Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions
Recent research has proven the existence of statistical relation among fragmented QRS and several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome, arrythmogenic cardiomyopathies, Brugada syndrome, and hypertrophic cardiomyopathy. One out of five hundred people suffer...
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| Format: | Article |
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MDPI AG
2019-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/17/3566 |
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| author | Francisco-Manuel Melgarejo-Meseguer Francisco-Javier Gimeno-Blanes María-Eladia Salar-Alcaraz Juan-Ramón Gimeno-Blanes Juan Martínez-Sánchez Arcadi García-Alberola José-Luis Rojo-Álvarez |
| author_facet | Francisco-Manuel Melgarejo-Meseguer Francisco-Javier Gimeno-Blanes María-Eladia Salar-Alcaraz Juan-Ramón Gimeno-Blanes Juan Martínez-Sánchez Arcadi García-Alberola José-Luis Rojo-Álvarez |
| author_sort | Francisco-Manuel Melgarejo-Meseguer |
| collection | DOAJ |
| description | Recent research has proven the existence of statistical relation among fragmented QRS and several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome, arrythmogenic cardiomyopathies, Brugada syndrome, and hypertrophic cardiomyopathy. One out of five hundred people suffer from hypertrophic cardiomyopathies. The relation among the fragmentation and arrhythmias drives the objective of this work, which is to propose a valid method for QRS fragmentation detection. With that aim, we followed a two-stage approach. First, we identified the features that better characterize the fragmentation by analyzing the physiological interpretation of multivariate approaches, such as principal component analysis (PCA) and independent component analysis (ICA). Second, we created an invariant transformation method for the multilead electrocardiogram (ECG), by scrutinizing the statistical distributions of the PCA eigenvectors and of the ICA transformation arrays, in order to anchor the desired elements in the suitable leads in the feature space. A complete database was compounded incorporating real fragmented ECGs, surrogate registers by synthetically adding fragmented activity to real non-fragmented ECG registers, and standard clean ECGs. Results showed that the creation of beat templates together with the application of PCA over eight independent leads achieves 0.995 fragmentation enhancement ratio and 0.07 dispersion coefficient. In the case of ICA over twelve leads, the results were 0.995 fragmentation enhancement ratio and 0.70 dispersion coefficient. We conclude that the algorithm presented in this work constructs a new paradigm, by creating a systematic and powerful tool for clinical anamnesis and evaluation based on multilead ECG. This approach consistently consolidates the inconspicuous elements present in multiple leads onto designated variables in the output space, hence offering additional and valid visual and non-visual information to standard clinical review, and opening the door to a more accurate automatic detection and statistically valid systematic approach for a wide number of applications. In this direction and within the companion paper, further developments are presented applying this technique to fragmentation detection. |
| first_indexed | 2024-12-19T03:55:04Z |
| format | Article |
| id | doaj.art-72ac833933e04684873f31c7174565a7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-19T03:55:04Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-72ac833933e04684873f31c7174565a72022-12-21T20:36:50ZengMDPI AGApplied Sciences2076-34172019-08-01917356610.3390/app9173566app9173566Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal DecompositionsFrancisco-Manuel Melgarejo-Meseguer0Francisco-Javier Gimeno-Blanes1María-Eladia Salar-Alcaraz2Juan-Ramón Gimeno-Blanes3Juan Martínez-Sánchez4Arcadi García-Alberola5José-Luis Rojo-Álvarez6Unidad de Arritmias, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 El Palmar, SpainDepartamento de Ingeniería de Comunicaciones, Universidad Miguel Hernández, 03202 Elche, SpainUnidad de Arritmias, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 El Palmar, SpainUnidad de Arritmias, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 El Palmar, SpainUnidad de Arritmias, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 El Palmar, SpainUnidad de Arritmias, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 El Palmar, SpainDepartamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28943 Fuenlabrada, SpainRecent research has proven the existence of statistical relation among fragmented QRS and several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome, arrythmogenic cardiomyopathies, Brugada syndrome, and hypertrophic cardiomyopathy. One out of five hundred people suffer from hypertrophic cardiomyopathies. The relation among the fragmentation and arrhythmias drives the objective of this work, which is to propose a valid method for QRS fragmentation detection. With that aim, we followed a two-stage approach. First, we identified the features that better characterize the fragmentation by analyzing the physiological interpretation of multivariate approaches, such as principal component analysis (PCA) and independent component analysis (ICA). Second, we created an invariant transformation method for the multilead electrocardiogram (ECG), by scrutinizing the statistical distributions of the PCA eigenvectors and of the ICA transformation arrays, in order to anchor the desired elements in the suitable leads in the feature space. A complete database was compounded incorporating real fragmented ECGs, surrogate registers by synthetically adding fragmented activity to real non-fragmented ECG registers, and standard clean ECGs. Results showed that the creation of beat templates together with the application of PCA over eight independent leads achieves 0.995 fragmentation enhancement ratio and 0.07 dispersion coefficient. In the case of ICA over twelve leads, the results were 0.995 fragmentation enhancement ratio and 0.70 dispersion coefficient. We conclude that the algorithm presented in this work constructs a new paradigm, by creating a systematic and powerful tool for clinical anamnesis and evaluation based on multilead ECG. This approach consistently consolidates the inconspicuous elements present in multiple leads onto designated variables in the output space, hence offering additional and valid visual and non-visual information to standard clinical review, and opening the door to a more accurate automatic detection and statistically valid systematic approach for a wide number of applications. In this direction and within the companion paper, further developments are presented applying this technique to fragmentation detection.https://www.mdpi.com/2076-3417/9/17/3566ECGfragmentation analysismultivariate techniquesICAPCAfragmentation detection |
| spellingShingle | Francisco-Manuel Melgarejo-Meseguer Francisco-Javier Gimeno-Blanes María-Eladia Salar-Alcaraz Juan-Ramón Gimeno-Blanes Juan Martínez-Sánchez Arcadi García-Alberola José-Luis Rojo-Álvarez Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions Applied Sciences ECG fragmentation analysis multivariate techniques ICA PCA fragmentation detection |
| title | Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions |
| title_full | Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions |
| title_fullStr | Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions |
| title_full_unstemmed | Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions |
| title_short | Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions |
| title_sort | electrocardiographic fragmented activity i physiological meaning of multivariate signal decompositions |
| topic | ECG fragmentation analysis multivariate techniques ICA PCA fragmentation detection |
| url | https://www.mdpi.com/2076-3417/9/17/3566 |
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