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+# Preprocessing Data
+The preprocessing of imaging data is essential to effective training and testing of ML models. In these notebooks we review how to remove outliers, baseline wander, and complete per-lead normalization. The first file is the most important. Rolling averages and flatline removals are non-essential preprocessing steps but may be beneficial in other use cases. 
+
+## 1. Waveform_Array_Generation_Truncation_Normalization.ipynb
+Truncation, Baseline Wander Removal, and Per-Lead Normalization
+
+## 2. Visualizing_ECGs_Rolling_Average.ipynb
+Denoise ECGs using rolling average screening techniques
+
+## 3. Finding_Flatlines.ipynb
+Find flatlines in ECG data of various length for additional filtering