Sudden Cardiac Death Detection by Using an Hybrid Method Based on TWA and Dictionary Learning: A Data Experimentation
Sudden Cardiac Death (SCD) is considered one of the main causes of mortality worldwide. Understanding the origin of this heart disease continues to be a challenge for the scientific community. T-wave alternans (TWA) is the term used to describe changes in the T wave’s amplitude or shape t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10128134/ |
| _version_ | 1797810631301136384 |
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| author | Nancy C. Betancourt Marco Flores-Calero Carlos Almeida |
| author_facet | Nancy C. Betancourt Marco Flores-Calero Carlos Almeida |
| author_sort | Nancy C. Betancourt |
| collection | DOAJ |
| description | Sudden Cardiac Death (SCD) is considered one of the main causes of mortality worldwide. Understanding the origin of this heart disease continues to be a challenge for the scientific community. T-wave alternans (TWA) is the term used to describe changes in the T wave’s amplitude or shape that are seen. According to the literature review, T wave alternans has been considered an important, non-invasive indicator to detect and stratify the risk of sudden cardiac death. On the other hand, dictionary learning is a digital signal processing technique that allows identify the main characteristics of a signal using a sparse representation. In this context, a new non-invasive method is proposed by mixing TWA spectral methods and dictionary learning. The method identifies the main characteristics of ECG signal by obtaining a sparse representation that adapts a matrix (dictionary) in order to use it for highlighting the TWA characteristics and then use these characteristics for detecting SCD. Experimental results show an improvement of 32% compared to the Physionet TWAnalyser program by using synthetic data set and an improvement of 20% over public databases. |
| first_indexed | 2024-03-13T07:11:44Z |
| format | Article |
| id | doaj.art-477ebeccc6b24621a9010aabfe80b96f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T07:11:44Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-477ebeccc6b24621a9010aabfe80b96f2023-06-05T23:00:51ZengIEEEIEEE Access2169-35362023-01-0111530065301810.1109/ACCESS.2023.327739610128134Sudden Cardiac Death Detection by Using an Hybrid Method Based on TWA and Dictionary Learning: A Data ExperimentationNancy C. Betancourt0https://orcid.org/0000-0002-5988-0992Marco Flores-Calero1https://orcid.org/0000-0001-7507-3325Carlos Almeida2https://orcid.org/0000-0002-7301-963XDepartamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito, EcuadorDepartamento de Eléctrica, Electrónica y Telecomunicaciones, Universidad de las Fuerzas Armadas-ESPE, Sangolquí, EcuadorDepartamento de Matemática, Escuela Politécnica Nacional, Quito, EcuadorSudden Cardiac Death (SCD) is considered one of the main causes of mortality worldwide. Understanding the origin of this heart disease continues to be a challenge for the scientific community. T-wave alternans (TWA) is the term used to describe changes in the T wave’s amplitude or shape that are seen. According to the literature review, T wave alternans has been considered an important, non-invasive indicator to detect and stratify the risk of sudden cardiac death. On the other hand, dictionary learning is a digital signal processing technique that allows identify the main characteristics of a signal using a sparse representation. In this context, a new non-invasive method is proposed by mixing TWA spectral methods and dictionary learning. The method identifies the main characteristics of ECG signal by obtaining a sparse representation that adapts a matrix (dictionary) in order to use it for highlighting the TWA characteristics and then use these characteristics for detecting SCD. Experimental results show an improvement of 32% compared to the Physionet TWAnalyser program by using synthetic data set and an improvement of 20% over public databases.https://ieeexplore.ieee.org/document/10128134/ECGSCDdetectiondictionary learningTWA |
| spellingShingle | Nancy C. Betancourt Marco Flores-Calero Carlos Almeida Sudden Cardiac Death Detection by Using an Hybrid Method Based on TWA and Dictionary Learning: A Data Experimentation IEEE Access ECG SCD detection dictionary learning TWA |
| title | Sudden Cardiac Death Detection by Using an Hybrid Method Based on TWA and Dictionary Learning: A Data Experimentation |
| title_full | Sudden Cardiac Death Detection by Using an Hybrid Method Based on TWA and Dictionary Learning: A Data Experimentation |
| title_fullStr | Sudden Cardiac Death Detection by Using an Hybrid Method Based on TWA and Dictionary Learning: A Data Experimentation |
| title_full_unstemmed | Sudden Cardiac Death Detection by Using an Hybrid Method Based on TWA and Dictionary Learning: A Data Experimentation |
| title_short | Sudden Cardiac Death Detection by Using an Hybrid Method Based on TWA and Dictionary Learning: A Data Experimentation |
| title_sort | sudden cardiac death detection by using an hybrid method based on twa and dictionary learning a data experimentation |
| topic | ECG SCD detection dictionary learning TWA |
| url | https://ieeexplore.ieee.org/document/10128134/ |
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