Revolutionizing Cardiac Disease Detection: A Multi-Model Approach for ECG Signal Analysis

Problem Definition

From the literature review, it is evident that there is a pressing need to improve the detection of heart diseases at early stages using Electrocardiogram (ECG) signals. While various AI-based methods have been developed for this purpose, they have shown limitations in accuracy and complexity due to the lack of a proper feature extraction model. Traditional models have failed to consider the specific features of ECG signals that are crucial for identifying different heart diseases, such as peaks in signal amplitude and time-related features. As a result, the existing models focus solely on understanding the general pattern of ECG signals, making it challenging for them to accurately detect heart diseases. Therefore, there is a critical need for an updated system that incorporates a feature model for ECG signals in conjunction with Convolutional Neural Networks (CNN) to enhance the detection of heart diseases effectively and efficiently.

Objective

The objective is to develop a novel method that combines Convolutional Neural Network (CNN) and Feed Forward Artificial Neural Network (FFANN) to enhance the detection of heart diseases at early stages using Electrocardiogram (ECG) signals. This approach aims to address the limitations of existing AI-based methods by incorporating a feature extraction model that considers specific features of ECG signals crucial for identifying different heart diseases. By extracting key features and utilizing a voting mechanism to combine the outputs of both classifiers, the proposed model seeks to improve accuracy in detecting ECG heartbeat abnormalities, ultimately contributing to early detection and treatment of heart diseases.

Proposed Work

To address the research gap of accurately detecting heart diseases at early stages, a novel method combining Convolutional Neural Network (CNN) and Feed Forward Artificial Neural Network (FFANN) is proposed in this study. The traditional models lacked feature extraction models for ECG signals, leading to inaccurate and complex analysis. The proposed model aims to utilize the pattern-based training of CNN and feature-based training of FFANN to improve detection accuracy. By extracting crucial features such as mean, variance, number of R waves, and frequency domain characteristics, the proposed model can enhance the performance of the FF-ANN algorithm. Additionally, a voting mechanism is introduced to combine the outputs of both classifiers, ensuring a more reliable detection decision based on weightage.

This approach of integrating CNN, FFANN, and feature extraction models aims to enhance the accuracy of detecting ECG heartbeat abnormalities, ultimately contributing to early detection and treatment of heart diseases.

Application Area for Industry

This project can be utilized in various industrial sectors such as healthcare, medical devices, and artificial intelligence. The proposed solutions can be applied within different industrial domains by addressing the specific challenges faced by industries in detecting heart diseases at early stages. By incorporating a feature extraction model for ECG signals and utilizing a combination of CNN and FFANN classifiers, the project aims to improve the accuracy and efficiency of heart disease detection. Industries in the healthcare sector can benefit from the implementation of these solutions as it can lead to early diagnosis, better patient outcomes, and reduced healthcare costs. Moreover, the use of advanced technologies like AI in medical devices can revolutionize the way heart diseases are diagnosed and treated, providing a significant competitive advantage to companies operating in this sector.

Application Area for Academics

The proposed project on detecting heart diseases using a combination of CNN and FFANN models along with feature extraction can significantly enrich academic research, education, and training in the field of AI and healthcare. This project can provide researchers, MTech students, and PHD scholars with a valuable resource for studying innovative research methods and data analysis techniques in the context of ECG signal analysis. By incorporating CNN and FFANN models, the project offers a unique approach to pattern-based and feature-based training, providing valuable insights for future research in the field of medical diagnostics. The use of feature extraction models along with advanced classification algorithms can help in enhancing the accuracy and efficiency of detecting heart diseases from ECG signals. This project can be beneficial for researchers working in the domain of AI, machine learning, and healthcare.

They can use the code and literature provided in this project to understand the implementation of CNN and FFANN models for ECG signal analysis and further enhance their own research work in this area. MTech students and PHD scholars can also leverage the insights and methodologies presented in this project to develop their own research projects focused on improving the accuracy of heart disease detection using AI techniques. Future scope of this project includes exploring the integration of other advanced algorithms and techniques such as deep learning, reinforcement learning, and ensemble methods for further enhancing the accuracy and reliability of heart disease detection from ECG signals. Additionally, the project can be extended to include real-time monitoring and prediction of heart diseases, paving the way for the development of intelligent healthcare systems.

Algorithms Used

The proposed work utilizes a combination of convolutional neural network (CNN) and feed forward artificial neural network (FFANN) to accurately detect ECG heartbeat abnormalities. The CNN is used for pattern-based training, while the FFANN is used for feature-based training, making the model efficient in recognizing testing signals. The model also includes a feature extraction module to extract crucial features from the ECG signals such as Mean, variance, number of R waves, Frequency domain characteristics, Average heart rate, standard deviation of R-R series, sample entropy, power spectral entropy, mean R-R interval distance, and standard deviation of heart rate of ECG signal. The output from both CNN and FFANN is fed into a voting mechanism to make the final detection decision based on weightage.

Keywords

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SEO Tags

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