# Identification of Electrocardiographic Patterns Related to Mortality with COVID-19
## 2 Materials and Methods
### Signal Pre-Processing and Feature Extraction  
All computations were performed on MATLAB (Mathworks, version 2022a). The ECG recordings included in the database were filtered with a digital bidirectional third-order Butterworth high-pass filter (cut-off frequency equal to 0.5 Hz) to reduce baseline wander. On lead I, R-peak positions were identified by the [PERSON]'s algorithm [26] and considered as synchronization points for all heartbeats in all 12 ECG leads. For each ECG lead, synchronization points were used to select beat-related ECG segments spanning the time interval between 0.25 s before and 0.40\(\cdot\)RR\({}^{1/2}\) s after the synchronization point (where RR corresponds to the median RR interval in s) [24]. These ECG segments were used to compute 12 lead-dependent median ECG beats. Next, median VCG beats of the X, Y, and Z leads were computed by multiplying the median ECG beats of the 8 independent leads I, II, V1-V6, by using the Kors matrix [27; 28; 29]. From the median VCG components, the median vector magnitude signal was obtained. The ECG landmarks were then identified in the 12 median ECG beats and in the vector-magnitude signal. These landmarks are the positions of the P-wave peak, QRS onset, R peak position, QRS offset (or J point), T-wave peak, and T-wave offset [24; 30]. These landmarks in time plus the synchronization points were finally used to characterize each ECG recording by using the 254 features in Tables 1-3: 216 features (18 \(\times\) 12 [features \(\times\) leads], ID from f1 to f216 in Table 1) describe the morphological and temporal characteristics regarding 12-lead median ECG beats' lead-dependent features; 16 features (ID from f217 to f232 in Table 2) are related to the global QT interval, global QRS duration, heart rate (HR), HR variability, and dispersion of the ECG temporal characteristics, that are variability among ECG leads' lead-independent features; 22 features (ID from f233 to f254 in Table 3) are related to the median vector-magnitude signal. These 254 features are designed to represent the most clinically relevant morphological, spatial, and temporal information of 10-second 12-lead ECG signals. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature ID** & **Feature Name** & **Feature Description** \\ \hline f1–f12 & PA (\(\mu\)V) & P-wave peak amplitude \\ f13–f24 & QA (\(\mu\)V) & Q-wave amplitude at QRS-complex onset position \\ f25–f36 & QRSA (\(\mu\)V) & QRS-complex amplitude at R-wave peak position \\ f37–f48 & SA (\(\mu\)V) & 1-point amplitude \\ f49–f60 & TA (\(\mu\)V) & T-wave peak amplitude \\ f61–f72 & QRS/P (radi) & Ratio between the QRSA and PA \\ f73–f84 & QRS/T (adi) & Ratio between the QRSA and TA \\ f85–f96 & ToTpS (\({}^{\circ}\)) & Angle between the baseline and the left front of the T wave \\ f97–f108 & TpTeS (\({}^{\circ}\)) & Angle between the baseline and the right front of the T wave \\ f109–f120 & PR (ms) & Time interval between P-wave peak and R-wave peak \\ f121–f132 & PS (ms) & Time interval between P-wave peak and I point \\ f133–f144 & PT (ms) & Time interval between P-wave peak and T-wave end \\ f145–f156 & QT (ms) & Time interval between QRS-complex onset and T-wave end \\ f157–f168 & QRS (ms) & Time interval between QRS-complex onset and QRS-complex end \\ f169–f180 & RS (ms) & Time interval between QRS-complex peak and QRS-complex end \\ f181–f192 & ToTp (ms) & Time interval between T-wave onset and T-wave peak \\ f193–f204 & TpTe (ms) & Time interval between T-wave peak and T-wave end \\ f205–f216 & \(\rho\) (adi) & Median correlation coefficient between ECG beats and median ECG beat \\ \hline \hline \end{tabular}  
* For each lead-dependent feature, the feature ID considers the leads listed in the following order: I, II, III, aV\({}_{\rm{R}}\), aV\({}_{\rm{L}}\), aV\({}_{\rm{F}}\), V1, V2, V3, V4, V5, V6.  
\end{table} 
Table 1: Lead-dependent ECG features. The 18 features reported here, considered in all 12 leads for a total of 216 features, are related to morphological and temporal information of the ECG.