Identification Of Abnormalities In The Heart Using Extreme Learning Machine (ELM)

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Identification of Abnormalities in the Heart using Extreme Learning Machine (ELM)

Introduction

Abnormalities in the heart are abnormal conditions that can cause chronic heart disease if not treated immediately. The heart acts as the center of blood circulation in the body, so that every disorder of this vital organ can have serious consequences for health. Unfortunately, heart disorders cannot be seen in plain view. To identify this disorder, a heart rate record is needed in the form of an electrocardiogram (EKG). Therefore, an approach that utilizes image processing and artificial neural networks becomes very important.

Background

The heart is a vital organ that plays a crucial role in maintaining the body's overall health. Any disorder or abnormality in the heart can have serious consequences, including heart failure, stroke, and even death. Early detection and treatment of heart disorders are essential to prevent these complications. However, manual interpretation of EKG signals can be a challenge, even for experienced medical professionals. This is where artificial intelligence-based technology, such as Extreme Learning Machine (ELM), comes into play.

Methodology

In this study, the method used to identify cardiac abnormalities is Extreme Learning Machine (ELM). ELM is one of the algorithms of artificial neural networks known for its ability to learn quickly. The process of taking the characteristics of the ECG is done using invariants moment, which helps in detecting important features of the ECG signal. In this study, researchers used 57 ECG images as training data and 22 ECG images as test data. The identified disorders include atrial fibrillation, Wave-T, ST segment, and normal heart condition.

Experimental Design

This study tests three choices of activation functions in Extreme Learning Machine, namely sigmoid, sine, and hard climate. In addition, the parameter of hidden neurons is also tested using variations of 10, 30, and 50 neurons. The results of the test show that the level of accuracy of the system in identifying cardiac abnormalities reaches 90.9% with the use of sigmoid functions and the number of hidden neurons of 50.

Results

The results of this study show that the use of ELM with sigmoid activation function and 50 hidden neurons achieves the highest accuracy of 90.9% in identifying cardiac abnormalities. This is a significant improvement over manual interpretation of EKG signals, which can be prone to errors. The use of invariants moment as a feature extraction method also provides more significant features for ELM model training.

Discussion

Success in identifying heart disorders with high accuracy is very important, given the early detection of heart disorders can save lives. ECG is a tool commonly used in the medical world to monitor heart activity, but the EKG manual interpretation can be a challenge, even for experienced medical professionals. Therefore, the use of artificial intelligence-based technology such as Extreme Learning Machine is very promising.

Another advantage of using ELM is the speed in the training process compared to other conventional neural network methods. In addition, its ability to process large amounts of data is very helpful in analyzing complex ECG images. By using invariants moment as a feature extraction method, this research is able to provide more significant features for ELM model training.

Conclusion

The use of Extreme Learning Machine (ELM) with sigmoid activation function and 50 hidden neurons achieves the highest accuracy of 90.9% in identifying cardiac abnormalities. This is a significant improvement over manual interpretation of EKG signals, which can be prone to errors. The use of invariants moment as a feature extraction method also provides more significant features for ELM model training. The results of this study show that the use of ELM is a promising approach for identifying cardiac abnormalities.

Future Directions

The use of modern technology and methods, such as ELM, provides new hope for future heart health management. Further research is needed to explore other combinations of activation functions and hidden neuron parameters that might increase further accuracy. Additionally, the use of ELM in other medical applications, such as identifying other types of diseases, is also an interesting area of research.

Limitations

This study has some limitations. The number of ECG images used in this study is relatively small, and further research is needed to validate the results with a larger dataset. Additionally, the use of ELM in other medical applications, such as identifying other types of diseases, is also an interesting area of research.

Conclusion

In conclusion, the use of Extreme Learning Machine (ELM) with sigmoid activation function and 50 hidden neurons achieves the highest accuracy of 90.9% in identifying cardiac abnormalities. This is a significant improvement over manual interpretation of EKG signals, which can be prone to errors. The use of invariants moment as a feature extraction method also provides more significant features for ELM model training. The results of this study show that the use of ELM is a promising approach for identifying cardiac abnormalities.

Recommendations

Based on the results of this study, the following recommendations are made:

  1. Further research is needed to explore other combinations of activation functions and hidden neuron parameters that might increase further accuracy.
  2. The use of ELM in other medical applications, such as identifying other types of diseases, is also an interesting area of research.
  3. The use of invariants moment as a feature extraction method is also a promising approach for identifying cardiac abnormalities.

Future Research Directions

The use of modern technology and methods, such as ELM, provides new hope for future heart health management. Further research is needed to explore other combinations of activation functions and hidden neuron parameters that might increase further accuracy. Additionally, the use of ELM in other medical applications, such as identifying other types of diseases, is also an interesting area of research.

Conclusion

In conclusion, the use of Extreme Learning Machine (ELM) with sigmoid activation function and 50 hidden neurons achieves the highest accuracy of 90.9% in identifying cardiac abnormalities. This is a significant improvement over manual interpretation of EKG signals, which can be prone to errors. The use of invariants moment as a feature extraction method also provides more significant features for ELM model training. The results of this study show that the use of ELM is a promising approach for identifying cardiac abnormalities.
Q&A: Identification of Abnormalities in the Heart using Extreme Learning Machine (ELM)

Q: What is Extreme Learning Machine (ELM)?

A: Extreme Learning Machine (ELM) is a type of artificial neural network algorithm that is known for its ability to learn quickly. It is a supervised learning algorithm that can be used for classification, regression, and clustering tasks.

Q: How does ELM work?

A: ELM works by first training a hidden layer of neurons using a set of input data. The hidden layer is then used to generate a set of output values that are used to classify or predict the input data.

Q: What is the advantage of using ELM in identifying cardiac abnormalities?

A: The advantage of using ELM in identifying cardiac abnormalities is that it can learn quickly and accurately from a large dataset of ECG images. This allows for the identification of cardiac abnormalities with high accuracy, which can be difficult to achieve using manual interpretation of EKG signals.

Q: What is the role of invariants moment in ELM?

A: Invariants moment is a feature extraction method that is used to extract important features from the ECG signal. These features are then used to train the ELM model, which can learn to identify cardiac abnormalities.

Q: What are the different activation functions used in ELM?

A: The different activation functions used in ELM are sigmoid, sine, and hard climate. Each of these activation functions has its own strengths and weaknesses, and the choice of activation function can affect the accuracy of the ELM model.

Q: What is the significance of the number of hidden neurons in ELM?

A: The number of hidden neurons in ELM can affect the accuracy of the model. A larger number of hidden neurons can lead to a more complex model that can learn to identify more complex patterns in the data.

Q: What are the limitations of this study?

A: The limitations of this study include the small number of ECG images used in the study, and the need for further research to validate the results with a larger dataset.

Q: What are the future directions of this research?

A: The future directions of this research include exploring other combinations of activation functions and hidden neuron parameters that might increase further accuracy, and using ELM in other medical applications, such as identifying other types of diseases.

Q: What are the recommendations of this study?

A: The recommendations of this study include further research to explore other combinations of activation functions and hidden neuron parameters that might increase further accuracy, and using ELM in other medical applications, such as identifying other types of diseases.

Q: What is the conclusion of this study?

A: The conclusion of this study is that the use of Extreme Learning Machine (ELM) with sigmoid activation function and 50 hidden neurons achieves the highest accuracy of 90.9% in identifying cardiac abnormalities. This is a significant improvement over manual interpretation of EKG signals, which can be prone to errors.

Q: What are the implications of this study?

A: The implications of this study are that the use of ELM can provide a more accurate and efficient way of identifying cardiac abnormalities, which can lead to better patient outcomes and improved healthcare.

Q: What are the future applications of this research?

A: The future applications of this research include using ELM in other medical applications, such as identifying other types of diseases, and developing more accurate and efficient methods for identifying cardiac abnormalities.

Q: What are the potential benefits of this research?

A: The potential benefits of this research include improved patient outcomes, reduced healthcare costs, and improved healthcare efficiency.

Q: What are the potential risks of this research?

A: The potential risks of this research include the potential for errors in the ELM model, and the need for further research to validate the results with a larger dataset.