Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection
Abstract
:1. Introduction
2. Background
3. Materials and Methods
3.1. Databases
3.2. Classifiers
3.3. Processing and Features Spaces
4. Results
4.1. Fragmentation Detection Based on Linear Models
4.2. Features Relevance and New Fragmented Subrogated Model
4.3. Fibrosis Detection Based on Linear Models and Statistical Relevance
4.4. Fragmentation and Fibrosis Detection Based on Non-Linear Models
5. Discussion
Author Contributions
Funding
Conflicts of Interest
References
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Classifier | Input Space | Signal Selection | Sen | Spe | PPV | NPV | Acc |
---|---|---|---|---|---|---|---|
NuSVM | Statistics + 3PCA | Non-normalized Beat | 0.70 | 0.75 | 0.76 | 0.69 | 0.73 |
NuSVM | Statistics + 3PCA | Normalized Beat | 0.70 | 0.81 | 0.80 | 0.71 | 0.75 |
NuSVM | Statistics + 3PCA | Non-normalized QRS | 0.76 | 0.88 | 0.87 | 0.77 | 0.82 |
NuSVM | Statistics + PCA | Normalized QRS | 0.75 | 0.90 | 0.89 | 0.76 | 0.82 |
CSVM | Statistics + ICA | Non-normalized Beat | 0.78 | 0.63 | 0.70 | 0.72 | 0.71 |
CSVM | Statistics + PCA | Normalized Beat | 0.65 | 0.88 | 0.85 | 0.69 | 0.76 |
CSVM | Statistics + ICA | Non-normalized QRS | 0.76 | 0.79 | 0.80 | 0.75 | 0.78 |
CSVM | Statistics + PCA | Normalized QRS | 0.70 | 0.86 | 0.85 | 0.72 | 0.78 |
Classifier | Input Space | Signal Selection | Sen | Spe | PPV | NPV | Acc |
---|---|---|---|---|---|---|---|
NuSVM | Statistics + 8-Ld | Non-normalized Beat | 0.55 | 0.73 | 0.67 | 0.63 | 0.64 |
NuSVM | Concat + 8-Ld | Normalized Beat | 0.74 | 0.58 | 0.63 | 0.70 | 0.66 |
NuSVM | Statistics + 12-Ld | Non-normalized QRS | 0.65 | 0.71 | 0.67 | 0.69 | 0.68 |
NuSVM | Statistics + 12-Ld | Normalized QRS | 0.70 | 0.65 | 0.69 | 0.71 | 0.68 |
CSVM | Sum + 8-Ld | Non-normalized Beat | 0.59 | 0.75 | 0.69 | 0.65 | 0.67 |
CSVM | Concat + 8-Ld | Normalized Beat | 0.67 | 0.62 | 0.63 | 0.66 | 0.64 |
CSVM | Concat + 8-Ld | Non-normalized QRS | 0.61 | 0.75 | 0.69 | 0.68 | 0.68 |
CSVM | Sum + 8-Ld | Normalized QRS | 0.54 | 0.71 | 0.63 | 0.63 | 0.63 |
Classifier | Signal Selection | Input Space | Accuracy | Classifier | Signal Selection | Input Space | Accuracy | Classifier | Signal Selection | Input Space | Accuracy |
---|---|---|---|---|---|---|---|---|---|---|---|
C-SVM | Normalized QRS | Stats + PCA | 0.79 | C-SVM | Non-normalized QRS | Stats + 3PCA | 0.77 | C-SVM | Normalized QRS | Sum + 8 Ld | 0.91 |
Nu-SVM | Non-normalized beat | Stats + ICA | 0.78 | Nu-SVM | Non-normalized QRS | Stats + 3PCA | 0.78 | Nu-SVM | Normalized QRS | Sum + 8 Ld | 0.91 |
KNN | Non-normalized QRS | Stats +PCA | 0.65 | KNN | Normalized QRS | Stats + PCA | 0.72 | KNN | Non-normalized QRS | Stats + 8 Ld | 0.79 |
MLP | Non-normalized QRS | Stats + ICA | 0.78 | MLP | Non-normalized QRS | Stats + ICA | 0.78 | MLP | Normalized Beat | Stats + 8 Ld | 0.78 |
DT | Normalized QRS | Stats + 8Ld | 0.81 | DT | Non-normalized QRS | Stats + PCA | 0.78 | DT | Non-normalized QRS | Stats + 12 Ld | 0.79 |
NB | Non-normalized QRS | Stats +PCA | 0.83 | NB | Non-normalized QRS | Stats + PCA | 0.83 | NB | Normalized QRS | Stats + 8 Ld | 0.79 |
(a) | (b) | (c) |
Classifier | Signal Selection | Input Space | Accuracy |
---|---|---|---|
C-SVM | Normalized QRS | Sum + 8 Ld | 0.68 |
-SVM | Normalized QRS | Sum + 8 Ld | 0.63 |
KNN | Non-normalized QRS | Stats + 8 Ld | 0.65 |
MLP | Non-normalized QRS | Stats + 12 Ld | 0.63 |
DT | Non-normalized QRS | Stats + 8 Ld | 0.61 |
NB | Non-normalized QRS | Stats + 8 Ld | 0.70 |
Data Base | Classifier | Signal Selection | Input Space | Sen | Spe | PPV | NPV | Acc |
---|---|---|---|---|---|---|---|---|
Sfrag | Linear -SVM | Normalized QRS | Stats + PCA | 0.730 | 0.772 | 0.780 | 0.721 | 0.750 |
NB | Non-normalized QRS | Stats + PCA | 0.778 | 0.965 | 0.961 | 0.797 | 0.867 | |
SWfrag | Linear -SVM | Normalized QRS | Stats + PCA | 0.698 | 0.860 | 0.846 | 0.721 | 0.775 |
NB | Non-normalized QRS | Stats + PCA | 0.762 | 0.895 | 0.889 | 0.773 | 0.825 | |
FHCM | Linear -SVM | Normalized Beat | PowSum + RegPCA | 0.733 | 0.941 | 0.917 | 0.800 | 0.844 |
RBF CSVM | Normalized QRS | Sum + 8 Ld | 0.941 | 0.875 | 0.889 | 0.933 | 0.909 | |
HCM | Linear -SVM | Non-normalized QRS | Stats + 12 Ld | 0.649 | 0.714 | 0.673 | 0.692 | 0.683 |
NB | Non-normalized QRS | Stats + 8 Ld | 0.474 | 0.905 | 0.818 | 0.655 | 0.700 |
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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 (II): A Machine Learning Approach to Detection. Appl. Sci. 2019, 9, 3565. https://doi.org/10.3390/app9173565
Melgarejo-Meseguer F-M, Gimeno-Blanes F-J, Salar-Alcaraz M-E, Gimeno-Blanes J-R, Martínez-Sánchez J, García-Alberola A, Rojo-Álvarez JL. Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection. Applied Sciences. 2019; 9(17):3565. https://doi.org/10.3390/app9173565
Chicago/Turabian StyleMelgarejo-Meseguer, Francisco-Manuel, Francisco-Javier Gimeno-Blanes, María-Eladia Salar-Alcaraz, Juan-Ramón Gimeno-Blanes, Juan Martínez-Sánchez, Arcadi García-Alberola, and José Luis Rojo-Álvarez. 2019. "Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection" Applied Sciences 9, no. 17: 3565. https://doi.org/10.3390/app9173565
APA StyleMelgarejo-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. (2019). Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection. Applied Sciences, 9(17), 3565. https://doi.org/10.3390/app9173565