject. In some embodiments, the physiological parameter may be selected from brainwave, sleep pattern, respiratory distress, blood parameters, cardiac parameters, skin parameters and combination thereof.
Brief Description of the Drawings
[00018] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00019] FIG. 1 is a flowchart illustrating a method for categorizing placebo responders, according to some embodiments of the present disclosure.
[00020] FIG. 2 is a block diagram illustrating a system, according to some embodiments of the present disclosure.
Detailed Description
[00021] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00022] The use of the terms a and an and the and at least one and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term at least one followed by a list of one or more items (for example, at least one of A and B) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms comprising, having, including, and containing are to be construed as open-ended terms (i.e., meaning including, but not limited to,) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., such as) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00023] The present invention relates generally to medical treatment, more particularly to a system and method to identify a placebo responder in a person.
[00024] FIG. 1 is a flowchart that describes a method for categorizing placebo responders, according to some embodiments of the present disclosure. In some embodiments, at 110, the method may include collecting a first EEG data from a sample of participants before and after placebo administration. At 120, the method may include providing an external stimuli to participants. At 130, the method may include collecting a second EEG data from participants before and after providing stimuli. At 140, the method may include analysing the collected first and second EEG data using machine learning algorithms. At 150, the method may include using the analysed EEG data to categorize participants as placebo responders or non-responders. The steps of, the method may include 110 to 150.
[00025] In some embodiments, the EEG data may be collected using a portable or stationary EEG recording device. In some embodiments, the machine learning algorithms may use feature selection and extraction techniques to identify the most important EEG features associated with placebo response. In some embodiments, the machine learning algorithms may use supervised learning techniques to train a model for predicting placebo response. In some embodiments, the externa stimuli may be selected from electric, chemical, light, aroma, audio, video, mechanical and combination thereof.
[00026] FIG. 2 is a block diagram that describes a system 200, according to some embodiments of the present disclosure. In some embodiments, the system 200 may include a first EEG recording device 210 for collecting a first EEG data from participants, an external stimulation 220 providing device may be arranged to provide an external stimuli to participants, a second EEG recording device 230 for collecting a second EEG data from participants, a data analysis module 240 for analyzing the collected first and second EEG data using machine learning algorithms, and a categorization module 250 for using the analyzed EEG data to categorize participants as placebo responders or non-responders. In some embodiments, the data analysis module 240 may include a cloud-based platform that enables the use of scalable machine learning algorithms. In some embodiments, the categorization module 250 may provide personalized feedback and recommendations to participants based on their placebo response category.
[00027] The present invention provides a method for categorizing placebo responders using EEG data. The method includes the following steps:
Step 1: Study Design
[00028] The first step involves designing a study to administer a placebo treatment to individuals. The study can include a randomized controlled trial with a placebo group and a control group. The placebo treatment can be a sugar pill or other inert substance.
Step 2: EEG Data Collection
[00029] The second step involves collecting EEG data from the individuals before and after administering the placebo treatment. The EEG data can be collected using a commercially available EEG device or a custom-built device. The EEG data can be collected while the individual is resting with their eyes closed.
Step 3: EEG Data Pre-processing
[00030] The collected EEG data is pre-processed to remove noise, correct errors, and convert data into a suitable format for analysis. The pre-processing steps include data cleaning, filtering, and artifact removal.
Step 4: EEG Data Analysis
[00031] The pre-processed EEG data is then analyzed to identify patterns of neural activity that are indicative of placebo response. The analysis can include time-frequency analysis, coherence analysis, and connectivity analysis. The analysis can be performed using commercially available software or custom-built algorithms.
Step 5: Categorization of Placebo Responders
[00032] Based on the EEG data analysis, the method categorizes individuals into placebo responders and non-responders. The categorization can be based on the amplitude, frequency, and connectivity of neural activity observed in the EEG data. Individuals who exhibit patterns of neural activity that are indicative of placebo response are categorized as placebo responders, while individuals who do not exhibit these patterns are categorized as non-responders.
Step 6: Clinical Trial Design
[00033] The categorized individuals can then be used to design clinical trials that take into account placebo response. For example, individuals who are categorized as placebo responders can be included in the placebo arm of the trial, while individuals who are categorized as non-responders can be excluded from the placebo arm.
Advantages:
[00034] The present invention has several advantages over traditional methods for identifying placebo responders. The use of EEG data allows for a non-invasive method of measuring neural activity and identifying patterns that are indicative of placebo response. This allows for more accurate identification of placebo responders, which can lead to more effective clinical trial design.
[00035] This disclosure may also be embodied as a technique for classifying placebo responders using EEG data, the method including the steps of obtaining a first EEG data from a sample of subjects before and after placebo administration. Participants may additionally be given an external stimulus in certain embodiments. Other embodiments may additionally include obtaining a second set of EEG readings from participants before and after they are shown stimuli. In certain embodiments, the recorded first and second-generation EEG data is analysed using machine learning methods. Certain embodiments may additionally include classifying people as placebo responders or non-responders based on the results of an analysis of their EEG data.
[00036] In certain implementations, a mobile or fixed EEG recording equipment is used to acquire the EEG data. Some implementations of machine learning algorithms use feature selection and extraction strategies to isolate the most relevant EEG characteristics related with placebo response. In other implementations, the machine learning algorithms develop a model to predict placebo response using supervised learning approaches. Electric, chemical, illuminant, olfactory, auditory, visual, mechanical, and any combination thereof may all be used as extrinsic stimuli in various implementations.
[00037] Included in certain implementations of the present disclosure is a system for classifying placebo responders based on EEG data, which may comprise a first EEG recording device for obtaining a first EEG data from participants. Certain embodiments may additionally make use of a device that may be set up to present participants with external stimuli. A second EEG recording device for gathering additional EEG data from participants is also possible in certain embodiments. The obtained first and second EEG data may be analysed using machine learning methods utilising a data analysis module, which some embodiments may include. Using the results of the EEG analysis, embodiments may classify subjects as either placebo responders or non-responders. In certain implementations, scalable machine learning techniques may be used thanks to the data analysis module's incorporation into a cloud-based platform. A participant's placebo response category may inform the categorization module's suggestions and feedback in certain implementations.
[00038] For example, in certain implementations, the. Artificial neural networks (ANNs) or simulation neural networks (SNNs), deep neural networks (DNNs), radial basis function neural networks (RBFs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) are all viable options for the AI implementation in various cases (RNN).
[00039] Brain imaging data may be used to make educated guesses about the magnitude of a placebo effect in certain implementations of the approach. The subject's physiological parameters may be used to assess the subject's pain ratings in certain implementations. Brainwave, sleep pattern, respiratory distress, blood parameters, heart parameters, skin parameters, and combinations thereof may all be used as the physiological parameter in various implementations.
[00040] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00041] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00042] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

 

Claims
I/We Claim:
1. A method for categorizing placebo responders using EEG data, comprising the steps of: collecting a first EEG data from a sample of participants before and after placebo administration; providing an external stimuli to participants; collecting a second EEG data from participants before and after providing stimuli; analysing the collected first and second EEG data using machine learning algorithms; and using the analysed EEG data to categorize participants as placebo responders or non-responders.
2. The method of claim 1, wherein the EEG data is collected using a portable or stationary EEG recording device.
3. The method of claim 1, wherein the machine learning algorithms use feature selection and extraction techniques to identify the most important EEG features associated with placebo response.
4. The method of claim 1, wherein the machine learning algorithms use supervised learning techniques to train a model for predicting placebo response.
5. The method of claim 1, wherein the externa stimuli is selected from electric, chemical, light, aroma, audio, video, mechanical and combination thereof.
6. A system for categorizing placebo responders using EEG data, comprising: a first EEG recording device for collecting a first EEG data from participants; an external stimulation providing device is arranged to provide an external stimuli to participants; a second EEG recording device for collecting a second EEG data from participants; a data analysis module for analyzing the collected first and second EEG data using machine learning algorithms; and a categorization module for using the analyzed EEG data to categorize participants as placebo responders or non-responders.
7. The system of claim 6, wherein the data analysis module includes a cloud-based platform that enables the use of scalable machine learning algorithms.
8. The system of claim 6, wherein the categorization module provides personalized feedback and recommendations to participants based on their placebo response category. 

PSYCHOLOGICAL TEST TO DETERMINE PLACEBO RESPONSE IN PATIENT
Abstract
Possible implementations of the disclosed methodology for classifying placebo responders using EEG data include obtaining a first EEG data from a sample of subjects before and after placebo treatment. Participants may additionally be given an external stimulus in certain embodiments. Other embodiments may additionally include obtaining a second set of EEG readings from participants before and after they are shown stimuli. In certain embodiments, the recorded first and second-generation EEG data is analysed using machine learning methods. Certain embodiments may additionally include classifying people as placebo responders or non-responders based on the results of an analysis of their EEG data. , C , Claims:Claims
I/We Claim:
1. A method for categorizing placebo responders using EEG data, comprising the steps of: collecting a first EEG data from a sample of participants before and after placebo administration; providing an external stimuli to participants; collecting a second EEG data from participants before and after providing stimuli; analysing the collected first and second EEG data using machine learning algorithms; and using the analysed EEG data to categorize participants as placebo responders or non-responders.
2. The method of claim 1, wherein the EEG data is collected using a portable or stationary EEG recording device.
3. The method of claim 1, wherein the machine learning algorithms use feature selection and extraction techniques to identify the most important EEG features associated with placebo response.
4. The method of claim 1, wherein the machine learning algorithms use supervised learning techniques to train a model for predicting placebo response.
5. The method of claim 1, wherein the externa stimuli is selected from electric, chemical, light, aroma, audio, video, mechanical and combination thereof.
6. A system for categorizing placebo responders using EEG data, comprising: a first EEG recording device for collecting a first EEG data from participants; an external stimulation providing device is arranged to provide an external stimuli to participants; a second EEG recording device for collecting a second EEG data from participants; a data analysis module for analyzing the collected first and second EEG data using machine learning algorithms; and a categorization module for using the analyzed EEG data to categorize participants as placebo responders or non-responders.
7. The system of claim 6, wherein the data analysis module includes a cloud-based platform that enables the use of scalable machine learning algorithms.
8. The system of claim 6, wherein the categorization module provides personalized feedback and recommendations to participants based on their placebo response category.