A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identification
In this work, a data-driven, modal decomposition method, the higher order dynamic mode decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to improve the classification accuracy of several cardiac diseases using echocardiography images. The HODMD algorithm is used f...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025002312 |
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| author | Nourelhouda Groun María Villalba-Orero Lucía Casado-Martín Enrique Lara-Pezzi Eusebio Valero Jesús Garicano-Mena Soledad Le Clainche |
| author_facet | Nourelhouda Groun María Villalba-Orero Lucía Casado-Martín Enrique Lara-Pezzi Eusebio Valero Jesús Garicano-Mena Soledad Le Clainche |
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| description | In this work, a data-driven, modal decomposition method, the higher order dynamic mode decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to improve the classification accuracy of several cardiac diseases using echocardiography images. The HODMD algorithm is used first as feature extraction technique for the echocardiography datasets, taken from both healthy mice and mice afflicted by different cardiac diseases (Diabetic Cardiomyopathy, Obesity, TAC Hypertrophy and Myocardial Infarction). A total number of 130 echocardiography datasets are used in this work. The dominant features related to each cardiac disease were identified and represented by the HODMD algorithm as a set of DMD modes, which then are used as the input to the CNN. In a way, the database dimension was augmented, hence HODMD has been used, for the first time to the authors knowledge, for data augmentation in the machine learning framework. Six sets of the original echocardiography databases were hold out to be used as unseen data to test the performance of the CNN. In order to demonstrate the efficiency of the HODMD technique, two testcases are studied: the CNN is first trained using the original echocardiography images only, and second training the CNN using a combination of the original images and the DMD modes. The classification performance of the designed trained CNN shows that combining the original images with the DMD modes improves the results in all the testcases, as it improves the accuracy by up to 22%. These results show the great potential of using the HODMD algorithm as a data augmentation technique. |
| format | Article |
| id | doaj-art-9fa378b39cf74720ae88f7fa2d72b1a8 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-9fa378b39cf74720ae88f7fa2d72b1a82025-01-30T05:14:52ZengElsevierResults in Engineering2590-12302025-03-0125104143A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identificationNourelhouda Groun0María Villalba-Orero1Lucía Casado-Martín2Enrique Lara-Pezzi3Eusebio Valero4Jesús Garicano-Mena5Soledad Le Clainche6ETSI Aeronáutica y del Espacio - Universidad Politécnica de Madrid, Pl. del Cardenal Cisneros, 3, 28040, Madrid, Spain; ETSI Telecomunicación - Universidad Politécnica de Madrid, Av. Complutense, 30, 28040, Madrid, Spain; Corresponding author.Departamento de Medicina y Cirugía Animal, Facultad de Veterinaria - Universidad Complutense de Madrid, Av. Puerta de Hierro, 28040, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), C. de Melchor Fernández Almagro, 3, 28029, Madrid, SpainDepartamento de Medicina y Cirugía Animal, Facultad de Veterinaria - Universidad Complutense de Madrid, Av. Puerta de Hierro, 28040, Madrid, SpainCentro Nacional de Investigaciones Cardiovasculares (CNIC), C. de Melchor Fernández Almagro, 3, 28029, Madrid, SpainETSI Aeronáutica y del Espacio - Universidad Politécnica de Madrid, Pl. del Cardenal Cisneros, 3, 28040, Madrid, Spain; Center for Computational Simulation (CCS), Boadilla del Monte, 28660, Madrid, SpainETSI Aeronáutica y del Espacio - Universidad Politécnica de Madrid, Pl. del Cardenal Cisneros, 3, 28040, Madrid, Spain; Center for Computational Simulation (CCS), Boadilla del Monte, 28660, Madrid, SpainETSI Aeronáutica y del Espacio - Universidad Politécnica de Madrid, Pl. del Cardenal Cisneros, 3, 28040, Madrid, Spain; Center for Computational Simulation (CCS), Boadilla del Monte, 28660, Madrid, SpainIn this work, a data-driven, modal decomposition method, the higher order dynamic mode decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to improve the classification accuracy of several cardiac diseases using echocardiography images. The HODMD algorithm is used first as feature extraction technique for the echocardiography datasets, taken from both healthy mice and mice afflicted by different cardiac diseases (Diabetic Cardiomyopathy, Obesity, TAC Hypertrophy and Myocardial Infarction). A total number of 130 echocardiography datasets are used in this work. The dominant features related to each cardiac disease were identified and represented by the HODMD algorithm as a set of DMD modes, which then are used as the input to the CNN. In a way, the database dimension was augmented, hence HODMD has been used, for the first time to the authors knowledge, for data augmentation in the machine learning framework. Six sets of the original echocardiography databases were hold out to be used as unseen data to test the performance of the CNN. In order to demonstrate the efficiency of the HODMD technique, two testcases are studied: the CNN is first trained using the original echocardiography images only, and second training the CNN using a combination of the original images and the DMD modes. The classification performance of the designed trained CNN shows that combining the original images with the DMD modes improves the results in all the testcases, as it improves the accuracy by up to 22%. These results show the great potential of using the HODMD algorithm as a data augmentation technique.http://www.sciencedirect.com/science/article/pii/S2590123025002312Deep learningHigher order dynamic mode decompositionClassificationData augmentationEchocardiography |
| spellingShingle | Nourelhouda Groun María Villalba-Orero Lucía Casado-Martín Enrique Lara-Pezzi Eusebio Valero Jesús Garicano-Mena Soledad Le Clainche A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identification Results in Engineering Deep learning Higher order dynamic mode decomposition Classification Data augmentation Echocardiography |
| title | A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identification |
| title_full | A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identification |
| title_fullStr | A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identification |
| title_full_unstemmed | A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identification |
| title_short | A novel data augmentation tool for enhancing machine learning classification: A new application of the higher order dynamic mode decomposition for improved cardiac disease identification |
| title_sort | novel data augmentation tool for enhancing machine learning classification a new application of the higher order dynamic mode decomposition for improved cardiac disease identification |
| topic | Deep learning Higher order dynamic mode decomposition Classification Data augmentation Echocardiography |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025002312 |
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