A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death
Cardiovascular diseases are among the major global health problems. For example, sudden cardiac death (SCD) accounts for approximately 4 million deaths worldwide. In particular, an SCD event can subtly change the electrocardiogram (ECG) signal before onset, which is generally undetectable by the pat...
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MDPI AG
2025-06-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/6/130 |
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| author | Manuel A. Centeno-Bautista Andrea V. Perez-Sanchez Juan P. Amezquita-Sanchez David Camarena-Martinez Martin Valtierra-Rodriguez |
| author_facet | Manuel A. Centeno-Bautista Andrea V. Perez-Sanchez Juan P. Amezquita-Sanchez David Camarena-Martinez Martin Valtierra-Rodriguez |
| author_sort | Manuel A. Centeno-Bautista |
| collection | DOAJ |
| description | Cardiovascular diseases are among the major global health problems. For example, sudden cardiac death (SCD) accounts for approximately 4 million deaths worldwide. In particular, an SCD event can subtly change the electrocardiogram (ECG) signal before onset, which is generally undetectable by the patient. Hence, timely detection of these changes in ECG signals could help develop a tool to anticipate an SCD event and respond appropriately in patient care. In this sense, this work proposes a novel computational methodology that combines the maximal overlap discrete wavelet packet transform (MODWPT) with stacked autoencoders (SAEs) to discover suitable features in ECG signals and associate them with SCD prediction. The proposed method efficiently predicts an SCD event with an accuracy of 98.94% up to 30 min before the onset, making it a reliable tool for early detection while providing sufficient time for medical intervention and increasing the chances of preventing fatal outcomes, demonstrating the potential of integrating signal processing and deep learning techniques within computational biology to address life-critical health problems. |
| format | Article |
| id | doaj-art-aef5feb22c004a02b11cbc18ed581a6b |
| institution | Kabale University |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-aef5feb22c004a02b11cbc18ed581a6b2025-08-20T03:26:11ZengMDPI AGComputation2079-31972025-06-0113613010.3390/computation13060130A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac DeathManuel A. Centeno-Bautista0Andrea V. Perez-Sanchez1Juan P. Amezquita-Sanchez2David Camarena-Martinez3Martin Valtierra-Rodriguez4ENAP-RG, CA Sistemas Dinámicos y Control, Facultad de Ingeniería, Departamento de Electromecánica, Universidad Autónoma de Querétaro, Campus San Juan del Río, San Juan del Río 76807, MexicoENAP-RG, CA Sistemas Dinámicos y Control, Facultad de Ingeniería, Departamento de Electromecánica, Universidad Autónoma de Querétaro, Campus San Juan del Río, San Juan del Río 76807, MexicoENAP-RG, CA Sistemas Dinámicos y Control, Facultad de Ingeniería, Departamento de Electromecánica, Universidad Autónoma de Querétaro, Campus San Juan del Río, San Juan del Río 76807, MexicoENAP-RG, División de Ingeniería, Universidad de Guanajuato (UG), Campus Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, MexicoENAP-RG, CA Sistemas Dinámicos y Control, Facultad de Ingeniería, Departamento de Electromecánica, Universidad Autónoma de Querétaro, Campus San Juan del Río, San Juan del Río 76807, MexicoCardiovascular diseases are among the major global health problems. For example, sudden cardiac death (SCD) accounts for approximately 4 million deaths worldwide. In particular, an SCD event can subtly change the electrocardiogram (ECG) signal before onset, which is generally undetectable by the patient. Hence, timely detection of these changes in ECG signals could help develop a tool to anticipate an SCD event and respond appropriately in patient care. In this sense, this work proposes a novel computational methodology that combines the maximal overlap discrete wavelet packet transform (MODWPT) with stacked autoencoders (SAEs) to discover suitable features in ECG signals and associate them with SCD prediction. The proposed method efficiently predicts an SCD event with an accuracy of 98.94% up to 30 min before the onset, making it a reliable tool for early detection while providing sufficient time for medical intervention and increasing the chances of preventing fatal outcomes, demonstrating the potential of integrating signal processing and deep learning techniques within computational biology to address life-critical health problems.https://www.mdpi.com/2079-3197/13/6/130sudden cardiac deathelectrocardiogram signalmaximum overlap discrete wavelet packet transformstacked autoencodersdeep learning |
| spellingShingle | Manuel A. Centeno-Bautista Andrea V. Perez-Sanchez Juan P. Amezquita-Sanchez David Camarena-Martinez Martin Valtierra-Rodriguez A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death Computation sudden cardiac death electrocardiogram signal maximum overlap discrete wavelet packet transform stacked autoencoders deep learning |
| title | A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death |
| title_full | A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death |
| title_fullStr | A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death |
| title_full_unstemmed | A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death |
| title_short | A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death |
| title_sort | computational methodology based on maximum overlap discrete wavelet transform and autoencoders for early prediction of sudden cardiac death |
| topic | sudden cardiac death electrocardiogram signal maximum overlap discrete wavelet packet transform stacked autoencoders deep learning |
| url | https://www.mdpi.com/2079-3197/13/6/130 |
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