A Kohonen Self Organizing Map (KSOM) Technique for Classification of Electrocardiogram (ECG) Signals

Abstract

ABSTRACT

Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases. Handling noisy ECG data, which is common in real-world situations makes accurate classification a critical task. Because ECG signals are faint and are quickly disrupted, classification accuracy can be poor, hence the need for improvement in the automatic ECG categorization system's recognition accuracy. The Kohonen Self Organising Map (KSOM) is known for its ability to cluster high-dimensional data in a low-dimensional space, hence its adoption in this research. The procedure employed include collection and pre-processing of diverse ECG data, including normal and abnormal cardiac rhythms. Inherent noise was removed from the data to ensure better-quality input data into the classification algorithm. A Kohonen SOM neural network (MiniSom model) was trained using the preprocessed ECG data. The KSOM organizes ECG signals into clusters on a topological map, preserving similarities and dissimilarities between different cardiac rhythms. Subsequently, the trained SOM serves as a reference model for classifying unseen ECG signals, indicating the corresponding cardiac rhythm. Benchmarked on two different dataset, evaluation of the classification performance of the technique was carried out. Cross-validation was done to assess the model's robustness and generalizability. Comparative analysis was conducted to measure the effectiveness and efficiency of the SOM-based approach against other common ECG signal classification techniques based on accuracy, precision, recall and fi-score. The result obtained shows that the average accuracy of 94.2%, precision of 83%, recall of 100% and f1-score of 91% achieved by the MiniSOM model outperformed the other models.