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Being electrocardiogram already an established method for analyzing cardiac health, it gained many researchers' interests to classify heartbeats accurately. In spite of having numerous works in this field, it still lacks obtaining high accuracy scores. In this paper, some well-known machine learning approaches are used by tuning and compared with other state-of-the-art related methodologies. The datasets are used in this research work, are highly imbalanced and handled with penalizing the loss value of the Artificial Neural Network (ANN) by assigning class weights. Two different enriched ECG datasets are selected for this research. They are MIT-BIH Arrhythmia which contains five classes and PTB Diagnostic ECG which contains two classes. About 98.06% and 97.664% accuracy are achieved with proposed approaches for MIT-BIH Arrhythmia and PTB Diagnostic ECG dataset respectively. Both cases this research outperforms all the other state-of-the-art methodologies.

An architecture is proposed in this paper, which combines both the U-net and Neural Ordinary Differential Equations for semantic segmentation. This method consumes very lower memory and at the same time in many cases outperforms some state-of-the-art methodologies in terms of very well known performance metrics for semantic segmentation. The proposed approach is tested on three datasets, two of them are medical images and another one is self-supervised dataset. For all the datasets, the proposed approach outperforms the state-of-the-art methods with the same environmental setup.