diff --git a/Main_Quality_Relabel.py b/Main_Quality_Relabel.py
index 448d8b0..76d759f 100644
--- a/Main_Quality_Relabel.py
+++ b/Main_Quality_Relabel.py
@@ -56,13 +56,13 @@ if __name__ == '__main__':
     opt = parser.parse_args()
 
     focus_event_list = []
-    if 'OSA' in opt.focus_event_list:
+    if 'OSA' in opt.event:
         focus_event_list.append("Obstructive apnea")
-    if 'CSA' in opt.focus_event_list:
+    if 'CSA' in opt.event:
         focus_event_list.append("Central apnea")
-    if 'MSA' in opt.focus_event_list:
+    if 'MSA' in opt.event:
         focus_event_list.append("Mixed apnea")
-    if 'HPY' in opt.focus_event_list:
+    if 'HPY' in opt.event:
         focus_event_list.append("Hypopnea")
 
     qualityRelabel = Quality_Relabel(all_path=all_path, sampNo=opt.sampNo, frequency=frequency,
diff --git a/utils/Quality_Relabel.py b/utils/Quality_Relabel.py
index a4e8e5d..032c85f 100644
--- a/utils/Quality_Relabel.py
+++ b/utils/Quality_Relabel.py
@@ -63,12 +63,12 @@ class Quality_Relabel:
     #      3      红色    阻塞型
     #      4      灰色    混合型
     #      5      绿色    血氧饱和度下降
-    #      6      橙色    大体动
-    #      7      橙色    小体动
-    #      8      橙色    深呼吸
-    #      9      橙色    脉冲体动
-    #      10     橙色    无效片段
-    color_cycle = ["black", "pink", "blue", "red", "silver", "green", "orange", "orange", "orange", "orange", "orange"]
+    #      6      黄色    大体动
+    #      7      黄色    小体动
+    #      8      黄色    深呼吸
+    #      9      黄色    脉冲体动
+    #      10     黄色    无效片段
+    color_cycle = ["black", "pink", "blue", "red", "silver", "green", "yellow", "yellow", "yellow", "yellow", "yellow"]
 
     # assert len(color_cycle) == len(base_event) + 1, "基础事件数量与颜色数量不一致"
 
@@ -118,6 +118,7 @@ class Quality_Relabel:
         self.check_channel()
         self.read_data(frequency, bcg_frequency)
         self.read_event()
+        self.read_artifact_label()
 
     def check_channel(self):
         for i in self.channel_list:
@@ -266,20 +267,25 @@ class Quality_Relabel:
 
     def read_artifact_label(self):
         all_offset_length = pd.read_excel(self.Artifact_Offset_Path)
-        offset_length = all_offset_length[all_offset_length['数据编号'] == self.sampNo]['from_code']
+        offset_length = all_offset_length[all_offset_length['数据编号'] == self.sampNo]['from_code'].values[0]
         artifact_label_path = self.Artifact_Label_Path / f"Artifact_{self.sampNo}.txt"
         artifact_label = pd.read_csv(artifact_label_path, header=None).to_numpy().reshape(-1, 4)
 
         self.artifact_event_label = np.zeros(len(self.bcg_event_label))
 
         for i, artifact_type, SP, EP in artifact_label:
-            SP = (SP + offset_length) // (1000 // self.frequency)
-            EP = (SP + offset_length) // (1000 // self.frequency)
-            artifact_label += 5
-            if EP > len(self.bcg_event_label):
+            SP = (int(SP) + offset_length) // (1000 // self.frequency)
+            EP = (int(EP) + offset_length) // (1000 // self.frequency)
+            artifact_type = int(artifact_type) + 5
+
+            SP = 0 if SP < 0 else SP
+            if EP < 0:
                 continue
 
-            self.artifact_event_label[SP:EP] = artifact_label
+            if SP > len(self.bcg_event_label):
+                continue
+
+            self.artifact_event_label[SP:EP] = artifact_type
 
     # assert len(self.ecg_event_label_filtered_df) == len(self.bcg_event_label_filtered_df), \
     #     f"PSG与心晓事件数量不一致, PSG事件数量{len(self.ecg_event_label_filtered_df)},