Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain
Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and inter...
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MDPI AG
2024-09-01
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| author | Pratixita Bhattacharjee Piotr Augustyniak |
| author_facet | Pratixita Bhattacharjee Piotr Augustyniak |
| author_sort | Pratixita Bhattacharjee |
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| description | Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies’ efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson’s CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals. |
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| spelling | doaj-art-a7e7f6dbfc3d4747b29a1c73623ae7862025-08-20T01:47:44ZengMDPI AGApplied Sciences2076-34172024-09-011419877210.3390/app14198772Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling DomainPratixita Bhattacharjee0Piotr Augustyniak1Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, 30-059 Kraków, PolandFaculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, 30-059 Kraków, PolandElectrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies’ efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson’s CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals.https://www.mdpi.com/2076-3417/14/19/8772electrocardiogram (ECG)non-uniform samplingneural networkstransformer modeltemporal convolutional network (TCN)signal fidelity |
| spellingShingle | Pratixita Bhattacharjee Piotr Augustyniak Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain Applied Sciences electrocardiogram (ECG) non-uniform sampling neural networks transformer model temporal convolutional network (TCN) signal fidelity |
| title | Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain |
| title_full | Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain |
| title_fullStr | Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain |
| title_full_unstemmed | Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain |
| title_short | Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain |
| title_sort | comparative evaluation of neural network models for optimizing ecg signal in non uniform sampling domain |
| topic | electrocardiogram (ECG) non-uniform sampling neural networks transformer model temporal convolutional network (TCN) signal fidelity |
| url | https://www.mdpi.com/2076-3417/14/19/8772 |
| work_keys_str_mv | AT pratixitabhattacharjee comparativeevaluationofneuralnetworkmodelsforoptimizingecgsignalinnonuniformsamplingdomain AT piotraugustyniak comparativeevaluationofneuralnetworkmodelsforoptimizingecgsignalinnonuniformsamplingdomain |