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danibene/nk_issue_721_comp_hrv.ipynb

Created October 13, 2022 16:02
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nk_issue_721_comp_hrv.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyOD3U4N8mNM2ED/o+8ckVQb",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/danibene/d9e17256d004e92393e940be0025e7d7/nk_issue_721_comp_hrv.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "w3u9vxZ9AOUF",
"outputId": "d3bc62d1-4c80-4895-a31b-065353c1f091"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting git+https://github.com/rhenanbartels/hrv.git\n",
" Cloning https://github.com/rhenanbartels/hrv.git to /tmp/pip-req-build-s2sdfsg5\n",
" Running command git clone -q https://github.com/rhenanbartels/hrv.git /tmp/pip-req-build-s2sdfsg5\n",
"Requirement already satisfied: matplotlib>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from hrv==0.2.10) (3.2.2)\n",
"Requirement already satisfied: numpy>=1.14.4 in /usr/local/lib/python3.7/dist-packages (from hrv==0.2.10) (1.21.6)\n",
"Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from hrv==0.2.10) (1.7.3)\n",
"Collecting spectrum>=0.7.3\n",
" Downloading spectrum-0.8.1.tar.gz (230 kB)\n",
"\u001b[K |████████████████████████████████| 230 kB 4.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2.2->hrv==0.2.10) (1.4.4)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2.2->hrv==0.2.10) (0.11.0)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2.2->hrv==0.2.10) (2.8.2)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2.2->hrv==0.2.10) (3.0.9)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.2.2->hrv==0.2.10) (4.1.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=2.2.2->hrv==0.2.10) (1.15.0)\n",
"Collecting easydev\n",
" Downloading easydev-0.12.0.tar.gz (47 kB)\n",
"\u001b[K |████████████████████████████████| 47 kB 3.6 MB/s \n",
"\u001b[?25hCollecting colorama\n",
" Downloading colorama-0.4.5-py2.py3-none-any.whl (16 kB)\n",
"Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from easydev->spectrum>=0.7.3->hrv==0.2.10) (4.8.0)\n",
"Collecting colorlog\n",
" Downloading colorlog-6.7.0-py2.py3-none-any.whl (11 kB)\n",
"Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect->easydev->spectrum>=0.7.3->hrv==0.2.10) (0.7.0)\n",
"Building wheels for collected packages: hrv, spectrum, easydev\n",
" Building wheel for hrv (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for hrv: filename=hrv-0.2.10-py2.py3-none-any.whl size=34896 sha256=94456c0549f2414ac13833e6accf1d532ea3c267da17fcca1247eef0192e2d4d\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-tfi56ujl/wheels/80/02/ce/1fd7efe6ff858e235b160cae58cf2f5ee770ba30ea250e0ed4\n",
" Building wheel for spectrum (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for spectrum: filename=spectrum-0.8.1-cp37-cp37m-linux_x86_64.whl size=235158 sha256=a7013f807911ba52f32b78c1ed10f1b2867ce470acef5bc387d3694235c4e7b1\n",
" Stored in directory: /root/.cache/pip/wheels/79/db/9c/92fa684ca088447807d08672e7609b48102c6161ac9c7e3c62\n",
" Building wheel for easydev (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for easydev: filename=easydev-0.12.0-py3-none-any.whl size=64232 sha256=f9a28f9cdc7ffc4660adaa1b3d5137f0b10902ebc293014b6298bbe4b9e970ba\n",
" Stored in directory: /root/.cache/pip/wheels/82/ab/83/fdfc4017ea44a585b6754752cc5f63f2d0d63fcc1317e7174b\n",
"Successfully built hrv spectrum easydev\n",
"Installing collected packages: colorlog, colorama, easydev, spectrum, hrv\n",
"Successfully installed colorama-0.4.5 colorlog-6.7.0 easydev-0.12.0 hrv-0.2.10 spectrum-0.8.1\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting neurokit2\n",
" Downloading neurokit2-0.2.1-py2.py3-none-any.whl (1.2 MB)\n",
"\u001b[K |████████████████████████████████| 1.2 MB 4.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from neurokit2) (1.21.6)\n",
"Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from neurokit2) (1.0.2)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from neurokit2) (1.3.5)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from neurokit2) (1.7.3)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from neurokit2) (3.2.2)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0.0->neurokit2) (1.2.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0.0->neurokit2) (3.1.0)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2) (3.0.9)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2) (0.11.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2) (1.4.4)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2) (2.8.2)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->neurokit2) (4.1.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->neurokit2) (1.15.0)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->neurokit2) (2022.4)\n",
"Installing collected packages: neurokit2\n",
"Successfully installed neurokit2-0.2.1\n"
]
}
],
"source": [
"!pip install git+https://github.com/rhenanbartels/hrv.git\n",
"!pip install neurokit2"
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import neurokit2 as nk\n",
"from hrv.detrend import smoothness_priors"
],
"metadata": {
"id": "hHLvp5FLAW7K"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"ecg = nk.ecg_simulate(duration=120, sampling_rate=1000, heart_rate=110, random_state=42)\n",
"\n",
"_, peaks = nk.ecg_process(ecg, sampling_rate=1000)\n",
"peaks = peaks[\"ECG_R_Peaks\"]\n",
"rri = np.diff(peaks).astype(float)\n",
"rri_time = peaks[1:]/1000"
],
"metadata": {
"id": "g6sw2hd9Ao0k"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"detrended_rri_nk = nk.signal_detrend(rri, method=\"tarvainen2002\")"
],
"metadata": {
"id": "1Jekcsi_AqZR"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"interpolation_rate = 4\n",
"detrended_rri_hrv = smoothness_priors(rri, fs=interpolation_rate)\n",
"time_interp_hrv = np.arange(rri_time[0], rri_time[-1], 1.0 / interpolation_rate)"
],
"metadata": {
"id": "nxSHMgStEX22"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plt.figure()\n",
"plt.plot(rri_time, rri, label=\"original\")\n",
"plt.plot(rri_time, detrended_rri_nk, label=\"detrended with nk\")\n",
"plt.plot(time_interp_hrv, detrended_rri_hrv, label=\"detrended with hrv\")\n",
"plt.legend()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"id": "8AKpLdIuAr4E",
"outputId": "a72a91ee-7dae-40a5-b3e3-e54119350bc3"
},
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "PQ_4zc5OAtiR"
},
"execution_count": null,
"outputs": []
}
]
}
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