WO2023099966A1

WO2023099966A1 - A system and a method for breath based diagnosis

Info

Publication number: WO2023099966A1
Application number: PCT/IB2022/050507
Authority: WO
Inventors: Ankur JAISWAL; Pushkar Shripad Bhagwat; Kushal VYAS; Sagar Rajendra Hosur
Original assignee: Jaiswal Ankur; Pushkar Shripad Bhagwat
Priority date: 2021-12-03
Filing date: 2022-01-21
Publication date: 2023-06-08

Abstract

A system (10) for breath-based diagnosis is disclosed. The system includes an IOT based diagnostic device (20) including a breath flow tube (30) to receive breath from the users. The IOT based diagnostic device includes a breath chamber (40) to hold the breath. The breath chamber includes gas sensors (50) to sense parameters from the breath. The system includes a processing subsystem (60) including a parameter processing module (90) to filter the parameters into a structured format of gaseous parameter data. The processing subsystem includes a diagnostic module (110) to compare attribute metric derived from the structured format of gaseous parameter data with historical attribute metric records. The diagnostic module is to identify medical conditions corresponding the users. The processing subsystem includes a monitoring module (120) to classify the users into categories. The monitoring module is monitor the medical conditions identified for a predefined duration corresponding to the categories. The processing subsystem includes a recommendation module (130) to provide recommendations to the users.

Description

A SYSTEM AND A METHOD FOR BREATH BASED DIAGNOSIS

EARLIEST PRIORITY DATE:

This Application claims priority from a Complete patent application filed in India having Patent Application No. 202121056136, filed on December 03, 2021 and titled “A SYSTEM AND A METHOD FOR BREATH BASED DIAGNOSIS”.

FIELD OF INVENTION

Embodiments of the present disclosure relate to the field of medical diagnosis and more particularly to a system and method for breath-based diagnosis.

BACKGROUND

Diseases may cause alterations in physiology and metabolism. The diseases may be characterized by changes at a level of gene regulation, a protein expression, and a metabolite production. The changes may be specific to the diseases and may be used as biosignatures for diagnosing the diseases. Occurrence of certain volatile organic compounds in exhaled breath as a result of the alterations in various metabolic pathways may distinguish an unhealthy state from a healthy state. Hence, breath analysis may be performed to diagnose the diseases. Variety of the diseases which may be diagnosed by the breath analysis may include, renal failure, liver dysfunction, cirrhosis of liver, peptic ulcer, halitosis, lung disorders, intestine and colon related disease, breast cancer, liver diseases, asthma, and the like.

Even though, the breath analysis may be used to diagnose the diseases, there exists a variety of challenges associated with the breath analysis. Various constraints such as time constrains, economic constraints, geographical constraints prevents patients from undergoing the breath analysis. Existing systems may not be able to detect the diseases associated with vital organs accurately. Also, the existing systems fails to provide dietary recommendations or predictive diagnosis to the patients. Lack of accessible medical infrastructure and availability of a medical professional also prevents the patients from getting the diseases diagnosed.

Hence, there is a need for an improved system and method for breath-based diagnosis to address the aforementioned issue(s). BRIEF DESCRIPTION

In accordance with an embodiment of the present disclosure, a system for enabling breathbased diagnosis is provided. The system includes an internet of things (IOT) based diagnostic device located in proximity of one or more users. The IOT based diagnostic device includes a breath flow tube adapted to receive breath from the corresponding one or more users. The IOT based diagnostic device also includes a breath chamber coupled to the breath flow tube. The breath chamber is adapted to hold the breath received by the breath flow tube for a predefined time. The breath chamber includes a plurality of gas sensors adapted to sense a plurality of parameters from the breath held by the breath chamber. The system also includes a processing subsystem operatively coupled to the IOT based diagnostic device. The processing subsystem is hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a parameter processing module operatively coupled to an integrated database. The parameter processing module is configured to filter the plurality of parameters received into a structured format of gaseous parameter data by using a data filtration technique. The processing subsystem also includes a diagnostic module operatively coupled to the integrated database. The diagnostic module is configured to compare one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database. The diagnostic module also configured to identify one or more medical conditions corresponding the one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records. The processing subsystem further includes a monitoring module operatively coupled to the integrated database. The monitoring module is configured to classify the one or more users into one or more categories based on the one or more medical conditions identified. The monitoring module is also configured to monitor the one or more medical conditions identified corresponding to the one or more users for a predefined duration corresponding to the one or more categories. The processing subsystem also includes a recommendation module operatively coupled to the integrated database. The recommendation module is configured to provide a plurality of recommendations to the one or more users for the predefined duration corresponding to the one or more categories when the one or more medical conditions are following a predefined trend. The plurality of recommendations includes at least one of a dietary recommendations, lifestyle recommendations or a combination thereof. In accordance with another embodiment of the present disclosure, a method for enabling breath-based diagnosis is provided. The method includes receiving, by a breath flow tube, breath from the corresponding one or more users. The method also includes holding, by a breath chamber, the breath received by the breath flow tube for a predefined time. The method further includes sensing, by a plurality of gas sensors, a plurality of parameters from the breath held by the breath chamber. The method also includes filtering, by a parameter processing module, the plurality of parameters received into a structured format of gaseous parameter data by using a data filtration technique. The method also includes comparing, by a diagnostic module, one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database. The method also includes identifying, by the diagnostic module, one or more medical conditions corresponding the one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records. The method also includes classifying, by a monitoring module, the one or more users in to one or more categories based on the one or more medical conditions identified. The method further includes monitoring, by the monitoring module, the one or more medical conditions identified corresponding to the one or more users for a predefined duration corresponding to the one or more categories.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

FIG. 1 is a block diagram representation of a system for enabling breath-based diagnosis in accordance with an embodiment of the present disclosure;

FIG. 2 is a schematic representation of one embodiment of the system of FIG. 1, depicting an IOT based diagnostic device in accordance with an embodiment of the present disclosure;

FIG. 3 is a block diagram representation of another embodiment of the system of FIG. 1, in accordance with an embodiment of the present disclosure;

FIG. 4 is a schematic representation of an exemplary embodiment of the system of FIG. 1, in accordance with an embodiment of the present disclosure; FIG. 5 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and

FIG. 6(a) and FIG. 6(b) is a flow chart representing the steps involved in a method for enabling pulse-based diagnosis in accordance with an embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures, or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Embodiments of the present disclosure relate to a system and a method for breath-based diagnosis. In accordance with an embodiment of the present disclosure, a system and method for breath-based diagnosis is provided. The system includes an internet of things (IOT) based diagnostic device located in proximity of one or more users. The IOT based diagnostic device includes a breath flow tube adapted to receive breath from the corresponding one or more users. The IOT based diagnostic device also includes a breath chamber coupled to the breath flow tube. The breath chamber is adapted to hold the breath received by the breath flow tube for a predefined time. The breath chamber includes a plurality of gas sensors adapted to sense a plurality of parameters from the breath held by the breath chamber. The system also includes a processing subsystem operatively coupled to the IOT based diagnostic device. The processing subsystem is hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a parameter processing module operatively coupled to an integrated database. The parameter processing module is configured to filter the plurality of parameters received into a structured format of gaseous parameter data by using a data filtration technique. The processing subsystem also includes a diagnostic module operatively coupled to the integrated database. The diagnostic module is configured to compare one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database. The diagnostic module also configured to identify one or more medical conditions corresponding the one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records. The processing subsystem further includes a monitoring module operatively coupled to the integrated database. The monitoring module is configured to classify the one or more users in to one or more categories based on the one or more medical conditions identified. The monitoring module is also configured to monitor the one or more medical conditions identified corresponding to the one or more users for a predefined duration corresponding to the one or more categories. The processing subsystem also includes a recommendation module operatively coupled to the integrated database. The recommendation module is configured to provide a plurality of recommendations to the one or more users for the predefined duration corresponding to the one or more categories when the one or more medical conditions are following a predefined trend. The plurality of recommendations includes at least one of a dietary recommendations, lifestyle recommendations or a combination thereof.

FIG. 1 is a block diagram representation of a system (10) for enabling breath-based diagnosis in accordance with an embodiment of the present disclosure. The system (10) includes an internet of things (IOT) based diagnostic device (20) located in proximity of one or more users. In one embodiment, the one or more users may include, but not limited to, a patient, a medical professional, or any person intends to undergo breath analysis. In a specific embodiment, the IOT based diagnosis device may be a wearable device. In some embodiments, the IOT based diagnosis may be a handheld device. The IOT based diagnostic device (20) includes a breath flow tube (30) adapted to receive breath from the corresponding one or more users. In one embodiment, the breath flow tube (30) may include a flow meter adapted to sense pressure difference in the breath flow tube (30) to control one or more valves provided in the breath flow tube (30). In such an embodiment, the flow meter may include, but not limited to, a resistive air pressure sensor, a capacitive air pressure sensor, an inductive air pressure sensor. In an exemplary embodiment, the one or more users may breathe into the breath flow tube (30) after holding the breath for a predefined time. In some embodiments, the one or more valves may be actuated by corresponding one or more servo motors. In such an embodiment, the one or more valves may be actuated to enable reception of the breath into the breath flow tube (30) when the flow meter senses a pressure difference in the breath flow tube (30).

Further, the IOT based diagnostic device (20) also includes a breath chamber (40) coupled to the breath flow tube (30). The breath chamber (40) is adapted to hold the breath received by the breath flow tube (30) for a predefined time. The breath chamber (40) includes a plurality of gas sensors (50) adapted to sense a plurality of parameters from the breath held by the breath chamber (40). In one embodiment, the breath chamber (40) may hold the breath for a predefined time to enable dynamic settling of the plurality of parameters sensed by the plurality of gas sensors (50) in a prior measurement to ensure accuracy of successive measurements. In one embodiment, the plurality of gas sensors (50) may include at least one metal oxide sensor. In such an embodiment, the plurality of gas sensors (50) may be able to sense volatile organic compounds present in the breath. In an exemplary embodiment, the plurality of gas sensors (50) may be tin oxide (SnOz) sensors. In some embodiments, the plurality of gas sensors (50) may be capable of sensing the presence of volatile organic compounds in parts per million (ppm) and parts per billion (ppb) levels. In one embodiment, the plurality of parameters may include, but not limited to, at least one of a type of a gas, concentration of the gas, resistance value corresponding to the gas or a combination thereof. In a specific embodiment, the plurality of gas sensors (50) may be adapted to preheat the breath held by the breath chamber (40) prior to sensing of the plurality of parameters. In some embodiments, the plurality of gas sensors (50) may be dynamically calibrated prior to sensing of the plurality of parameters.

Also, in detail, output of the plurality of gas sensors (50) may be one or more voltages corresponding to concentration of a plurality gases present in the breath. Initially, the one or more voltages may be converted to one or more resistance values corresponding to the plurality of gases. The one or more resistance values may be reiterated for a predefined number of time to ensure accuracy. In one embodiment, the IOT based diagnostic device (20) may include a breath outlet (FIG. 3, 140) coupled to the breath chamber (40). The breath outlet (FIG. 3, 140) may be adapted to expel the breath held by the breath chamber (40) to an outside environment upon sensing the plurality of parameters from the breath by the plurality of gas sensors (50). In such an embodiment, the breath outlet (FIG. 3, 140) may include one or more outlet valves actuated by the corresponding one or more servo motors. In one embodiment, the IOT based diagnostic device (20) may include a poison level detection sensor (FIG. 3, 220) adapted to sense a poison level in a body of the corresponding one or more users. A schematic representation of the IOT based diagnostic device (20) depicting a top view (170), a sectional view (180) and a front view (190) is shown in FIG. 2.

Furthermore, referring back to FIG. 1, the system (10) also includes a processing subsystem (60) operatively coupled to the IOT based diagnostic device (20). The processing subsystem (60) is hosted on a server (70). In one embodiment, the server (70) may be a cloud-based server. In another embodiment, the server (70) may be a local server. The processing subsystem (60) is configured to execute on a network (80) to control bidirectional communications among a plurality of modules. In one embodiment, the network (80) may include one or more terrestrial and/or satellite networks interconnected to communicatively connect a user device to web server engine and a web crawler. In one example, the network (80) may be a private or public local area network (LAN) or wide area network (WAN), such as the Internet. In another embodiment, the network (80) may include both wired and wireless communications according to one or more standards and/or via one or more transport mediums. In one example, the network (80) may include wireless communications according to one of the 802.11 or bluetooth specification sets, or another standard or proprietary wireless communication protocol. In yet another embodiment, the network (80) may also include communications over a terrestrial cellular network, including, a GSM (global system for mobile communications), CDMA (code division multiple access), and/or EDGE (enhanced data for global evolution) network.

Also, the processing subsystem (60) includes a parameter processing module (90) operatively coupled to an integrated database (100). In a specific embodiment, the integrated database (100) may include, but not limited to, a SQL based database, non-SQL based database, object- oriented database, hierarchical database, columnar database and the like. In some embodiments, the integrated database (100) may store a structured format of gaseous parameter data, one or more historical attribute metric records. In one embodiment, the one or more historical attribute metric records may include permissible levels of a plurality of gases in a human body corresponding to different age groups of the one or more users. In some embodiments, the plurality of gases may include, but not limited to, ammonia, acetone, carbon monoxide, methane, butane, ethanol, benzene, hydrogen, hydrogen sulphide, and toluene.

Additionally, the parameter processing module (90) is configured to filter the plurality of parameters received into the structured format of gaseous parameter data by using a data filtration technique. In one embodiment, the parameter processing module (90) may be configured to receive the plurality of parameters via a communication protocol. In some embodiments, the communication protocol may include, but not limited to, bluetooth, zig-bee, near field communication, wireless fidelity and the like. In one embodiment, the structured format of gaseous parameter data may include, the one or more resistance values corresponding to the plurality of gases. In a specific embodiment, the data filtration technique may filter the plurality of parameters into one or more formats by eliminating null values, duplicate values, one or more spurious signals and the like. In one embodiment, the data filtration technique may include a median filtering technique, a kalman filtering technique or a low pass filtering technique.

Further, the processing subsystem (60) also includes a diagnostic module (110) operatively coupled to the integrated database (100). The diagnostic module (110) is configured to compare one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database (100). In one embodiment, the one or more attribute metric derived from the structured format of gaseous parameter data may include one or more concentration levels of the corresponding plurality of gases.

Moreover, the diagnostic module (110) also configured to identify one or more medical conditions corresponding the one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records. For example, consider a scenario in which acetone levels in the breath of a user X is 1.7 parts per million based on the one or more attribute metric. Normal value of the acetone levels in a healthy individual may be below 1 parts per million based on the historical attribute metric records. Upon comparing the acetone levels of the user X with the historical attribute metric records, the diagnostic module (110) may identify the variation in the acetone levels of the user X and a corresponding one or more medical conditions may be identified by the diagnosis module. In one embodiment, the one or more medical conditions may include, but not limited to, liver health status, lung health status, heart health status, kidney health status, diabetic health status, stomach health status, respiratory system health status or the like.

Also, the processing subsystem (60) further includes a monitoring module (120) operatively coupled to the integrated database (100). The monitoring module (120) is configured to classify the one or more users in to one or more categories based on the one or more medical conditions identified. In one embodiment, the one or more categories may include, but not limited to, a healthy stage category, a low stage category, a moderate stage category, a serious stage category. For example, consider the scenario of the user X. The acetone levels of the user X is 1.7 parts per million. Based on the acetone levels the monitoring module (120) may classify the user X into the low stage category. The monitoring module (120) may classify the user X into the serious stage category when the acetone levels of the user X is between 75 parts per billion and 1250 parts per billion.

Additionally, the monitoring module (120) is also configured to monitor the one or more medical conditions identified corresponding to the one or more users for a predefined duration corresponding to the one or more categories. For example, consider a scenario in which the user X may be categorized as the healthy stage category. In such a scenario, the one or more medical conditions identified corresponding to the user X may be monitored by the monitoring module (120) for a 30 day duration. Similarly, the monitoring module (120) may monitor the one or more medical conditions identified corresponding to the user X, for a 14 day duration when the user X is categorized as the low stage category. Also, the monitoring module (120) may monitor the one or more medical conditions identified corresponding to the user X, for a 7 day and 4 day duration when the user X is categorized as the moderate stage category and the serious stage category respectively.

Further, the processing subsystem (60) also includes a recommendation module (130) operatively coupled to the integrated database (100). The recommendation module (130) is configured to provide a plurality of recommendations to the one or more users for the predefined duration corresponding to the one or more categories when the one or more medical conditions are following a predefined trend. The plurality of recommendations includes at least one of a dietary recommendations, lifestyle recommendations or a combination thereof. In one embodiment, the predefined trend may include, but not limited to, a liner regression trend, a nonlinear regression trend, an exponential trend and the like. For example, consider the scenario of the user X. The one or more medical conditions associated with the user X may be monitored by the monitoring module (120) for the 14 day duration the user X is categorized as the low stage category. The recommendation module (130) may provide the dietary recommendations and the lifestyle recommendations to the user X for a specific duration when the acetone levels of the user X follows the nonlinear regression trend. The recommendation module (130) may advice the user X to seek medical advice from the medical professional when the acetone levels of the user X follows the linear regression trend.

FIG. 3 is a block diagram representation of another embodiment of the system (10) of FIG. 1, in accordance with an embodiment of the present disclosure. The system (10) of FIG. 1 includes the parameter processing module (90), the diagnostic module (110), the monitoring module (120), the recommendation module (130). In one embodiment, the system (10) of FIG. 1 may include the processing subsystem (60) including a disease prediction module (150) which is operatively coupled to the integrated database (100). In one embodiment, the disease prediction module (150) configured to predict an occurrence of one or more diseases in future upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records. For example, consider a scenario in the one or more attribute metric corresponding to a user Y may include high acetaldehyde levels. The one or more historical attribute metric may be able to map high acetaldehyde levels to lung cancer or lever diseases. Upon comparing the one or more attribute metric corresponding to the user Y with the one or more historical attribute metric, the disease prediction module (150) may predict the occurrence of the lung caner or lever diseases for the user Y. In one embodiment, the processing subsystem (60) may include a poison level detection module (160) configured to sense a poison level in a body of the corresponding one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records.

FIG. 4 is a schematic representation of an exemplary embodiment (200) of the system (10) of FIG. 1 in accordance with an embodiment of the present disclosure. Consider an example of a user A (210) where the user A (210) is undergoing the breath based diagnosis. The user A (210) may have to breathe into the breath flow tube (FIG. 1, 30) of the IOT based diagnostic device (20). The flow meter may sense the pressure difference in the breath flow tube (FIG. 1, 30) and may open the one or more valves in the breath flow tube (FIG. 1, 30) thereby enabling the breath chamber (FIG. 1, 40) to receive the breath. The plurality of gas sensors (FIG. 1, 50) present in the breath chamber (FIG. 1, 40) may preheat the breath and may analyze the one or more resistance values of the corresponding plurality of gases present in the breath. The parameter processing module (90) may filter the one or more resistance values of the corresponding plurality of gases into the structured format of the gaseous parameter data using the data filtration technique. The diagnostic module (110) may compare the one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database (100).

Also, the one or more attribute metric may include the one or more concentration levels of the corresponding plurality of gases present in the breath of user A (210). Consider a scenario in which ammonia levels in the breath of the user A (210) is above 2500 parts per million. The diagnostic module (110) may identify the one or more medical conditions of the user A (210) upon comparing the ammonia levels of the user A (210) with the one or more historical attribute metric. Since, 2500 parts per million is a dangerous level of the ammonia, the monitoring module (120) may categorize the user A (210) as the serious stage category and may monitor the ammonia levels of the user A (210) for the next 4 day duration. The recommendation module (130) may advice the user A (210) to seek help of the medical professional when the ammonia levels of the user A (210) follows a linear regression during the 4 day duration along with providing one or more recommendations regarding dietary habits, sleeping habits, physical exercises and the like. The user A (210) may also receive a detailed medical report generated by the diagnostic module (110) along with the recommendations provided by the recommendation module (130). The disease prediction module (150) may predict the occurrence of the one or more diseases based on the comparison of the one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database (100). Also, the poison level detection module (160) may be configured to sense a poison level in a body of the user A (210) upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records

FIG. 5 is a block diagram of a computer or a server (70) in accordance with an embodiment of the present disclosure. The server (70) includes processor(s) (230), and memory (240) operatively coupled to the bus (250). The processor(s) (230), as used herein, includes any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

The memory (240) includes several subsystems stored in the form of executable program which instructs the processor to perform the method steps illustrated in FIG. 1. The memory (240) is substantially similar to system (10) of FIG.1. The memory (240) has the following subsystems: a processing subsystem (60) including the parameter processing module (90), the diagnostic module (110), the monitoring module (120), the recommendation module (130), the disease prediction module (150), the poison level detection module (160). The plurality of modules of the processing subsystem (60) performs the functions as stated in FIG. 1 and FIG. 3. The bus (250) as used herein refers to be the internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (250) includes a serial bus or a parallel bus, wherein the serial bus transmit data in bit-serial format and the parallel bus transmit data across multiple wires. The bus (250) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus, and the like.

Further, the processing subsystem (60) includes a parameter processing module (90) operatively coupled to an integrated database (100). The parameter processing module (90) is configured to filter the plurality of parameters received into a structured format of gaseous parameter data by using a data filtration technique. The processing subsystem (60) also includes a diagnostic module (110) operatively coupled to the integrated database (100). The diagnostic module (110) is configured to compare one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database (100). The diagnostic module (110) also configured to identify one or more medical conditions corresponding the one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records. The processing subsystem (60) further includes a monitoring module (120) operatively coupled to the integrated database (100).

Also, the monitoring module (120) is configured to classify the one or more users into one or more categories based on the one or more medical conditions identified. The monitoring module (120) is also configured to monitor the one or more medical conditions identified corresponding to the one or more users for a predefined duration corresponding to the one or more categories. The processing subsystem (60) also includes a recommendation module (130) operatively coupled to the integrated database (100). The recommendation module (130) is configured to provide a plurality of recommendations to the one or more users for the predefined duration corresponding to the one or more categories when the one or more medical conditions are following a predefined trend. The plurality of recommendations includes at least one of a dietary recommendations, lifestyle recommendations or a combination thereof. The processing subsystem (60) also includes a disease prediction module (150) configured to predict occurrence of one or more diseases in future upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records . The processing subsystem (60) also includes a poison level detection module (160) configured to sense a poison level in a body of the corresponding one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records.

Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (230). FIG. 6(a) and FIG. 6(b) is a flow chart representing the steps involved in a method (500) for breath-based diagnostics in accordance with an embodiment of the present disclosure. The method (500) includes receiving breath from the corresponding one or more users in step 510. In one embodiment, receiving breath from the corresponding one or more users includes receiving breath from the corresponding one or more users by a breath flow tube. In one embodiment, the one or more users may include, but not limited to, a patient, a medical professional, or any person intends to undergo breath analysis. In one embodiment, the breath flow tube may include a flow meter adapted to sense pressure difference in the breath flow tube to control one or more valves provided in the breath flow tube. In such an embodiment, the flow meter may include, but not limited to, a resistive air pressure sensor, a capacitive air pressure sensor, an inductive air pressure sensor. In an exemplary embodiment, the one or more users may breathe into the breath flow tube after holding the breath for a predefined time. In some embodiments, the one or more valves may be actuated by corresponding one or more servo motors. In such an embodiment, the one or more valves may be actuated to enable reception of the breath into the breath flow tube when the flow meter senses a pressure difference in the breath flow tube.

The method (500) also includes holding the breath received by the breath flow tube for a predefined time in step 520. In one embodiment, holding the breath received by the breath flow tube for a predefined time includes holding the breath received by the breath flow tube for a predefined time by a breath chamber.

The method (500) further includes sensing a plurality of parameters from the breath held by the breath chamber in step 530. In one embodiment, sensing a plurality of parameters from the breath held by the breath chamber includes sensing a plurality of parameters from the breath held by the breath chamber by a plurality of gas sensors. In one embodiment, the plurality of gas sensors may include at least one metal oxide sensor. In such an embodiment, the plurality of gas sensors may be able to sense volatile organic compounds present in the breath. In an exemplary embodiment, the plurality of gas sensors may be tin oxide (Snth) sensors. In some embodiments, the plurality of gas sensors may be capable of sensing the presence of volatile organic compounds in parts per million (ppm) and parts per billion (ppb) levels. In one embodiment, the plurality of parameters may include, but not limited to, at least one of a type of a gas, concentration of the gas, resistance value corresponding to the gas or a combination thereof. In a specific embodiment, the plurality of gas sensors may be adapted to preheat the breath held by the breath chamber prior to sensing of the plurality of parameters. In some embodiments, the plurality of gas sensors may be dynamically calibrated prior to sensing of the plurality of parameters.

The method (500) also includes filtering the plurality of parameters received into a structured format of gaseous parameter data by using a data filtration technique in step 540. In one embodiment, filtering the plurality of parameters received into a structured format of gaseous parameter data by using a data filtration technique includes filtering the plurality of parameters received into a structured format of gaseous parameter data by using a data filtration technique by a parameter processing module. In one embodiment, the parameter processing module may be configured to receive the plurality of parameters via a communication protocol. In some embodiments, the communication protocol may include, but not limited to, bluetooth, zig-bee, near field communication, wireless fidelity and the like. In one embodiment, the structured format of gaseous parameter data may include, the one or more resistance values corresponding to the plurality of gases. In a specific embodiment, the data filtration technique may filter the plurality of parameters into one or more formats by eliminating null values, duplicate values, one or more spurious signals and the like. In one embodiment, the data filtration technique may include a median filtering technique, a kalman filtering technique or a low pass filtering technique.

The method (500) also includes comparing one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database in step 550. In one embodiment, comparing one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database includes comparing one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database by a diagnostic module. In one embodiment, the one or more attribute metric derived from the structured format of gaseous parameter data may include one or more concentration levels of the corresponding plurality of gases.

The method (500) also includes identifying one or more medical conditions corresponding the one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records in step 560. In one embodiment, identifying one or more medical conditions corresponding the one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records includes identifying one or more medical conditions corresponding the one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records by the diagnostic module. In one embodiment, the one or more medical conditions may include, but not limited to, liver health status, lung health status, heart health status, kidney health status, diabetic health status, stomach health status, respiratory system health status.

The method (500) also includes classifying the one or more users in to one or more categories based on the one or more medical conditions identified in step 570. In one embodiment, classifying the one or more users in to one or more categories based on the one or more medical conditions identified includes classifying the one or more users in to one or more categories based on the one or more medical conditions identified by a monitoring module. In one embodiment, the one or more categories may include, but not limited to, a healthy stage category, a low stage category, a moderate stage category, a serious stage category.

The method (500) also includes monitoring the one or more medical conditions identified corresponding to the one or more users for a predefined duration corresponding to the one or more categories in step 580. In one embodiment, monitoring the one or more medical conditions identified corresponding to the one or more users for a predefined duration corresponding to the one or more categories includes monitoring the one or more medical conditions identified corresponding to the one or more users for a predefined duration corresponding to the one or more categories by the monitoring module.

The method (500) further includes providing a plurality of recommendations to the one or more users for the predefined duration corresponding to the one or more categories when the one or more medical conditions are following a predefined trend in step 590. In one embodiment, providing a plurality of recommendations to the one or more users for the predefined duration corresponding to the one or more categories when the one or more medical conditions are following a predefined trend includes providing a plurality of recommendations to the one or more users for the predefined duration corresponding to the one or more categories when the one or more medical conditions are following a predefined trend by a recommendation module. In one embodiment, the predefined trend may include, but not limited to, a liner regression trend, a nonlinear regression trend, an exponential trend and the like. Various embodiments of the system and method for breath-based diagnosis described above enable various advantages. The system enables the patients to diagnose the diseases without any constraints such as the time constraints, economic constraints, geographical constraints since the system is portable, light weight and cost effective. The system is easy to operate thereby eliminating a need for the medical professional to operate the system. The system is capable of detecting the diseases associated with the vital organs accurately along with providing the lifestyle recommendations and the dietary recommendations to the one or more users. Provision of an energy management system enables the system to be used for multiple times eliminating the need of frequent replacement of batteries. Also, the system is compact and user friendly.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof. While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

WE CLAIM:

1. A system (10) for breath-based diagnosis comprising: an internet of things (IOT) based diagnostic device (20) located in proximity of one or more users, wherein the internet of things (IOT) based diagnostic device (20) comprises: a breath flow tube (30) adapted to receive breath from the corresponding one or more users; a breath chamber (40) coupled to the breath flow tube (30), wherein the breath chamber (40) is adapted to hold the breath received by the breath flow tube (30) for a predefined time, wherein the breath chamber (40) comprises a plurality of gas sensors (50) adapted to sense a plurality of parameters from the breath held by the breath chamber (40); a processing subsystem (60) operatively coupled to the internet of things (IOT) based diagnostic device (20), wherein the processing subsystem (60) is hosted on a server (70) and configured to execute on a network (80) to control bidirectional communications among a plurality of modules comprising: a parameter processing module (90) operatively coupled to an integrated database (100), wherein the parameter processing module (90) is configured to filter the plurality of parameters received into a structured format of gaseous parameter data by using a data filtration technique; a diagnostic module (110) operatively coupled to the integrated database (100), wherein the diagnostic module (110) is configured to: compare one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database (100); identify one or more medical conditions corresponding the one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records; a monitoring module (120) operatively coupled to the integrated database (100), wherein the monitoring module (120) is configured to: classify the one or more users in to one or more categories based on the one or more medical conditions identified; monitor the one or more medical conditions identified corresponding to the one or more users for a predefined duration corresponding to the one or more categories; and a recommendation module (130) operatively coupled to the integrated database (100), wherein the recommendation module (130) is configured to provide a plurality of recommendations to the one or more users for the predefined duration corresponding to the one or more categories when the one or more medical conditions are following a predefined trend, wherein the plurality of recommendations comprises at least one of a dietary recommendations, lifestyle recommendations or a combination thereof.

2. The system (10) as claimed in claiml, wherein the breath flow tube (30) comprises a flow meter adapted to sense pressure difference in the breath flow tube (30) to control one or more valves provided in the breath flow tube (30).

3. The system (10) as claimed in claim 2, wherein the one or more valves provided in the breath flow tube (30) and a breath outlet (140) are actuated by corresponding one or more servo motors.

4. The system (10) as claimed in claiml, wherein the internet of things (IOT) based diagnostic device (20) comprises a breath outlet (140) coupled to the breath chamber (40), wherein the breath outlet (140) is adapted to expel the breath held by the breath chamber (40) to an outside environment upon sensing the plurality of parameters from the breath by the plurality of gas sensors (50).

5. The system (10) as claimed in claim 1, wherein the plurality of gas sensors (50) comprises at least one metal oxide sensor.

6. The system (10) as claimed in claim 1, wherein the plurality of gas sensors (50) are adapted to preheat the breath held by the breath chamber (40) prior to sensing of the plurality of parameters.

7. The system (10) as claimed in claim 1, wherein the plurality of parameters comprises at least one of a type of a gas, concentration of the gas, resistance value corresponding to the gas or a combination thereof.

8. The system (10) as claimed in claim 1, wherein the processing subsystem (60) comprises a disease prediction module (150) configured to predict an occurrence of one or more diseases in future upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records.

9. The system (10) as claimed in claim 1, wherein the processing subsystem (60) comprises a poison level detection module (160) configured to sense a poison level in a body of the corresponding one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records.

10. A method (500) comprising: receiving, by a breath flow tube, breath from the corresponding one or more users; (510) holding, by a breath chamber, the breath received by the breath flow tube for a predefined time; (520) sensing, by a plurality of gas sensors, a plurality of parameters from the breath held by the breath chamber; (530) filtering, by a parameter processing module, the plurality of parameters received into a structured format of gaseous parameter data by using a data filtration technique; (540) comparing, by a diagnostic module, one or more attribute metric derived from the structured format of gaseous parameter data with corresponding one or more historical attribute metric records stored in the integrated database; (550) identifying, by the diagnostic module, one or more medical conditions corresponding the one or more users upon comparing the one or more attribute metric with the corresponding one or more historical attribute metric records; (560) 21 classifying, by a monitoring module, the one or more users in to one or more categories based on the one or more medical conditions identified; (570) monitoring, by the monitoring module, the one or more medical conditions identified corresponding to the one or more users for a predefined duration corresponding to the one or more categories; (580) providing, by a recommendation module, a plurality of recommendations to the one or more users for the predefined duration corresponding to the one or more categories when the one or more medical conditions are following a predefined trend. (590)