2D ECG classification system based on machine learning and LBP.

One of the most common and practical tools for predicting and diagnosing cardiac problems is the electrocardiogram (ECG) signal. It is an advanced medical device fundamental that monitors track of cardiac excitability, transmission, and recovery. It Provides essential information on the functional features of the heart and cardiovascular system. In this work, a technique for classifying an ECG image of a patient case into four classes is proposed. This class includes (MI, Arrhythmia, History MI, and Normal case). There are three steps to the proposed system: The procedure includes "image pre-processing, feature extraction, and classification". In order to prepare and enhance the input images, certain image preprocessing techniques are utilized in the early step. Color images are converted to binary images. Utilizing local binary pattern modulation, features are extracted from the images in the second step (LBP). The extracted features are sent into a 1D ML algorithm as input in the third step. Three ML algorithms are utilized. For ECG classification, these algorithms are "K-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR)." Algorithms achieved successful results during the ECG classification process, The findings show that the suggested technique is excellent in RF the range of this classification precision is 93.66 percent, and very good in K-NN the range of this classification precision is 80.53 percent, and good degree in LR the range of this classification precision is 70.87 percent. As evidenced by the low number of fault alarms and high accuracy rate, the suggested system performs exceptionally well in classifying ECG images. [ABSTRACT FROM AUTHOR]