dor_id: 4149282

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

561.#.#.u: https://www.icat.unam.mx/

650.#.4.x: Ingenierías

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/1844/1002

100.1.#.a: Thangaraju, Tamilselvan; Sharma, Om Prakash

524.#.#.a: Thangaraju, Tamilselvan, et al. (2023). Hybird RNN based feature extraction for early prediction of CVDs using ECG Signals for type 2 diabetic patients. Journal of Applied Research and Technology; Vol. 21 Núm. 3, 2023; 424-432. Recuperado de https://repositorio.unam.mx/contenidos/4149282

245.1.0.a: Hybird RNN based feature extraction for early prediction of CVDs using ECG Signals for type 2 diabetic patients

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2023

264.#.1.c: 2023-06-27

653.#.#.a: Diabetes mellitus; Rajan Transform; electrocardiogram; Cardio Vascular Disease; hybrid Recurrent Neural Network

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/1844

001.#.#.#: 074.oai:ojs2.localhost:article/1844

041.#.7.h: eng

520.3.#.a: Diabetes mellitus patients are at an increased risk of cardiovascular illness, and cardiovascular complications are the primary cause of morbidity. Diabetes is linked to both morbidity and mortality. Type-2 Diabetes causes a prothrombotic state that leads to acute coronary syndromes by causing endothelial damage and lowering antiaggregant factors like nitric oxide and prostacyclin, as well as increasing thrombotic substances like fibrinogen and factor VII, and suppressing fibrinolysis with factors like plasminogen activator inhibitors. The accurate identification and diagnosis of CVD (Cardio Vascular Disease) is dependent on the correct detection of the ECG signal from the heart. The ECG signal is extremely important in the early detection of cardiac problems. The ECG signal of diabetic individuals offers vital information about the heart and is one of the most important diagnostic tools used by doctors to identify cardiovascular disorders. The time gap between two consecutive QRS complexes appearing contiguous in an ECG is known as heart rate. The most appealing feature is that HRV (Heart Rate Variability) measurement is non-invasive and repeatable. A number of machine learning techniques have been proposed for the non-invasive automated identification of diabetes. This paper discusses innovative methods for analyzing electrocardiogram (ECG) signals in order to extract important diagnostic information. The ECG signal is first treated using a dual tree complex wavelet transform (DTCWT-SG) with threshold method. Subsequently, the features are extracted from detailed coefficients of DTCWT-SG filter, Eigen vectors by minimum normalization method and Rajan Transform. Main key features are extracted using these three methods. These features are classified and analyzed by different machine learning classifiers. The proposed approach was tested on DICARDIA, MIT-BIH and Physionet database and the performance analysis shows that the hybrid Recurrent Neural Network (RNN) (LSTM+GRU Gated Recurrent Units) achieves better prediction of 98.8% compared to state of art techniques.

773.1.#.t: Journal of Applied Research and Technology; Vol. 21 Núm. 3 (2023); 424-432

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

300.#.#.a: Páginas: 424-432

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2023.21.3.1844

harvesting_date: 2023-11-08 13:10:00.0

856.#.0.q: application/pdf

file_creation_date: 2023-06-23 21:57:16.0

file_modification_date: 2023-06-23 21:57:16.0

file_creator: Yolanda G.G.

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last_modified: 2024-03-19 14:00:00

license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es

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