An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.

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In this paper, we introduce a new color image segmentation by using superpixels as feature representation and Manhattan Nonnegative Matrix Factorization (MahNMF) for accurate segmentation. Firstly, the image pixels are grouped into superpixels and considered as the coarse features. The next step is then conducted by factorizing the matrix feature into two nonnegative matrices, which respectively imply representative features and their combination coefficients per superpixel. Exploiting superpixels as features can avoid using too much global information to obtain an advance in time complexity, and using MahNMF can analyze these features for getting segmented image. The experiments show the promise of this new approach.

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