diff --git a/train_12ECG_classifier.py b/train_12ECG_classifier.py
index 7e67f6e..03bfa81 100644
--- a/train_12ECG_classifier.py
+++ b/train_12ECG_classifier.py
@@ -17,15 +17,6 @@ def train_12ECG_classifier(input_directory, output_directory):
             header_files.append(g)
 
     classes = get_classes(input_directory, header_files)
-    num_classes = len(classes)
-    num_files = len(header_files)
-    recordings = list()
-    headers = list()
-
-    for i in range(num_files):
-        recording, header = load_challenge_data(header_files[i])
-        recordings.append(recording)
-        headers.append(header)
 
     # Train model.
     print('Training model...')
@@ -33,21 +24,11 @@ def train_12ECG_classifier(input_directory, output_directory):
     features = list()
     labels = list()
 
+    num_files = len(header_files)
     for i in range(num_files):
-        recording = recordings[i]
-        header = headers[i]
-
-        tmp = get_12ECG_features(recording, header)
-        features.append(tmp)
-
-        for l in header:
-            if l.startswith('#Dx:'):
-                labels_act = np.zeros(num_classes)
-                arrs = l.strip().split(' ')
-                for arr in arrs[1].split(','):
-                    class_index = classes.index(arr.rstrip()) # Only use first positive index
-                    labels_act[class_index] = 1
-        labels.append(labels_act)
+        feats, labs = process_single_recording(header_files[i], classes)
+        features.append(feats)
+        labels.append(labs)
 
     features = np.array(features)
     labels = np.array(labels)
@@ -67,6 +48,7 @@ def train_12ECG_classifier(input_directory, output_directory):
     filename = os.path.join(output_directory, 'finalized_model.sav')
     joblib.dump(final_model, filename, protocol=0)
 
+
 # Load challenge data.
 def load_challenge_data(header_file):
     with open(header_file, 'r') as f:
@@ -76,6 +58,7 @@ def load_challenge_data(header_file):
     recording = np.asarray(x['val'], dtype=np.float64)
     return recording, header
 
+
 # Find unique classes.
 def get_classes(input_directory, filenames):
     classes = set()
@@ -87,3 +70,22 @@ def get_classes(input_directory, filenames):
                     for c in tmp:
                         classes.add(c.strip())
     return sorted(classes)
+
+
+# Process a single sample.
+def process_single_recording(header_file, classes):
+
+    recording, header = load_challenge_data(header_file)
+
+    feats = get_12ECG_features(recording, header)
+    num_classes = len(classes)
+
+    for l in header:
+        if l.startswith('#Dx:'):
+            labels_act = np.zeros(num_classes)
+            arrs = l.strip().split(' ')
+            for arr in arrs[1].split(','):
+                class_index = classes.index(arr.rstrip()) # Only use first positive index
+                labels_act[class_index] = 1
+
+    return feats, labels_act