# A Novel 1-D CCANet for ECG Classification
## 3 Proposed Method  
The input of the proposed method is a two-channel ECG segment obtained after a preliminary segmentation that consists in the isolation of heartbeats in each record. Given the R-peak positions, any heartbeat is isolated by retaining \(T_{1}\) and \(T_{2}\) samples to the left and to the right of the R-peak, respectively. For each of the two leads, a vector denoted as \(x_{h}\) (\(h=1,2\)) is built with the values of the ECG (in Volts), of size \((T_{2}~{}+~{}T_{1})\). The values of \(T_{1}\) and \(T_{2}\) are 160-200 and 120-136 for MIT-BIH and INCART, respectively. These values are given in Table 3, and are similar to the one used in [20].  
The architecture of the whole process is shown Figure 1a,b. The first stage is feature extraction. The input of the process is, for each lead, a vector \(x_{h}\) (with \(h=1,2\)) containing raw values of the segmented heartbeat. Each lead \(x_{h}\) (\(h=1,2\)) is normalized (see the \"Normalization\" module in Figure 1a by using a rescaling procedure so that the resulting vector \(x_{h,\text{norm}}\) has an intensity that ranges from 0 to 1, as per equation:  
\[x_{h,\text{norm}}=\frac{x_{h}-\min(x_{h})}{\max(x_{h})-\min(x_{h})}. \tag{1}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type** & **Name** & **Quantity** \\ \hline r & Rhythm change & 200 \\ N & Normal beat & 1000 \\ A & Atrial premature beat & 200 \\ V & Premature ventricular & 200 \\ P & Paced beat & 200 \\ x & Non-conducted P-wave & 100 \\ F & Fusion of ventricular contraction & 200 \\ j & Nodal (junction) escape beat & 200 \\ L & Left bundle branch block beat & 200 \\ a & Aberrated atrial premature beat & 100 \\ J & Nodal (junction) premature beat & 50 \\ R & Left bundle branch block beat & 200 \\! & Ventricular flutter & 200 \\ E & Ventricular escape beat & 100 \\ f & Fusion of paced and normal beat & 200 \\ \hline Tot & & 3350 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the categories for MIT-BIH database.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Type** & **Name** & **Quantity** \\ \hline N & Normal beat & 500 \\ A & Atrial premature beat & 200 \\ V & Premature ventricular & 500 \\ n & Supraventricular escape beat & 30 \\ F & Fusion of ventricular contraction & 200 \\ j & Nodal (junction) escape beat & 90 \\ R & Left bundle branch block beat & 200 \\ \hline Tot & & 1720 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the categories for INCART database.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Database** & **Sampling Rate (Hz)** & \(T_{1}\) & \(T_{2}\) \\ \hline MIT-BIH & 360 & 160 & 200 \\ INCART & 257 & 120 & 136 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sampling rates, values of \(T_{1}\) and \(T_{2}\) for both MIT-BIH and INCART databases.  
At the same time, hand-crafted features are extracted from each lead \(x_{h}\) (\(h=1,2\)): frequency-domain features \(x_{h,\text{spec}}\), and autoregression features \(x_{h,\text{ar}}\). A single time-domain features vector \(x_{\text{time}}\) is also computed for both leads. Frequency-domain features, autoregression features and the normalized segmented heartbeat \(x_{h,\text{norm}}\) are concatenated to obtain the vector \(x_{h,\text{cat}}=[x_{h,\text{ar}}\ x_{h,\text{spec}}\ x_{h,\text{norm}}]\) (see Figure 1a). The \(x_{h,\text{cat}}\ (h=1,2)\) vector is processed by the neural module to produce a single output vector \(f_{\text{neur}}\) for the two leads (see Figure 1b). The vector \(f_{\text{neur}}\) is then reduced in dimensions by using Principal Component Analysis (PCA) thus obtaining the vector \(f_{\text{pca}}\). The concatenation of the time-domain features \(x_{\text{time}}\) and \(f_{\text{pca}}\) is the input of a Support Vector Machine classifier. The output of the classifier is the predicted heartbeat class. In the following subsections the feature extraction and neural module are discussed more in detail.