System and method to detect and predict heart anomalies based on vital parameters

Abstract

A system (10) to detect and predict one or more heart anomalies based on a plurality of vital parameters is disclosed The system includes a monitoring device (20) including sensors to sense electrocardiogram signals of a user. The system includes a processing subsystem (40) including a signal processing module to filter out noise from the electrocardiogram signals to obtain filtered signals. The processing subsystem includes a signal analysis module to align the filtered signals with respect to corresponding historical signals to extract features. The signal analysis module is to generate a similarity matrix based on the features extracted to calculate a similarity score. The processing subsystem includes a detection module to detect the heart anomalies based on the similarity score. The processing subsystem includes an assessment module to assess a criticality of the heart anomalies. The processing subsystem includes a suggestion module to provide suggestions based on the criticality assessed.

Description

[0001] SYSTEM AND METHOD TO DETECT AND PREDICT HEART ANOMALIES BASED ON VITAL PARAMETERS

[0002] EARLIEST PRIORITY DATE

[0003] This Application claims priority from a Complete patent application filed in India having Patent Application No. 202341061417, filed on September 12, 2023, and titled “SYSTEM AND METHOD TO DETECT AND PREDICT HEART ANOMALIES BASED ON VITAL PARAMETERS”.

[0004] FIELD OF INVENTION

[0005] Embodiments of the present disclosure relate to a field of electrocardiography and more particularly to a system and a method to detect and predict one or more heart anomalies based on a plurality of vital parameters.

[0006] BACKGROUND

[0007] Electrocardiogram (ECG) is a medical test used to diagnose various problems of heart. The ECG captures electrical signals generated by the heart through electrodes attached to a patient. The electrical signals coordinate contraction and relaxation of different chambers of the heart to pump blood throughout the body. The ECG captures the electrical signals and represents the electrical signals as a graph for diagnosing the various problems.

[0008] Conventionally, the patient needs to be physically present at a medical facility to perform the ECG, and results of the ECG need to be analyzed by medical practitioners to figure out the various problems. Occasionally, unavailability of the medical practitioners and technicians delays a treatment, thereby affecting well-being of the patient and resulting in co-morbidities. Additionally, an apparatus used for performing the ECG is bulky, thereby restricting a freedom of movement of the patient. Furthermore, the apparatus fails to predict anomalies associated with the heart.

[0009] Hence, there is a need for an improved system and method to detect and predict one or more heart anomalies based on a plurality of vital parameters to address the aforementioned issue(s). OBJECTIVE OF THE INVENTION

[0010] An objective of the invention is to provide a system and a method to detect and predict one or more heart anomalies based on a plurality of vital parameters bymonitoring the plurality of vital parameters in real-time.

[0011] Yet another objective of the invention is to suggest one or more recovery plans to the user based on a criticality of the one or more heart anomalies.

[0012] Yet another objective of the invention is to modify the one or more recovery plans based on one or more inputs from at least one of a monitoring device, a medical practitioner and a bystander.

[0013] Yet another objective of the invention is to identify an emergency situation and notify an emergency situation regarding the emergency situation.

[0014] BRIEF DESCRIPTION

[0015] In accordance with an embodiment of the present disclosure, a system to detect and predict one or more heart anomalies based on a plurality of vital parameters is provided. The system includes a monitoring device worn by a user. The monitoring device includes one or more sensors adapted to sense a plurality of electrocardiogram signals of the user through at least two leads of the monitoring device. The system also includes a processing subsystem operatively coupled to the 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 signal processing module configured to filter out noise from the plurality of electrocardiogram signals by a plurality of noise cancellation techniques to obtain a plurality of filtered signals. The processing subsystem also includes a signal analysis module configured to align the plurality of filtered signals with respect to a plurality of corresponding historical signals to extract a plurality of features from each of the plurality of filtered signals and the plurality of corresponding historical signals. The signal analysis module is also configured to generate a similarity matrix based on the plurality of features extracted to calculate a similarity score using a plurality of statistical analysis techniques. The processing subsystem also includes a detection module configured to detect the one or more heart anomalies based on the similarity score calculated, thereby detecting the one or more heart anomalies based on the plurality of vital parameters. The processing subsystem also includes an assessment module configured to assess a criticality of the one or more heart anomalies detected based on at least one of a severity of the one or more heart anomalies, duration of onset of the one or more heart anomalies and a treatment required for the one or more heart anomalies. The processing subsystem further includes a suggestion module configured to provide one or more suggestions to the user regarding a treatment strategy based on the criticality assessed. The treatment strategy includes at least one of a lifestyle change and consultation of a medical practitioner.

[0016] In accordance with another embodiment of the present disclosure, a method to detect and predict one or more heart anomalies based on a plurality of vital parameters is provided. The method includes sensing, by one or more sensors, a plurality of electrocardiogram signals of the user through at least two leads of the monitoring device. The method also includes filtering out, by a signal processing module, noise from the plurality of electrocardiogram signals by a plurality of noise cancellation techniques to obtain a plurality of filtered signals. The method further includes aligning, by the signal analysis module, the plurality of filtered signals with respect to a plurality of corresponding historical signals to extract a plurality of features from each of the plurality of filtered signals and the plurality of corresponding historical signals. The method also includes generating, by the signal analysis module, a similarity matrix based on the plurality of features extracted to calculate a similarity score using a plurality of statistical analysis techniques. The method further includes detecting, by a detection module, the one or more heart anomalies based on the similarity score calculated. The method also includes assessing, by the assessment module, assess a criticality of the one or more heart anomalies detected based on at least one of a severity of the one or more heart anomalies, duration of onset of the one or more heart anomalies and a treatment required for the one or more heart anomalies. The method further includes providing a suggestion module configured to provide one or more suggestions to the user regarding a treatment strategy based on the criticality assessed. The treatment strategy includes at least one of a lifestyle change and consultation of a medical practitioner. 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.

[0017] BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

[0019] FIG. 1 is a block diagram representation of a system to detect and predict one or more heart anomalies based on a plurality of vital parameters in accordance with an embodiment of the present disclosure;

[0020] 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;

[0021] 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;

[0022] FIG. 4 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and

[0023] FIG. 5 is a flow chart representing the steps involved in a method to detect and predict one or more heart anomalies based on a plurality of vital parameters in accordance with an embodiment of the present disclosure.

[0024] 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

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Embodiments of the present disclosure relate to a system and a method to detect and predict one or more heart anomalies based on a plurality of vital parameters. The system includes a monitoring device worn by a user. The monitoring device includes one or more sensors adapted to sense a plurality of electrocardiogram signals of the user through at least two leads of the monitoring device. The system also includes a processing subsystem operatively coupled to the 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 signal processing module configured to filter out noise from the plurality of electrocardiogram signals by a plurality of noise cancellation techniques to obtain a plurality of filtered signals. The processing subsystem also includes a signal analysis module configured to align the plurality of filtered signals with respect to a plurality of corresponding historical signals to extract a plurality of features from each of the plurality of filtered signals and the plurality of corresponding historical signals. The signal analysis module is also configured to generate a similarity matrix based on the plurality of features extracted to calculate a similarity score using a plurality of statistical analysis techniques. The processing subsystem also includes a detection module configured to detect the one or more heart anomalies based on the similarity score calculated, thereby detecting the one or more heart anomalies based on the plurality of vital parameters. The processing subsystem also includes an assessment module configured to assess a criticality of the one or more heart anomalies detected based on at least one of a severity of the one or more heart anomalies, duration of onset of the one or more heart anomalies and a treatment required for the one or more heart anomalies. The processing subsystem further includes a suggestion module configured to provide one or more suggestions to the user regarding a treatment strategy based on the criticality assessed. The treatment strategy includes at least one of a lifestyle change and consultation of a medical practitioner.

[0030] FIG. 1 is a block diagram representation of a system (10) to detect and predict one or more heart anomalies based on a plurality of vital parameters in accordance with an embodiment of the present disclosure. The system (10) includes a monitoring device (20) worn by a user. In one embodiment, the monitoring device (20) may be associated with a wrist band, a wristwatch, a neck band and the like. In one embodiment, the integrated database (30) may include a relational database, non- Structured Query Language (SQL) database, object-oriented database, hierarchical database, network database, graph database, time-series database, document database, key-value stores, columnar database, in-memory database, spatial database and the like.

[0031] Further, the monitoring device (20) includes one or more sensors (not shown in FIG. 1) adapted to sense a plurality of electrocardiogram signals of the user through at least two leads of the monitoring device (20). In one embodiment, the one or more sensors may include electrodes, wireless sensors and the like. In some embodiments, the one or more sensors may be adapted to sense electrical conductivity of heart of the user, heart rate of the user, oxygen saturation level of the user, and blood pressure of the user. In one embodiment, the monitoring device (20) may include a plurality of micro needles adapted to collect one or more blood samples from the user to identify one or more biomarkers from the one or more blood samples. In such an embodiment, the one or more biomarkers may include, but not limited to, glucose, lipids, hemoglobin, complete blood count, liver enzymes, kidney function biomarkers, c-reactive protein, prostate antigen, thyroid hormones, cardiac biomarkers and the like. In one embodiment, the monitoring device (20) may be configured to analyze the plurality of electrocardiogram signals on-board to inform the user regarding a cardiac related event upon meeting one or more predefined conditions. In such an embodiment, the one or more predefined conditions may include, absence of network coverage, low battery and the like.

[0032] Furthermore, the system (10) includes a processing subsystem (40) operatively coupled to the monitoring device (20). The processing subsystem (40) is hosted on a server (50) and configured to execute on a network (60) to control bidirectional communications among a plurality of modules. In one embodiment, the processing subsystem may be operatively coupled to an integrated database (30) to store data being processed. In a specific embodiment, functioning of the plurality of modules may be based on an artificial intelligence model. In such an embodiment, the artificial intelligence model may be trained using historical electrocardiogram signals of various patients. In one embodiment, a user device (210) may be communicatively coupled with at least one of the monitoring device (20) and the plurality of modules through the network (60). In such an embodiment, the user device (210) may include, but is not limited to, a phone, computer, personal digital assistant and the like. In some embodiments, the user device (210) may be utilized to provide one or more inputs to the plurality of modules. In such an embodiment, the one or more inputs may include one or more data formats corresponding to the plurality of electrocardiogram signals of the user. In one embodiment, the one or more data formats may include, text, image, visual and the like. In a specific embodiment, the user device (210) may be configured to display information obtained from the 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. Further, 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.

[0033] Moreover, 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. In one embodiment, the server (50) may include a cloud server. In some embodiments, the server (50) may include a local server.

[0034] Additionally, the processing subsystem (40) includes a signal processing module (70) configured to filter out noise from the plurality of electrocardiogram signals by a plurality of noise cancellation techniques to obtain a plurality of filtered signals. In some embodiments, the monitoring device (20) may be configured to provide the plurality of electrocardiogram signals to the signal processing module (70) based on an occurrence of one or more events. As used herein, the one or more events may be defined as a significant variation of the plurality of electrocardiogram signals from an average value of the same measured over a predefined interval.

[0035] Also, in one embodiment, the noise may include, baseline wander, power line interference, muscle artifacts, electrode motion artifacts, external interference, electrical interference and the like. In one embodiment, the plurality of noise cancellation techniques may include at least one of adaptive filtering, spectral subtraction, wavelet transform, kalman filtering, and wiener filtering. In one embodiment, the filtered signals may be obtained by a series of steps including processing the ECG reports, interpolating the processed ECG reports, and filtering out noise using various image processing techniques. For example, consider a scenario in which a user X wears the monitoring device (20) as the wrist band. The one or more sensors senses the plurality of electrocardiogram signals of the user X. The electrocardiogram signals may include the baseline wander noise. The signal processing module (70) may further filter out the baseline wander noise by a high pass filtering technique to obtain the plurality of filtered signals.

[0036] Further, the processing subsystem (40) includes a signal analysis module (80) configured to align the plurality of filtered signals with respect to a plurality of corresponding historical signals to extract a plurality of features from each of the plurality of filtered signals and the plurality of corresponding historical signals. The signal analysis module (80) is also configured to generate a similarity matrix based on the plurality of features extracted to calculate a similarity score using a plurality of statistical analysis techniques. In some embodiments, the plurality of corresponding historical signals may include a plurality of electrocardiogram signals of cardiac patients experiencing various health issues and undergoing treatment for the same. In one embodiment, the plurality of features may include amplitude of P wave, duration of P wave, duration of Q wave, duration of R wave, duration of S wave, peak value of the R wave, heart rate, duration of P-P wave, duration of R-R wave, duration of S-T wave and P-QRS-T morphology.

[0037] Furthermore, in some embodiments, the plurality of statistical analysis techniques may include correlation analysis, cluster analysis, dimensionality reduction, hypothesis testing, network analysis, and non-parametric tests. In continuation with the ongoing example, the signal analysis module (80) aligns the filtered signals with the plurality of corresponding historical signals and extracts the plurality of features that are similar in each of the filtered signals and the plurality of corresponding historical signals.

[0038] Moreover, the signal analysis module (80) generate the similarity matrix based on the plurality of features extracted to calculate the similarity score. In one embodiment, the signal analysis module (80) may generate the similarity matrix based on the plurality of features extracted over a period of time. Consider a situation in which the signal analysis module (80) aligned the filtered signals with the plurality of corresponding signals of each of a healthy person B and a heart patient C to calculate the similarity score. The similarity score calculated by the signal analysis module (80) for the user X and the heart patient C is greater than the similarity score calculated for the user X and the user C since the filtered signals and the plurality of corresponding signals of the heart patient C lack QRS complexes.

[0039] Additionally, the processing subsystem (40) includes a detection module (90) configured to detect the one or more heart anomalies based on the similarity score calculated, thereby detecting the one or more heart anomalies based on the plurality of vital parameters. In one embodiment, the one or more heart anomalies may include, but not limited to, arrhythmias, myocardial infarction, ischemia, hypertrophy, conduction abnormalities, cardiac arrest, long qt syndrome, brugada syndrome, tachycardia, heart block. In continuation with the ongoing example, the detection module (90) detects presence of the heart block in the user X based on the similarity score generated by the signal analysis module (80).

[0040] Also, the processing subsystem (40) includes an assessment module (100) configured to assess a criticality of the one or more heart anomalies detected based on at least one of a severity of the one or more heart anomalies, duration of onset of the one or more heart anomalies and a treatment required for the one or more heart anomalies. In continuation with the ongoing example, the assessment module (100), assess the criticality of the heart block detected by the detection module (90) based on the severity of the heart block. The assessment module (100) may assess the criticality of the heart block based on one or more medical reports of the heart patient C. The assessment module (100) may assess the criticality of the heart block as a first degree block.

[0041] Further, the processing subsystem (40) includes a suggestion module (110) configured to provide one or more suggestions to the user regarding a treatment strategy based on the criticality assessed. The treatment strategy includes at least one of a lifestyle change and consultation of a medical practitioner. In continuation with the ongoing example, the suggestion module may suggest the user X to change the lifestyle as the treatment strategy upon assessing the criticality of the heart block as the first degree block.

[0042] 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 signal processing module (70), the signal analysis module (80), the detection module (90), the assessment module (100) and the suggestion module (110). In one embodiment, the system (10) of FIG. 1 may include the processing subsystem (40) including a recommendation module (120) configured to suggest one or more recovery plans to the user based on at least one of the criticality assessed and a plurality of physical factors. In such an embodiment, the one or more recovery plans may include at least one of a dietary plan, an exercise plan, medication plan, sleep cycle, and stress management plan. In one embodiment, the plurality of physical factors may include, medical history, age, health condition, demography, and the like. In continuation with the ongoing example, the recommendation module (120) suggests the exercise plan for the user X to manage the underlying conditions of the heart block.

[0043] Further, in one embodiment, the processing subsystem (40) may include a feedback module (130) configured to modify one or more recovery plans based on one or more inputs from at least one of the monitoring device (20), a medical practitioner and a bystander. In continuation with the ongoing example, consider the scenario in which the user X follows the exercise plan suggested by the recommendation module (120) and made improvements in health conditions. The feedback module (130) modifies the exercise plan based on the one or more input provided by the monitoring device (20) and the medical practitioner.

[0044] Furthermore, in some embodiments, the processing subsystem (40) may include an emergency module (140) configured to identify an emergency situation based on an input from the one or sensors. In such an embodiment, the emergency module (140) may be configured to notify an emergency contact regarding the emergency situation by generating one or more alerts. In one embodiment, the one or more alerts may include, audio alerts, visual alerts, vibration alerts and the like. In one embodiment, the emergency module (140) may be configured to guide the emergency contact to perform one or more first aid procedures through at least one of a visual, image and an audio clip.

[0045] Moreover, in continuation with the ongoing example, consider the scenario in which the heart rate of the user X drops during the exercise. The emergency module (140) detects the one or more heart anomalies based on the inputs from the one or more sensors and notifies an emergency contact Y by sending a text message to the emergency contact Y. The emergency module (140) may be able to distinguish the heart rate change during the one or more heart anomalies from the heart rate change during a workout cycle to avoid erroneous detection. The emergency module (140) also guide the emergency contact Y to perform Cardio-Pulmonary Resuscitation (CPR) by playing the visual in a user interface associated with the monitoring device (20).

[0046] Additionally, the processing subsystem (40) may include a prediction module (200) configured to predict the one or more heart anomalies based on the similarity score calculated. In continuation with the ongoing example, the prediction module (200) may predict a possible heart attack for the user X in a near future based on the similarity score calculated. Based on the prediction, the user X may take informed decisions.

[0047] FIG. 3 is a schematic representation of an exemplary embodiment (150) of the system (10) of FIG. 1 in accordance with an embodiment of the present disclosure. Consider a scenario in which the user E (160) wears the monitoring device (20). The one or more sensors present in the monitoring device (20) sense the plurality of electrocardiogram signals of the user E (160) through at least two leads of the monitoring device (20). The signal processing module (70) filters the noise from the plurality of electrocardiogram signals to obtain the plurality of filtered signals. The signal analysis module (80) is configured to align the plurality of filtered signals with respect to the plurality of corresponding historical signals of various persons to extract the plurality of features from the plurality of filtered signals and the plurality of corresponding historical signals.

[0048] Further, the various persons may include healthy persons as well as cardiac patients. The signal analysis module (80) may generate the similarity matrix based on the features extracted to calculate the similarity score between each of the plurality of corresponding historical signals and the filtered signal. In detail, consider an instance in which the filtered signals match with the plurality of corresponding signals of the cardiac patients who experienced a heart attack. In such a scenario, the similarity score calculated by the signal analysis module (80) may be a higher value compared to the similarity score calculated by the signal analysis module (80) considering the plurality of filtered signals of the user E (160) and the plurality of corresponding signals of a healthy person. Furthermore, the detection module (90) may detect the heart attack based on the similarity score and the assessment module (100) may assess the criticality of the heart attack. The suggestion module may suggest the user E (160) to consult the medical practitioner considering the criticality of the heart attack assessed by the assessment module (100). Further, the recommendation module (120) suggest the dietary plan and the medication plan to the user E (160) to act in response to the heart attack and the feedback module (130) may change the dietary plan and the medication plan based on the one or more inputs provide by the monitoring device (20). Consider another instance in which the blood pressure of the user E (160) drops suddenly. The emergency module (140) may identify the emergency condition and may notify the emergency contact regarding the emergency by generating an alarm. The emergency module (140) may further guide the emergency contact to perform the one or more first aid procedures by playing the audio clip. The prediction module (200) may also predict the one or more heart anomalies for the user E (160) based on the similarity score calculated.

[0049] FIG. 4 is a block diagram of a computer or a server (50) in accordance with an embodiment of the present disclosure. The server (50) includes processor(s) (170), and memory (180) operatively coupled to the bus (190). The processor(s) (170), 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.

[0050] The memory (180) 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 (180) is substantially similar to the system (10) of FIG.1. The memory (180) has the following subsystems: the processing subsystem (40) including the signal processing module (70), the detection module (90), the assessment module (100), the suggestion module, the recommendation module (120), the feedback module (130), and the emergency module (140). The plurality of modules of the processing subsystem (40) performs the functions as stated in FIG. 1 and FIG. 2. The bus (190) as used herein refers to be the internal memory channels or computer network (60) that is used to connect computer components and transfer data between them. The bus (190) 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 (190) 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.

[0051] The processing subsystem (40) includes a signal processing module (70) configured to filter out noise from the plurality of electrocardiogram signals by a plurality of noise cancellation techniques to obtain a plurality of filtered signals. The processing subsystem (40) also includes a signal analysis module (80) configured to align the plurality of filtered signals with respect to a plurality of corresponding historical signals to extract a plurality of features from each of the plurality of filtered signals and the plurality of corresponding historical signals. The signal analysis module (80) is also configured to generate a similarity matrix based on the plurality of features extracted to calculate a similarity score using a plurality of statistical analysis techniques. The processing subsystem (40) also includes a detection module (90) configured to detect the one or more heart anomalies based on the similarity score calculated, thereby detecting the one or more heart anomalies based on the plurality of vital parameters. The processing subsystem (40) also includes an assessment module (100) configured to assess a criticality of the one or more heart anomalies detected based on at least one of a severity of the one or more heart anomalies, duration of onset of the one or more heart anomalies and a treatment required for the one or more heart anomalies. The processing subsystem (40) further includes a suggestion module (110) configured to provide one or more suggestions to the user regarding a treatment strategy based on the criticality assessed. The treatment strategy includes at least one of a lifestyle change and consultation of a medical practitioner.

[0052] The processing subsystem (40) also includes a recommendation module (120) configured to suggest one or more recovery plans to the user based on at least one of the criticality assessed and a plurality of physical factors. The one or more recovery plans includes at least one of a dietary plan, an exercise plan, medication plan, sleep cycle, and stress management plan. The processing subsystem (40) also includes a feedback module (130) configured to modify one or more recovery plans based on one or more inputs from at least one of the monitoring device (20), a medical practitioner and a bystander.

[0053] The processing subsystem (40) also includes an emergency module (140) configured to identify an emergency situation based on an input from the one or sensors. The emergency module (140) is also configured to notify an emergency contact regarding the emergency situation by generating one or more alerts. The emergency module (140) is further configured to guide the emergency contact to perform one or more first aid procedures through at least one of a visual, image and an audio clip.

[0054] The processing subsystem (40) also include a prediction module (200) configured to predict the one or more heart anomalies based on the similarity score calculated.

[0055] 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) (170).

[0056] FIG. 5 is a flow chart representing the steps involved in a method (400) to detect and predict one or more heart anomalies based on a plurality of vital parameters in accordance with an embodiment of the present disclosure. The method (400) includes sensing a plurality of electrocardiogram signals of the user through at least two leads of the monitoring device in step 410. In one embodiment, sensing a plurality of electrocardiogram signals of the user through at least two leads of the monitoring device includes sensing a plurality of electrocardiogram signals of the user through at least two leads of the monitoring device by one or more sensors. In one embodiment, the monitoring device may be associated with a wrist band, a wristwatch, a neck band and the like. In one embodiment, the one or more sensors may include electrodes, wireless sensors and the like. In some embodiments, the one or more sensors may be adapted to sense electrical conductivity of heart of the user, heart rate of the user, oxygen saturation level of the user, and blood pressure of the user. In one embodiment, the monitoring device may include a plurality of micro needles adapted to collect one or more blood samples from the user to identify one or more biomarkers from the one or more blood samples. In such an embodiment, the one or more biomarkers may include, but not limited to, glucose, lipids, hemoglobin, complete blood count, liver enzymes, kidney function biomarkers, c-reactive protein, prostate antigen, thyroid hormones, cardiac biomarkers and the like.

[0057] The method (400) also includes filtering out noise from the plurality of electrocardiogram signals by a plurality of noise cancellation techniques to obtain a plurality of filtered signals in step 420. In one embodiment, filtering out noise from the plurality of electrocardiogram signals by a plurality of noise cancellation techniques to obtain a plurality of filtered signals includes filtering out noise from the plurality of electrocardiogram signals by a plurality of noise cancellation techniques to obtain a plurality of filtered signals by a signal processing module. In one embodiment, the noise may include, baseline wander, power line interference, muscle artifacts, electrode motion artifacts, external interference, electrical interference and the like. In one embodiment, the plurality of noise cancellation techniques may include at least one of adaptive filtering, spectral subtraction, wavelet transform, kalman filtering, and wiener filtering.

[0058] The method (400) also includes aligning the plurality of filtered signals with respect to a plurality of corresponding historical signals to extract a plurality of features from each of the plurality of filtered signals and the plurality of corresponding historical signals in step 430. In one embodiment, aligning the plurality of filtered signals with respect to a plurality of corresponding historical signals to extract a plurality of features from each of the plurality of filtered signals and the plurality of corresponding historical signals includes aligning the plurality of filtered signals with respect to a plurality of corresponding historical signals to extract a plurality of features from each of the plurality of filtered signals and the plurality of corresponding historical signals by the signal analysis module. In some embodiments, the plurality of corresponding historical signals may include the plurality of electrocardiogram signals of other cardiac patients who has been experiencing various health issues and undergoing treatment for the same. In one embodiment, the plurality of features may include amplitude of P wave, duration of P wave, duration of Q wave, duration of R wave, duration of S wave, peak value of the R wave, heart rate, duration of P-P wave, duration of R-R wave, duration of S-T wave and P-QRS-T morphology. In some embodiments, the plurality of statistical analysis techniques may include correlation analysis, cluster analysis, dimensionality reduction, hypothesis testing, network analysis, and non-parametric tests.

[0059] The method (400) further includes generating a similarity matrix based on the plurality of features extracted to calculate a similarity score using a plurality of statistical analysis techniques in step 440. In one embodiment, generating a similarity matrix based on the plurality of features extracted to calculate a similarity score using a plurality of statistical analysis techniques includes generating a similarity matrix based on the plurality of features extracted to calculate a similarity score using a plurality of statistical analysis techniques by the signal analysis module.

[0060] The method (400) also includes detecting the one or more heart anomalies based on the similarity score calculated in step 450. In one embodiment, detecting the one or more heart anomalies based on the similarity score calculated includes detecting the one or more heart anomalies based on the similarity score calculated by a detection module. In one embodiment, the one or more heart anomalies may include, but not limited to, arrhythmias, myocardial infarction, ischemia, hypertrophy, conduction abnormalities, cardiac arrest, long qt syndrome, brugada syndrome, tachycardia, heart block.

[0061] The method (400) also includes assessing a criticality of the one or more heart anomalies detected based on at least one of a severity of the one or more heart anomalies, duration of onset of the one or more heart anomalies and a treatment required for the one or more heart anomalies in step 460. In one embodiment, assessing a criticality of the one or more heart anomalies detected based on at least one of a severity of the one or more heart anomalies, duration of onset of the one or more heart anomalies and a treatment required for the one or more heart anomalies includes assessing a criticality of the one or more heart anomalies detected based on at least one of a severity of the one or more heart anomalies, duration of onset of the one or more heart anomalies and a treatment required for the one or more heart anomalies by the assessment module.

[0062] The method (400) also includes providing one or more suggestions to the user regarding a treatment strategy based on the criticality assessed in step 470. In one embodiment, providing one or more suggestions to the user regarding a treatment strategy based on the criticality assessed includes providing one or more suggestions to the user regarding a treatment strategy based on the criticality assessed by a suggestion module.

[0063] Various embodiments of the system and method to detect and predict one or more heart anomalies based on a plurality of vital parameters described above enable various advantages. Combination of the signal processing module, the signal analysis module and the detection module is capable of detecting the one or more heart anomalies, thereby reducing dependency on the medical practitioners along with eliminating undue delay in detecting the one or more heart anomalies. The system is also capable of predicting the anomalies, thereby enabling the user to take informed decisions.

[0064] Further, combination of the assessment module and the suggestion module is capable of making suggestions to the user weather the user needs to visit the medical practitioner considering the criticality of the one or more heart anomalies, thereby saving time of the user by avoiding unwanted hospital visits. The recommendation module is capable of suggesting the one or more recovery plans to the user based on at least one of the criticality of the one or more heart anomalies and the plurality of physical factors, thereby providing tailor fit recovery plans to the user to optimize efficiency of a treatment plan.

[0065] Furthermore, the feedback module modifies the one or more recovery plans based on the one or more inputs from at least one of the monitoring device, the medical practitioner and the bystander, thereby enhancing the effectiveness of the treatment. Further, the emergency module is capable of identifying the emergency situation and notifying the emergency contact regarding the emergency situation, thereby ensuring timely medical care to the user. Also, the capability of the emergency module to guide the emergency contact to perform the one or more first aid procedures enhances survival chances of the user.

[0066] Miniature size of the monitoring device ensures freedom of movement to the user. Also, the monitoring device is capable of sensing the plurality of electrocardiogram signals of the user through at least two leads, thereby providing accurate measurements. The monitoring device is capable of being configured to communicate with the plurality of modules based on the occurrence of the one or more events, thereby ensuring efficient utilization of the battery. Also, the monitoring device is capable of analyzing the plurality of cardiogram signals on-board during the one or more predefined conditions to inform the user regarding the cardiac related event, thereby ensuring safety of the user. Also, the system is capable of receiving and processing the one or more inputs provided by the user device, thereby showcasing the flexibility of the system. The user device is capable of displaying the information received from the plurality of modules. The system is capable of detecting and predicting the one or more heart anomalies based on the progressing disease condition, thereby achieving contextual based detection and prediction.

[0067] 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.

[0068] 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) to detect and predict one or more heart anomalies based on a plurality of vital parameters comprising: characterized in that: a monitoring device (20) worn by a user, wherein the monitoring device (20) comprises one or more sensors adapted to sense a plurality of electrocardiogram signals of the user through at least two leads of the monitoring device (20); a processing subsystem (40) operatively coupled to the monitoring device (20), wherein the processing subsystem (40) is hosted on a server (50) and configured to execute on a network (60) to control bidirectional communications among a plurality of modules comprising: a signal processing module (70) configured to filter out noise from the plurality of electrocardiogram signals by a plurality of noise cancellation techniques to obtain a plurality of filtered signals; a signal analysis module (80) configured to: align the plurality of filtered signals with respect to a plurality of corresponding historical signals to extract a plurality of features from each of the plurality of filtered signals and the plurality of corresponding historical signals; generate a similarity matrix based on the plurality of features extracted to calculate a similarity score using a plurality of statistical analysis techniques; a detection module (90) configured to detect the one or more heart anomalies based on the similarity score calculated, thereby detecting the one or more heart anomalies based on the plurality of vital parameters; and an assessment module (100) configured to assess a criticality of the one or more heart anomalies detected based on at least one of a severity of the one ormore heart anomalies, duration of onset of the one or more heart anomalies and a treatment required for the one or more heart anomalies; and a suggestion module (110) configured to provide one or more suggestions to the user regarding a treatment strategy based on the criticality assessed, wherein the treatment strategy comprises at least one of a lifestyle change and consultation of a medical practitioner.

2. The system (10) as claimed in claim 1, wherein the monitoring device (20) comprises a plurality of micro needles adapted to collect one or more blood samples from the user to identify one or more biomarkers from the one or more blood samples.

3. The system (10) as claimed in claim 1, wherein the one or more sensors are adapted to sense electrical conductivity of heart of the user, heart rate of the user, oxygen saturation level of the user, and blood pressure of the user.

4. The system (10) as claimed in claim 1, wherein the noise cancellation techniques comprises at least one of adaptive filtering, spectral subtraction, wavelet transform, kalman filtering, and wiener filtering.

5. The system (10) as claimed in claim 1, wherein the plurality of features comprises amplitude of P wave, duration of P wave, duration of Q wave, duration of R wave, duration of S wave, peak value of the R wave, heart rate, duration of P-P wave, duration of R-R wave, duration of S-T segment and P-QRS-T morphology.

6. The system (10) as claimed in claim 1, wherein the statistical analysis techniques comprises correlation analysis, cluster analysis, dimensionality reduction, hypothesis testing, network (60) analysis, and non-parametric tests.

7. The system (10) as claimed in claiml, wherein the processing subsystem (40) comprises a recommendation module (120) configured to suggest one or more recovery plans to the user based on at least one of the criticality assessed and a plurality of physical factors, wherein the one or more recovery plans comprises at least one of a dietary plan, an exercise plan, medication plan, sleep cycle, and stress management plan.

8. The system (10) as claimed in claiml, wherein the processing subsystem (40) comprises a feedback module (130) configured to modify one or more recovery plans based on one or more inputs from at least one of the monitoring device (20), a medical practitioner and a bystander.

9. The system (10) as claimed in claim 1, wherein the processing subsystem (40) comprises an emergency module (140) configured to: identify an emergency situation based on an input from the one or sensors; notify an emergency contact regarding the emergency situation by generating one or more alerts; and guide the emergency contact to perform one or more first aid procedures through at least one of a visual, image and an audio clip.

10. The system (10) as claimed in claim 1, wherein the processing subsystem (40) comprises a prediction module (200) configured to predict the one or more heart anomalies based on the similarity score calculated.

11. A method (400) comprising: characterized in that: sensing, by one or more sensors, a plurality of electrocardiogram signals of the user through at least two leads of the monitoring device; (410) filtering out, by a signal processing module, noise from the plurality of electrocardiogram signals by a plurality of noise cancellation techniques to obtain a plurality of filtered signals; (420) aligning, by the signal analysis module, the plurality of filtered signals with respect to a plurality of corresponding historical signals to extract a plurality of features from each of the plurality of filtered signals and the plurality of corresponding historical signals; (430)generating, by the signal analysis module, a similarity matrix based on the plurality of features extracted to calculate a similarity score using a plurality of statistical analysis techniques; (440) detecting, by a detection module, the one or more heart anomalies based on the similarity score calculated; (450) assessing, by the assessment module, a criticality of the one or more heart anomalies detected based on at least one of a severity of the one or more heart anomalies, duration of onset of the one or more heart anomalies and a treatment required for the one or more heart anomalies; (460) and providing, by a suggestion module, one or more suggestions to the user regarding a treatment strategy based on the criticality assessed, wherein the treatment strategy comprises at least one of a lifestyle change and consultation of a medical practitioner. (470)

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