A Deep Learning Framework for ECG Classification Based on 2D-to-1D Data Transformation Using Autoencoder and LSTM

In this study, a deep learning framework for multiclass ECG classification based on a 2D-to-1D data transformation is presented. The proposed methodology integrates a deep autoencoder and a long short-term memory network to enable sequential modeling of compressed 2D signal representations. This strategy effectively addresses the challenge of data format compatibility, enabling the training of models on data initially unsuitable for such architectures. ECG signals were transformed into 2D representations using techniques such as recurrence plots, visibility matrices, Gram matrices, and Markov transition fields, followed by compression into low-dimensional vectors suitable for sequence processing. The proposed method was rigorously evaluated using the MIT-BIH arrhythmia database and demonstrated superior performance compared to contemporary state-of-the-art models. The method achieved high metric values, with MTFs yielding the best results: 99.8% accuracy, 99.8% precision, 99.8% recall, 99.8% specificity, 99.8% F1-score, 99.7% Matthews correlation coefficient, and 99.3% Cohen’s kappa, confirming its effectiveness for real-time ECG classification in clinical scenarios. Furthermore, the model’s versatility was validated through its successful application to image classification tasks, highlighting its adaptability to a wide range of data types beyond ECG signals.