Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks
<b>Background:</b> Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different 1D-to-2D transformation methods...
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2025-01-01
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| author | Georgios Lekkas Eleni Vrochidou George A. Papakostas |
| author_facet | Georgios Lekkas Eleni Vrochidou George A. Papakostas |
| author_sort | Georgios Lekkas |
| collection | DOAJ |
| description | <b>Background:</b> Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different 1D-to-2D transformation methods to classify electrocardiogram (ECG) and electroencephalogram (EEG) signals. <b>Methods:</b> We select five transformation methods: Continuous Wavelet Transform (CWT), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Signal Reshaping (SR), and Recurrence Plots (RPs). We used the MIT-BIH Arrhythmia Database for ECG signals and the Epilepsy EEG Dataset from the University of Bonn for EEG signals. After converting the signals from 1D to 2D, using the aforementioned methods, we employed two types of 2D CNNs: a minimal CNN and the LeNet-5 model. Our results indicate that RPs, CWT, and STFT are the methods to achieve the highest accuracy across both CNN architectures. <b>Results:</b> These top-performing methods achieved accuracies of 99%, 98%, and 95%, respectively, on the minimal 2D CNN and accuracies of 99%, 99%, and 99%, respectively, on the LeNet-5 model for the ECG signals. For the EEG signals, all three methods achieved accuracies of 100% on the minimal 2D CNN and accuracies of 100%, 99%, and 99% on the LeNet-5 2D CNN model, respectively. <b>Conclusions:</b> This superior performance is most likely related to the methods’ capacity to capture time–frequency information and nonlinear dynamics inherent in time-dependent signals such as ECGs and EEGs. These findings underline the significance of using appropriate transformation methods, suggesting that the incorporation of time–frequency analysis and nonlinear feature extraction in the transformation process improves the effectiveness of CNN-based classification for biological data. |
| format | Article |
| id | doaj-art-1e858ed4613441a2ac1abbfcc5a53ea6 |
| institution | OA Journals |
| issn | 2673-7426 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | BioMedInformatics |
| spelling | doaj-art-1e858ed4613441a2ac1abbfcc5a53ea62025-08-20T02:11:12ZengMDPI AGBioMedInformatics2673-74262025-01-0151710.3390/biomedinformatics5010007Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural NetworksGeorgios Lekkas0Eleni Vrochidou1George A. Papakostas2MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, GreeceMLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, GreeceMLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece<b>Background:</b> Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different 1D-to-2D transformation methods to classify electrocardiogram (ECG) and electroencephalogram (EEG) signals. <b>Methods:</b> We select five transformation methods: Continuous Wavelet Transform (CWT), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Signal Reshaping (SR), and Recurrence Plots (RPs). We used the MIT-BIH Arrhythmia Database for ECG signals and the Epilepsy EEG Dataset from the University of Bonn for EEG signals. After converting the signals from 1D to 2D, using the aforementioned methods, we employed two types of 2D CNNs: a minimal CNN and the LeNet-5 model. Our results indicate that RPs, CWT, and STFT are the methods to achieve the highest accuracy across both CNN architectures. <b>Results:</b> These top-performing methods achieved accuracies of 99%, 98%, and 95%, respectively, on the minimal 2D CNN and accuracies of 99%, 99%, and 99%, respectively, on the LeNet-5 model for the ECG signals. For the EEG signals, all three methods achieved accuracies of 100% on the minimal 2D CNN and accuracies of 100%, 99%, and 99% on the LeNet-5 2D CNN model, respectively. <b>Conclusions:</b> This superior performance is most likely related to the methods’ capacity to capture time–frequency information and nonlinear dynamics inherent in time-dependent signals such as ECGs and EEGs. These findings underline the significance of using appropriate transformation methods, suggesting that the incorporation of time–frequency analysis and nonlinear feature extraction in the transformation process improves the effectiveness of CNN-based classification for biological data.https://www.mdpi.com/2673-7426/5/1/7biomedical signal processingAI in healthcarebiomedical AIelectrocardiogram (ECG) classificationECGsdeep learning |
| spellingShingle | Georgios Lekkas Eleni Vrochidou George A. Papakostas Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks BioMedInformatics biomedical signal processing AI in healthcare biomedical AI electrocardiogram (ECG) classification ECGs deep learning |
| title | Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks |
| title_full | Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks |
| title_fullStr | Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks |
| title_full_unstemmed | Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks |
| title_short | Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks |
| title_sort | time frequency transformations for enhanced biomedical signal classification with convolutional neural networks |
| topic | biomedical signal processing AI in healthcare biomedical AI electrocardiogram (ECG) classification ECGs deep learning |
| url | https://www.mdpi.com/2673-7426/5/1/7 |
| work_keys_str_mv | AT georgioslekkas timefrequencytransformationsforenhancedbiomedicalsignalclassificationwithconvolutionalneuralnetworks AT elenivrochidou timefrequencytransformationsforenhancedbiomedicalsignalclassificationwithconvolutionalneuralnetworks AT georgeapapakostas timefrequencytransformationsforenhancedbiomedicalsignalclassificationwithconvolutionalneuralnetworks |