notebooks/16_EEG-HRV_coupling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brain—body coupling in YASA\n",
    "\n",
    "This notebook illustrates how to calculate the overnight correlation between brain activity (EEG bandpower) and cardiovascular features (heart rate and HRV).\n",
    "\n",
    "* Author: Dr. Raphael Vallat (raphaelvallat9@gmail.com)\n",
    "* Date: July 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mne\n",
    "import yasa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(font_scale=1.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "We'll use the polysomnography data from the [YASA quickstart](https://raphaelvallat.com/yasa/build/html/quickstart.html). Make sure to download the EDF and hypnogram files on your computer and then update the `wdir` path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define paths\n",
    "wdir = \"/Users/raphael/Drive/yasa_quickstart/\"\n",
    "path_edf = wdir + \"yasa_example_night_young.edf\"\n",
    "path_hypno = wdir + \"yasa_example_night_young_hypno.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Measurement date</th>\n",
       "        \n",
       "        <td>August 20, 2010  23:59:06 GMT</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Experimenter</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "        <th>Participant</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Digitized points</th>\n",
       "        \n",
       "        <td>Not available</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Good channels</th>\n",
       "        <td>2 EEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Bad channels</th>\n",
       "        <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>EOG channels</th>\n",
       "        <td>Not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>ECG channels</th>\n",
       "        <td>Not available</td>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Sampling frequency</th>\n",
       "        <td>200.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Highpass</th>\n",
       "        <td>0.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Lowpass</th>\n",
       "        <td>100.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Filenames</th>\n",
       "        <td>yasa_example_night_young.edf</td>\n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Duration</th>\n",
       "        <td>08:01:60 (HH:MM:SS)</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<RawEDF | yasa_example_night_young.edf, 2 x 5784000 (28920.0 s), ~88.3 MB, data loaded>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use MNE to load the EDF file\n",
    "raw = mne.io.read_raw_edf(path_edf, preload=True, verbose=False)\n",
    "# Keep only one EEG channel (C4-A1) and one ECG\n",
    "raw.pick_channels(['C4-A1', 'EKG-R-EKG-L'], ordered=True)\n",
    "raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ -69.,  -70.,  -72., ..., -111., -122., -128.]),\n",
       " array([27., 27., 26., ...,  4.,  6.,  7.]),\n",
       " 200.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the EEG and ECG data, in microVolts\n",
    "eeg, ecg = data = raw.get_data(units=\"uV\")\n",
    "sf = raw.info[\"sfreq\"]\n",
    "eeg, ecg, sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5784000,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the hypnogram\n",
    "hypno_orig = pd.read_csv(path_hypno).squeeze(\"columns\")\n",
    "hypno = yasa.hypno_upsample_to_data(hypno_orig, sf_hypno=1/30, data=raw)\n",
    "hypno.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brain-body coupling on the entire recording\n",
    "\n",
    "### Calculate cardiovascular features\n",
    "\n",
    "Using the [yasa.hrv_stage](https://raphaelvallat.com/yasa/build/html/generated/yasa.hrv_stage.html#yasa.hrv_stage) function, we can calculate heart rate and HRV in consecutive epochs of 2.5 minutes throughout the entire night. We get the following columns:\n",
    "\n",
    "- `start`: the start of the epoch, in seconds from the beginning of the recording\n",
    "- `duration`: the duration of the epoch, in this case it is always 150 seconds (2.5 min)\n",
    "- `hr_mean`: the mean heart rate (BPM) in current epoch\n",
    "- `hr_std`: the standard deviation of the heart rate in current epoch\n",
    "- `hrv_rmssd`: the HRV in the current epoch. Specifically, the root mean square of successive differences between normal heartbeats (RMSSD). The RMSSD reflects the beat-to-beat variance in heart rate and is the primary time-domain measure used to estimate the vagally mediated changes reflected in HRV ([Shaffer and Ginsberg 2017](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624990/))\n",
    "\n",
    "The ECG heartbeat detection is performed with the [sleepecg](https://github.com/cbrnr/sleepecg) package. A minimal outlier rejection is performed, consisting of removing and interpolating inter-beat intervals outside the range of 500 to 1600 ms (37.5 to 120 bpm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04-Dec-22 10:05:05 | WARNING | No hypnogram was passed. The entire recording will be used, i.e. hypno will be set to np.zeros(data.size) and include will be set to 0.\n"
     ]
    },
    {
     "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",
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       "    }\n",
       "\n",
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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></th>\n",
       "      <th>start</th>\n",
       "      <th>duration</th>\n",
       "      <th>hr_mean</th>\n",
       "      <th>hr_std</th>\n",
       "      <th>hrv_rmssd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <th>epoch</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 rowspan=\"10\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>56.0484</td>\n",
       "      <td>6.4150</td>\n",
       "      <td>93.0896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>53.1473</td>\n",
       "      <td>4.4523</td>\n",
       "      <td>117.8534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.8612</td>\n",
       "      <td>9.5444</td>\n",
       "      <td>116.4574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>450.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0265</td>\n",
       "      <td>14.2854</td>\n",
       "      <td>108.0822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>54.6351</td>\n",
       "      <td>3.3517</td>\n",
       "      <td>89.4281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>750.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>56.2327</td>\n",
       "      <td>4.0530</td>\n",
       "      <td>83.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>900.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>54.8637</td>\n",
       "      <td>2.9856</td>\n",
       "      <td>78.8270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>53.5248</td>\n",
       "      <td>2.7591</td>\n",
       "      <td>69.1373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>53.9280</td>\n",
       "      <td>5.6145</td>\n",
       "      <td>89.6373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1350.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>51.2966</td>\n",
       "      <td>2.4314</td>\n",
       "      <td>68.1618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               start  duration  hr_mean   hr_std  hrv_rmssd\n",
       "values epoch                                               \n",
       "0      0         0.0     150.0  56.0484   6.4150    93.0896\n",
       "       1       150.0     150.0  53.1473   4.4523   117.8534\n",
       "       2       300.0     150.0  55.8612   9.5444   116.4574\n",
       "       3       450.0     150.0  60.0265  14.2854   108.0822\n",
       "       4       600.0     150.0  54.6351   3.3517    89.4281\n",
       "       5       750.0     150.0  56.2327   4.0530    83.0750\n",
       "       6       900.0     150.0  54.8637   2.9856    78.8270\n",
       "       7      1050.0     150.0  53.5248   2.7591    69.1373\n",
       "       8      1200.0     150.0  53.9280   5.6145    89.6373\n",
       "       9      1350.0     150.0  51.2966   2.4314    68.1618"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hrv, _ = yasa.hrv_stage(ecg, sf, threshold=\"2.5min\", equal_length=True, rr_limit=(500, 1400))\n",
    "hrv.head(10).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the HR and HRV across the night\n",
    "hrv[\"start_hours\"] = hrv[\"start\"] / 3600\n",
    "\n",
    "ax = hrv.plot(\n",
    "    x=\"start_hours\", y=[\"hr_mean\", \"hrv_rmssd\"], subplots=True, figsize=(11, 6), \n",
    "    xlabel=\"Time (hours)\", xlim=(0, 8), lw=1.5,\n",
    "    color=[\"tab:red\", \"tab:orange\"])\n",
    "\n",
    "ax[0].set_ylabel(\"Heart rate (bpm)\")\n",
    "ax[1].set_ylabel(\"HRV (ms)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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81KpVqzp9ozAAAIA71BpqfvnLX9pv5I2JidHSpUvVpk0bXb58Wd988418fHzk5+dnSKEAAAC1ueqNwps3b9batWvtgSY7O1txcXE6ceKEPDw8dP/992vBggUOn+AAAAAYrdYbhT/44APNmjVLt956q1q2bCnpyp9vFxQUaNWqVXr33Xe1f/9+vfXWW4YUCwAAUJNaQ826des0depUvfjii2rXrp0yMjJ06NAhPfroo7r99tsVFhamqVOn8qgDAABgulpDTWZmpsMDJ//5z3/Kw8NDd955p70tMDBQ3377rfsqBAAAcEKtoaasrEw2m83+eu/evfL29lZISIi97eLFi/bvrgEAADBLraGmW7duSktLkyQVFBRoz549uv322+Xh4WGf58MPP1T37t3dWyUAAMBV1PrXT6NGjdLChQuVmZmpr776ShcvXtTo0aMlSWfOnNHWrVu1cuVKLViwwJBiAQAAalJrqBk+fLguXbqkDRs2qGnTpnr11VfVo0cPSdLSpUu1ceNGjRs3TsOHDzegVAAAgJpd9XtqHnroIT300ENV2sePH68pU6aoXbt2bikMAACgLpx+SndlfJMwAACoT5x+SjcAAEB9RqgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWYFioCQwMVHx8fJX2ZcuWKSYmxv768OHDevTRR9WzZ08NGzZM7733nlElAgCABszQT2o2b96stLS0Gqfn5+crLi5OwcHBev/99zV16lQtXLhQH3/8sXFFAgCABsnQUNOxY0ctWLBAJSUl1U7/9NNP1aRJE8XHx8vf31/R0dEaPny4tm7damSZAACgATI01EyZMkXffvutkpKSqp3eq1cvvfLKK2rSxLGsgoICI8oDAAANmKeRG7v55ps1ceJEvfbaa4qOjpaPj4/D9A4dOqhDhw7212fPntUHH3yg3/72t0aWqczMDMXGxji8DgwMqnZaZWlpex1eR0T0cWgfOfIxzZgx29UlV2vx4heUmXlYaWl77XXUpGIfXeVqY1UX1fWhfEwjIvrIZvNUUVFxtdMr11JxucrzlbeXb6+2sau4T69s47ACA2+z/1xxveXrMpIrx786NfXflcd3+TFcHZvN0y3HrRGud9+UH+/OvLfronx95fuxfPzLj+ukpLWSjD2Pmcld76HK41xRxfO2pCrzVXxPlM/TurW36e8Dd55vtmzZ7PS8hoYaSRozZow2bdqkxYsXKzExscb5Ll26pKlTp8rHx0cPPfRQnbbh49P6muvr0SNMNpvjsISGhig4OFiSqky7msrz5+RkydfX+5rru5qK687JybIf9FlZmfrVr35V43LlfXRVbdWN47X6+uuvJdXch/LtVNxe+TLlQkNDalyusqysTElyeuzKT/QV11m+jnIVA01kZKRLx1pSlXW5cvyvJicnS9KVPtpsni7tV05OVq3j7+rj1giu2jflx9jVjk9nlb9n0tL2Ki1tr5KS1ioyMtL+uqKkpLUOx31ubq7D9PL+VW53FSP2t7veQ9WNc2U33HCD/eesrEyH+SIjI6vscyPeB+XjUd12jDzfXI3hVdhsNiUkJGjcuHEaMWJEtfNcvHhRTz75pLKyspSUlKSWLVvWaRtnzhSotLTsmuqbPPnZa1quXI8ejml5+fI1Du1FRcXKy8u/rm3UxNfX22HdFT+56N490F5LbVxV2/WOY0WxsTFKS9tbpQ/lY7p8+ZoqfS9fJiKij1avXuewvorLVXxdrnv3QIeTeE1jV3k56V9jXnkd5SIi+tjX5aqxrtx3ybXjX5OKx3Q5Vx/fRUXFNY5/xX676z3lDq7YN76+3rr33uHVvi+uVfl7pqLKn37WpPL4ly/njv1S3fHuDu56D1U3zpVVPH9UPpdUfE9UPpe5c1wqjocZ5xtnmfI9Nf3799fQoUO1YMEClZaWOkz7+eefFRcXp4MHD+rNN99UQECAGSUCAIAGxrQv34uPj9fx48eVnJzs0D59+nR98803euuttxQU1PCulQMAAHOYFmo6duyoCRMmOFxz3b59u3bs2KF58+bJx8dHeXl5ysvL0/nz580qEwAANBCm3tkzduxYvf/++/bXH330kSTp6aefdpivb9++WrfO8b4IAACAigwLNZmZmVXabDabtm/fbn/98ssv6+WXXzaqJAAAYCE80BIAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFgCoQYAAFiCYaEmMDBQ8fHxVdqXLVummJgY++uUlBQNHz5cYWFhuvfee7V7926jSgQAAA2YoZ/UbN68WWlpaTVO/+GHHzRp0iRFR0dr27ZteuCBB/Tkk0/q+++/N7BKAADQEBkaajp27KgFCxaopKSk2uknTpzQ8OHD9cQTT6hLly4aM2aMvLy8lJ6ebmSZAACgATI01EyZMkXffvutkpKSqp3es2dPzZs3T5JUXFysjRs3qqioSGFhYUaWCQAAGiBPIzd28803a+LEiXrttdcUHR0tHx+faufLzc3VkCFDVFJSomnTpqlz585GlgkAABogQ0ONJI0ZM0abNm3S4sWLlZiYWO08bdq00Z/+9Cft27dPixYtUteuXTVs2DCDK3WPzMwMxcbGXH3Ga2CzeaqoqNhhWzBW+ZgHBgaZXIlxKh5nrj6+MzMzGtVY1jfOnkMq73P2m/swtrUzPNTYbDYlJCRo3LhxGjFiRLXztG7dWsHBwQoODtaRI0f09ttv1ynU+Pi0dlW5dZabm1tj+9y5c3Xo0CG3bt9m+9cuDQ0NUUpKiiIjIxUcHCxfX2+3bttdevQIk83mWaUPlce64rSalqluucqv586dK5vN86pjV75PV69ebW8LDQ2RJAUHB2vLls32fe7u/WDGvq18TKekpNj77yqhoSG1jllDPaZdobZj/HrWl5KSYm8rP4eUi42NlSSHY15yPO+UL+fOc05D3u/VjXN182zZslnSlfNRWtpe+7SKY1vT75vGzKOsrKzMiA0FBgZq7dq16tevn6Qr99ccP35cQ4YMUWpqqtatW6ejR4/q9OnT6tu3r3259evX65133tFf/vIXp7d15kyBSksN6Va94uvrrby8fLPLMAV9b3x9b6z9lug7fW9c6hJiTfvyvfj4eB0/flzJycn2ts8++0yzZs1SaWmpve3w4cMKCAgwo0QAANCAmBZqOnbsqAkTJjh8fBYdHa2CggK9+OKLOnbsmJKSkvTnP/9Z48ePN6tMAADQQJj6mISxY8fK39/f/trHx0erVq1Senq67rvvPq1bt06vvvqqfvWrX5lXJAAAaBAMu1E4MzOzSpvNZtP27dsd2kJDQ/Xuu+8aVRYAALAIHmgJAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAsgVADAAAswdPsAtyhSRMPs0swDX1vnBpr3xtrvyX63lg15r47w6OsrKzM7CIAAACuF5efAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJVgm1Fy8eFHx8fGKiIjQHXfcoeTkZLNLMty5c+cUGRmp77//3uxSDPHdd99p/Pjx6t27t6KiorR8+XKVlpaaXZYhDh8+rEceeUTh4eEaNmyYNm/ebHZJpnjiiScUHx9vdhmG+eSTTxQYGOjwb8qUKWaXZYiioiLNnz9fffv2VWRkpJYsWSKrfyF+cnJylf0dGBiooKAgs0szxIkTJzR+/Hj16tVLUVFRevPNN6+6jGWe/ZSYmKisrCytX79eGRkZmjt3rvz9/dWrVy+zSzPEjz/+qIkTJ+rs2bNml2KI4uJiTZo0Sd26ddOGDRuUm5urmTNn6oYbbtCoUaPMLs+tLl68qLi4ON19991KTEzUvn37NHv2bHXp0kURERFml2eYbdu2affu3br//vvNLsUwR48eVf/+/fXiiy/a25o3b25iRcZZuHChUlNTtWrVKl24cEHTpk1Tp06d9PDDD5tdmttER0drwIAB9tfFxcUaPXq07rjjDvOKMtAzzzwjPz8/JScn68iRI5o+fbo6deqkIUOG1LiMJUJNYWGhNm3apDVr1igoKEhBQUHav3+/kpKSGkWo+eKLLzRz5ky1atXK7FIMc+jQIeXk5Ojdd99Vq1at9Mtf/lJjxozR1q1bLR9qfvjhB/Xr10+zZs1S06ZN9Ytf/EJr1qzRl19+2WhCzfnz55WYmKiwsDCzSzFUdna2unXrJl9fX7NLMdT58+e1adMmrV271r7PH3/8ce3fv9/SoaZFixZq0aKF/fWKFStUVlam6dOnm1iVMQoKCvTll19q69at8vf3l7+/vwYMGKCUlJRaQ40lLj9lZGSouLhY4eHh9raePXtq//79JlZlnE8//VT/9V//pT/84Q9ml2KYLl26aOXKlQ5BzsPDQwUFBSZWZQx/f38tWbJETZs2VWlpqXbt2qWcnBz16dPH7NIMk5iYqHvuuUe33nqr2aUYKjs7W/7+/maXYbi0tDR5e3s7hPa4uDgtWrTIxKqMdf78eb3xxhuaNm2abDab2eW4nc1mk5eXl5KTk3X58mUdPXpUX375pW677bZal7NEqMnLy1P79u3l6fmvD55uvPFGnTp1ysSqjPPUU09p0qRJatq0qdmlGKZdu3b693//d/vrS5cuaePGjfq3f/s3E6syVklJicLDwzVx4kTdd9996tGjh9klGSIlJUV79+7Vb3/7W7NLMVx2drb27NmjYcOGafDgwXr55ZdVVFRkdllu991336lTp05KTk7W3XffrbvuukvLli1rNPfQSdI777yjm266SXfffbfZpRjCZrNp3rx52rBhg8LDw/Uf//EfGjhwoB588MFal7PE5aeff/5ZzZo1c2hr1qxZo3izQyotLdXs2bOVn5+v8ePHm12OYcrKyvTOO+/o2LFjmj9/vvz9/fX444+bXZZbXbp0SfPmzdPcuXPVsmVLs8sx1NmzZ3X+/Hl5enrqv//7v5Wbm6uFCxfq4sWLmjNnjtnluVVhYaGOHTumTZs2adGiRTp9+rTmzp2rFi1aaOzYsWaX53ZlZWXauHGjxo0bZ3YphsrOztbgwYM1evRoZWVl6fnnn1dkZKTuvffeGpexRKhp3ry5Ll++7NB2+fLlRnfSa4xKSko0Z84c7dixQ6tXr25U9xp4enoqJCREISEhOnnypNatW2f5ULN06VKFhIRo4MCBZpdiuPbt2+vzzz+Xt7e3mjRpottuu02lpaWaPn26/f4qq/L09FRBQYGWLFkiPz8/SbIf840h1Bw4cEAnT57Ur3/9a7NLMcyePXv03nvvaffu3WrRooVCQ0N18uRJLV++3PqhpkOHDjp//rxKSkrsb+zTp083ql9wjVFJSYlmzJihHTt2aPny5erdu7fZJRnixIkT+uabbzRo0CB7W7du3XTu3DkTqzLGtm3bdPr0afXs2VOS7J/GHjhwQNu2bTOzNEO0adPG4XVAQIAuXbqkH3/8Ue3btzepKvfz9fWVl5eXPdBI0i233KITJ06YWJVx/vGPf6h3795V9r+VHThwQF26dHG4UTo4OFivv/56rctZ4p6aoKAgeXh46Ouvv7a3ffnll43mHoPGKjExUTt27NDrr7/ucH+N1R0+fFhTp07VhQsX7G2HDh1SQECAiVUZY926ddq6das2b96szZs366677lJUVJRWrlxpdmlu98UXXygiIkI//fSTve3w4cNq27atpQONJIWHh6uwsFA5OTn2tuzsbHXq1MnEqoxz4MCBRvGXvBXddNNNOnbsmMNVmKNHj6pLly61LmeJUOPl5aX77rtP8+bN06FDh7Rlyxa9//77euSRR8wuDW5y4MABrV27Vk899ZS6deumvLw85eXlNYrv6bn99tt10003KSEhQUePHtW2bdv0xhtvaOLEiWaX5nadOnVS165d7f9at26tVq1aNYpfbiEhIWrbtq3mzJmjo0eP6pNPPtHixYsbxX0WAQEBuuOOOzRr1ixlZGTos88+04oVKxrNOf7IkSPq1q2b2WUYKioqSi1atFBCQoJycnK0a9cuvf766xo9enSty3mUWeQrGS9cuKC5c+dq586dateunaZMmdKovpRLkr7//nsNHjxYO3fuVOfOnc0ux61effXVaj+G7NSpk3bt2mVCRcY6fvy45s+fr6+++krt2rXThAkTNGLECLPLMtxzzz2nkpIShy+js7Ls7Gy98MIL+uqrr+Tl5aWHH35YTz75pDw8PMwuze3y8/P1/PPP66OPPlLLli01cuTIRtP3nj176n/+538cvoivMThy5Ih+//vfKz09Xe3bt1dMTIwee+yxWpexTKgBAACNmyUuPwEAABBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqANRZfHy8AgMDa/wXFRWl5ORkBQcHG1pXVFSUvYazZ88qPj5eY8aMcdn6V65caV//smXLXLZeAK5hiWc/ATDWc889p2nTpkm68iyqBx98UMuWLVNYWJgkqWnTpvLy8jLlwZNxcXEaPXq02rVr5/J1P/roo7r//vv1m9/8xuXrBnD9CDUA6szb21ve3t6SpEuXLkm68rDFyg+RrfgwOqN4eXm57WG2Xl5e8vLysvQTsYGGjMtPANyi8uWnwMBAbdy4UQ8//LBCQ0MVHR2tffv2KSkpSYMGDVKvXr30zDPP2J+8LV15iOPDDz+ssLAwDR48WEuWLLGHKGddvnxZv//979WvXz/16tVLM2bMUGFhoSQpNTVVoaGhWrZsmfr27auYmBiVlJQoMTFRAwYMUEhIiO655x799a9/dc2gAHArQg0Aw7zyyit64okn9Oc//1mtW7fWE088oZ07d+qNN97QokWL9OGHH+pPf/qTpCtPoI6NjdWQIUO0detWLVy4UH//+9/1u9/9rk7b/OKLL1RcXKwNGzbolVde0d/+9jf98Y9/tE8vKipSamqqNm7cqDlz5igpKUkfffSR/vCHP+hvf/ub7r77bk2bNk3fffedK4cCgBsQagAYZsSIEYqKilJAQIDuu+8+/fjjj/rd736nW2+9VcOGDdNtt92mrKwsSdLq1as1aNAgxcbGqmvXroqMjNT8+fOVnJysU6dOOb1NPz8/zZ07V/7+/rrjjjt0++236+DBgw7zjBs3Tl27dlVgYKCOHz+uli1bqlOnTurcubMmTZqkFStWqG3btq4cCgBuwD01AAzzi1/8wv5zy5Yt1aRJE4cnyrdo0cJ++enw4cM6fvy4evbsaZ9e/vzd7Oxs3XTTTU5vs+KTnNu0aaOTJ086zNOlSxf7zyNHjtRHH32kgQMHKiQkRAMGDNA999xjv4cIQP1FqAFgGE9Px1OOh4eHQ+CoqFmzZho+fLji4uKqTKvLjcDV3dRbHo7KVbyhOSAgQDt27FBKSoo+++wzbdu2TStWrNCqVasUGRnp9HYBGI/LTwDqpW7duik7O1tdu3a1/zt79qwSExN14cIFt213/fr1+vDDDzVw4EDNmjVLf/3rX3XLLbdo+/btbtsmANcg1ACol+Li4pSenq5FixYpOztbn3/+uWbOnKn8/Hy3/cm2JJ07d07PP/+8/v73vys3N1c7d+7U999/r/DwcLdtE4BrcPkJQL0UGBioFStW6LXXXlNSUpK8vb115513asaMGW7d7oQJE3Tx4kXNnz9fp0+f1s0336zJkyfr/vvvd+t2AVw/j7LKF5cBoIGKiorSb37zG02aNMkS2wFQN1x+AmAphYWFysvLq3IzsCvXXVJS4vJ1A7h+hBoAlvLGG2+of//+OnfunMvX/fbbb6t///764YcfXL5uANePy08AAMAS+KQGAABYAqEGAABYAqEGAABYAqEGAABYAqEGAABYAqEGAABYwv8By5rSUmDeCmwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the hypnogram\n",
    "_, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
    "yasa.plot_hypnogram(hypno_orig, ax=ax)\n",
    "ax.set_xlim(0, 8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plots above, it is pretty clear that heart rate is higher during REM sleep and wakefulness, and lower during NREM sleep. Finding such patterns for HRV is more difficult, but we do see a clear outlier peak around the 6 hours mark. Here, we'll leave it as is, but for an actual project, we would definitely need to remove this outlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate EEG powers\n",
    "\n",
    "For each of these 2.5-min epochs, we now calculate the corresponding EEG sigma power. While this could be done with a for loop, here we leverage the [yasa.sliding_window](https://raphaelvallat.com/yasa/build/html/generated/yasa.sliding_window.html#yasa.sliding_window) function to convert our continuous EEG data into epochs of 2.5-minutes, and then calculate the bandpower on each epoch. Please see [08_bandpower.ipynb](https://github.com/raphaelvallat/yasa/blob/master/notebooks/08_bandpower.ipynb) for more details on the [yasa.bandpower](https://raphaelvallat.com/yasa/build/html/generated/yasa.bandpower.html#yasa.bandpower) function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Delta</th>\n",
       "      <th>Theta</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>Sigma</th>\n",
       "      <th>Beta</th>\n",
       "      <th>Gamma</th>\n",
       "      <th>TotalAbsPow</th>\n",
       "      <th>FreqRes</th>\n",
       "      <th>Relative</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>epoch</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>0.4470</td>\n",
       "      <td>0.1874</td>\n",
       "      <td>0.2946</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>132.5558</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3679</td>\n",
       "      <td>0.2311</td>\n",
       "      <td>0.3485</td>\n",
       "      <td>0.0230</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>147.9583</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3451</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.3756</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>105.6220</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2767</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.3555</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>0.1346</td>\n",
       "      <td>0.0633</td>\n",
       "      <td>191.2137</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3916</td>\n",
       "      <td>0.1547</td>\n",
       "      <td>0.3799</td>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0386</td>\n",
       "      <td>0.0092</td>\n",
       "      <td>123.5907</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.6694</td>\n",
       "      <td>0.2514</td>\n",
       "      <td>0.0435</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>96.1594</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.7061</td>\n",
       "      <td>0.2106</td>\n",
       "      <td>0.0470</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>102.2158</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.7321</td>\n",
       "      <td>0.1930</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>0.0154</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>115.5555</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.7596</td>\n",
       "      <td>0.1718</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>133.2436</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.7252</td>\n",
       "      <td>0.1923</td>\n",
       "      <td>0.0405</td>\n",
       "      <td>0.0297</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>146.1788</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Delta   Theta   Alpha   Sigma    Beta   Gamma  TotalAbsPow  FreqRes  \\\n",
       "epoch                                                                         \n",
       "0      0.4470  0.1874  0.2946  0.0265  0.0369  0.0077     132.5558     0.25   \n",
       "1      0.3679  0.2311  0.3485  0.0230  0.0243  0.0053     147.9583     0.25   \n",
       "2      0.3451  0.2030  0.3756  0.0339  0.0363  0.0062     105.6220     0.25   \n",
       "3      0.2767  0.1100  0.3555  0.0599  0.1346  0.0633     191.2137     0.25   \n",
       "4      0.3916  0.1547  0.3799  0.0260  0.0386  0.0092     123.5907     0.25   \n",
       "...       ...     ...     ...     ...     ...     ...          ...      ...   \n",
       "187    0.6694  0.2514  0.0435  0.0153  0.0173  0.0031      96.1594     0.25   \n",
       "188    0.7061  0.2106  0.0470  0.0153  0.0172  0.0037     102.2158     0.25   \n",
       "189    0.7321  0.1930  0.0422  0.0154  0.0146  0.0028     115.5555     0.25   \n",
       "190    0.7596  0.1718  0.0371  0.0182  0.0115  0.0018     133.2436     0.25   \n",
       "191    0.7252  0.1923  0.0405  0.0297  0.0106  0.0017     146.1788     0.25   \n",
       "\n",
       "       Relative  \n",
       "epoch            \n",
       "0          True  \n",
       "1          True  \n",
       "2          True  \n",
       "3          True  \n",
       "4          True  \n",
       "...         ...  \n",
       "187        True  \n",
       "188        True  \n",
       "189        True  \n",
       "190        True  \n",
       "191        True  \n",
       "\n",
       "[192 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t, eeg_2d = yasa.sliding_window(eeg, sf, window=150)\n",
    "bp = yasa.bandpower(eeg_2d, sf).reset_index(drop=True)\n",
    "bp.index.name = \"epoch\"\n",
    "bp.round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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></th>\n",
       "      <th>start</th>\n",
       "      <th>duration</th>\n",
       "      <th>hr_mean</th>\n",
       "      <th>hr_std</th>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <th>start_hours</th>\n",
       "      <th>sigma</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <th>epoch</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 rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>56.048</td>\n",
       "      <td>6.415</td>\n",
       "      <td>93.090</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>53.147</td>\n",
       "      <td>4.452</td>\n",
       "      <td>117.853</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.861</td>\n",
       "      <td>9.544</td>\n",
       "      <td>116.457</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>450.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.026</td>\n",
       "      <td>14.285</td>\n",
       "      <td>108.082</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>54.635</td>\n",
       "      <td>3.352</td>\n",
       "      <td>89.428</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              start  duration  hr_mean  hr_std  hrv_rmssd  start_hours  sigma  \\\n",
       "values epoch                                                                    \n",
       "0      0        0.0     150.0   56.048   6.415     93.090        0.000  0.026   \n",
       "       1      150.0     150.0   53.147   4.452    117.853        0.042  0.023   \n",
       "       2      300.0     150.0   55.861   9.544    116.457        0.083  0.034   \n",
       "       3      450.0     150.0   60.026  14.285    108.082        0.125  0.060   \n",
       "       4      600.0     150.0   54.635   3.352     89.428        0.167  0.026   \n",
       "\n",
       "              delta  \n",
       "values epoch         \n",
       "0      0      0.447  \n",
       "       1      0.368  \n",
       "       2      0.345  \n",
       "       3      0.277  \n",
       "       4      0.392  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine with the HRV dataframe. We'll focus on the delta and sigma power.\n",
    "hrv[\"sigma\"] = bp[\"Sigma\"].to_numpy()\n",
    "hrv[\"delta\"] = bp[\"Delta\"].to_numpy()\n",
    "\n",
    "hrv.head().round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate EEG-HRV correlations\n",
    "\n",
    "We can now run some correlations between the cardiovascular features and the EEG bandpower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the EEG delta and sigma power across the night (as well as HR and HRV)\n",
    "ax = hrv.plot(\n",
    "    x=\"start_hours\", y=[\"hr_mean\", \"hrv_rmssd\", \"delta\", \"sigma\"], subplots=True, figsize=(11, 11), \n",
    "    xlabel=\"Time (hours)\", xlim=(0, 8), lw=1.5,\n",
    "    color=[\"tab:red\", \"tab:orange\", \"tab:blue\", \"tab:purple\"])\n",
    "\n",
    "ax[0].set_ylabel(\"Heart rate (bpm)\")\n",
    "ax[1].set_ylabel(\"HRV (ms)\");\n",
    "ax[2].set_ylabel(\"EEG delta\");\n",
    "ax[3].set_ylabel(\"EEG sigma\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>hr_mean</th>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <th>delta</th>\n",
       "      <th>sigma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hr_mean</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.114</td>\n",
       "      <td>-0.366</td>\n",
       "      <td>-0.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <td>0.114</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.285</td>\n",
       "      <td>-0.062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hr_mean  hrv_rmssd  delta  sigma\n",
       "hr_mean      1.000      0.114 -0.366 -0.346\n",
       "hrv_rmssd    0.114      1.000 -0.285 -0.062"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Spearman correlations\n",
    "hrv[[\"hr_mean\", \"hrv_rmssd\", \"delta\", \"sigma\"]].corr(method=\"spearman\").round(3).iloc[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some conclusions:**\n",
    "\n",
    "- There is a negative correlation between heart rate and both delta and sigma power. This makes sense since heart rate is typically lower in NREM sleep when delta and sigma power are high. Conversely, heart rate is high during wakefulness, while delta and sigma power are low.\n",
    "- HRV is also negatively correlated with delta, but not really with sigma. This would suggest that high HRV occurs during periods of deep NREM sleep, when delta power is at is maximum. Conversely, HRV should be lower during wakefulness or REM sleep, when delta power is reduced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "********\n",
    "\n",
    "## For NREM and REM separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 4])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge N2 and N3 into NREM sleep\n",
    "hypno[hypno == 3] = 2\n",
    "np.unique(hypno)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate cardiovascular features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116 epochs of NREM sleep were found\n",
      "56 epochs of REM sleep were found\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>duration</th>\n",
       "      <th>hr_mean</th>\n",
       "      <th>hr_std</th>\n",
       "      <th>hrv_rmssd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <th>epoch</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 rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>990.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>53.325</td>\n",
       "      <td>2.580</td>\n",
       "      <td>66.723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1140.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>53.358</td>\n",
       "      <td>3.493</td>\n",
       "      <td>84.776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1410.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>51.272</td>\n",
       "      <td>2.237</td>\n",
       "      <td>65.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1560.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>50.995</td>\n",
       "      <td>2.160</td>\n",
       "      <td>60.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1710.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>51.702</td>\n",
       "      <td>1.896</td>\n",
       "      <td>48.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">4</th>\n",
       "      <th>51</th>\n",
       "      <td>25980.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>57.219</td>\n",
       "      <td>5.084</td>\n",
       "      <td>88.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>26130.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>54.266</td>\n",
       "      <td>5.150</td>\n",
       "      <td>97.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>26280.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.157</td>\n",
       "      <td>4.350</td>\n",
       "      <td>79.374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>26430.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>57.144</td>\n",
       "      <td>4.550</td>\n",
       "      <td>71.559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>26580.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>56.644</td>\n",
       "      <td>4.255</td>\n",
       "      <td>70.416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                start  duration  hr_mean  hr_std  hrv_rmssd\n",
       "values epoch                                               \n",
       "2      0        990.0     150.0   53.325   2.580     66.723\n",
       "       1       1140.0     150.0   53.358   3.493     84.776\n",
       "       2       1410.0     150.0   51.272   2.237     65.858\n",
       "       3       1560.0     150.0   50.995   2.160     60.188\n",
       "       4       1710.0     150.0   51.702   1.896     48.782\n",
       "...               ...       ...      ...     ...        ...\n",
       "4      51     25980.0     150.0   57.219   5.084     88.565\n",
       "       52     26130.0     150.0   54.266   5.150     97.766\n",
       "       53     26280.0     150.0   55.157   4.350     79.374\n",
       "       54     26430.0     150.0   57.144   4.550     71.559\n",
       "       55     26580.0     150.0   56.644   4.255     70.416\n",
       "\n",
       "[172 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pass the hypnogram and apply separately for NREM and REM\n",
    "hrv, _ = yasa.hrv_stage(\n",
    "    ecg, sf, hypno=hypno, include=(2, 4), threshold=\"2.5min\", equal_length=True, rr_limit=(500, 1400))\n",
    "\n",
    "print(f\"{hrv.xs(2).shape[0]} epochs of NREM sleep were found\")\n",
    "print(f\"{hrv.xs(4).shape[0]} epochs of REM sleep were found\")\n",
    "hrv.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate EEG powers\n",
    "\n",
    "Let's use a good old _for loop_ here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>duration</th>\n",
       "      <th>hr_mean</th>\n",
       "      <th>hr_std</th>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <th>delta</th>\n",
       "      <th>theta</th>\n",
       "      <th>alpha</th>\n",
       "      <th>sigma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values</th>\n",
       "      <th>epoch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>990.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>53.325</td>\n",
       "      <td>2.580</td>\n",
       "      <td>66.723</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1140.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>53.358</td>\n",
       "      <td>3.493</td>\n",
       "      <td>84.776</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1410.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>51.272</td>\n",
       "      <td>2.237</td>\n",
       "      <td>65.858</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1560.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>50.995</td>\n",
       "      <td>2.160</td>\n",
       "      <td>60.188</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1710.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>51.702</td>\n",
       "      <td>1.896</td>\n",
       "      <td>48.782</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">4</th>\n",
       "      <th>51</th>\n",
       "      <td>25980.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>57.219</td>\n",
       "      <td>5.084</td>\n",
       "      <td>88.565</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>26130.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>54.266</td>\n",
       "      <td>5.150</td>\n",
       "      <td>97.766</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>26280.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>55.157</td>\n",
       "      <td>4.350</td>\n",
       "      <td>79.374</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>26430.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>57.144</td>\n",
       "      <td>4.550</td>\n",
       "      <td>71.559</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>26580.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>56.644</td>\n",
       "      <td>4.255</td>\n",
       "      <td>70.416</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                start  duration  hr_mean  hr_std  hrv_rmssd  delta  theta  \\\n",
       "values epoch                                                                \n",
       "2      0        990.0     150.0   53.325   2.580     66.723  0.632  0.272   \n",
       "       1       1140.0     150.0   53.358   3.493     84.776  0.674  0.226   \n",
       "       2       1410.0     150.0   51.272   2.237     65.858  0.698  0.188   \n",
       "       3       1560.0     150.0   50.995   2.160     60.188  0.670  0.211   \n",
       "       4       1710.0     150.0   51.702   1.896     48.782  0.708  0.181   \n",
       "...               ...       ...      ...     ...        ...    ...    ...   \n",
       "4      51     25980.0     150.0   57.219   5.084     88.565  0.651  0.257   \n",
       "       52     26130.0     150.0   54.266   5.150     97.766  0.682  0.235   \n",
       "       53     26280.0     150.0   55.157   4.350     79.374  0.666  0.250   \n",
       "       54     26430.0     150.0   57.144   4.550     71.559  0.655  0.260   \n",
       "       55     26580.0     150.0   56.644   4.255     70.416  0.648  0.260   \n",
       "\n",
       "              alpha  sigma  \n",
       "values epoch                \n",
       "2      0      0.045  0.021  \n",
       "       1      0.050  0.022  \n",
       "       2      0.055  0.028  \n",
       "       3      0.058  0.037  \n",
       "       4      0.055  0.044  \n",
       "...             ...    ...  \n",
       "4      51     0.052  0.016  \n",
       "       52     0.047  0.015  \n",
       "       53     0.046  0.015  \n",
       "       54     0.048  0.014  \n",
       "       55     0.054  0.014  \n",
       "\n",
       "[172 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize empty columns\n",
    "hrv[\"delta\"] = np.nan\n",
    "hrv[\"theta\"] = np.nan\n",
    "hrv[\"alpha\"] = np.nan\n",
    "hrv[\"sigma\"] = np.nan\n",
    "\n",
    "\n",
    "# Loop across each epoch\n",
    "for idx, row in hrv.iterrows():\n",
    "    # Find indices (in samples) of start and end of epoch\n",
    "    start = int(row[\"start\"] * sf)\n",
    "    end = int(sf * (row[\"start\"] + row[\"duration\"]))\n",
    "    # Calculate bandpower\n",
    "    bp = yasa.bandpower(eeg[start:end], sf)\n",
    "    # Add to DataFrame\n",
    "    hrv.loc[idx, \"delta\"] = bp[\"Delta\"].iloc[0]\n",
    "    hrv.loc[idx, \"theta\"] = bp[\"Theta\"].iloc[0]\n",
    "    hrv.loc[idx, \"alpha\"] = bp[\"Alpha\"].iloc[0]\n",
    "    hrv.loc[idx, \"sigma\"] = bp[\"Sigma\"].iloc[0]\n",
    "    \n",
    "hrv.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate EEG-HRV correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>hr_mean</th>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <th>delta</th>\n",
       "      <th>theta</th>\n",
       "      <th>alpha</th>\n",
       "      <th>sigma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hr_mean</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <td>0.05</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.07</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hr_mean  hrv_rmssd  delta  theta  alpha  sigma\n",
       "hr_mean       1.00       0.05   0.13  -0.08  -0.24  -0.34\n",
       "hrv_rmssd     0.05       1.00  -0.14   0.18   0.14  -0.07"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation in NREM sleep\n",
    "cols = [\"hr_mean\", \"hrv_rmssd\", \"delta\", \"theta\", \"alpha\", \"sigma\"]\n",
    "hrv.xs(2)[cols].corr(method=\"spearman\").round(2).iloc[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hr_mean</th>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <th>delta</th>\n",
       "      <th>theta</th>\n",
       "      <th>alpha</th>\n",
       "      <th>sigma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hr_mean</th>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <td>-0.55</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           hr_mean  hrv_rmssd  delta  theta  alpha  sigma\n",
       "hr_mean       1.00      -0.55   0.11  -0.13  -0.14  -0.15\n",
       "hrv_rmssd    -0.55       1.00  -0.02  -0.07   0.15   0.30"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation in REM sleep\n",
    "hrv.xs(4)[cols].corr(method=\"spearman\").round(2).iloc[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some conclusions:**\n",
    "\n",
    "- Overall the correlations between HRV and EEG are weaker when considering stage-specific associations instead of the entire night. This is likely because we removed the strong effect of wakefulness.\n",
    "- In NREM sleep, sigma power is the strongest predictor of heart rate. Specifically, higher sigma power is associated with lower heart rate. The strongest predictor of HRV is theta power, with higher theta predicting higher HRV (a good outcome).\n",
    "- In REM sleep, sigma power is the strongest predictor of both heart rate and HRV. Here, higher sigma is associated with a lower heart rate and a higher HRV, i.e. more restorative sleep. This is perhaps surprising since the sigma band is not typically associated with REM sleep.\n",
    "\n",
    "There are many other ways that we could leverage YASA to calculate brain—body coupling. For example, [Mikutta et al 2021](https://onlinelibrary.wiley.com/doi/10.1111/jsr.13466) showed a correlation between slow oscillations—spindle coupling (and phase) and heart rate variability in NREM sleep. Also, changing some of the parameters of this analysis — such as the epoch length or the outlier rejection thresholds — may strongly impact the results. On the latter, the [yasa.hrv_stage](https://raphaelvallat.com/yasa/build/html/generated/yasa.hrv_stage.html#yasa.hrv_stage) function also returns the indices of all the detected ECG peaks, so we could easily apply a different outlier rejection method and/or calculate other relevant HRV metrics."
   ]
  }
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