ECG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Quad_Cat</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>MeanRR</th>\n",
       "      <th>MedianRR</th>\n",
       "      <th>MinRR</th>\n",
       "      <th>MaxRR</th>\n",
       "      <th>LF</th>\n",
       "      <th>HF</th>\n",
       "      <th>VLF</th>\n",
       "      <th>Ibi</th>\n",
       "      <th>Bpm</th>\n",
       "      <th>Sdnn</th>\n",
       "      <th>Sdsd</th>\n",
       "      <th>Rmssd</th>\n",
       "      <th>Pnn50</th>\n",
       "      <th>pnn20</th>\n",
       "      <th>Pnn50pnn20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9.404760e-07</td>\n",
       "      <td>-0.741816</td>\n",
       "      <td>0.919643</td>\n",
       "      <td>1531</td>\n",
       "      <td>1562.0</td>\n",
       "      <td>14</td>\n",
       "      <td>3130</td>\n",
       "      <td>1.194750e-09</td>\n",
       "      <td>2.231830e-06</td>\n",
       "      <td>4.403520e-11</td>\n",
       "      <td>763.823158</td>\n",
       "      <td>78.559522</td>\n",
       "      <td>245.875789</td>\n",
       "      <td>469.052909</td>\n",
       "      <td>102253.7461</td>\n",
       "      <td>0.478903</td>\n",
       "      <td>0.481013</td>\n",
       "      <td>0.995614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4.330640e-07</td>\n",
       "      <td>-0.386809</td>\n",
       "      <td>0.452853</td>\n",
       "      <td>1582</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>1336</td>\n",
       "      <td>1695</td>\n",
       "      <td>1.060430e-10</td>\n",
       "      <td>1.283180e-07</td>\n",
       "      <td>2.946190e-12</td>\n",
       "      <td>794.173554</td>\n",
       "      <td>75.573801</td>\n",
       "      <td>240.813984</td>\n",
       "      <td>468.780827</td>\n",
       "      <td>127309.4367</td>\n",
       "      <td>0.477178</td>\n",
       "      <td>0.477178</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>9.034100e-06</td>\n",
       "      <td>-0.311829</td>\n",
       "      <td>0.398468</td>\n",
       "      <td>1637</td>\n",
       "      <td>1659.5</td>\n",
       "      <td>1421</td>\n",
       "      <td>1798</td>\n",
       "      <td>2.679310e-09</td>\n",
       "      <td>6.225430e-08</td>\n",
       "      <td>8.177790e-10</td>\n",
       "      <td>781.661290</td>\n",
       "      <td>76.868796</td>\n",
       "      <td>263.073272</td>\n",
       "      <td>492.561472</td>\n",
       "      <td>130746.3059</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.607510e-05</td>\n",
       "      <td>-0.330817</td>\n",
       "      <td>0.405026</td>\n",
       "      <td>1724</td>\n",
       "      <td>1727.0</td>\n",
       "      <td>1574</td>\n",
       "      <td>1895</td>\n",
       "      <td>1.345210e-08</td>\n",
       "      <td>1.348180e-07</td>\n",
       "      <td>4.147950e-09</td>\n",
       "      <td>720.920000</td>\n",
       "      <td>83.241534</td>\n",
       "      <td>243.280744</td>\n",
       "      <td>419.797884</td>\n",
       "      <td>163218.7363</td>\n",
       "      <td>0.424749</td>\n",
       "      <td>0.438127</td>\n",
       "      <td>0.969466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3.100000e-06</td>\n",
       "      <td>-0.303375</td>\n",
       "      <td>0.390093</td>\n",
       "      <td>1634</td>\n",
       "      <td>1654.0</td>\n",
       "      <td>1354</td>\n",
       "      <td>1837</td>\n",
       "      <td>7.953400e-10</td>\n",
       "      <td>1.135080e-07</td>\n",
       "      <td>2.688100e-10</td>\n",
       "      <td>779.417204</td>\n",
       "      <td>77.044532</td>\n",
       "      <td>266.303744</td>\n",
       "      <td>502.914461</td>\n",
       "      <td>121215.4100</td>\n",
       "      <td>0.480603</td>\n",
       "      <td>0.487069</td>\n",
       "      <td>0.986726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Quad_Cat          Mean       Min       Max  MeanRR  MedianRR  MinRR  MaxRR  \\\n",
       "0         1  9.404760e-07 -0.741816  0.919643    1531    1562.0     14   3130   \n",
       "1         0  4.330640e-07 -0.386809  0.452853    1582    1599.0   1336   1695   \n",
       "2         0  9.034100e-06 -0.311829  0.398468    1637    1659.5   1421   1798   \n",
       "3         3 -1.607510e-05 -0.330817  0.405026    1724    1727.0   1574   1895   \n",
       "4         1  3.100000e-06 -0.303375  0.390093    1634    1654.0   1354   1837   \n",
       "\n",
       "             LF            HF           VLF         Ibi        Bpm  \\\n",
       "0  1.194750e-09  2.231830e-06  4.403520e-11  763.823158  78.559522   \n",
       "1  1.060430e-10  1.283180e-07  2.946190e-12  794.173554  75.573801   \n",
       "2  2.679310e-09  6.225430e-08  8.177790e-10  781.661290  76.868796   \n",
       "3  1.345210e-08  1.348180e-07  4.147950e-09  720.920000  83.241534   \n",
       "4  7.953400e-10  1.135080e-07  2.688100e-10  779.417204  77.044532   \n",
       "\n",
       "         Sdnn        Sdsd        Rmssd     Pnn50     pnn20  Pnn50pnn20  \n",
       "0  245.875789  469.052909  102253.7461  0.478903  0.481013    0.995614  \n",
       "1  240.813984  468.780827  127309.4367  0.477178  0.477178    1.000000  \n",
       "2  263.073272  492.561472  130746.3059  0.459459  0.459459    1.000000  \n",
       "3  243.280744  419.797884  163218.7363  0.424749  0.438127    0.969466  \n",
       "4  266.303744  502.914461  121215.4100  0.480603  0.487069    0.986726  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ecg_all = pd.read_csv(\"VREED_Dataset/05 ECG-GSR Data/02 ECG-GSR (Features Extracted)/ECG_FeaturesExtracted.csv\")\n",
    "df_ecg_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalize data using MinMaxScaler\n",
    "scaler = preprocessing.MinMaxScaler()\n",
    "df_ecg_drop = df_ecg_all.drop(['Quad_Cat'], axis=1)\n",
    "names = df_ecg_drop.columns\n",
    "fit = scaler.fit_transform(df_ecg_drop)\n",
    "scaled_df = pd.DataFrame(fit, columns=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>MeanRR</th>\n",
       "      <th>MedianRR</th>\n",
       "      <th>MinRR</th>\n",
       "      <th>MaxRR</th>\n",
       "      <th>LF</th>\n",
       "      <th>HF</th>\n",
       "      <th>VLF</th>\n",
       "      <th>Ibi</th>\n",
       "      <th>Bpm</th>\n",
       "      <th>Sdnn</th>\n",
       "      <th>Sdsd</th>\n",
       "      <th>Rmssd</th>\n",
       "      <th>Pnn50</th>\n",
       "      <th>pnn20</th>\n",
       "      <th>Pnn50pnn20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.620218</td>\n",
       "      <td>0.247688</td>\n",
       "      <td>0.723824</td>\n",
       "      <td>0.024380</td>\n",
       "      <td>0.022320</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.018784</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.073382</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.236841</td>\n",
       "      <td>0.604383</td>\n",
       "      <td>0.620706</td>\n",
       "      <td>0.870455</td>\n",
       "      <td>0.435817</td>\n",
       "      <td>0.687615</td>\n",
       "      <td>0.601416</td>\n",
       "      <td>0.991855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.619265</td>\n",
       "      <td>0.712832</td>\n",
       "      <td>0.299935</td>\n",
       "      <td>0.027428</td>\n",
       "      <td>0.024254</td>\n",
       "      <td>0.613469</td>\n",
       "      <td>0.003607</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.004205</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.282384</td>\n",
       "      <td>0.546729</td>\n",
       "      <td>0.605248</td>\n",
       "      <td>0.869882</td>\n",
       "      <td>0.546639</td>\n",
       "      <td>0.684441</td>\n",
       "      <td>0.592299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.635420</td>\n",
       "      <td>0.811074</td>\n",
       "      <td>0.250549</td>\n",
       "      <td>0.030714</td>\n",
       "      <td>0.027416</td>\n",
       "      <td>0.652675</td>\n",
       "      <td>0.004696</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.002032</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.263608</td>\n",
       "      <td>0.571735</td>\n",
       "      <td>0.673227</td>\n",
       "      <td>0.920031</td>\n",
       "      <td>0.561840</td>\n",
       "      <td>0.651832</td>\n",
       "      <td>0.550169</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.588257</td>\n",
       "      <td>0.786194</td>\n",
       "      <td>0.256505</td>\n",
       "      <td>0.035913</td>\n",
       "      <td>0.030945</td>\n",
       "      <td>0.723247</td>\n",
       "      <td>0.005722</td>\n",
       "      <td>0.004286</td>\n",
       "      <td>0.004418</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>0.172463</td>\n",
       "      <td>0.694793</td>\n",
       "      <td>0.612781</td>\n",
       "      <td>0.766585</td>\n",
       "      <td>0.705466</td>\n",
       "      <td>0.587955</td>\n",
       "      <td>0.499448</td>\n",
       "      <td>0.943293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.624274</td>\n",
       "      <td>0.822150</td>\n",
       "      <td>0.242944</td>\n",
       "      <td>0.030535</td>\n",
       "      <td>0.027129</td>\n",
       "      <td>0.621771</td>\n",
       "      <td>0.005108</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.003718</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.260241</td>\n",
       "      <td>0.575129</td>\n",
       "      <td>0.683093</td>\n",
       "      <td>0.941863</td>\n",
       "      <td>0.519685</td>\n",
       "      <td>0.690744</td>\n",
       "      <td>0.615816</td>\n",
       "      <td>0.975348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>0.628281</td>\n",
       "      <td>0.791413</td>\n",
       "      <td>0.094741</td>\n",
       "      <td>0.462982</td>\n",
       "      <td>0.072239</td>\n",
       "      <td>0.754151</td>\n",
       "      <td>0.633721</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.184827</td>\n",
       "      <td>0.676852</td>\n",
       "      <td>0.499249</td>\n",
       "      <td>0.542272</td>\n",
       "      <td>0.651343</td>\n",
       "      <td>0.626166</td>\n",
       "      <td>0.669424</td>\n",
       "      <td>0.766397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>0.691427</td>\n",
       "      <td>0.777482</td>\n",
       "      <td>0.131714</td>\n",
       "      <td>0.048939</td>\n",
       "      <td>0.048821</td>\n",
       "      <td>0.033210</td>\n",
       "      <td>0.039185</td>\n",
       "      <td>0.032671</td>\n",
       "      <td>0.009497</td>\n",
       "      <td>0.012896</td>\n",
       "      <td>0.211157</td>\n",
       "      <td>0.640930</td>\n",
       "      <td>0.534037</td>\n",
       "      <td>0.658210</td>\n",
       "      <td>0.554501</td>\n",
       "      <td>0.939139</td>\n",
       "      <td>0.939291</td>\n",
       "      <td>0.977535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>0.668528</td>\n",
       "      <td>0.789992</td>\n",
       "      <td>0.132297</td>\n",
       "      <td>0.021094</td>\n",
       "      <td>0.010846</td>\n",
       "      <td>0.023524</td>\n",
       "      <td>0.021650</td>\n",
       "      <td>0.016022</td>\n",
       "      <td>0.005076</td>\n",
       "      <td>0.006629</td>\n",
       "      <td>0.193857</td>\n",
       "      <td>0.663990</td>\n",
       "      <td>0.460736</td>\n",
       "      <td>0.538035</td>\n",
       "      <td>0.491848</td>\n",
       "      <td>0.784623</td>\n",
       "      <td>0.794122</td>\n",
       "      <td>0.899405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>0.812129</td>\n",
       "      <td>0.802167</td>\n",
       "      <td>0.120148</td>\n",
       "      <td>0.037944</td>\n",
       "      <td>0.043019</td>\n",
       "      <td>0.385148</td>\n",
       "      <td>0.023860</td>\n",
       "      <td>0.111845</td>\n",
       "      <td>0.033263</td>\n",
       "      <td>0.041298</td>\n",
       "      <td>0.186640</td>\n",
       "      <td>0.676580</td>\n",
       "      <td>0.444202</td>\n",
       "      <td>0.536770</td>\n",
       "      <td>0.428582</td>\n",
       "      <td>0.873665</td>\n",
       "      <td>0.848664</td>\n",
       "      <td>0.984127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>0.435144</td>\n",
       "      <td>0.809541</td>\n",
       "      <td>0.092797</td>\n",
       "      <td>0.035136</td>\n",
       "      <td>0.013695</td>\n",
       "      <td>0.378229</td>\n",
       "      <td>0.058593</td>\n",
       "      <td>0.092862</td>\n",
       "      <td>0.024901</td>\n",
       "      <td>0.036974</td>\n",
       "      <td>0.220141</td>\n",
       "      <td>0.629022</td>\n",
       "      <td>0.512378</td>\n",
       "      <td>0.632387</td>\n",
       "      <td>0.464016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>312 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mean       Min       Max    MeanRR  MedianRR     MinRR     MaxRR  \\\n",
       "0    0.620218  0.247688  0.723824  0.024380  0.022320  0.003690  0.018784   \n",
       "1    0.619265  0.712832  0.299935  0.027428  0.024254  0.613469  0.003607   \n",
       "2    0.635420  0.811074  0.250549  0.030714  0.027416  0.652675  0.004696   \n",
       "3    0.588257  0.786194  0.256505  0.035913  0.030945  0.723247  0.005722   \n",
       "4    0.624274  0.822150  0.242944  0.030535  0.027129  0.621771  0.005108   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "307  0.628281  0.791413  0.094741  0.462982  0.072239  0.754151  0.633721   \n",
       "308  0.691427  0.777482  0.131714  0.048939  0.048821  0.033210  0.039185   \n",
       "309  0.668528  0.789992  0.132297  0.021094  0.010846  0.023524  0.021650   \n",
       "310  0.812129  0.802167  0.120148  0.037944  0.043019  0.385148  0.023860   \n",
       "311  0.435144  0.809541  0.092797  0.035136  0.013695  0.378229  0.058593   \n",
       "\n",
       "           LF        HF       VLF       Ibi       Bpm      Sdnn      Sdsd  \\\n",
       "0    0.000358  0.073382  0.000016  0.236841  0.604383  0.620706  0.870455   \n",
       "1    0.000009  0.004205  0.000000  0.282384  0.546729  0.605248  0.869882   \n",
       "2    0.000834  0.002032  0.000316  0.263608  0.571735  0.673227  0.920031   \n",
       "3    0.004286  0.004418  0.001608  0.172463  0.694793  0.612781  0.766585   \n",
       "4    0.000230  0.003718  0.000103  0.260241  0.575129  0.683093  0.941863   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "307  0.000895  0.001449  0.000178  0.184827  0.676852  0.499249  0.542272   \n",
       "308  0.032671  0.009497  0.012896  0.211157  0.640930  0.534037  0.658210   \n",
       "309  0.016022  0.005076  0.006629  0.193857  0.663990  0.460736  0.538035   \n",
       "310  0.111845  0.033263  0.041298  0.186640  0.676580  0.444202  0.536770   \n",
       "311  0.092862  0.024901  0.036974  0.220141  0.629022  0.512378  0.632387   \n",
       "\n",
       "        Rmssd     Pnn50     pnn20  Pnn50pnn20  \n",
       "0    0.435817  0.687615  0.601416    0.991855  \n",
       "1    0.546639  0.684441  0.592299    1.000000  \n",
       "2    0.561840  0.651832  0.550169    1.000000  \n",
       "3    0.705466  0.587955  0.499448    0.943293  \n",
       "4    0.519685  0.690744  0.615816    0.975348  \n",
       "..        ...       ...       ...         ...  \n",
       "307  0.651343  0.626166  0.669424    0.766397  \n",
       "308  0.554501  0.939139  0.939291    0.977535  \n",
       "309  0.491848  0.784623  0.794122    0.899405  \n",
       "310  0.428582  0.873665  0.848664    0.984127  \n",
       "311  0.464016  1.000000  1.000000    1.000000  \n",
       "\n",
       "[312 rows x 18 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Quad_Cat</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
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       "      <th>Sdnn</th>\n",
       "      <th>Sdsd</th>\n",
       "      <th>Rmssd</th>\n",
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       "      <td>0.024380</td>\n",
       "      <td>0.022320</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.018784</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.073382</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.236841</td>\n",
       "      <td>0.604383</td>\n",
       "      <td>0.620706</td>\n",
       "      <td>0.870455</td>\n",
       "      <td>0.435817</td>\n",
       "      <td>0.687615</td>\n",
       "      <td>0.601416</td>\n",
       "      <td>0.991855</td>\n",
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       "      <td>0.619265</td>\n",
       "      <td>0.712832</td>\n",
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       "      <td>0.027428</td>\n",
       "      <td>0.024254</td>\n",
       "      <td>0.613469</td>\n",
       "      <td>0.003607</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.004205</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.282384</td>\n",
       "      <td>0.546729</td>\n",
       "      <td>0.605248</td>\n",
       "      <td>0.869882</td>\n",
       "      <td>0.546639</td>\n",
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       "      <td>0.030714</td>\n",
       "      <td>0.027416</td>\n",
       "      <td>0.652675</td>\n",
       "      <td>0.004696</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.002032</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.263608</td>\n",
       "      <td>0.571735</td>\n",
       "      <td>0.673227</td>\n",
       "      <td>0.920031</td>\n",
       "      <td>0.561840</td>\n",
       "      <td>0.651832</td>\n",
       "      <td>0.550169</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.588257</td>\n",
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       "      <td>0.256505</td>\n",
       "      <td>0.035913</td>\n",
       "      <td>0.030945</td>\n",
       "      <td>0.723247</td>\n",
       "      <td>0.005722</td>\n",
       "      <td>0.004286</td>\n",
       "      <td>0.004418</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>0.172463</td>\n",
       "      <td>0.694793</td>\n",
       "      <td>0.612781</td>\n",
       "      <td>0.766585</td>\n",
       "      <td>0.705466</td>\n",
       "      <td>0.587955</td>\n",
       "      <td>0.499448</td>\n",
       "      <td>0.943293</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.624274</td>\n",
       "      <td>0.822150</td>\n",
       "      <td>0.242944</td>\n",
       "      <td>0.030535</td>\n",
       "      <td>0.027129</td>\n",
       "      <td>0.621771</td>\n",
       "      <td>0.005108</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.003718</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.260241</td>\n",
       "      <td>0.575129</td>\n",
       "      <td>0.683093</td>\n",
       "      <td>0.941863</td>\n",
       "      <td>0.519685</td>\n",
       "      <td>0.690744</td>\n",
       "      <td>0.615816</td>\n",
       "      <td>0.975348</td>\n",
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       "      <td>0.094741</td>\n",
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       "      <td>0.072239</td>\n",
       "      <td>0.754151</td>\n",
       "      <td>0.633721</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.184827</td>\n",
       "      <td>0.676852</td>\n",
       "      <td>0.499249</td>\n",
       "      <td>0.542272</td>\n",
       "      <td>0.651343</td>\n",
       "      <td>0.626166</td>\n",
       "      <td>0.669424</td>\n",
       "      <td>0.766397</td>\n",
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       "      <td>0.691427</td>\n",
       "      <td>0.777482</td>\n",
       "      <td>0.131714</td>\n",
       "      <td>0.048939</td>\n",
       "      <td>0.048821</td>\n",
       "      <td>0.033210</td>\n",
       "      <td>0.039185</td>\n",
       "      <td>0.032671</td>\n",
       "      <td>0.009497</td>\n",
       "      <td>0.012896</td>\n",
       "      <td>0.211157</td>\n",
       "      <td>0.640930</td>\n",
       "      <td>0.534037</td>\n",
       "      <td>0.658210</td>\n",
       "      <td>0.554501</td>\n",
       "      <td>0.939139</td>\n",
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       "      <td>0.016022</td>\n",
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       "      <td>0.120148</td>\n",
       "      <td>0.037944</td>\n",
       "      <td>0.043019</td>\n",
       "      <td>0.385148</td>\n",
       "      <td>0.023860</td>\n",
       "      <td>0.111845</td>\n",
       "      <td>0.033263</td>\n",
       "      <td>0.041298</td>\n",
       "      <td>0.186640</td>\n",
       "      <td>0.676580</td>\n",
       "      <td>0.444202</td>\n",
       "      <td>0.536770</td>\n",
       "      <td>0.428582</td>\n",
       "      <td>0.873665</td>\n",
       "      <td>0.848664</td>\n",
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       "      <td>0.092797</td>\n",
       "      <td>0.035136</td>\n",
       "      <td>0.013695</td>\n",
       "      <td>0.378229</td>\n",
       "      <td>0.058593</td>\n",
       "      <td>0.092862</td>\n",
       "      <td>0.024901</td>\n",
       "      <td>0.036974</td>\n",
       "      <td>0.220141</td>\n",
       "      <td>0.629022</td>\n",
       "      <td>0.512378</td>\n",
       "      <td>0.632387</td>\n",
       "      <td>0.464016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "</table>\n",
       "<p>312 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Quad_Cat      Mean       Min       Max    MeanRR  MedianRR     MinRR  \\\n",
       "0           1  0.620218  0.247688  0.723824  0.024380  0.022320  0.003690   \n",
       "1           0  0.619265  0.712832  0.299935  0.027428  0.024254  0.613469   \n",
       "2           0  0.635420  0.811074  0.250549  0.030714  0.027416  0.652675   \n",
       "3           3  0.588257  0.786194  0.256505  0.035913  0.030945  0.723247   \n",
       "4           1  0.624274  0.822150  0.242944  0.030535  0.027129  0.621771   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "307         2  0.628281  0.791413  0.094741  0.462982  0.072239  0.754151   \n",
       "308         0  0.691427  0.777482  0.131714  0.048939  0.048821  0.033210   \n",
       "309         1  0.668528  0.789992  0.132297  0.021094  0.010846  0.023524   \n",
       "310         3  0.812129  0.802167  0.120148  0.037944  0.043019  0.385148   \n",
       "311         3  0.435144  0.809541  0.092797  0.035136  0.013695  0.378229   \n",
       "\n",
       "        MaxRR        LF        HF       VLF       Ibi       Bpm      Sdnn  \\\n",
       "0    0.018784  0.000358  0.073382  0.000016  0.236841  0.604383  0.620706   \n",
       "1    0.003607  0.000009  0.004205  0.000000  0.282384  0.546729  0.605248   \n",
       "2    0.004696  0.000834  0.002032  0.000316  0.263608  0.571735  0.673227   \n",
       "3    0.005722  0.004286  0.004418  0.001608  0.172463  0.694793  0.612781   \n",
       "4    0.005108  0.000230  0.003718  0.000103  0.260241  0.575129  0.683093   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "307  0.633721  0.000895  0.001449  0.000178  0.184827  0.676852  0.499249   \n",
       "308  0.039185  0.032671  0.009497  0.012896  0.211157  0.640930  0.534037   \n",
       "309  0.021650  0.016022  0.005076  0.006629  0.193857  0.663990  0.460736   \n",
       "310  0.023860  0.111845  0.033263  0.041298  0.186640  0.676580  0.444202   \n",
       "311  0.058593  0.092862  0.024901  0.036974  0.220141  0.629022  0.512378   \n",
       "\n",
       "         Sdsd     Rmssd     Pnn50     pnn20  Pnn50pnn20  \n",
       "0    0.870455  0.435817  0.687615  0.601416    0.991855  \n",
       "1    0.869882  0.546639  0.684441  0.592299    1.000000  \n",
       "2    0.920031  0.561840  0.651832  0.550169    1.000000  \n",
       "3    0.766585  0.705466  0.587955  0.499448    0.943293  \n",
       "4    0.941863  0.519685  0.690744  0.615816    0.975348  \n",
       "..        ...       ...       ...       ...         ...  \n",
       "307  0.542272  0.651343  0.626166  0.669424    0.766397  \n",
       "308  0.658210  0.554501  0.939139  0.939291    0.977535  \n",
       "309  0.538035  0.491848  0.784623  0.794122    0.899405  \n",
       "310  0.536770  0.428582  0.873665  0.848664    0.984127  \n",
       "311  0.632387  0.464016  1.000000  1.000000    1.000000  \n",
       "\n",
       "[312 rows x 19 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ecg = pd.concat([df_ecg_all['Quad_Cat'],scaled_df], axis = 1)\n",
    "df_ecg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>Sdnn</th>\n",
       "      <th>Sdsd</th>\n",
       "      <th>Rmssd</th>\n",
       "      <th>Pnn50</th>\n",
       "      <th>pnn20</th>\n",
       "      <th>Pnn50pnn20</th>\n",
       "      <th>Arousal</th>\n",
       "      <th>Valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.620218</td>\n",
       "      <td>0.247688</td>\n",
       "      <td>0.723824</td>\n",
       "      <td>0.024380</td>\n",
       "      <td>0.022320</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.018784</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.073382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236841</td>\n",
       "      <td>0.604383</td>\n",
       "      <td>0.620706</td>\n",
       "      <td>0.870455</td>\n",
       "      <td>0.435817</td>\n",
       "      <td>0.687615</td>\n",
       "      <td>0.601416</td>\n",
       "      <td>0.991855</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.619265</td>\n",
       "      <td>0.712832</td>\n",
       "      <td>0.299935</td>\n",
       "      <td>0.027428</td>\n",
       "      <td>0.024254</td>\n",
       "      <td>0.613469</td>\n",
       "      <td>0.003607</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.004205</td>\n",
       "      <td>...</td>\n",
       "      <td>0.282384</td>\n",
       "      <td>0.546729</td>\n",
       "      <td>0.605248</td>\n",
       "      <td>0.869882</td>\n",
       "      <td>0.546639</td>\n",
       "      <td>0.684441</td>\n",
       "      <td>0.592299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.635420</td>\n",
       "      <td>0.811074</td>\n",
       "      <td>0.250549</td>\n",
       "      <td>0.030714</td>\n",
       "      <td>0.027416</td>\n",
       "      <td>0.652675</td>\n",
       "      <td>0.004696</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.002032</td>\n",
       "      <td>...</td>\n",
       "      <td>0.263608</td>\n",
       "      <td>0.571735</td>\n",
       "      <td>0.673227</td>\n",
       "      <td>0.920031</td>\n",
       "      <td>0.561840</td>\n",
       "      <td>0.651832</td>\n",
       "      <td>0.550169</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.588257</td>\n",
       "      <td>0.786194</td>\n",
       "      <td>0.256505</td>\n",
       "      <td>0.035913</td>\n",
       "      <td>0.030945</td>\n",
       "      <td>0.723247</td>\n",
       "      <td>0.005722</td>\n",
       "      <td>0.004286</td>\n",
       "      <td>0.004418</td>\n",
       "      <td>...</td>\n",
       "      <td>0.172463</td>\n",
       "      <td>0.694793</td>\n",
       "      <td>0.612781</td>\n",
       "      <td>0.766585</td>\n",
       "      <td>0.705466</td>\n",
       "      <td>0.587955</td>\n",
       "      <td>0.499448</td>\n",
       "      <td>0.943293</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.624274</td>\n",
       "      <td>0.822150</td>\n",
       "      <td>0.242944</td>\n",
       "      <td>0.030535</td>\n",
       "      <td>0.027129</td>\n",
       "      <td>0.621771</td>\n",
       "      <td>0.005108</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.003718</td>\n",
       "      <td>...</td>\n",
       "      <td>0.260241</td>\n",
       "      <td>0.575129</td>\n",
       "      <td>0.683093</td>\n",
       "      <td>0.941863</td>\n",
       "      <td>0.519685</td>\n",
       "      <td>0.690744</td>\n",
       "      <td>0.615816</td>\n",
       "      <td>0.975348</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>2</td>\n",
       "      <td>0.628281</td>\n",
       "      <td>0.791413</td>\n",
       "      <td>0.094741</td>\n",
       "      <td>0.462982</td>\n",
       "      <td>0.072239</td>\n",
       "      <td>0.754151</td>\n",
       "      <td>0.633721</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>...</td>\n",
       "      <td>0.184827</td>\n",
       "      <td>0.676852</td>\n",
       "      <td>0.499249</td>\n",
       "      <td>0.542272</td>\n",
       "      <td>0.651343</td>\n",
       "      <td>0.626166</td>\n",
       "      <td>0.669424</td>\n",
       "      <td>0.766397</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>0</td>\n",
       "      <td>0.691427</td>\n",
       "      <td>0.777482</td>\n",
       "      <td>0.131714</td>\n",
       "      <td>0.048939</td>\n",
       "      <td>0.048821</td>\n",
       "      <td>0.033210</td>\n",
       "      <td>0.039185</td>\n",
       "      <td>0.032671</td>\n",
       "      <td>0.009497</td>\n",
       "      <td>...</td>\n",
       "      <td>0.211157</td>\n",
       "      <td>0.640930</td>\n",
       "      <td>0.534037</td>\n",
       "      <td>0.658210</td>\n",
       "      <td>0.554501</td>\n",
       "      <td>0.939139</td>\n",
       "      <td>0.939291</td>\n",
       "      <td>0.977535</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>1</td>\n",
       "      <td>0.668528</td>\n",
       "      <td>0.789992</td>\n",
       "      <td>0.132297</td>\n",
       "      <td>0.021094</td>\n",
       "      <td>0.010846</td>\n",
       "      <td>0.023524</td>\n",
       "      <td>0.021650</td>\n",
       "      <td>0.016022</td>\n",
       "      <td>0.005076</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193857</td>\n",
       "      <td>0.663990</td>\n",
       "      <td>0.460736</td>\n",
       "      <td>0.538035</td>\n",
       "      <td>0.491848</td>\n",
       "      <td>0.784623</td>\n",
       "      <td>0.794122</td>\n",
       "      <td>0.899405</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>3</td>\n",
       "      <td>0.812129</td>\n",
       "      <td>0.802167</td>\n",
       "      <td>0.120148</td>\n",
       "      <td>0.037944</td>\n",
       "      <td>0.043019</td>\n",
       "      <td>0.385148</td>\n",
       "      <td>0.023860</td>\n",
       "      <td>0.111845</td>\n",
       "      <td>0.033263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186640</td>\n",
       "      <td>0.676580</td>\n",
       "      <td>0.444202</td>\n",
       "      <td>0.536770</td>\n",
       "      <td>0.428582</td>\n",
       "      <td>0.873665</td>\n",
       "      <td>0.848664</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>3</td>\n",
       "      <td>0.435144</td>\n",
       "      <td>0.809541</td>\n",
       "      <td>0.092797</td>\n",
       "      <td>0.035136</td>\n",
       "      <td>0.013695</td>\n",
       "      <td>0.378229</td>\n",
       "      <td>0.058593</td>\n",
       "      <td>0.092862</td>\n",
       "      <td>0.024901</td>\n",
       "      <td>...</td>\n",
       "      <td>0.220141</td>\n",
       "      <td>0.629022</td>\n",
       "      <td>0.512378</td>\n",
       "      <td>0.632387</td>\n",
       "      <td>0.464016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>312 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Quad_Cat      Mean       Min       Max    MeanRR  MedianRR     MinRR  \\\n",
       "0           1  0.620218  0.247688  0.723824  0.024380  0.022320  0.003690   \n",
       "1           0  0.619265  0.712832  0.299935  0.027428  0.024254  0.613469   \n",
       "2           0  0.635420  0.811074  0.250549  0.030714  0.027416  0.652675   \n",
       "3           3  0.588257  0.786194  0.256505  0.035913  0.030945  0.723247   \n",
       "4           1  0.624274  0.822150  0.242944  0.030535  0.027129  0.621771   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "307         2  0.628281  0.791413  0.094741  0.462982  0.072239  0.754151   \n",
       "308         0  0.691427  0.777482  0.131714  0.048939  0.048821  0.033210   \n",
       "309         1  0.668528  0.789992  0.132297  0.021094  0.010846  0.023524   \n",
       "310         3  0.812129  0.802167  0.120148  0.037944  0.043019  0.385148   \n",
       "311         3  0.435144  0.809541  0.092797  0.035136  0.013695  0.378229   \n",
       "\n",
       "        MaxRR        LF        HF  ...       Ibi       Bpm      Sdnn  \\\n",
       "0    0.018784  0.000358  0.073382  ...  0.236841  0.604383  0.620706   \n",
       "1    0.003607  0.000009  0.004205  ...  0.282384  0.546729  0.605248   \n",
       "2    0.004696  0.000834  0.002032  ...  0.263608  0.571735  0.673227   \n",
       "3    0.005722  0.004286  0.004418  ...  0.172463  0.694793  0.612781   \n",
       "4    0.005108  0.000230  0.003718  ...  0.260241  0.575129  0.683093   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "307  0.633721  0.000895  0.001449  ...  0.184827  0.676852  0.499249   \n",
       "308  0.039185  0.032671  0.009497  ...  0.211157  0.640930  0.534037   \n",
       "309  0.021650  0.016022  0.005076  ...  0.193857  0.663990  0.460736   \n",
       "310  0.023860  0.111845  0.033263  ...  0.186640  0.676580  0.444202   \n",
       "311  0.058593  0.092862  0.024901  ...  0.220141  0.629022  0.512378   \n",
       "\n",
       "         Sdsd     Rmssd     Pnn50     pnn20  Pnn50pnn20  Arousal  Valence  \n",
       "0    0.870455  0.435817  0.687615  0.601416    0.991855        0        1  \n",
       "1    0.869882  0.546639  0.684441  0.592299    1.000000        1        1  \n",
       "2    0.920031  0.561840  0.651832  0.550169    1.000000        1        1  \n",
       "3    0.766585  0.705466  0.587955  0.499448    0.943293        1        0  \n",
       "4    0.941863  0.519685  0.690744  0.615816    0.975348        0        1  \n",
       "..        ...       ...       ...       ...         ...      ...      ...  \n",
       "307  0.542272  0.651343  0.626166  0.669424    0.766397        0        0  \n",
       "308  0.658210  0.554501  0.939139  0.939291    0.977535        1        1  \n",
       "309  0.538035  0.491848  0.784623  0.794122    0.899405        0        1  \n",
       "310  0.536770  0.428582  0.873665  0.848664    0.984127        1        0  \n",
       "311  0.632387  0.464016  1.000000  1.000000    1.000000        1        0  \n",
       "\n",
       "[312 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read column names for X and y\n",
    "df_ecg['Arousal'] = df_ecg['Quad_Cat'].apply(lambda x: 0 if ((x == 1) | (x == 2))  else 1)\n",
    "df_ecg['Valence'] = df_ecg['Quad_Cat'].apply(lambda x: 0 if ((x == 2) | (x == 3))  else 1)\n",
    "\n",
    "column_names = scaled_df.columns\n",
    "\n",
    "X_ecg = df_ecg[column_names]\n",
    "y_ecg_quad = df_ecg['Quad_Cat']\n",
    "y_ecg_ar = df_ecg['Arousal']\n",
    "y_ecg_va = df_ecg['Valence']\n",
    "df_ecg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder=LabelEncoder()\n",
    "label_encoder.fit(y_ecg_quad)\n",
    "y=label_encoder.transform(y_ecg_quad)\n",
    "classes_quad=label_encoder.classes_\n",
    "print(classes_quad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "label_encoder=LabelEncoder()\n",
    "label_encoder.fit(y_ecg_ar)\n",
    "y=label_encoder.transform(y_ecg_ar)\n",
    "classes_ar=label_encoder.classes_\n",
    "print(classes_ar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "label_encoder=LabelEncoder()\n",
    "label_encoder.fit(y_ecg_va)\n",
    "y=label_encoder.transform(y_ecg_va)\n",
    "classes_va=label_encoder.classes_\n",
    "print(classes_va)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECG 4-class accuracy: 31.0256\n",
      "ECG 4-class precision: 0.3197\n",
      "ECG 4-class recall: 0.3148\n",
      "ECG 4-class F1: 0.3084\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier as GBDT\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "\n",
    "ecg_quad = RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
    "                       criterion='gini', max_depth=None, max_features='auto',\n",
    "                       max_leaf_nodes=None, max_samples=None,\n",
    "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                       min_samples_leaf=1, min_samples_split=2,\n",
    "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
    "                       n_jobs=-1, oob_score=False, random_state=2313, verbose=0,\n",
    "                       warm_start=False)\n",
    "\n",
    "ecg_ar = RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
    "                       criterion='gini', max_depth=None, max_features='auto',\n",
    "                       max_leaf_nodes=None, max_samples=None,\n",
    "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                       min_samples_leaf=1, min_samples_split=2,\n",
    "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
    "                       n_jobs=-1, oob_score=False, random_state=2313, verbose=0,\n",
    "                       warm_start=False)\n",
    "\n",
    "ecg_va = GBDT(learning_rate=0.1,max_depth=9,min_samples_leaf=60,min_samples_split=5,n_estimators=31,random_state=7)\n",
    "\n",
    "def CrossValidation(X,y,classifier):\n",
    "    accuracy = []\n",
    "\n",
    "    cv = ShuffleSplit(n_splits=5, test_size=0.25, random_state=False)\n",
    "    cv.get_n_splits(X, y)\n",
    "\n",
    "    for train, test in cv.split(X, y):\n",
    "        X_train, X_test = X.iloc[train], X.iloc[test]\n",
    "        y_train, y_test = y.iloc[train], y.iloc[test]\n",
    "\n",
    "        classifier.fit(X_train, y_train)\n",
    "        feature = classifier.feature_importances_\n",
    "        \n",
    "        prediction = classifier.predict(X_test)\n",
    "        score = metrics.accuracy_score(y_test, prediction)\n",
    "        accuracy.append(score)\n",
    "        \n",
    "        pred_prob = classifier.predict_proba(X_test)\n",
    "        #roc_auc_score(y_test, pred_proba, multi_class = \"ovr\")\n",
    "        #print(pred_proba.shape)\n",
    "        \n",
    "        report = metrics.classification_report(y_test, prediction,output_dict=True)\n",
    "        precision =  report['macro avg']['precision'] \n",
    "        recall = report['macro avg']['recall']    \n",
    "        f1 = report['macro avg']['f1-score']\n",
    "        \n",
    "        confusion_matrix = metrics.confusion_matrix(y_test, prediction)\n",
    "        \n",
    "\n",
    "\n",
    "    return accuracy, feature, precision, recall, f1, confusion_matrix, y_test, pred_prob\n",
    "\n",
    "all_quad_accuracy, all_quad_feature, all_quad_precision, all_quad_recall, all_quad_f1, all_quad_cm, y_test_quad, pred_prob_quad = CrossValidation(X_ecg, y_ecg_quad, ecg_quad)\n",
    "all_quad_accuracy_mean = (np.array(all_quad_accuracy).mean()) * 100\n",
    "print(\"ECG 4-class accuracy: %.4f\" % all_quad_accuracy_mean)\n",
    "print(\"ECG 4-class precision: %.4f\" % all_quad_precision)\n",
    "print(\"ECG 4-class recall: %.4f\" % all_quad_recall)\n",
    "print(\"ECG 4-class F1: %.4f\" % all_quad_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECG Arousal accuracy: 56.1538\n",
      "ECG Arousal precision: 0.4616\n",
      "ECG Arousal recall: 0.4613\n",
      "ECG Arousal F1: 0.4486\n"
     ]
    }
   ],
   "source": [
    "all_ar_accuracy, all_ar_feature, all_ar_precision, all_ar_recall, all_ar_f1, all_ar_cm, y_test_ar, pred_prob_ar = CrossValidation(X_ecg, y_ecg_ar, ecg_ar)\n",
    "all_ar_accuracy_mean = (np.array(all_ar_accuracy).mean()) * 100\n",
    "\n",
    "print(\"ECG Arousal accuracy: %.4f\" % all_ar_accuracy_mean)\n",
    "print(\"ECG Arousal precision: %.4f\" % all_ar_precision)\n",
    "print(\"ECG Arousal recall: %.4f\" % all_ar_recall)\n",
    "print(\"ECG Arousal F1: %.4f\" % all_ar_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECG Valence accuracy: 56.6667\n",
      "ECG Valence precision: 0.5442\n",
      "ECG Valence recall: 0.5442\n",
      "ECG Valence F1: 0.5385\n"
     ]
    }
   ],
   "source": [
    "all_va_accuracy, all_va_feature, all_va_precision, all_va_recall, all_va_f1, all_va_cm, y_test_va, pred_prob_va = CrossValidation(X_ecg, y_ecg_va, ecg_va)\n",
    "all_va_accuracy_mean = (np.array(all_va_accuracy).mean()) * 100\n",
    "\n",
    "print(\"ECG Valence accuracy: %.4f\" % all_va_accuracy_mean)\n",
    "print(\"ECG Valence precision: %.4f\" % all_va_precision)\n",
    "print(\"ECG Valence recall: %.4f\" % all_va_recall)\n",
    "print(\"ECG Valence F1: %.4f\" % all_va_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_binarized=label_binarize(y_test_va,classes=classes_quad)\n",
    "classes = classes_va\n",
    "fpr = {}\n",
    "tpr = {}\n",
    "thresh ={}\n",
    "roc_auc = dict()\n",
    "\n",
    "n_class = classes.shape[0]\n",
    "\n",
    "for i in range(n_class):    \n",
    "    fpr[i], tpr[i], thresh[i] = roc_curve(y_test_binarized[:,i], pred_prob_va[:,i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "    plt.plot(fpr[i], tpr[i], linestyle='--',  label='%s' % classes[i]) \n",
    "\n",
    "plt.plot([0,1],[0,1],'b--')\n",
    "plt.xlim([0,1])\n",
    "plt.ylim([0,1.05])\n",
    "plt.title('ROC curve for Valence')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive rate')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Feature Importance graph plot for 4-class\n",
    "\n",
    "plt.figure(1,figsize=(18,5))\n",
    "xaxis = column_names\n",
    "clrs = ['blue' if (x < 0.06) else 'red' for x in all_quad_feature ]\n",
    "plt.bar(xaxis, all_quad_feature, color = clrs)\n",
    "plt.title('Feature Importance: 4-Class')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Feature Importance graph plot for Arousal\n",
    "\n",
    "plt.figure(1,figsize=(18,5))\n",
    "xaxis = column_names\n",
    "clrs = ['blue' if (x < 0.06) else 'red' for x in all_ar_feature ]\n",
    "plt.bar(xaxis, all_ar_feature, color = clrs)\n",
    "plt.title('Feature Importance: Arousal')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Feature Importance graph plot for Valence\n",
    "\n",
    "plt.figure(1,figsize=(18,5))\n",
    "xaxis = column_names\n",
    "clrs = ['blue' if (x < 0.10) else 'red' for x in all_va_feature ]\n",
    "plt.bar(xaxis, all_va_feature, color = clrs)\n",
    "plt.title('Feature Importance: Valence')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "#Confusion Matrix for 4-Class\n",
    "ax = sns.heatmap(all_quad_cm/np.sum(all_quad_cm), annot=True, fmt='.1%', cmap='Reds')\n",
    "\n",
    "ax.set_title('4-Class Confusion Matrix\\n\\n');\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values ');\n",
    "\n",
    "ax.xaxis.set_ticklabels(['0','1','2','3'])\n",
    "ax.yaxis.set_ticklabels(['0','1','2','3'])\n",
    "\n",
    "# Display the visualization of the Confusion Matrix.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix for Arousal\n",
    "\n",
    "ax = sns.heatmap(all_ar_cm, annot=True, cmap='Reds')\n",
    "\n",
    "ax.set_title('Arousal Confusion Matrix\\n\\n');\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values ');\n",
    "\n",
    "ax.xaxis.set_ticklabels(['0','1'])\n",
    "ax.yaxis.set_ticklabels(['0','1'])\n",
    "\n",
    "# Display the visualization of the Confusion Matrix.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix for Valence\n",
    "\n",
    "ax = sns.heatmap(all_va_cm, annot=True, cmap='Reds')\n",
    "\n",
    "ax.set_title('Valence Confusion Matrix\\n\\n');\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values ');\n",
    "\n",
    "ax.xaxis.set_ticklabels(['0','1'])\n",
    "ax.yaxis.set_ticklabels(['0','1'])\n",
    "\n",
    "# Display the visualization of the Confusion Matrix.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}