AI- BASED HEART STROKE PREDICTION AND CLASSIFICATION USING ECG AND PPG BIO-SIGNALS
Objective
This project aims to predict heart strokes using artificial intelligence, primarily utilizing artificial neural networks (ANN) trained on public databases to achieve over 92% accuracy, followed by classification with KNN.
Abstract
This project uses artificial intelligence (AI)-based decision makers to forecast heart strokes based on information gathered from various patients. The information will be gathered from the physionbankatm database or any other publicly accessible databases of heart attacks. Artificial Neural Networks (ANN) will be used in our work as the decision-making tool. The ANN will first be trained using the various datasets that were obtained from the aforementioned databases. The training phase will then be validated, and the ANN's accuracy will be tested during the test phase. We anticipate that utilizing ANN, we will be able to predict heart attacks with an accuracy of above 92%. Finally, the prediction is further classified to whether it is Hemorrhage or Ischemia using the same heart rate and by implementing KNN classifier. The neural network model used in the proposed study is based on deep learning and is therefore more reliable and accurate than conventional machine learning decision-making methods. Our goal is to create findings that are more accurate than those of earlier works or algorithms.
Keywords: Heart Stroke, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Machine Learning (ML), Accuracy.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Signal Processing?
· About Signal Processing
· Introduction to Signal Processing
· How analog and digital signal is formed
· Importing the signal via signal acquisition tools
· Analyzing and manipulation of signals.
· Phases of signal processing:
· Acquisition
· Signal enhancement
· Signal restoration
· Medical Signal Processing
· Medical Signal Analysis
· Medical Signal Diagnosis
· Filtering techniques
· Machine Learning Algorithms
· Deep Learning Algorithms etc.
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
