diff --git "a/data-processing.ipynb" "b/data-processing.ipynb"
--- "a/data-processing.ipynb"
+++ "b/data-processing.ipynb"
@@ -9,7 +9,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.0.3\n"
+      "2.2.1\n"
      ]
     }
    ],
@@ -23,13 +23,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
     "# loading emg data & time marker from test-1 folder\n",
-    "emg_data_path = 'test-1/0-New_Task-recording-0.csv'\n",
-    "time_marker_path = 'test-1/time_marker.csv'\n",
+    "emg_data_path = 'test-new/0-New_Task-recording-0.csv'\n",
+    "time_marker_path = 'test-new/time_marker.csv'\n",
     "\n",
     "emg_data = pd.read_csv(emg_data_path, skiprows=[0,1,3,4])\n",
     "time_marker = pd.read_csv(time_marker_path)"
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -68,27 +68,14 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>10</th>\n",
-       "      <th>...</th>\n",
-       "      <th>13</th>\n",
-       "      <th>14</th>\n",
-       "      <th>15</th>\n",
-       "      <th>16</th>\n",
        "      <th>17</th>\n",
        "      <th>18</th>\n",
        "      <th>19</th>\n",
        "      <th>20</th>\n",
        "      <th>21</th>\n",
        "      <th>22</th>\n",
+       "      <th>23</th>\n",
+       "      <th>24</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Channels</th>\n",
@@ -100,141 +87,63 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>24</td>\n",
-       "      <td>18</td>\n",
-       "      <td>28</td>\n",
-       "      <td>-12</td>\n",
-       "      <td>17</td>\n",
-       "      <td>29</td>\n",
-       "      <td>-14</td>\n",
-       "      <td>2</td>\n",
-       "      <td>42</td>\n",
-       "      <td>-16</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>42</td>\n",
-       "      <td>76</td>\n",
-       "      <td>-48</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-145</td>\n",
-       "      <td>33</td>\n",
-       "      <td>60</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-87</td>\n",
+       "      <td>2508</td>\n",
+       "      <td>1311</td>\n",
+       "      <td>2601</td>\n",
+       "      <td>1099</td>\n",
+       "      <td>1212</td>\n",
+       "      <td>1028</td>\n",
+       "      <td>-1143</td>\n",
+       "      <td>-1249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2202</th>\n",
-       "      <td>51</td>\n",
-       "      <td>7</td>\n",
-       "      <td>81</td>\n",
-       "      <td>-47</td>\n",
-       "      <td>3</td>\n",
-       "      <td>63</td>\n",
-       "      <td>47</td>\n",
-       "      <td>-31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>-24</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-13</td>\n",
-       "      <td>9</td>\n",
-       "      <td>117</td>\n",
-       "      <td>4</td>\n",
-       "      <td>34</td>\n",
-       "      <td>-156</td>\n",
-       "      <td>81</td>\n",
-       "      <td>13</td>\n",
-       "      <td>-172</td>\n",
-       "      <td>-93</td>\n",
+       "      <th>1600</th>\n",
+       "      <td>1865</td>\n",
+       "      <td>1182</td>\n",
+       "      <td>754</td>\n",
+       "      <td>94</td>\n",
+       "      <td>68</td>\n",
+       "      <td>11</td>\n",
+       "      <td>-1138</td>\n",
+       "      <td>-1130</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4404</th>\n",
-       "      <td>56</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>86</td>\n",
-       "      <td>-53</td>\n",
-       "      <td>-21</td>\n",
-       "      <td>39</td>\n",
-       "      <td>56</td>\n",
-       "      <td>-45</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-27</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-35</td>\n",
-       "      <td>-11</td>\n",
-       "      <td>119</td>\n",
-       "      <td>14</td>\n",
-       "      <td>20</td>\n",
-       "      <td>-188</td>\n",
-       "      <td>52</td>\n",
-       "      <td>-78</td>\n",
-       "      <td>-219</td>\n",
-       "      <td>-121</td>\n",
+       "      <th>3200</th>\n",
+       "      <td>382</td>\n",
+       "      <td>961</td>\n",
+       "      <td>-327</td>\n",
+       "      <td>240</td>\n",
+       "      <td>-462</td>\n",
+       "      <td>-75</td>\n",
+       "      <td>-445</td>\n",
+       "      <td>-66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6606</th>\n",
-       "      <td>35</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55</td>\n",
-       "      <td>-46</td>\n",
-       "      <td>-7</td>\n",
-       "      <td>5</td>\n",
-       "      <td>25</td>\n",
-       "      <td>-39</td>\n",
-       "      <td>6</td>\n",
-       "      <td>-16</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-42</td>\n",
-       "      <td>-26</td>\n",
-       "      <td>84</td>\n",
-       "      <td>-14</td>\n",
-       "      <td>-13</td>\n",
-       "      <td>-195</td>\n",
-       "      <td>21</td>\n",
-       "      <td>-39</td>\n",
-       "      <td>-218</td>\n",
-       "      <td>-114</td>\n",
+       "      <th>4800</th>\n",
+       "      <td>-493</td>\n",
+       "      <td>-87</td>\n",
+       "      <td>-1505</td>\n",
+       "      <td>-375</td>\n",
+       "      <td>-872</td>\n",
+       "      <td>-558</td>\n",
+       "      <td>-722</td>\n",
+       "      <td>-211</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8808</th>\n",
-       "      <td>3</td>\n",
-       "      <td>29</td>\n",
-       "      <td>23</td>\n",
-       "      <td>-46</td>\n",
-       "      <td>-7</td>\n",
-       "      <td>-21</td>\n",
-       "      <td>-36</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-19</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-36</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>70</td>\n",
-       "      <td>-18</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-182</td>\n",
-       "      <td>-19</td>\n",
-       "      <td>-25</td>\n",
-       "      <td>-150</td>\n",
-       "      <td>-104</td>\n",
+       "      <th>6400</th>\n",
+       "      <td>-1565</td>\n",
+       "      <td>-666</td>\n",
+       "      <td>-2092</td>\n",
+       "      <td>-724</td>\n",
+       "      <td>-1142</td>\n",
+       "      <td>-809</td>\n",
+       "      <td>-769</td>\n",
+       "      <td>255</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -246,178 +155,86 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>92534528</th>\n",
-       "      <td>397</td>\n",
-       "      <td>-1928</td>\n",
-       "      <td>-91</td>\n",
-       "      <td>-7</td>\n",
-       "      <td>-5</td>\n",
-       "      <td>319</td>\n",
-       "      <td>54</td>\n",
-       "      <td>-893</td>\n",
-       "      <td>135</td>\n",
-       "      <td>-639</td>\n",
-       "      <td>...</td>\n",
-       "      <td>254</td>\n",
-       "      <td>70</td>\n",
-       "      <td>119</td>\n",
-       "      <td>20</td>\n",
-       "      <td>209</td>\n",
-       "      <td>203</td>\n",
-       "      <td>200</td>\n",
-       "      <td>-2388</td>\n",
-       "      <td>-217</td>\n",
-       "      <td>134</td>\n",
+       "      <th>95198400</th>\n",
+       "      <td>-2717</td>\n",
+       "      <td>-2701</td>\n",
+       "      <td>-2697</td>\n",
+       "      <td>-2706</td>\n",
+       "      <td>-2692</td>\n",
+       "      <td>-2691</td>\n",
+       "      <td>-2680</td>\n",
+       "      <td>-2703</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>92536730</th>\n",
-       "      <td>354</td>\n",
-       "      <td>-1996</td>\n",
-       "      <td>-121</td>\n",
-       "      <td>-183</td>\n",
-       "      <td>7</td>\n",
-       "      <td>262</td>\n",
-       "      <td>15</td>\n",
-       "      <td>-1008</td>\n",
-       "      <td>111</td>\n",
-       "      <td>-611</td>\n",
-       "      <td>...</td>\n",
-       "      <td>248</td>\n",
-       "      <td>67</td>\n",
-       "      <td>90</td>\n",
-       "      <td>-21</td>\n",
-       "      <td>172</td>\n",
-       "      <td>125</td>\n",
-       "      <td>144</td>\n",
-       "      <td>-2357</td>\n",
-       "      <td>-487</td>\n",
-       "      <td>112</td>\n",
+       "      <th>95194910</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>92538932</th>\n",
-       "      <td>282</td>\n",
-       "      <td>-2043</td>\n",
-       "      <td>-184</td>\n",
-       "      <td>-336</td>\n",
-       "      <td>2</td>\n",
-       "      <td>184</td>\n",
-       "      <td>-75</td>\n",
-       "      <td>-1088</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-573</td>\n",
-       "      <td>...</td>\n",
-       "      <td>218</td>\n",
-       "      <td>30</td>\n",
-       "      <td>46</td>\n",
-       "      <td>-62</td>\n",
-       "      <td>132</td>\n",
-       "      <td>186</td>\n",
-       "      <td>102</td>\n",
-       "      <td>-2320</td>\n",
-       "      <td>-598</td>\n",
-       "      <td>16</td>\n",
+       "      <th>95196510</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>92541134</th>\n",
-       "      <td>217</td>\n",
-       "      <td>-2065</td>\n",
-       "      <td>-212</td>\n",
-       "      <td>-461</td>\n",
-       "      <td>-5</td>\n",
-       "      <td>161</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-1148</td>\n",
-       "      <td>-58</td>\n",
-       "      <td>-540</td>\n",
-       "      <td>...</td>\n",
-       "      <td>85</td>\n",
-       "      <td>-27</td>\n",
-       "      <td>-67</td>\n",
-       "      <td>-145</td>\n",
-       "      <td>27</td>\n",
-       "      <td>204</td>\n",
-       "      <td>22</td>\n",
-       "      <td>-2275</td>\n",
-       "      <td>-701</td>\n",
-       "      <td>-12</td>\n",
+       "      <th>95198110</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>92543336</th>\n",
-       "      <td>153</td>\n",
-       "      <td>-2087</td>\n",
-       "      <td>-240</td>\n",
-       "      <td>-587</td>\n",
-       "      <td>-12</td>\n",
-       "      <td>139</td>\n",
-       "      <td>-120</td>\n",
-       "      <td>-1209</td>\n",
-       "      <td>-117</td>\n",
-       "      <td>-507</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-48</td>\n",
-       "      <td>-84</td>\n",
-       "      <td>-180</td>\n",
-       "      <td>-228</td>\n",
-       "      <td>-78</td>\n",
-       "      <td>222</td>\n",
-       "      <td>-57</td>\n",
-       "      <td>-2230</td>\n",
-       "      <td>-805</td>\n",
-       "      <td>-41</td>\n",
+       "      <th>95199710</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>41854 rows × 22 columns</p>\n",
+       "<p>59504 rows × 8 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "            1     2    3    4   5    6    7     8    9   10  ...   13  14  \\\n",
-       "Channels                                                     ...            \n",
-       "0          24    18   28  -12  17   29  -14     2   42  -16  ...   -2  42   \n",
-       "2202       51     7   81  -47   3   63   47   -31   22  -24  ...  -13   9   \n",
-       "4404       56    -2   86  -53 -21   39   56   -45    0  -27  ...  -35 -11   \n",
-       "6606       35     0   55  -46  -7    5   25   -39    6  -16  ...  -42 -26   \n",
-       "8808        3    29   23  -46  -7  -21  -36    -1    8  -19  ...  -36  -9   \n",
-       "...       ...   ...  ...  ...  ..  ...  ...   ...  ...  ...  ...  ...  ..   \n",
-       "92534528  397 -1928  -91   -7  -5  319   54  -893  135 -639  ...  254  70   \n",
-       "92536730  354 -1996 -121 -183   7  262   15 -1008  111 -611  ...  248  67   \n",
-       "92538932  282 -2043 -184 -336   2  184  -75 -1088    1 -573  ...  218  30   \n",
-       "92541134  217 -2065 -212 -461  -5  161  -97 -1148  -58 -540  ...   85 -27   \n",
-       "92543336  153 -2087 -240 -587 -12  139 -120 -1209 -117 -507  ...  -48 -84   \n",
-       "\n",
-       "           15   16   17   18   19    20   21   22  \n",
-       "Channels                                           \n",
-       "0          76  -48    1 -145   33    60  -97  -87  \n",
-       "2202      117    4   34 -156   81    13 -172  -93  \n",
-       "4404      119   14   20 -188   52   -78 -219 -121  \n",
-       "6606       84  -14  -13 -195   21   -39 -218 -114  \n",
-       "8808       70  -18    0 -182  -19   -25 -150 -104  \n",
-       "...       ...  ...  ...  ...  ...   ...  ...  ...  \n",
-       "92534528  119   20  209  203  200 -2388 -217  134  \n",
-       "92536730   90  -21  172  125  144 -2357 -487  112  \n",
-       "92538932   46  -62  132  186  102 -2320 -598   16  \n",
-       "92541134  -67 -145   27  204   22 -2275 -701  -12  \n",
-       "92543336 -180 -228  -78  222  -57 -2230 -805  -41  \n",
+       "            17    18    19    20    21    22    23    24\n",
+       "Channels                                                \n",
+       "0         2508  1311  2601  1099  1212  1028 -1143 -1249\n",
+       "1600      1865  1182   754    94    68    11 -1138 -1130\n",
+       "3200       382   961  -327   240  -462   -75  -445   -66\n",
+       "4800      -493   -87 -1505  -375  -872  -558  -722  -211\n",
+       "6400     -1565  -666 -2092  -724 -1142  -809  -769   255\n",
+       "...        ...   ...   ...   ...   ...   ...   ...   ...\n",
+       "95198400 -2717 -2701 -2697 -2706 -2692 -2691 -2680 -2703\n",
+       "95194910     0     0     0     0     0     0     0     0\n",
+       "95196510     0     0     0     0     0     0     0     0\n",
+       "95198110     0     0     0     0     0     0     0     0\n",
+       "95199710     0     0     0     0     0     0     0     0\n",
        "\n",
-       "[41854 rows x 22 columns]"
+       "[59504 rows x 8 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -430,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -438,34 +255,20 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 41854 entries, 0 to 92543336\n",
-      "Data columns (total 22 columns):\n",
+      "Index: 59504 entries, 0 to 95199710\n",
+      "Data columns (total 8 columns):\n",
       " #   Column  Non-Null Count  Dtype\n",
       "---  ------  --------------  -----\n",
-      " 0   1       41854 non-null  int64\n",
-      " 1   2       41854 non-null  int64\n",
-      " 2   3       41854 non-null  int64\n",
-      " 3   4       41854 non-null  int64\n",
-      " 4   5       41854 non-null  int64\n",
-      " 5   6       41854 non-null  int64\n",
-      " 6   7       41854 non-null  int64\n",
-      " 7   8       41854 non-null  int64\n",
-      " 8   9       41854 non-null  int64\n",
-      " 9   10      41854 non-null  int64\n",
-      " 10  11      41854 non-null  int64\n",
-      " 11  12      41854 non-null  int64\n",
-      " 12  13      41854 non-null  int64\n",
-      " 13  14      41854 non-null  int64\n",
-      " 14  15      41854 non-null  int64\n",
-      " 15  16      41854 non-null  int64\n",
-      " 16  17      41854 non-null  int64\n",
-      " 17  18      41854 non-null  int64\n",
-      " 18  19      41854 non-null  int64\n",
-      " 19  20      41854 non-null  int64\n",
-      " 20  21      41854 non-null  int64\n",
-      " 21  22      41854 non-null  int64\n",
-      "dtypes: int64(22)\n",
-      "memory usage: 7.3 MB\n"
+      " 0   17      59504 non-null  int64\n",
+      " 1   18      59504 non-null  int64\n",
+      " 2   19      59504 non-null  int64\n",
+      " 3   20      59504 non-null  int64\n",
+      " 4   21      59504 non-null  int64\n",
+      " 5   22      59504 non-null  int64\n",
+      " 6   23      59504 non-null  int64\n",
+      " 7   24      59504 non-null  int64\n",
+      "dtypes: int64(8)\n",
+      "memory usage: 4.1 MB\n"
      ]
     }
    ],
@@ -475,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -489,17 +292,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get signal data from difference of emg_data\n",
+    "signal_left_lateral = emg_data['17'] - emg_data['18']\n",
+    "signal_left_medial = emg_data['19'] - emg_data['20']\n",
+    "\n",
+    "signal_right_lateral = emg_data['23'] - emg_data['24']\n",
+    "signal_right_medial = emg_data['21'] - emg_data['22']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean of RMS Signal 1:  442.605, Std Dev of RMS Signal 1:  628.329\n",
-      "Mean of RMS Signal 2:  366.791, Std Dev of RMS Signal 2:  681.991\n",
-      "Mean of RMS Signal 3:  135.842, Std Dev of RMS Signal 3:  212.442\n",
-      "Mean of RMS Signal 4:  206.513, Std Dev of RMS Signal 4:  403.897\n"
+      "Mean of RMS Signal 1:  414.735, Std Dev of RMS Signal 1:  702.679\n",
+      "Mean of RMS Signal 2:  443.660, Std Dev of RMS Signal 2:  578.622\n",
+      "Mean of RMS Signal 3:  440.785, Std Dev of RMS Signal 3:  622.244\n",
+      "Mean of RMS Signal 4:  483.905, Std Dev of RMS Signal 4:  758.514\n"
      ]
     }
    ],
@@ -543,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -551,39 +368,39 @@
       "text/plain": [
        "Channels\n",
        "0           NaN\n",
-       "2202        NaN\n",
-       "4404        NaN\n",
-       "6606        NaN\n",
-       "8808        NaN\n",
-       "11010       NaN\n",
-       "13212       NaN\n",
-       "15414       NaN\n",
-       "17616       NaN\n",
-       "19818       NaN\n",
-       "22020       NaN\n",
-       "24222       NaN\n",
-       "26424       NaN\n",
-       "28626       NaN\n",
-       "30828       NaN\n",
-       "33030       NaN\n",
-       "35232       NaN\n",
-       "37434       NaN\n",
-       "39636       NaN\n",
-       "41838       NaN\n",
-       "44040       NaN\n",
-       "46242       NaN\n",
-       "48444       NaN\n",
-       "50646       NaN\n",
-       "52848    254.96\n",
-       "55050    259.68\n",
-       "57252    261.56\n",
-       "59454    256.00\n",
-       "61656    248.76\n",
-       "63858    243.28\n",
+       "1600        NaN\n",
+       "3200        NaN\n",
+       "4800        NaN\n",
+       "6400        NaN\n",
+       "8000        NaN\n",
+       "9600        NaN\n",
+       "11200       NaN\n",
+       "12800       NaN\n",
+       "14400       NaN\n",
+       "16000       NaN\n",
+       "17600       NaN\n",
+       "19200       NaN\n",
+       "20800       NaN\n",
+       "22400       NaN\n",
+       "24000       NaN\n",
+       "25600       NaN\n",
+       "27200       NaN\n",
+       "28800       NaN\n",
+       "30400       NaN\n",
+       "32000       NaN\n",
+       "33600       NaN\n",
+       "35200       NaN\n",
+       "36800       NaN\n",
+       "38400    101.80\n",
+       "40000    187.36\n",
+       "41600    257.64\n",
+       "43200    258.04\n",
+       "44800    249.12\n",
+       "46400    213.04\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -594,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -603,7 +420,7 @@
        "131.65497112394493"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -614,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -623,7 +440,7 @@
        "101.73584628144596"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -634,16 +451,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "101.7471471252777"
+       "90.46881927178374"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -661,7 +478,143 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>event_time</th>\n",
+       "      <th>description</th>\n",
+       "      <th>tag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>78357902.0</td>\n",
+       "      <td>Cough</td>\n",
+       "      <td>start</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>79670999.0</td>\n",
+       "      <td>Cough</td>\n",
+       "      <td>end</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>81227489.0</td>\n",
+       "      <td>Bite</td>\n",
+       "      <td>start</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>82465323.0</td>\n",
+       "      <td>Bite</td>\n",
+       "      <td>end</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84239727.0</td>\n",
+       "      <td>Swallow</td>\n",
+       "      <td>start</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>85434346.0</td>\n",
+       "      <td>Swallow</td>\n",
+       "      <td>end</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>86628547.0</td>\n",
+       "      <td>Swallow</td>\n",
+       "      <td>start</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>87951834.0</td>\n",
+       "      <td>Swallow</td>\n",
+       "      <td>end</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>89673825.0</td>\n",
+       "      <td>Swallow</td>\n",
+       "      <td>start</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>91158663.0</td>\n",
+       "      <td>Swallow</td>\n",
+       "      <td>end</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>92257779.0</td>\n",
+       "      <td>Cough</td>\n",
+       "      <td>start</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>93714668.0</td>\n",
+       "      <td>Cough</td>\n",
+       "      <td>end</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    event_time description    tag\n",
+       "0   78357902.0       Cough  start\n",
+       "1   79670999.0       Cough    end\n",
+       "2   81227489.0        Bite  start\n",
+       "3   82465323.0        Bite    end\n",
+       "4   84239727.0     Swallow  start\n",
+       "5   85434346.0     Swallow    end\n",
+       "6   86628547.0     Swallow  start\n",
+       "7   87951834.0     Swallow    end\n",
+       "8   89673825.0     Swallow  start\n",
+       "9   91158663.0     Swallow    end\n",
+       "10  92257779.0       Cough  start\n",
+       "11  93714668.0       Cough    end"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_marker = pd.read_csv(time_marker_path)\n",
+    "time_marker = time_marker[['0-New_Task-recording_time(us)', 'description', 'tag']]\n",
+    "time_marker = time_marker.rename(columns={'0-New_Task-recording_time(us)': 'event_time'})\n",
+    "time_marker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -797,7 +750,7 @@
        "13  88152623.0    cough    end"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -811,7 +764,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select column value with odd/even index\n",
+    "event_start_times = time_marker.loc[0::2]['event_time'].values.astype(int)\n",
+    "event_end_times = time_marker.loc[1::2]['event_time'].values.astype(int)\n",
+    "event_names = time_marker.loc[0::2]['description'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -823,15 +788,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "signal LL basic 10s std :  101.747\n",
-      "signal RL basic 10s std :  14.723\n"
+      "signal LL basic 10s std :  90.469\n",
+      "signal RL basic 10s std :  64.398\n"
      ]
     }
    ],
@@ -846,7 +811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -917,46 +882,255 @@
     "    plt.show()\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "78357902"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "event_start_times[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "79670999"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "event_end_times[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.int32"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(event_start_times[0])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Event 1: bite\n",
-      "Start time:  32.030 sec, End time:  56.294 sec\n",
-      "left std ratio:  6.765, right std ratio:  18.278\n",
-      "LM_max_index:  51.882, LL_max_index:  51.882, left delta t:  0.000\n",
-      "RM_max_index:  51.900, RL_max_index:  52.496, right delta t: -0.596\n"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(signal_left_lateral_RMS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+       "Channels\n",
+       "78358400    369.00\n",
+       "78360000    380.12\n",
+       "78361600    285.88\n",
+       "78363200    238.68\n",
+       "78364800    215.96\n",
+       "             ...  \n",
+       "79664000     36.44\n",
+       "79665600     34.52\n",
+       "79667200     32.32\n",
+       "79668800     29.92\n",
+       "79670400     14.52\n",
+       "Length: 821, dtype: float64"
       ]
      },
+     "execution_count": 78,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = (signal_left_lateral_RMS.index >= event_start_times[0]) & (signal_left_lateral_RMS.index <= event_end_times[0])\n",
+    "signal_left_lateral_RMS.iloc[mask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "78357902",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2606\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2630\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 78357902",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[76], line 6\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m series\u001b[38;5;241m.\u001b[39mindex[series\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_loc(timestamp)]\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Then use:\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m start_idx \u001b[38;5;241m=\u001b[39m \u001b[43mget_nearest_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43msignal_left_lateral_RMS\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent_start_times\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m  \n\u001b[0;32m      7\u001b[0m end_idx \u001b[38;5;241m=\u001b[39m get_nearest_index(signal_left_lateral_RMS, event_end_times[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m      8\u001b[0m event_data \u001b[38;5;241m=\u001b[39m signal_left_lateral_RMS\u001b[38;5;241m.\u001b[39mloc[start_idx:end_idx]\n",
+      "Cell \u001b[1;32mIn[76], line 3\u001b[0m, in \u001b[0;36mget_nearest_index\u001b[1;34m(series, timestamp)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_nearest_index\u001b[39m(series, timestamp):\n\u001b[1;32m----> 3\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m series\u001b[38;5;241m.\u001b[39mindex[\u001b[43mseries\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimestamp\u001b[49m\u001b[43m)\u001b[49m]\n",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3810\u001b[0m     ):\n\u001b[0;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[1;31mKeyError\u001b[0m: 78357902"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert microsecond timestamps to array indices\n",
+    "def get_nearest_index(series, timestamp):\n",
+    "    return series.index[series.index.get_loc(timestamp)]\n",
+    "\n",
+    "# Then use:\n",
+    "start_idx = get_nearest_index(signal_left_lateral_RMS, event_start_times[0])  \n",
+    "end_idx = get_nearest_index(signal_left_lateral_RMS, event_end_times[0])\n",
+    "event_data = signal_left_lateral_RMS.loc[start_idx:end_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "78357902",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2606\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2630\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 78357902",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[70], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m start_time \u001b[38;5;241m=\u001b[39m \u001b[43msignal_left_lateral_RMS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mevent_start_times\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m start_time\n",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3810\u001b[0m     ):\n\u001b[0;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[1;31mKeyError\u001b[0m: 78357902"
+     ]
+    }
+   ],
+   "source": [
+    "start_time = signal_left_lateral_RMS.index.get_loc(int(event_start_times[0]))\n",
+    "start_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "78357902",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2606\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:2630\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 78357902",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[72], line 5\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Use nearest method to find the closest index values\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# start_time = signal_left_lateral_RMS.index.get_loc(event_start_times[0], method='nearest')\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# end_time = signal_left_lateral_RMS.index.get_loc(event_end_times[0], method='nearest')\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m \u001b[43msignal_left_lateral_RMS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mevent_start_times\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent_end_times\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexing.py:1191\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1189\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m   1190\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_deprecated_callable_usage(key, maybe_callable)\n\u001b[1;32m-> 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexing.py:1411\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1409\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m   1410\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1411\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_slice_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1412\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[0;32m   1413\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getbool_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexing.py:1443\u001b[0m, in \u001b[0;36m_LocIndexer._get_slice_axis\u001b[1;34m(self, slice_obj, axis)\u001b[0m\n\u001b[0;32m   1440\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m   1442\u001b[0m labels \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m-> 1443\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[43mlabels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1445\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(indexer, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m   1446\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_slice(indexer, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6662\u001b[0m, in \u001b[0;36mIndex.slice_indexer\u001b[1;34m(self, start, end, step)\u001b[0m\n\u001b[0;32m   6618\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mslice_indexer\u001b[39m(\n\u001b[0;32m   6619\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   6620\u001b[0m     start: Hashable \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   6621\u001b[0m     end: Hashable \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   6622\u001b[0m     step: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   6623\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mslice\u001b[39m:\n\u001b[0;32m   6624\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   6625\u001b[0m \u001b[38;5;124;03m    Compute the slice indexer for input labels and step.\u001b[39;00m\n\u001b[0;32m   6626\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   6660\u001b[0m \u001b[38;5;124;03m    slice(1, 3, None)\u001b[39;00m\n\u001b[0;32m   6661\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 6662\u001b[0m     start_slice, end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_locs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6664\u001b[0m     \u001b[38;5;66;03m# return a slice\u001b[39;00m\n\u001b[0;32m   6665\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(start_slice):\n",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6879\u001b[0m, in \u001b[0;36mIndex.slice_locs\u001b[1;34m(self, start, end, step)\u001b[0m\n\u001b[0;32m   6877\u001b[0m start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   6878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 6879\u001b[0m     start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mleft\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6880\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start_slice \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   6881\u001b[0m     start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6804\u001b[0m, in \u001b[0;36mIndex.get_slice_bound\u001b[1;34m(self, label, side)\u001b[0m\n\u001b[0;32m   6801\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_searchsorted_monotonic(label, side)\n\u001b[0;32m   6802\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[0;32m   6803\u001b[0m         \u001b[38;5;66;03m# raise the original KeyError\u001b[39;00m\n\u001b[1;32m-> 6804\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[0;32m   6806\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(slc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m   6807\u001b[0m     \u001b[38;5;66;03m# get_loc may return a boolean array, which\u001b[39;00m\n\u001b[0;32m   6808\u001b[0m     \u001b[38;5;66;03m# is OK as long as they are representable by a slice.\u001b[39;00m\n\u001b[0;32m   6809\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m is_bool_dtype(slc\u001b[38;5;241m.\u001b[39mdtype)\n",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6798\u001b[0m, in \u001b[0;36mIndex.get_slice_bound\u001b[1;34m(self, label, side)\u001b[0m\n\u001b[0;32m   6796\u001b[0m \u001b[38;5;66;03m# we need to look up the label\u001b[39;00m\n\u001b[0;32m   6797\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 6798\u001b[0m     slc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6799\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m   6800\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\snomed\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3810\u001b[0m     ):\n\u001b[0;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[1;31mKeyError\u001b[0m: 78357902"
+     ]
+    }
+   ],
+   "source": [
+    "# Use nearest method to find the closest index values\n",
+    "# start_time = signal_left_lateral_RMS.index.get_loc(event_start_times[0], method='nearest')\n",
+    "# end_time = signal_left_lateral_RMS.index.get_loc(event_end_times[0], method='nearest')\n",
+    "\n",
+    "signal_left_lateral_RMS.loc[event_start_times[0] : event_end_times[0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Event 2: swallow\n",
-      "Start time:  60.285 sec, End time:  62.479 sec\n",
-      "left std ratio:  7.153, right std ratio:  13.522\n",
-      "LM_max_index:  61.289, LL_max_index:  60.473, left delta t:  0.815\n",
-      "RM_max_index:  61.280, RL_max_index:  61.289, right delta t: -0.009\n"
+      "Event 1: Cough\n",
+      "Start time:  78.358 sec, End time:  79.671 sec\n",
+      "left std ratio:  1.008, right std ratio:  2.445\n",
+      "LM_max_index:  79.310, LL_max_index:  78.360, left delta t:  0.950\n",
+      "RM_max_index:  78.814, RL_max_index:  79.126, right delta t: -0.312\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -968,16 +1142,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Event 3: swallow\n",
-      "Start time:  67.893 sec, End time:  69.216 sec\n",
-      "left std ratio:  7.229, right std ratio:  17.350\n",
-      "LM_max_index:  67.902, LL_max_index:  67.920, left delta t: -0.018\n",
-      "RM_max_index:  67.967, RL_max_index:  68.663, right delta t: -0.696\n"
+      "Event 2: Bite\n",
+      "Start time:  81.227 sec, End time:  82.465 sec\n",
+      "left std ratio:  1.518, right std ratio:  6.700\n",
+      "LM_max_index:  81.672, LL_max_index:  81.954, left delta t: -0.282\n",
+      "RM_max_index:  81.611, RL_max_index:  81.656, right delta t: -0.045\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -989,16 +1163,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Event 4: swallow\n",
-      "Start time:  71.897 sec, End time:  73.035 sec\n",
-      "left std ratio:  11.192, right std ratio:  16.181\n",
-      "LM_max_index:  71.898, LL_max_index:  71.944, left delta t: -0.046\n",
-      "RM_max_index:  71.946, RL_max_index:  71.898, right delta t:  0.048\n"
+      "Event 3: Swallow\n",
+      "Start time:  84.240 sec, End time:  85.434 sec\n",
+      "left std ratio:  1.512, right std ratio:  5.680\n",
+      "LM_max_index:  84.914, LL_max_index:  85.347, left delta t: -0.434\n",
+      "RM_max_index:  85.064, RL_max_index:  85.357, right delta t: -0.293\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1010,16 +1184,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Event 5: cough\n",
-      "Start time:  77.099 sec, End time:  79.342 sec\n",
-      "left std ratio:  4.604, right std ratio:  9.801\n",
-      "LM_max_index:  78.709, LL_max_index:  78.628, left delta t:  0.081\n",
-      "RM_max_index:  77.962, RL_max_index:  79.038, right delta t: -1.075\n"
+      "Event 4: Swallow\n",
+      "Start time:  86.629 sec, End time:  87.952 sec\n",
+      "left std ratio:  1.689, right std ratio:  4.807\n",
+      "LM_max_index:  87.749, LL_max_index:  87.533, left delta t:  0.216\n",
+      "RM_max_index:  87.331, RL_max_index:  87.098, right delta t:  0.234\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1031,16 +1205,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Event 6: cough\n",
-      "Start time:  82.718 sec, End time:  83.992 sec\n",
-      "left std ratio:  1.160, right std ratio:  1.392\n",
-      "LM_max_index:  82.867, LL_max_index:  83.569, left delta t: -0.702\n",
-      "RM_max_index:  82.953, RL_max_index:  83.553, right delta t: -0.601\n"
+      "Event 5: Swallow\n",
+      "Start time:  89.674 sec, End time:  91.159 sec\n",
+      "left std ratio:  1.318, right std ratio:  4.252\n",
+      "LM_max_index:  90.611, LL_max_index:  90.832, left delta t: -0.221\n",
+      "RM_max_index:  90.851, RL_max_index:  90.176, right delta t:  0.675\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1052,16 +1226,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Event 7: cough\n",
-      "Start time:  86.345 sec, End time:  88.153 sec\n",
-      "left std ratio:  0.575, right std ratio:  1.769\n",
-      "LM_max_index:  86.460, LL_max_index:  87.226, left delta t: -0.766\n",
-      "RM_max_index:  86.672, RL_max_index:  87.228, right delta t: -0.555\n"
+      "Event 6: Cough\n",
+      "Start time:  92.258 sec, End time:  93.715 sec\n",
+      "left std ratio:  1.773, right std ratio:  4.874\n",
+      "LM_max_index:  93.262, LL_max_index:  93.605, left delta t: -0.342\n",
+      "RM_max_index:  93.386, RL_max_index:  92.814, right delta t:  0.571\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1083,11 +1257,17 @@
     "    print(f\"Start time: {float(event_start_time)/1000000: .3f} sec, End time: {float(event_end_time)/1000000: .3f} sec\")\n",
     "    \n",
     "    # Get event signal data with event time duration\n",
-    "    event_signal_LL = signal_left_lateral_RMS.loc[event_start_time:event_end_time]\n",
-    "    event_signal_LM = signal_left_medial_RMS.loc[event_start_time:event_end_time]\n",
+    "    mask_LL = (signal_left_lateral_RMS.index >= event_start_time) & (signal_left_lateral_RMS.index <= event_end_time)\n",
+    "    event_signal_LL = signal_left_lateral_RMS.iloc[mask_LL]\n",
+    "    \n",
+    "    mask_LM = (signal_left_medial_RMS.index >= event_start_time) & (signal_left_medial_RMS.index <= event_end_time)\n",
+    "    event_signal_LM = signal_left_medial_RMS.iloc[mask_LM]\n",
+    "    \n",
+    "    mask_RL = (signal_right_lateral_RMS.index >= event_start_time) & (signal_right_lateral_RMS.index <= event_end_time)\n",
+    "    event_signal_RL = signal_right_lateral_RMS.iloc[mask_RL]\n",
     "    \n",
-    "    event_signal_RL = signal_right_lateral_RMS.loc[event_start_time:event_end_time]\n",
-    "    event_signal_RM = signal_right_medial_RMS.loc[event_start_time:event_end_time]\n",
+    "    mask_RM = (signal_right_medial_RMS.index >= event_start_time) & (signal_right_medial_RMS.index <= event_end_time)\n",
+    "    event_signal_RM = signal_right_medial_RMS.iloc[mask_RM]\n",
     "    \n",
     "    # Calculate std ratio \n",
     "    left_event_std = event_signal_LL.std()\n",
@@ -1137,7 +1317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,