Next Article in Journal
Non-Linear Vibration Isolators with Unknown Excitation and Unmodelled Dynamics: Sliding Mode Active Control
Previous Article in Journal
Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection
Article

Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions

1
Unidad de Arritmias, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 El Palmar, Spain
2
Departamento de Medicina Interno, Universidad de Murcia, 30001 Murcia, Spain
3
Instituto Murciano de Investigación Biosanitaria Virgen de la Arrixaca (IMIB), 30120 El Palmar, Spain
4
Departamento de Ingeniería de Comunicaciones, Universidad Miguel Hernández, 03202 Elche, Spain
5
Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain
6
Center for Computational Simulation, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(17), 3566; https://doi.org/10.3390/app9173566
Received: 15 July 2019 / Revised: 19 August 2019 / Accepted: 27 August 2019 / Published: 31 August 2019
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. View Full-Text
Keywords: ECG; fragmentation analysis; multivariate techniques; ICA; PCA; fragmentation detection ECG; fragmentation analysis; multivariate techniques; ICA; PCA; fragmentation detection
Show Figures

Figure 1

MDPI and ACS Style

Melgarejo-Meseguer, F.-M.; Gimeno-Blanes, F.-J.; Salar-Alcaraz, M.-E.; Gimeno-Blanes, J.-R.; Martínez-Sánchez, J.; García-Alberola, A.; Rojo-Álvarez, J.-L. Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions. Appl. Sci. 2019, 9, 3566. https://doi.org/10.3390/app9173566

AMA Style

Melgarejo-Meseguer F-M, Gimeno-Blanes F-J, Salar-Alcaraz M-E, Gimeno-Blanes J-R, Martínez-Sánchez J, García-Alberola A, Rojo-Álvarez J-L. Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions. Applied Sciences. 2019; 9(17):3566. https://doi.org/10.3390/app9173566

Chicago/Turabian Style

Melgarejo-Meseguer, Francisco-Manuel; Gimeno-Blanes, Francisco-Javier; Salar-Alcaraz, María-Eladia; Gimeno-Blanes, Juan-Ramón; Martínez-Sánchez, Juan; García-Alberola, Arcadi; Rojo-Álvarez, José-Luis. 2019. "Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions" Appl. Sci. 9, no. 17: 3566. https://doi.org/10.3390/app9173566

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop