## 9 Conclusions  
As mentioned above, one of the main causes of sudden death is caused by the VF arrhythmia [3; 53]. The rapid and correct detection of \(VF\) and \(VT\) is of fundamental importance both for the use of an automatic external defibrillator and for monitoring the patient. In order to obtain a reliable algorithm to discriminate between the different arrhythmias, an attempt was made to perform this detection task using the lowest computational load. The methodology uses the ECG to monitor biomedical signals that have different morphological and spectral characteristics. We propose the analysis of the ECG signal for the real-time detection of the onset of ventricular fibrillation using a time-frequency method [7; 54]. Reduction of network interference and other noises, which correspond to high frequency noises in these signals was carried out. After performing the steps above, the data matrix of each \(TFR\) is converted to an image (\(TFRI\)) corresponding to the different cardiac pathologies of the processed ECG signal, allowing to obtain an appropriate representation capable of providing useful information about the problem to be solved and allowing practical applications to the diagnosis in real time. The novelty of this work lies in the fact of using a reference mark \(WRM\) to establish an analysis window \(t_{w}\), obtaining a time-frequency representation and its associated image (\(TFR\) and \(TFRI\) matrices, respectively) which are used as input to a combined classification algorithm without calculation of additional parameters for the classifier. This fact avoids the extraction of characteristics and thus, the loss of relevant information to discriminate between the different classes. Additionaly, we propose the use of combined specialized classifiers to improve classification. An analysis of several combination methodologies, and a comparative study between the individual performance of the \(KNN\), \(ANNC\), \(L2 RLR\), and \(BAGG\) algorithms was done. All of these individual and combined classifier algorithms were trained with the cross-validation method and evaluated based on sensitivity, specificity, accuracy, precision, and execution time. Using the \(TFRI\) strategy, we concluded that, using z-5 cross validation, the individual \(KNN\) classifier achieves good results retrieving a sensitivity of 94.97 \(\pm\) 0.70%, a specificity of 99.27 \(\pm\) 0.05%, an accuracy of 98.47 \(\pm\) 0.01%, and a precision of 97.09 \(\pm\) 0.14% for \(VF\). In case of \(VT\), a sensitivity of 93.47 \(\pm\) 0.19%, specificity of 99.39 \(\pm\) 0.15%, accuracy of 98.97 \(\pm\) 0.08%, and precision of 92.11 \(\pm\) 0.7%, with a running time \(t=\) 0.17 s. Using the \(TFRI\) strategy with combined classifiers in hierarchical form (HM) achieved a sensitivity of 95.58 \(\pm\) 0.40%, specificity of 99.31 \(\pm\) 0.08%, 98.6 \(\pm\) 0.04% accuracy, and a precision of 98.25 \(\pm\) 0.29% for \(VF\), with a sensitivity of 94.02 \(\pm\) 0.58%, specificity of 99.31 \(\pm\) 0.08%, accuracy of 99.14 \(\pm\) 0.43%, and a precision of 98.59 \(\pm\) 0.09% for \(VT\), with execution time between 0.07 s and 0.12 s.  
Different classifier robustness and classification analysis are performed to validate results: Sensibility, specificity, accuracy, precision, confusioin matrices, ROC, AUC, and A-test. All these analyses show that the used methodology is adequate and congruent results are obtained. Taking into consideration the performed study, we have concluded that the use of combined classifiers is the best way to integrate the information since they provide stronger and efficient estimates than a single classifier. The proposed methodology provides useful information for the detection of \(VF\) in real time with a low computational time, discriminating \(VF\) from the rest of the cardiac pathologies satisfactorily. This fact significantly improves the possibilities of correct diagnosis of the patient when presenting an episode with any of these arrhythmias.