## 3 Research Design and Procedure
### Data Acquisition and Preprocessing
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistics** & **Units** & **Description** & **Min** & **Max** & **Mean** & **StdDev** \\ \hline \multirow{3}{*}{QRS\_Duration} & \multirow{3}{*}{Milliseconds (ms)} & Time taken for ventricular depolarization & \multirow{3}{*}{55} & \multirow{3}{*}{188} & \multirow{3}{*}{88.92} & \multirow{3}{*}{15.364} \\ \cline{1-1} \cline{5-5}  & & & & \\ \hline BN & Microvolts (\(\mu\)V) & Baseline Noise & 0 & 92 & 31.23 & 27.949 \\ \hline CJ & ms & Conduction Junctions & 0 & 88 & 7.478 & 15.359 \\ \hline DK & ms & Duration of K-wave & 0 & 132 & 6.327 & 20.984 \\ \hline DZ & ms & Delta Z (impedance change) & 0 & 96 & 3.814 & 16.325 \\ \hline EB & Count & Ectopic Beat & 0 & 112 & 41.681 & 16.425 \\ \hline EM & Arbitrary units (AU) & Ectopic Measure & 0 & 88 & 3.239 & 11.531 \\ \hline \multirow{3}{*}{HR} & \begin{tabular}{c} Beats per minute \\ (ppm) \\ \end{tabular} & Heart Rate & \(-\)3.9 & 6.4 & \(-\)1.144 & 1.116 \\ \hline IN & ms & \
\begin{tabular}{c} Interval (possibly RR \\ or QT interval) \\ \end{tabular} & \(-\)5.5 & 7 & 0.868 & 1.053 \\ \hline IV & ms & Intrinsic Variability & 0 & 19.2 & 0.318 & 1.49 \\ \hline JB & \(\mu\)V & Junctional Beat & \(-\)216 & 268.9 & \(-\)18.738 & 23.715 \\ \hline JO & \(\mu\)V & Junctional Origin & \(-\)32.9 & 0 & \(-\)0.654 & 3.414 \\ \hline \multirow{3}{*}{JV} &
\begin{tabular}{c} Meters per second \\ (m/s) \\ \end{tabular} & Junctional Velocity & \(-\)11.8 & 18.8 & 3.895 & 2.991 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of Selected Attributes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Class** & **Entries** & **Description** \\ \hline
1 & Normal & 245 & Normal \\ \hline
2 & VPC & 3 & Ventricular Permature Contraction (PVC) \\ \hline
3 & IC-CAD & 44 & Ischemic changes (Coronary Artery Disease) \\ \hline
4 & 1-DAVB & 0 & 1. degree Atrio-Ventricular block \\ \hline
5 & LBBB & 9 & Left bundle branch block \\ \hline
6 & SB & 25 & Sinus bradycardiy \\ \hline
7 & ST & 13 & Sinus tachycardiy \\ \hline
8 & AF & 5 & Atrial Fibrillation or Flutter \\ \hline
9 & OIMI & 15 & Old Inferior Myocardial Infarction \\ \hline
10 & LVH & 4 & Left ventricular hypertrophy \\ \hline
11 & SPC & 2 & Supraventricular Premature Contraction \\ \hline
12 & RBBB & 50 & Right bundle branch block \\ \hline
13 & OAMI & 15 & Old Anterior Myocardial Infarction \\ \hline
14 & 3-DAVB & 0 & 3. degree AV block \\ \hline
15 & 2-DAVB & 0 & 2. degree AV block \\ \hline
16 & Others & 22 & Others \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of class attributes, descriptions, and number of entries [21]. Since ECG can reveal critical cardiovascular issues related to kidney dysfunction, there is a notable correlation between ECG data and kidney diseases, particularly CKD. For instance, hyperkalemia, a common condition in CKD, can cause distinctive ECG changes such as peaked T waves and enlarged QRS complexes, potentially indicating severe arrhythmias. Additionally, CKD-induced autonomic nervous system dysfunction often leads to reduced heart rate variability, detectable via ECG. ECG can also identify LVH, which is prevalent among CKD patients, underscoring the importance of cardiovascular risk management. Moreover, due to rapid fluid and electrolyte shifts in dialysis patients, continuous ECG monitoring is crucial for detecting arrhythmias. Certain ECG patterns, such as prolonged QT intervals, have been linked to CKD progression and increased mortality risk. Consequently, integrating ECG monitoring into CKD management can enhance patient outcomes and facilitate earlier detection of complications. The preprocessing approach consists of two different phases. The data did not have a structured format at first, thus it was converted into one. The second phase was fixing missing values. For this aim, mean imputation was used, which substituted missing values with the mean (average) of observed values inside the associated feature (column). A common method for handling missing data, especially numerical data, is mean imputation.