RT Journal Article
T1 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
A1 Groun, Nourelhouda
A1 Villalba Orero, María
A1 Casado Martín, Lucía
A1 Lara Pezzi, Enrique
A1 Valero, Eusebio
A1 Garicano Mena, Jesús
A1 Le Clainche, Soledad
AB 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.
PB Elsevier
SN 2590-1230
YR 2025
FD 2025-03
LK https://hdl.handle.net/20.500.14352/117897
UL https://hdl.handle.net/20.500.14352/117897
LA eng
NO 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, Vol 25, 2025, 104143, https://doi.org/10.1016/j.rineng.2025.104143.
NO Author Contributions:Nourelhouda Groun: Writing – original draft, Investigation, Formal analysis. María Villalba-Orero: Resources, Data curation. Lucía Casado-Martín: Data curation. Enrique Lara-Pezzi: Resources, Data curation. Eusebio Valero: Supervision, Project administration, Funding acquisition. Jesús Garicano-Mena: Writing – review & editing, Validation, Supervision. Soledad Le Clainche: Writing – review & editing, Supervision, Methodology, Funding acquisition, Conceptualization.
NO Ministerio de Ciencia e Innovación (España)
NO European Commission
DS Docta Complutense
RD 7 abr 2025