EARLIEST PRIORITY DATE:

This Application claims priority from a Complete patent application filed in India having Patent Application No. 202121046322, filed on October 11, 2021, and titled “A SYSTEM AND A METHOD FOR ENABLING PULSEBASED 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 enabling pulse-based diagnosis.

BACKGROUND

According to ayurveda, function of entire human body is governed by three aspects such as vata, pitta and kapha. The three aspects may be collectively called as tridosha. Equilibrium of the three aspects is responsible for proper functioning of physiology. Any imbalance in the equilibrium of the three aspects may cause a disorder. The imbalance may cause blood carrying vessels to contract or expand with respect to a normal position. Expansion and contraction of the blood carrying vessels may cause modulation of blood flow which in turn known as nadi. In detail, the nadi dictates a mode of blood circulation, which is governed by a physiological state of an individual. By sensing the blood circulation, a disease may be diagnosed.

Along with advancement in technology, many techniques came into picture for diagnosing the disease by sensing the blood circulation. The techniques involved exerting a constant pressure on a radial artery for measuring pulse signals. Application of constant pressure on the radial artery may be harmful. Requirement of an experienced medical practitioner further make the techniques undesirable. The techniques may not be able to analyse the pulse signals to diagnose a medical condition efficiently. Further, the techniques were not sufficient to identify dietary habits of a person and provide dietary recommendations to the person. Furthermore, the techniques failed to perform an appropriate diagnosis and a differential diagnosis of the person.

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

In accordance with an embodiment of the present disclosure, a system for enabling pulse-based diagnosis is provided. The system includes an internet of things (loT) based monitoring device operatively coupled to at least one body part of corresponding one or more patients. The loT based monitoring device includes a plurality of sensors adapted to sense one or more vital parameters of the corresponding one or more patients. The system also includes a processing subsystem operatively coupled to the loT based monitoring 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 registration module operatively coupled to an integrated database. The registration module is configured to register the one or more patients by creating one or more patient profiles in the integrated database upon receiving one or more patient details. The processing subsystem also includes a parameter processing module operatively coupled to the integrated database. The parameter processing module is configured to receive the one or more vital parameters of the one or more patients from the plurality of sensors via a communication protocol. The parameter processing module is also configured to filter the one or more vital parameters into a structured format of vital parameter data by using a data filtration technique. The processing subsystem further includes a diagnostic module operatively coupled to the integrated database.

The diagnostic module is configured to select one or more optimal features from the structured format of the vital parameter data for a predetermined time interval upon filtering by using a feature selection technique to obtain one or more attribute metric. The diagnostic module is also configured to evaluate the one or more attribute metric of each of the one or more optimal features selected from the vital parameter data for the predetermined time interval. The diagnostic module is also configured to compare the one or more attribute metric of each of the one or more optimal features evaluated for the predetermined time interval with corresponding one or more historical attribute metric records associated with the one or more patient profiles. The diagnostic module is further configured to identify one or more medical conditions of the one or more patients based on a comparison of the one or more attribute metric with the one or more historical attribute metric records. The processing subsystem further includes a dietary recommendation module operatively coupled to the integrated database. The dietary recommendation module is configured to identify one or more dietary habit of the one or more patients by comparing the one or more medical conditions of the one or more patients identified with a prestored data in the integrated database. The dietary recommendation module is also configured to provide one or more dietary recommendations to the one or more patients upon identifying the one or more dietary habits.

In accordance with another embodiment of the present disclosure, a method for enabling pulsebased diagnosis is provided. The method includes sensing, by a plurality of sensors, one or more vital parameters of the corresponding one or more patients. The method also includes registering, by a registration module, the one or more patients by creating one or more patient profiles in the integrated database upon receiving one or more patient details. The method further includes receiving by a parameter processing module, the one or more vital parameters of the one or more patients from the plurality of sensors via a communication protocol. The method also includes filtering, by the parameter processing module, the one or more vital parameters into a structured format of vital parameter data by using a data filtration technique. The method also includes selecting, by a diagnostic module, one or more optimal features from the structured format of the vital parameter data for a predetermined time interval upon filtering by using a feature selection technique to obtain one or more attribute metric.

The method also includes evaluating, by the diagnostic module, the one or more attribute metric of each of the one or more optimal features selected from the vital parameter data for the predetermined time interval. The method further includes comparing, by the diagnostic module, the one or more attribute metric of each of the one or more optimal features evaluated for the predetermined time interval with corresponding one or more historical attribute metric records associated with the one or more patient profiles. The method also includes identifying, by the diagnostic module, one or more medical conditions of the one or more patients based on a comparison of the one or more attribute metric with the one or more historical attribute metric records. The method also includes identifying, by a dietary recommendation module, one or more dietary habit of the one or more patients by comparing the one or more medical conditions of the one or more patients identified with a prestored data in the integrated database. The method further includes providing, by the dietary recommendation module, one or more dietary recommendations to the one or more patients upon identifying the one or more dietary habits. To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

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 pulse-based diagnosis in accordance with an embodiment of the present disclosure;

FIG. 2 is a block diagram representation of one embodiment of the system of FIG. 1, in accordance with an embodiment of the present disclosure;

FIG. 3 is a schematic representation of an exemplary embodiment of the system of FIG. 1, in accordance with an embodiment of the present disclosure;

FIG. 3(a) - 3(f) is a graphical representation of vata, pitta, kapha signals from the exemplary embodiment of the FIG. 3, in accordance with an embodiment of the present disclosure;

FIG. 4 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and

FIG. 5(a) and FIG. 5(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 enabling pulsebased diagnosis. In accordance with an embodiment of the present disclosure, a system and method for enabling pulse-based diagnosis is provided. The system includes an loT based monitoring device operatively coupled to at least one body part of corresponding one or more patients. The loT based monitoring device includes a plurality of sensors adapted to sense one or more vital parameters of the corresponding one or more patients. The system also includes a processing subsystem operatively coupled to the loT based monitoring 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 registration module operatively coupled to an integrated database. The registration module is configured to register the one or more patients by creating one or more patient profiles in the integrated database upon receiving one or more patient details.

The processing subsystem also includes a parameter processing module operatively coupled to the integrated database. The parameter processing module is configured to receive the one or more vital parameters of the one or more patients from the plurality of sensors via a communication protocol. The parameter processing module is also configured to filter the one or more vital parameters into a structured format of vital parameter data by using a data filtration technique. The processing subsystem further includes a diagnostic module operatively coupled to the integrated database. The diagnostic module is configured to select one or more optimal features from the structured format of the vital parameter data for a predetermined time interval upon filtering by using a feature selection technique to obtain one or more attribute metric.

The diagnostic module is also configured to evaluate the one or more attribute metric of each of the one or more optimal features selected from the vital parameter data for the predetermined time interval. The diagnostic module is also configured to compare the one or more attribute metric of each of the one or more optimal features evaluated for the predetermined time interval with corresponding one or more historical attribute metric records associated with the one or more patient profiles. The diagnostic module is further configured to identify one or more medical conditions of the one or more patients based on a comparison of the one or more attribute metric with the one or more historical attribute metric records. The processing subsystem further includes a dietary recommendation module operatively coupled to the integrated database. The dietary recommendation module is configured to identify one or more dietary habit of the one or more patients by comparing the one or more medical conditions of the one or more patients identified with a prestored data in the integrated database. The dietary recommendation module is also configured to provide one or more dietary recommendations to the one or more patients upon identifying the one or more dietary habits.

FIG. 1 is a block diagram representation of a system (10) for enabling pulse-based diagnosis in accordance with an embodiment of the present disclosure. The system (10) includes an internet of things (loT) based monitoring device (20) operatively coupled to at least one body part of corresponding one or more patients. In one embodiment, the loT based monitoring device (20) may include at least one of a wrist band, a patch, a point-based gaps or a combination thereof. In a specific embodiment, the loT based monitoring device may be wearable over any body part of the one or more patients. In some embodiments, the one or more patients may include any person seeking medical assistance from a medical practitioner. In a specific embodiment, the at least one body part of the one or more patients may include, but not limited to, wrist, chest, neck, ankles, or any body parts suitable for sensing human vitals.

Further, the loT based monitoring device (20) includes a plurality of sensors (30) adapted to sense one or more vital parameters of the corresponding one or more patients. In one embodiment, the plurality of sensors (30) may include one or more pulse sensors configured to sense one or more pulse signals of the one or more patients. In such an embodiment, the one or more pulse sensors may be configured to detect the one or more pulse signals at vata, pitta, kapha points on a wrist of the one or more patients. In a specific embodiment, the one or more pulse sensors may sense the one or more pulse signals by measuring an electric potential of the at least one body part from which the one or more pulse signals are being sensed. In some embodiments, the plurality of sensors (30) may include at least one of a temperature sensor, an accelerometer, a photoplethysmography sensor, an air pressure sensor or a combination thereof. In one embodiment, the temperature sensor may be configured to sense body temperature of the one or more patients. In some embodiments, the accelerometer may be configured to sense body movements of the one or more patients. In a specific embodiment, the photoplethysmography sensor may be configured to monitor pulse rate of the one or more patients. In one embodiment, the air pressure sensor may be configured to monitor oxygen level in blood of the one or more patients. In one embodiment, the one or more vital parameters of the corresponding one or more patients may include, but not limited to, the one or more pulse signals, oxygen concentration, blood pressure, body temperature and the like.

Furthermore, in one embodiment, the accelerometer may be adapted to sense a spatial movement of the loT based monitoring device (20) during sensing of the one or more vital parameters. In some embodiments, the spatial movement of the loT based monitoring device (20) may introduce noise while sensing the one or more vital parameters by the plurality of sensors (30). In one embodiment, the accelerometer may also be adapted to discard the one or more vital parameters sensed during the spatial movement of the loT based monitoring device (20). In some embodiments, the plurality of sensors (30) may be adapted to enable noise free sensing of the one or more vital parameters with minimal positional error by discarding the one or more vital parameters sensed during the spatial movement. Additionally, the system (10) also includes a processing subsystem (40) operatively coupled to the loT based monitoring device (20). The processing subsystem (40) is hosted on a server (50). In one embodiment, the server (50) may be a cloud-based server. In another embodiment, the server (50) may be a local server. The processing subsystem (40) is configured to execute on a network (60) to control bidirectional communications among a plurality of modules. In one embodiment, the network (60) 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 (60) may be a private or public local area network (LAN) or wide area network (WAN), such as the Internet. In another embodiment, the network (60) 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 (60) 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 (60) 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.

Besides, the processing subsystem (40) includes a registration module (70) operatively coupled to an integrated database (80). The registration module (70) is configured to register the one or more patients by creating one or more patient profiles in the integrated database (80) upon receiving one or more patient details. In one embodiment, the one or more patient details may include, but not limited to, name, email, phone number, location, address, medical records, registration number and the like. In a specific embodiment, the integrated database (80) 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 (80) may store a pre stored data, a historic data, a predefined range, a predefined data set one or more historical attribute metric records, medical reports and the like.

Moreover, the processing subsystem (40) also includes a parameter processing module (90) operatively coupled to the integrated database (80). The parameter processing module (90) is configured to receive the one or more vital parameters of the one or more patients from the plurality of sensors (30) via a communication protocol. In one embodiment, the loT based monitoring device (20) may include an analogue to digital converter configured to convert analogue output of the plurality of sensors (30) to corresponding digital signals compatible for communication. In some embodiments, the communication protocol may include, but not limited to, bluetooth, zig-bee, near field communication, wireless fidelity and the like. The parameter processing module (90) is also configured to filter the one or more vital parameters into a structured format of vital parameter data by using a data filtration technique. In one embodiment, the structured format of vital parameter data may include, the one or more vital parameters categorically arranged with time stamps. In a specific embodiment, the data filtration technique may filter the one or more vital parameters in 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.

Also, the processing subsystem (40) further includes a diagnostic module (100) operatively coupled to the integrated database (80). The diagnostic module (100) is configured to select one or more optimal features from the structured format of the vital parameter data for a predetermined time interval upon filtering by using a feature selection technique to obtain one or more attribute metric. In one embodiment, the one or more optimal features may include, but not limited to, rhythm, rate, force, equality and the like. In some embodiments, feature selection technique may include but not limited to, a chi-squared feature selection technique, a pearson correlation feature selection technique, a recursive feature elimination technique, a lasso feature selection technique, a tree-based feature selection technique. In a specific embodiment, the pre-defined time interval may be an hour, a day, a week, a period of ten days, a period of fifteen days, a month and the like

Further, the diagnostic module (100) is also configured to evaluate the one or more attribute metric of each of the one or more optimal features selected from the vital parameter data for the predetermined time interval. The evaluation of the one or more attribute metric of each of the one or more optimal features includes utilization of a computation technique. The computation technique includes at least one of a sum, an average, a median or a mode for evaluation of the one or more attribute metric for the predetermined time interval. The one or more attribute values for each of the one or more optimal features are collected for the predetermined time interval. Each of the one or more attribute values collected for each of the corresponding one or more optimal features over the predetermined time interval are further evaluated using the computation technique to obtain the one or more attribute metric. Furthermore, the diagnostic module (100) is also configured to compare the one or more attribute metric of each of the one or more optimal features evaluated for the predetermined time interval with corresponding one or more historical attribute metric records associated with the one or more patient profiles. In one embodiment, the one or more historical attribute metric records may include the one or more attribute metric evaluated and stored in the integrated database (80) during a prior diagnosis. In some embodiments, the one or more historical attribute metric records may include the one or more attribute metric obtained from an external source. The diagnostic module (100) is further configured to identify one or more medical conditions of the one or more patients based on a comparison of the one or more attribute metric with the one or more historical attribute metric records. For example, consider a scenario in which the one or more attribute metric of a patient is evaluated based on the one or more pulse signals of the patient. Also, the one or more historical attribute metric records stored in the database may include the one or more pulse signals corresponding to a healthy person. Upon comparing the one or more attribute metric of the patient with the one or more historical attribute metric records corresponding to the healthy person some irregularities in the one or more pulse signals of the patient may be found. Based on the irregularities found, the one or more medical conditions of the patient may be identified. In one embodiment, the one or more medical conditions may include, cardio-vascular diseases, udar roga, manas roga and the like. In one embodiment, selection of the one or more optimal features, evaluation of the one or more attribute metric, identification of the one or more medical conditions may be based on techniques such as neural networks, convolution, adaptive and transfer learning and the like.

Moreover, the processing subsystem (40) further includes a dietary recommendation module (110) operatively coupled to the integrated database (80). The dietary recommendation module (110) is configured to identify one or more dietary habit of the one or more patients by comparing the one or more medical conditions of the one or more patients identified with a prestored data in the integrated database (80). For example, consider a scenario in which the patient is diagnosed with the one or more medical conditions. The one or more medical conditions may be mapped to the irregularities found in the one or more pulse signals measured from the wrist of the patient. The one or more medical conditions may be compared with the prestored data to find out the one or more dietary habits causing the irregularities found in the one or more pulse signals of the patient. The dietary recommendation module (110) is also configured to provide one or more dietary recommendations to the one or more patients upon identifying the one or more dietary habits. For example, the one or more health conditions of the patient may be due to the one or more dietary habits followed by the patient. Once the one or more dietary habits of the patient is identified, the one or more dietary recommendations may be provided to the patient to alter the one or more dietary habits which may be acting as a cause of the one or more health conditions.

FIG. 2 is a block diagram representation of one 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 registration module (70), the parameter processing module (90), the diagnostic module (100), the dietary recommendation module (110). In one embodiment, the system (10) of FIG. 1 may include the processing subsystem (40) including a pre check module (120) which is operatively coupled to the integrated database (80). The pre check module (120) may be configured to compare the one or more vital parameters sensed by the plurality of sensors (30) with the predefined data set to identify status of the one or more vital parameters. In one embodiment, the predefined data set may include predefined range of the one or more vital parameters. The pre check module (120) may also configured to trigger the plurality of sensors (30) to sense the one or more vital parameters after a predefined time when the status of the one or more vital parameters are out of the predefined range upon comparison with the predefined data set. The pre check module (120) may further configured to disconnect the plurality of sensors (30) when the one or more vital parameters sensed are out of the predefined range. For example, the pre check module (120) may compare a pulse rate of the patient with the predefined range. The predefined range of the pulse rate may be between 60 and 100. The precheck module may trigger the plurality of sensors (30) to sense the pulse rate of the patient again when the pulse rate sensed is above 100 or below 60. The precheck module may disconnect the plurality of sensors (30) when the pulse rate sensed is out of the predefined range.

In one embodiment, the system (10) may include a display interface (130) which is configured to display the one or more vital parameters, the one or more medical conditions, the one or more dietary recommendations corresponding the one or more patients. In such an embodiment, the display interface (130) may be a liquid crystal display, a light emitting diode plasma, or cathode ray tube. In one embodiment, the display interface (130) may be in mounted mode. In another embodiment, the display interface (130) may be in handheld mode.

FIG. 3 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 patient X (210) where the patient X (210) is seeking assistance of a health care professional (220). A patient profile may be created in the integrated database (80) by the registration module (70). The registration module (70) may require one or more details of the patient X (210) such as name, address, email, phone number, previous medical records to create the patient profile. The health care professional (220) may attach the loT based monitoring device (20) on the wrist of the patient X (210) for further diagnosis. The loT based monitoring device (20) may be in a form of the wrist band for enabling attachment of the loT based monitoring device (20) on the wrist of the patient X (210). The loT based monitoring device (20) may sense the one or more pulse signals from the vata, pitta, kapha points on the wrist of the patient X (210) and may communicate the same to the parameter processing module (90). The pre check module (120) may monitor the one or more pulse signals sensed and may prompt the loT based monitoring device (20) to sense the one or more pulse signals again when the one or more pulse signals sensed are out of the predefined range.

Further, the parameter processing module (90) may receive the one or more pulse signals and may filter the one or more pulse signals into the structured format of vital parameter data. The structured format of vital parameter data may store the one or more pulse signals categorically with respective time stamps. The diagnostic module (100) may further select the one or more optimal features corresponding the one or more pulse signals. The optimal features may include, but not limited to, the rate, the rhythm, the force and the like. The diagnostic module (100) may also evaluate the one or more attribute metric of each of the one or more optimal features using at least one of the mean, the median, the mode-based evaluation technique. The one or more attribute metric evaluated may be compared with the one or more historical attribute metric records associated with the profile of the patient X (210) to identify the one or more medical conditions of the patient X (210). The dietary recommendation module (110) may compare the one or more medical conditions of the patient X (210) with the prestored data in the database to identify the one or more dietary habits of the patient X (210). The dietary recommendation module (110) may provide the one or more dietary recommendations to the patient X (210) upon identifying the one or more dietary habits of the patient X (210). The one or more pulse signals measured from the vata, pitta, kapha points on the wrist of the patient X (210) may be seen in FIG. 3(a)-3(c) and corresponding normal measurements may be seen in FIG. 3(d)-3(f). FIG. 4 is a block diagram of a computer or a server (600) in accordance with an embodiment of the present disclosure. The server (600) includes processor(s) (610), and memory (620) operatively coupled to the bus (630). The processor(s) (610), 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 (620) 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 (620) is substantially similar to system (10) of FIG.1. The memory (620) has the following subsystems: a processing subsystem (40) including a registration module (70), parameter processing module (90), diagnostic module (100), dietary recommendation module (110), pre check module (120). The plurality of modules of the processing subsystem (40) performs the functions as stated in FIG. 1 and FIG. 2. The bus (630) 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 (630) 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 (630) 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.

The processing subsystem (40) includes a registration module (70) configured to register the one or more patients by creating one or more patient profiles in the integrated database (80) upon receiving one or more patient details The processing subsystem (40) also includes a parameter processing module (90) operatively coupled to the integrated database (80). The parameter processing module (90) is configured to receive the one or more vital parameters of the one or more patients from the plurality of sensors (30) via a communication protocol. The parameter processing module (90) is also configured to filter the one or more vital parameters into a structured format of vital parameter data by using a data filtration technique. The processing subsystem (40) further includes a diagnostic module (100) operatively coupled to the integrated database (80). The diagnostic module (100) is configured to select one or more optimal features from the structured format of the vital parameter data for a predetermined time interval upon filtering by using a feature selection technique to obtain one or more attribute metric.

The diagnostic module (100) is also configured to evaluate the one or more attribute metric of each of the one or more optimal features selected from the vital parameter data for the predetermined time interval. The diagnostic module (100) is also configured to compare the one or more attribute metric of each of the one or more optimal features evaluated for the predetermined time interval with corresponding one or more historical attribute metric records associated with the one or more patient profiles. The diagnostic module (100) is further configured to identify one or more medical conditions of the one or more patients based on a comparison of the one or more attribute metric with the one or more historical attribute metric records. The processing subsystem (40) further includes a dietary recommendation module (110) operatively coupled to the integrated database (80). The dietary recommendation module (110) is configured to identify one or more dietary habit of the one or more patients by comparing the one or more medical conditions of the one or more patients identified with a prestored data in the integrated database (80). The dietary recommendation module (110) is also configured to provide one or more dietary recommendations to the one or more patients upon identifying the one or more dietary habits. The processing subsystem (40) further includes a precheck module configured to compare the one or more vital parameters sensed by the plurality of sensors (30) with a predefined data set to identify status of the one or more vital parameters. The precheck module is also configured to trigger the plurality of sensors (30) to sense the one or more vital parameters after a predefined time when the status of the one or more vital parameters are out of a predefined range upon comparison with the predefined data set. The precheck module is further configured to disconnect the plurality of sensors (30) when the one or more vital parameters sensed are out of the predefined range

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) (610). FIG. 5(a) and FIG. 5(b) is a flow chart representing the steps involved in a method (700) for enabling pulse-based diagnostics in accordance with an embodiment of the present disclosure. The method (700) includes sensing one or more vital parameters of the corresponding one or more patients. In one embodiment, includes sensing one or more vital parameters of the corresponding one or more patients includes sensing one or more vital parameters of the corresponding one or more patients by a plurality of sensors in step 710. In one embodiment, the plurality of sensors may include one or more pulse sensors configured to sense one or more pulse signals of the one or more patients. In such an embodiment, the one or more pulse sensors may be configured to detect the one or more pulse signals at vata, pitta, kapha points on a wrist of the one or more patients. In a specific embodiment, the one or more pulse sensors may sense the one or more pulse signals by measuring an electric potential of the at least one body part from which the one or more pulse signals are being sensed. In some embodiments, the plurality of sensors may include at least one of a temperature sensor, an accelerometer, a photoplethysmography sensor, an air pressure sensor or a combination thereof. In one embodiment, the one or more vital parameters of the corresponding one or more patients may include, but not limited to, the one or more pulse signals, oxygen concentration, blood pressure, body temperature and the like.

Further, in one embodiment, the accelerometer may be adapted to sense a spatial movement of the loT based monitoring device during sensing of the one or more vital parameters. In some embodiments, the spatial movement of the loT based monitoring device may introduce noise while sensing the one or more vital parameters by the plurality of sensors. In one embodiment, the accelerometer may also be adapted to discard the one or more vital parameters sensed during the spatial movement of the loT based monitoring device. In some embodiments, the plurality of sensors may be adapted to enable noise free sensing of the one or more vital parameters with minimal positional error by discarding the one or more vital parameters sensed during the spatial movement.

The method (700) also includes registering the one or more patients by creating one or more patient profiles in the integrated database upon receiving one or more patient details in step 720. In one embodiment, registering the one or more patients by creating one or more patient profiles in the integrated database upon receiving one or more patient details includes registering the one or more patients by creating one or more patient profiles in the integrated database upon receiving one or more patient details by a registration module. In one embodiment, the one or more patient details may include, but not limited to, name, email, phone number, location, address, medical records, registration number and the like. In a specific embodiment, the integrated database 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 may store a pre stored data, a historic data, a predefined range, a predefined data set one or more historical attribute metric records, medical reports and the like.

The method (700) further includes receiving the one or more vital parameters of the one or more patients from the plurality of sensors via a communication protocol in step 730. In one embodiment, receiving the one or more vital parameters of the one or more patients from the plurality of sensors via a communication protocol includes receiving the one or more vital parameters of the one or more patients from the plurality of sensors via a communication protocol by a parameter processing module. In one embodiment, the loT based monitoring device may include an analogue to digital converter configured to convert analogue output of the plurality of sensors to corresponding digital signals compatible for communication. In some embodiments, the communication protocol may include, but not limited to, bluetooth, zig-bee, near field communication, wireless fidelity and the like.

The method (700) also includes filtering the one or more vital parameters into a structured format of vital parameter data by using a data filtration technique in step 740. In one embodiment, filtering the one or more vital parameters into a structured format of vital parameter data by using a data filtration technique includes filtering the one or more vital parameters into a structured format of vital parameter data by using a data filtration technique by the parameter processing module. In one embodiment, the structured format of vital parameter data may include, the one or more vital parameters categorically arranged with time stamps. In a specific embodiment, the data filtration technique may filter the one or more vital parameters in 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 (700) also includes selecting one or more optimal features from the structured format of the vital parameter data for a predetermined time interval upon filtering by using a feature selection technique to obtain one or more attribute metric in step 750. In one embodiment, selecting one or more optimal features from the structured format of the vital parameter data for a predetermined time interval upon filtering by using a feature selection technique to obtain one or more attribute metric includes selecting one or more optimal features from the structured format of the vital parameter data for a predetermined time interval upon filtering by using a feature selection technique to obtain one or more attribute metric by a diagnostic module. In one embodiment, the one or more optimal features may include, but not limited to, rhythm, rate, force, equality and the like. In some embodiments, feature selection technique may include but not limited to, a chi-squared feature selection technique, a pearson correlation feature selection technique, a recursive feature elimination technique, a lasso feature selection technique, a tree-based feature selection technique. In a specific embodiment, the pre-defined time interval may be an hour, a day, a week, a period of ten days, a period of fifteen days, a month and the like

The method (700) also includes evaluating the one or more attribute metric of each of the one or more optimal features selected from the vital parameter data for the predetermined time interval in step 760. In one embodiment, the one or more attribute metric of each of the one or more optimal features selected from the vital parameter data for the predetermined time interval includes the one or more attribute metric of each of the one or more optimal features selected from the vital parameter data for the predetermined time interval by the diagnostic module.

The method (700) also includes comparing the one or more attribute metric of each of the one or more optimal features evaluated for the predetermined time interval with corresponding one or more historical attribute metric records associated with the one or more patient profiles in step 770. In one embodiment, comparing the one or more attribute metric of each of the one or more optimal features evaluated for the predetermined time interval with corresponding one or more historical attribute metric records associated with the one or more patient profiles includes comparing the one or more attribute metric of each of the one or more optimal features evaluated for the predetermined time interval with corresponding one or more historical attribute metric records associated with the one or more patient profiles by the diagnostic module. In one embodiment, the one or more historical attribute metric records may include the one or more attribute network evaluated and stored in the integrated database during a prior diagnosis. In some embodiments, the one or more historical attribute metric records may include the one or more attribute network obtained from an external source.

The method (700) also includes identifying one or more medical conditions of the one or more patients based on a comparison of the one or more attribute metric with the one or more historical attribute metric records in step 780. In one embodiment, identifying one or more medical conditions of the one or more patients based on a comparison of the one or more attribute metric with the one or more historical attribute metric records includes identifying one or more medical conditions of the one or more patients based on a comparison of the one or more attribute metric with the one or more historical attribute metric records by the diagnostic module.

The method (700) also includes identifying one or more dietary habit of the one or more patients by comparing the one or more medical conditions of the one or more patients identified with a prestored data in the integrated database in step 790. In one embodiment, identifying one or more dietary habit of the one or more patients by comparing the one or more medical conditions of the one or more patients identified with a prestored data in the integrated database includes identifying one or more dietary habit of the one or more patients by comparing the one or more medical conditions of the one or more patients identified with a prestored data in the integrated database by a dietary recommendation module.

The method (700) further includes providing one or more dietary recommendations to the one or more patients upon identifying the one or more dietary habits in step 800. In one embodiment, providing one or more dietary recommendations to the one or more patients upon identifying the one or more dietary habits includes providing one or more dietary recommendations to the one or more patients upon identifying the one or more dietary habits by the dietary recommendation module.

Various embodiments of the system and method for enabling pulse-based diagnosis described above enable various advantages. The loT based monitoring device enables the appropriate diagnostics and the differential diagnostics of the one or more patients. Also, the loT based monitoring device may sense the one or more vital parameters without causing any disturbances to the one or more patients with minimum possible assistance of a medical practitioner. The system is also capable of diagnosing the one or more medical conditions, identifying the one or more dietary habits, providing one or more dietary recommendations to the one or more patients which reduces frequent doctor interventions.

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.