# Electrocardiographic Fragmented Activity (II):
## 2 Background  
Very limited literature has been published according to this topic, specially related to automatic detection of singularities such as fragmentation and almost no paper with regard to systematic automatic detection of fibrosis using the ECG was found in our search of precedents. In particular, and to our best knowledge, there are just four main works available about fragmentation detection. In Reference [9], the authors proposed a method based on discrete wavelet transform (DWT),The algorithm firstly presents a signal accommodation stage for processing through denoising and detrending. Then, the DWT is computed by using Haar signals and the resulting coefficients are interpolated. Finally, fragmentation is detected and classified according to the values of the coefficients close to the zero-cross points. This method was tested over 31 registers from the PTB database from Physionet, achieving 0.90 sensitivity and 0.90 specificity.  
In Reference [10], the authors presented an algorithm based on intrinsic time-scale decomposition (ITD). First, the algorithm computes the first four components of ITD from the ECG. Second, the ECG is delineated according to the second and third ITD components. Then, the fragmentation index is computed by averaging the ratio between the wave-speed and the absolute amplitude of the half wave over the QRS, both of them obtained from the first ITD component. Finally, the algorithm is tested over the records of the PTB Physionet database, limiting the cases to those meeting the following criteria: QRS duration lower than 120 ms and noise level computed as the standard deviation of the first 80 ms of the record lesser than 0.02 mV. Results showed 0.96 area under the receiver operating characteristic curve.  
In Reference [11], the authors proposed an algorithm based on stationary wavelet transform (SWT). In this case, the ECG is detrended by using median filters. Then, fiducial point detection is made based in zero-crossing techniques. Finally, fragmented QRS are detected by computing the SWT, jointly together with Haar signals, from each beat, thus finding the zero-crossing of the transformed signal and finally applying certain rules based on morphology. Results applied over 51 patients from University Hospital of Southampton showed 0.90 accuracy.  
In Reference [12], the authors proposed a method to detect fragmentation. Their algorithm firstly presents a pre-processing stage where the signal is band-pass filtered and the baseline wander is processed. Afterwards, the beats are detected and delineated by using the variant mode decomposition (VMD). Then, ten features are computed from the signal and from the VMD output, namely: The average slope of the rising and downing flanks of QRS; The values of the linear fit of the averaged slopes; The peaks and main frequencies, of the 3 rd, 4 th and 5 th component of VMD; And the number of peaks of the QRS. Finally, a number of classifiers are tested using the previous computed features over a private database of 616 records labeled by 5 experts. The final algorithm exhibited 0.86 sensitivity, 0.89 specificity and 0.88 accuracy.  
With regard to fibrosis, and as mentioned earlier, to our knowledge there is no published paper devoted to automatic detection of fibrosis. Hence, this would be a new research line pioneered by the present paper.  
In this work, we used linear and non-linear classifiers in order to detect both fragmentation and fibrosis. We selected those classifiers that previously have presented good performance in other similar bio-signal processing fields, which are: The support vector machine (SVM), used for the vasovagal syncope detection [13]; The K-nearest neighbour (KNN), applied in beat classification tasks [14]; The multilayer perceptron (MLP), used for arrhythmia classification in implanted cardioverter defibrillators [15]; The naive Bayes (NB), applied also in arrhythmia classification tasks [16]; And the decision trees (DT), used to lung cancer detection [17].