A Proficient Evaluation with the Pre-Term Birth Classification in ECG Signal Using KNN
Objective
The rate of premature births is rising all over the world and there is still no forecast against the births. The purpose of this work is to classify the fetal ECG heartbeats, using the KNN classifier, and to predict preterm birth.
Abstract
In this work, preterm birth is classified using KNN algorithm with ECG signals. The rate of premature births is rising all over the world and there is still no forecast against the births. Recent research is based on ECG record analysis, which contains information on the Electrophysiological properties of the mother’s and fetal’s cardiac signals.
The purpose of this work is to classify the fetal ECG heartbeats, using the KNN classifier, and to predict preterm birth. 50 ECG signals were taken in this work, it is preprocessed using the FIR and NLMS filters. Using FFT the function was extracted based on the pre-processed signals. Classification of the signals with the extracted features is unclear.
So, the classification is done by using the Classification Learner app from the MATLAB software. In addition, identify the ECG signals according to the qualified features, selected features and target value. ECG signals were marked as a term or premature.
Keywords: Preterm, ECG signals, FIR, NLMS, FFT, KNN.
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 Requirements:
MATLAB R2018a or above
Hardware Requirements:
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 an Image/pixel?
- About image formats
- Introduction to Image Processing
- How digital image is formed
- Importing the image via image acquisition tools
- Analyzing and manipulation of image.
- Phases of image processing:
- Acquisition
- Image enhancement:
- Image restoration:
- Color image processing:
- Image compression:
- Morphological processing:
- Segmentation etc.,
- About Artificial Intelligence (AI)
- About Machine Learning
- About Deep Learning
- About layers in AI (input, hidden and output layers)
- Building AI (ANN/CNN) architecture using Matlab
- We will be able to know what’s the term “Training” means in Artificial Intelligence
- About requirements that can influence the AI training process:
- Data
- Training data
- Validation data
- Testing data
- Hardware requirements to train network
- How to evaluate the pre-term birth using AI
- How to extend our work to another real time applications
- Project development Skills
- Problem analyzing skills
- Problem-solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
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