notebooks/11_nonlinear_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Non-linear features\n",
    "\n",
    "This notebook demonstrates how to use YASA to calculate epoch-per-epoch non-linear features of a full-night single-channel EEG recording.\n",
    "\n",
    "Please make sure to install the latest version of YASA first by typing the following line in your terminal or command prompt:\n",
    "\n",
    "`pip install --upgrade yasa`\n",
    "\n",
    "In addition, you will also need to install the [AntroPy](https://github.com/raphaelvallat/antropy) package: `pip install --upgrade antropy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2161058,) [15.797 22.307 39.922 25.657 27.094]\n"
     ]
    }
   ],
   "source": [
    "import yasa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import antropy as ant\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1.2)\n",
    "\n",
    "# Load EEG data\n",
    "f = np.load('data_full_6hrs_100Hz_Cz+Fz+Pz.npz')\n",
    "data, ch_names = f['data'], f['chan']\n",
    "sf = 100.\n",
    "times = np.arange(data.size) / sf\n",
    "\n",
    "# Keep only Cz\n",
    "data = data[0, :]\n",
    "print(data.shape, np.round(data[0:5], 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720,) Unique values = [0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "# Load the hypnogram data\n",
    "hypno = np.loadtxt('data_full_6hrs_100Hz_hypno_30s.txt').astype(int)\n",
    "print(hypno.shape, 'Unique values =', np.unique(hypno))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720, 3000)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the EEG data to 30-sec data\n",
    "times, data_win = yasa.sliding_window(data, sf, window=30)\n",
    "\n",
    "# Convert times to minutes\n",
    "times /= 60\n",
    "\n",
    "data_win.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate non-linear features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perm_entropy</th>\n",
       "      <th>svd_entropy</th>\n",
       "      <th>sample_entropy</th>\n",
       "      <th>dfa</th>\n",
       "      <th>petrosian</th>\n",
       "      <th>katz</th>\n",
       "      <th>higuchi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.993005</td>\n",
       "      <td>0.615179</td>\n",
       "      <td>0.992649</td>\n",
       "      <td>1.275455</td>\n",
       "      <td>1.027439</td>\n",
       "      <td>2.794894</td>\n",
       "      <td>1.743780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.993249</td>\n",
       "      <td>0.565308</td>\n",
       "      <td>0.858924</td>\n",
       "      <td>1.384108</td>\n",
       "      <td>1.027283</td>\n",
       "      <td>2.593635</td>\n",
       "      <td>1.748034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.993561</td>\n",
       "      <td>0.573424</td>\n",
       "      <td>0.773124</td>\n",
       "      <td>1.406985</td>\n",
       "      <td>1.027411</td>\n",
       "      <td>2.633458</td>\n",
       "      <td>1.721437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.998517</td>\n",
       "      <td>0.495381</td>\n",
       "      <td>0.166400</td>\n",
       "      <td>1.453336</td>\n",
       "      <td>1.028952</td>\n",
       "      <td>2.259923</td>\n",
       "      <td>1.868283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.991045</td>\n",
       "      <td>0.737926</td>\n",
       "      <td>1.332437</td>\n",
       "      <td>1.152280</td>\n",
       "      <td>1.026885</td>\n",
       "      <td>3.484544</td>\n",
       "      <td>1.769742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   perm_entropy  svd_entropy  sample_entropy       dfa  petrosian      katz  \\\n",
       "0      0.993005     0.615179        0.992649  1.275455   1.027439  2.794894   \n",
       "1      0.993249     0.565308        0.858924  1.384108   1.027283  2.593635   \n",
       "2      0.993561     0.573424        0.773124  1.406985   1.027411  2.633458   \n",
       "3      0.998517     0.495381        0.166400  1.453336   1.028952  2.259923   \n",
       "4      0.991045     0.737926        1.332437  1.152280   1.026885  3.484544   \n",
       "\n",
       "    higuchi  \n",
       "0  1.743780  \n",
       "1  1.748034  \n",
       "2  1.721437  \n",
       "3  1.868283  \n",
       "4  1.769742  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import apply_along_axis as apply\n",
    "\n",
    "df_feat = {\n",
    "    # Entropy\n",
    "    'perm_entropy': apply(ant.perm_entropy, axis=1, arr=data_win, normalize=True),\n",
    "    'svd_entropy': apply(ant.svd_entropy, 1, data_win, normalize=True),\n",
    "    'sample_entropy': apply(ant.sample_entropy, 1, data_win),\n",
    "    # Fractal dimension\n",
    "    'dfa': apply(ant.detrended_fluctuation, 1, data_win),\n",
    "    'petrosian': apply(ant.petrosian_fd, 1, data_win),\n",
    "    'katz': apply(ant.katz_fd, 1, data_win),\n",
    "    'higuchi': apply(ant.higuchi_fd, 1, data_win),\n",
    "}\n",
    "\n",
    "df_feat = pd.DataFrame(df_feat)\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lziv(x):\n",
    "    \"\"\"Binarize the EEG signal and calculate the Lempel-Ziv complexity.\n",
    "    \"\"\"\n",
    "    return ant.lziv_complexity(x > x.mean(), normalize=True)\n",
    "\n",
    "df_feat['lziv'] = apply(lziv, 1, data_win)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add classic spectral power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>perm_entropy</th>\n",
       "      <th>svd_entropy</th>\n",
       "      <th>sample_entropy</th>\n",
       "      <th>dfa</th>\n",
       "      <th>petrosian</th>\n",
       "      <th>katz</th>\n",
       "      <th>higuchi</th>\n",
       "      <th>lziv</th>\n",
       "      <th>delta</th>\n",
       "      <th>theta</th>\n",
       "      <th>alpha</th>\n",
       "      <th>sigma</th>\n",
       "      <th>beta</th>\n",
       "      <th>gamma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.993005</td>\n",
       "      <td>0.615179</td>\n",
       "      <td>0.992649</td>\n",
       "      <td>1.275455</td>\n",
       "      <td>1.027439</td>\n",
       "      <td>2.794894</td>\n",
       "      <td>1.743780</td>\n",
       "      <td>0.565987</td>\n",
       "      <td>0.696338</td>\n",
       "      <td>0.116689</td>\n",
       "      <td>0.066675</td>\n",
       "      <td>0.020174</td>\n",
       "      <td>0.068901</td>\n",
       "      <td>0.031222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.993249</td>\n",
       "      <td>0.565308</td>\n",
       "      <td>0.858924</td>\n",
       "      <td>1.384108</td>\n",
       "      <td>1.027283</td>\n",
       "      <td>2.593635</td>\n",
       "      <td>1.748034</td>\n",
       "      <td>0.400426</td>\n",
       "      <td>0.802190</td>\n",
       "      <td>0.068789</td>\n",
       "      <td>0.045489</td>\n",
       "      <td>0.015227</td>\n",
       "      <td>0.045956</td>\n",
       "      <td>0.022349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.993561</td>\n",
       "      <td>0.573424</td>\n",
       "      <td>0.773124</td>\n",
       "      <td>1.406985</td>\n",
       "      <td>1.027411</td>\n",
       "      <td>2.633458</td>\n",
       "      <td>1.721437</td>\n",
       "      <td>0.550586</td>\n",
       "      <td>0.877757</td>\n",
       "      <td>0.047858</td>\n",
       "      <td>0.021888</td>\n",
       "      <td>0.007310</td>\n",
       "      <td>0.028541</td>\n",
       "      <td>0.016645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.998517</td>\n",
       "      <td>0.495381</td>\n",
       "      <td>0.166400</td>\n",
       "      <td>1.453336</td>\n",
       "      <td>1.028952</td>\n",
       "      <td>2.259923</td>\n",
       "      <td>1.868283</td>\n",
       "      <td>0.431228</td>\n",
       "      <td>0.697172</td>\n",
       "      <td>0.024946</td>\n",
       "      <td>0.041486</td>\n",
       "      <td>0.025344</td>\n",
       "      <td>0.108747</td>\n",
       "      <td>0.102304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.991045</td>\n",
       "      <td>0.737926</td>\n",
       "      <td>1.332437</td>\n",
       "      <td>1.152280</td>\n",
       "      <td>1.026885</td>\n",
       "      <td>3.484544</td>\n",
       "      <td>1.769742</td>\n",
       "      <td>0.539035</td>\n",
       "      <td>0.690355</td>\n",
       "      <td>0.098202</td>\n",
       "      <td>0.069255</td>\n",
       "      <td>0.023337</td>\n",
       "      <td>0.082457</td>\n",
       "      <td>0.036393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   perm_entropy  svd_entropy  sample_entropy       dfa  petrosian      katz  \\\n",
       "0      0.993005     0.615179        0.992649  1.275455   1.027439  2.794894   \n",
       "1      0.993249     0.565308        0.858924  1.384108   1.027283  2.593635   \n",
       "2      0.993561     0.573424        0.773124  1.406985   1.027411  2.633458   \n",
       "3      0.998517     0.495381        0.166400  1.453336   1.028952  2.259923   \n",
       "4      0.991045     0.737926        1.332437  1.152280   1.026885  3.484544   \n",
       "\n",
       "    higuchi      lziv     delta     theta     alpha     sigma      beta  \\\n",
       "0  1.743780  0.565987  0.696338  0.116689  0.066675  0.020174  0.068901   \n",
       "1  1.748034  0.400426  0.802190  0.068789  0.045489  0.015227  0.045956   \n",
       "2  1.721437  0.550586  0.877757  0.047858  0.021888  0.007310  0.028541   \n",
       "3  1.868283  0.431228  0.697172  0.024946  0.041486  0.025344  0.108747   \n",
       "4  1.769742  0.539035  0.690355  0.098202  0.069255  0.023337  0.082457   \n",
       "\n",
       "      gamma  \n",
       "0  0.031222  \n",
       "1  0.022349  \n",
       "2  0.016645  \n",
       "3  0.102304  \n",
       "4  0.036393  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.signal import welch\n",
    "freqs, psd = welch(data_win, sf, nperseg=int(4 * sf))\n",
    "bp = yasa.bandpower_from_psd_ndarray(psd, freqs)\n",
    "bp = pd.DataFrame(bp.T, columns=['delta', 'theta', 'alpha', 'sigma', 'beta', 'gamma'])\n",
    "df_feat = pd.concat([df_feat, bp], axis=1)\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ratio of spectral power\n",
    "# df_feat.eval('dt = delta / theta', inplace=True)\n",
    "# df_feat.eval('db = delta / beta', inplace=True)\n",
    "# df_feat.eval('at = alpha / theta', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find best features for sleep stage classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import f_classif\n",
    "\n",
    "# Extract sorted F-values\n",
    "fvals = pd.Series(f_classif(X=df_feat, y=hypno)[0], \n",
    "                  index=df_feat.columns\n",
    "                 ).sort_values()\n",
    "\n",
    "# Plot best features\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.barplot(y=fvals.index, x=fvals, palette='RdYlGn')\n",
    "plt.xlabel('F-values')\n",
    "plt.xticks(rotation=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot hypnogram and higuchi\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6), sharex=True)\n",
    "\n",
    "hypno = pd.Series(hypno).map({-1: -1, 0: 0, 1: 2, 2: 3, 3: 4, 4: 1}).values\n",
    "hypno_rem = np.ma.masked_not_equal(hypno, 1)\n",
    "\n",
    "# Plot the hypnogram\n",
    "ax1.step(times, -1 * hypno, color='k', lw=1.5)\n",
    "ax1.step(times, -1 * hypno_rem, color='r', lw=2.5)\n",
    "ax1.set_yticks([0, -1, -2, -3, -4])\n",
    "ax1.set_yticklabels(['W', 'R', 'N1', 'N2', 'N3'])\n",
    "ax1.set_ylim(-4.5, 0.5)\n",
    "ax1.set_ylabel('Sleep stage')\n",
    "\n",
    "# Plot the non-linear feature\n",
    "ax2.plot(times, df_feat['higuchi'])\n",
    "ax2.set_ylabel('Higuchi Fractal Dimension')\n",
    "ax2.set_xlabel('Time [minutes]')\n",
    "\n",
    "ax2.set_xlim(0, times[-1]);"
   ]
  }
 ],
 "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
}