Description:A Combined ECG and Clinical Parameter Approach for Heart Disease Prediction

The present invention relates to the field of biomedical engineering, artificial intelligence, and healthcare analytics, and more particularly to a system and method for early prediction of heart disease using a combined analysis of electrocardiogram (ECG) signals and clinical parameters through machine learning techniques.

BACKGROUND
[0001] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0002] The human heart is a vital organ that continuously pumps oxygenated blood throughout the body, supporting essential physiological functions. Any disruption in its activity can lead to serious health complications. Cardiovascular diseases (CVDs), including arrhythmia, myocardial infarction, and heart failure, are among the leading causes of death worldwide, creating a significant burden on healthcare systems.
[0003] Early detection of heart disease plays a crucial role in reducing mortality rates and improving patient outcomes. However, many individuals remain undiagnosed due to lack of awareness, limited access to healthcare facilities, and absence of continuous monitoring systems. This problem is more severe in rural and remote areas, where advanced diagnostic tools and expert medical professionals are not readily available.
[0004] Traditional methods for diagnosing heart disease include electrocardiogram (ECG) analysis, stress tests, and clinical examinations. Although these methods are effective, they are often time-consuming and require skilled interpretation by healthcare professionals. Moreover, such approaches typically focus on isolated data sources, either ECG signals or clinical parameters, which may limit the overall diagnostic accuracy.
[0005] With the advancement of artificial intelligence and machine learning, several automated systems have been developed for heart disease prediction. These systems analyze medical datasets to identify patterns and predict potential risks. However, many existing models rely on limited features or single-source data, resulting in inconsistent performance and reduced reliability in real-world scenarios.
[0006] There is a strong need for an integrated approach that combines ECG signal analysis with comprehensive clinical parameters to improve prediction accuracy. A hybrid system that utilizes both physiological signals and patient-specific data can provide more reliable and early detection of heart diseases, thereby supporting better clinical decision-making and enhancing overall healthcare outcomes.
[0007] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0008] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.

OBJECTS OF THE INVENTION
[0009] It is an object of the present disclosure to develop an intelligent system that combines electrocardiogram (ECG) signals and clinical parameters for accurate prediction of heart diseases.
[0010] It is another object of the present disclosure to enable early detection of cardiac abnormalities such as arrhythmia, myocardial infarction, and other cardiovascular conditions to reduce mortality risk.
[0011] It is another object of the present disclosure to integrate machine learning algorithms for analyzing large-scale medical data and improving prediction accuracy and efficiency.
[0012] It is another object of the present disclosure to provide a user-friendly and automated diagnostic framework that assists healthcare professionals in clinical decision-making.
[0013] It is another object of the present disclosure to support real-time monitoring and prediction through integration with wearable devices and remote healthcare systems.
SUMMARY
[0001] The present invention shows the combined ECG and clinical parameter approach for heart disease prediction.
[0002] The present disclosure provides a system and method for heart disease prediction by combining electrocardiogram (ECG) signals with clinical parameters. The system collects ECG data from sensors or wearable devices and integrates it with patient-specific clinical information such as age, blood pressure, cholesterol levels, and medical history. Advanced preprocessing and feature extraction techniques are applied to the ECG signals to identify significant patterns related to cardiac activity.
[0003] The extracted ECG features and clinical parameters are then processed using machine learning algorithms to classify and predict heart disease conditions. The proposed approach improves diagnostic accuracy by utilizing a hybrid data fusion mechanism. The system enables early detection, supports real-time monitoring, and assists healthcare professionals in making informed decisions, thereby reducing the risk of severe cardiac events.
[0004] One should appreciate that although the present disclosure has been explained with respect to a defined set of functional modules, any other module or set of modules can be added/deleted/modified/combined and any such changes in architecture/construction of the proposed method are completely within the scope of the present disclosure. Each module can also be fragmented into one or more functional sub-modules, all of which also completely within the scope of the present disclosure.
[0005] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the analysis of the present disclosure.
[0015] Figure 1: A Combined ECG and Clinical Parameter Approach for Heart Disease Prediction.

DETAILED DESCRIPTION
[0016] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0017] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0018] Exemplary embodiments will now be described more fully hereinafter with reference to the drawings, in which exemplary embodiments are shown. This disclosure, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure.
[0019] various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0020] In an embodiment of the present disclosure, the system comprises a data acquisition module configured to collect ECG signals using sensors, wearable devices, or medical instruments. Simultaneously, clinical parameters including demographic details, physiological measurements, and lifestyle factors are gathered from patient records or input interfaces. This dual data collection ensures comprehensive input for accurate analysis.
[0021] In another embodiment, the acquired ECG signals undergo preprocessing to remove noise and artifacts using filtering and normalization techniques. Following preprocessing, a feature extraction module identifies critical attributes such as heart rate variability, waveform intervals, and rhythm characteristics. These features are essential for detecting abnormalities such as arrhythmia and irregular cardiac patterns.
[0022] In a further embodiment, the extracted ECG features are integrated with clinical parameters to form a unified dataset. This combined dataset is fed into a machine learning model such as Gradient Boosting Machine, Random Forest, or Neural Network. The model is trained and validated using standard medical datasets to classify heart conditions into normal and abnormal categories with improved accuracy.
[0023] In yet another embodiment, the system generates prediction outputs through a decision support interface, indicating the presence or risk level of heart disease. The results can be displayed to healthcare professionals or patients through a user-friendly interface. Additionally, the system can be integrated with wearable devices for continuous monitoring, enabling timely intervention and improved patient care.
[001] Data Acquisition and Patient Registration (100): Patients are registered into the system through a secure interface where demographic and clinical details such as age, gender, medical history, blood pressure, and lifestyle factors are recorded. Simultaneously, ECG signals are acquired using sensors or wearable devices, ensuring accurate and continuous data collection.
[002] ECG Signal Preprocessing (101): The acquired ECG signals are processed to remove noise and unwanted artifacts using filtering and normalization techniques. This step enhances signal quality and prepares the data for accurate feature extraction and analysis.
[003] Feature Extraction from ECG (102): Significant features such as P-wave, QRS complex, T-wave intervals, heart rate variability, and RR intervals are extracted using automated algorithms. These features represent critical cardiac activity patterns required for disease detection.
[004] Clinical Data Integration (103): The extracted ECG features are combined with clinical parameters including cholesterol levels, blood pressure, diabetes status, and lifestyle habits to form a unified dataset. This integrated approach improves the robustness of the prediction model.
[005] Machine Learning-Based Prediction (104): The unified dataset is processed using machine learning algorithms such as Gradient Boosting Machine, Random Forest, or Neural Networks to classify heart conditions and predict the likelihood of heart disease with high accuracy.
[006] Result Generation and Decision Support (105): The system generates prediction outputs indicating normal or abnormal cardiac conditions along with risk levels. Results are displayed through a user-friendly interface for healthcare professionals, enabling timely diagnosis and effective decision-making. Additionally, the system can provide alerts for critical conditions and support real-time monitoring.
, Claims:I/We Claim
Claim 1: A system for prediction of heart disease using a combined electrocardiogram (ECG) and clinical parameter approach, comprising:
• a data acquisition module configured to collect ECG signals from sensors or wearable devices and clinical parameters including demographic, physiological, and lifestyle data;
• a preprocessing unit adapted to filter noise and normalize ECG signals for improved signal quality;
• a feature extraction module configured to derive significant ECG features including waveform intervals, heart rate variability, and rhythm characteristics;
• a data integration module adapted to combine extracted ECG features with clinical parameters into a unified dataset;
• a machine learning-based prediction engine configured to classify and predict heart disease conditions based on the combined dataset; and
• a decision support interface configured to display prediction results indicating the presence or risk level of heart disease.
Claim 2: The system as claimed in claim 1, wherein the ECG signal acquisition is performed using wearable devices enabling continuous and real-time monitoring of cardiac activity.
Claim 3: The system as claimed in claim 1, wherein the preprocessing unit utilizes filtering and normalization techniques to remove noise and artifacts from ECG signals to enhance feature quality.
Claim 4: The system as claimed in claim 1, wherein the machine learning-based prediction engine comprises algorithms selected from Gradient Boosting Machine, Random Forest, Support Vector Machine, or Neural Networks for accurate classification of cardiac conditions.
Claim 5: The system as claimed in claim 1, wherein the data integration module improves prediction accuracy by combining physiological ECG features with clinical parameters including age, blood pressure, cholesterol levels, diabetes status, and lifestyle habits.
Claim 6: The system as claimed in claim 1, wherein the decision support interface provides real-time diagnostic output and supports healthcare professionals in clinical decision-making through an interactive and user-friendly interface.