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": [
       "<details open>\n",
       "    <summary><strong>General</strong></summary>\n",
       "    <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",
       "        <tr>\n",
       "            <th>Participant</th>\n",
       "            \n",
       "            \n",
       "            <td>No</td>\n",
       "            \n",
       "            \n",
       "        </tr>\n",
       "    </table>\n",
       "    </details>\n",
       "    <details open>\n",
       "        <summary><strong>Channels</strong></summary>\n",
       "        <table class=\"table table-hover table-striped table-sm table-responsive small\">\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",
       "            </tr>\n",
       "        </table>\n",
       "        </details>\n",
       "        <details open>\n",
       "            <summary><strong>Data</strong></summary>\n",
       "            <table class=\"table table-hover table-striped table-sm table-responsive small\">\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",
       "                \n",
       "                <tr>\n",
       "                    <th>Duration</th>\n",
       "                    <td>08:01:60 (HH:MM:SS)</td>\n",
       "                </tr>\n",
       "                \n",
       "            </table>\n",
       "            </details>"
      ],
      "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(['C4-A1', 'EKG-R-EKG-L'])\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": [
      "/Users/raphael/.pyenv/versions/3.9.6/lib/python3.9/site-packages/sleepecg/heartbeats.py:640: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  _squared_moving_integration_numba = jit(_squared_moving_integration_py)\n",
      "/Users/raphael/.pyenv/versions/3.9.6/lib/python3.9/site-packages/sleepecg/heartbeats.py:641: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  _thresholding_numba = jit(_thresholding_py)\n",
      "13-Feb-24 22:51:26 | 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",
       "        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",
       "    </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 1100x600 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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\n",
      "text/plain": [
       "<Figure size 900x400 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 1100x1100 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>0.113</td>\n",
       "      <td>-0.366</td>\n",
       "      <td>-0.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <td>0.113</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.286</td>\n",
       "      <td>-0.063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hr_mean  hrv_rmssd  delta  sigma\n",
       "hr_mean      1.000      0.113 -0.366 -0.346\n",
       "hrv_rmssd    0.113      1.000 -0.286 -0.063"
      ]
     },
     "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": {
      "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",
       "    </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>57.075</td>\n",
       "      <td>5.002</td>\n",
       "      <td>83.927</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   57.075   5.002     83.927\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": {
      "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>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",
       "      <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",
       "      <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",
       "      <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 rowspan=\"5\" valign=\"top\">4</th>\n",
       "      <th>51</th>\n",
       "      <td>25980.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>57.075</td>\n",
       "      <td>5.002</td>\n",
       "      <td>83.927</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   57.075   5.002     83.927  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": {
      "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>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>0.04</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hr_mean  hrv_rmssd  delta  theta  alpha  sigma\n",
       "hr_mean       1.00       0.04   0.12  -0.07  -0.23  -0.31\n",
       "hrv_rmssd     0.04       1.00  -0.13   0.17   0.13  -0.08"
      ]
     },
     "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": {
      "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>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>-0.56</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hr_mean  hrv_rmssd  delta  theta  alpha  sigma\n",
       "hr_mean       1.00      -0.56   0.11  -0.13  -0.14  -0.16\n",
       "hrv_rmssd    -0.56       1.00  -0.01  -0.08   0.15   0.31"
      ]
     },
     "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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}