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danibene/cardioresp_feature_examples.ipynb

Created August 19, 2024 11:46
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cardioresp_feature_examples.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyMU/XDfHwugnnJCr0XlDK7+",
"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/9d9b03e4eee808b6551ac2b5bd5700fc/cardioresp_feature_examples.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"The purpose of this notebook is to demonstrate the types of features that can be extracted from cardiorespiratory data and the required inputs for each type of features extracted."
],
"metadata": {
"id": "xG9vcmFedHI2"
}
},
{
"cell_type": "markdown",
"source": [
"# Imports"
],
"metadata": {
"id": "0mSdst_NOemG"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zdkJRCiOOcdS",
"outputId": "4699b6af-8b00-483c-9db8-1cf3e8058a48"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting neurokit2\n",
" Downloading neurokit2-0.2.10-py2.py3-none-any.whl.metadata (37 kB)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from neurokit2) (2.32.3)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from neurokit2) (1.26.4)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from neurokit2) (2.1.4)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from neurokit2) (1.13.1)\n",
"Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from neurokit2) (1.3.2)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from neurokit2) (3.7.1)\n",
"Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->neurokit2) (1.4.2)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->neurokit2) (3.5.0)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->neurokit2) (1.2.1)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->neurokit2) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->neurokit2) (4.53.1)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->neurokit2) (1.4.5)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->neurokit2) (24.1)\n",
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->neurokit2) (9.4.0)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->neurokit2) (3.1.2)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->neurokit2) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->neurokit2) (2024.1)\n",
"Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->neurokit2) (2024.1)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->neurokit2) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->neurokit2) (3.7)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->neurokit2) (2.0.7)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->neurokit2) (2024.7.4)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->neurokit2) (1.16.0)\n",
"Downloading neurokit2-0.2.10-py2.py3-none-any.whl (693 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m693.1/693.1 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: neurokit2\n",
"Successfully installed neurokit2-0.2.10\n"
]
}
],
"source": [
"!pip install neurokit2"
]
},
{
"cell_type": "code",
"source": [
"import neurokit2 as nk\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
],
"metadata": {
"id": "_KylQs6rOjUk"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Export example data"
],
"metadata": {
"id": "CSOE8p-eOmuP"
}
},
{
"cell_type": "markdown",
"source": [
"## Download data from neurokit2"
],
"metadata": {
"id": "xj0aNsqJPce-"
}
},
{
"cell_type": "code",
"source": [
"# Download example data\n",
"data = nk.data(\"bio_resting_5min_100hz\")\n",
"sampling_rate = 100"
],
"metadata": {
"id": "FRApxqbzOmBU"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Export waveforms"
],
"metadata": {
"id": "ojCkfUjdPhLB"
}
},
{
"cell_type": "code",
"source": [
"# Select relevant data for this notebook\n",
"cardioresp_waveform = data.loc[:, [\"ECG\", \"RSP\"]]\n",
"\n",
"# Add timestamp\n",
"cardioresp_waveform[\"timestampInMilliseconds\"] = (cardioresp_waveform.index / sampling_rate) * 1000\n",
"\n",
"# Export to CSV\n",
"cardioresp_waveform.to_csv(\"cardioresp_waveform.csv\")\n",
"cardioresp_waveform.head()"
],
"metadata": {
"id": "MKpm7s32Pbe2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 226
},
"outputId": "c80e0d25-3f5a-4a97-ae04-653b5b820fff"
},
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" ECG RSP timestampInMilliseconds\n",
"0 0.003766 0.494652 0.0\n",
"1 -0.017466 0.502483 10.0\n",
"2 -0.015679 0.511102 20.0\n",
"3 -0.001598 0.518791 30.0\n",
"4 0.002483 0.528669 40.0"
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" <div id=\"df-e24cf971-94b3-4c6c-9bee-255e5549182b\" class=\"colab-df-container\">\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "cardioresp_waveform",
"summary": "{\n \"name\": \"cardioresp_waveform\",\n \"rows\": 30000,\n \"fields\": [\n {\n \"column\": \"ECG\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.041127735521766,\n \"min\": -0.7084760177701148,\n \"max\": 0.8945318018647915,\n \"num_unique_values\": 28963,\n \"samples\": [\n -0.0038046606141498,\n 0.0105750898041808,\n -0.0548180982868685\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"RSP\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2255575236988667,\n \"min\": -0.3422629785636023,\n \"max\": 1.7665763473795082,\n \"num_unique_values\": 25852,\n \"samples\": [\n 0.8259586368197356,\n 0.4924202559365574,\n 0.4545509795868194\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"timestampInMilliseconds\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 86603.98374208891,\n \"min\": 0.0,\n \"max\": 299990.0,\n \"num_unique_values\": 30000,\n \"samples\": [\n 23080.0,\n 224040.0,\n 233970.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"source": [
"## Export timestamps of extracted points"
],
"metadata": {
"id": "JJHBmEWSPji-"
}
},
{
"cell_type": "code",
"source": [
"# Extract cardiac peaks\n",
"_, cardiac_peaks = nk.ecg_process(cardioresp_waveform[\"ECG\"], sampling_rate=sampling_rate)\n",
"cardiac_peaks_in_ms = (cardiac_peaks[\"ECG_R_Peaks\"]/sampling_rate)*1000\n",
"\n",
"# Export to CSV\n",
"pd.DataFrame({\"cardiacPeaksInMilliseconds\": cardiac_peaks_in_ms}).to_csv(\"cardiac_peaks_in_ms.csv\")\n",
"\n",
"# Extract respiration troughs (inhalation onsets)\n",
"_, rsp_peaks = nk.rsp_process(cardioresp_waveform[\"RSP\"], sampling_rate=sampling_rate)\n",
"rsp_troughs_in_ms = (rsp_peaks[\"RSP_Troughs\"]/sampling_rate)*1000\n",
"\n",
"# Export to CSV\n",
"pd.DataFrame({\"rspTroughsInMilliseconds\": rsp_troughs_in_ms}).to_csv(\"rsp_troughs_in_ms.csv\")\n",
"\n",
"# Extract respiration peaks\n",
"_, rsp_peaks = nk.rsp_process(cardioresp_waveform[\"RSP\"], sampling_rate=sampling_rate)\n",
"rsp_peaks_in_ms = (rsp_peaks[\"RSP_Peaks\"]/sampling_rate)*1000\n",
"\n",
"# Export to CSV\n",
"pd.DataFrame({\"rspPeaksInMilliseconds\": rsp_peaks_in_ms}).to_csv(\"rsp_peaks_in_ms.csv\")"
],
"metadata": {
"id": "TmzQv8dxOsmR"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Extract features"
],
"metadata": {
"id": "B4mcTINwPoVw"
}
},
{
"cell_type": "markdown",
"source": [
"## Heart rate variability features"
],
"metadata": {
"id": "c9IyzrBVQBhn"
}
},
{
"cell_type": "code",
"source": [
"# Load cardiac peak timestamps\n",
"cardiac_peaks_in_ms = pd.read_csv(\"cardiac_peaks_in_ms.csv\")[\"cardiacPeaksInMilliseconds\"].values\n",
"\n",
"# Convert to dictionary\n",
"# I directly pass the R-R intervals and their timestamps\n",
"# In case there was missing data detected\n",
"# Some of Neurokit's HRV functions have methods for dealing with missing data\n",
"peaks = {\"RRI\": np.diff(cardiac_peaks_in_ms),\n",
" \"RRI_Time\": cardiac_peaks_in_ms[1:]}\n",
"\n",
"# Extract features\n",
"hrv = nk.hrv(peaks)\n",
"\n",
"# Export to CSV\n",
"hrv.to_csv(\"hrv.csv\")\n",
"hrv.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 130
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"id": "rn_TwYm2O5z8",
"outputId": "302b0945-2dca-4c80-d582-c5eaac2e0b2c"
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"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" HRV_MeanNN HRV_SDNN HRV_SDANN1 HRV_SDNNI1 HRV_SDANN2 HRV_SDNNI2 \\\n",
"0 694.756381 49.036043 7.277185 48.83361 NaN NaN \n",
"\n",
" HRV_SDANN5 HRV_SDNNI5 HRV_RMSSD HRV_SDSD ... HRV_SampEn HRV_ShanEn \\\n",
"0 NaN NaN 38.837766 38.882559 ... 1.978637 4.307829 \n",
"\n",
" HRV_FuzzyEn HRV_MSEn HRV_CMSEn HRV_RCMSEn HRV_CD HRV_HFD HRV_KFD \\\n",
"0 1.268694 1.404138 1.469801 2.57268 1.832294 1.846507 2.722348 \n",
"\n",
" HRV_LZC \n",
"0 0.873124 \n",
"\n",
"[1 rows x 91 columns]"
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "hrv"
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},
"metadata": {},
"execution_count": 6
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]
},
{
"cell_type": "markdown",
"source": [
"## Respiratory rate variability features"
],
"metadata": {
"id": "sg6kXEQITBBr"
}
},
{
"cell_type": "code",
"source": [
"rsp_troughs_in_ms = pd.read_csv(\"rsp_troughs_in_ms.csv\")[\"rspTroughsInMilliseconds\"].values\n",
"\n",
"# Calculate mean respiratory rate interpolated at 1000 Hz\n",
"interpolation_rate = 1000\n",
"rsp_rate_in_bpm = 60000/np.diff(rsp_troughs_in_ms)\n",
"n_samples = int(np.round((np.max(rsp_troughs_in_ms) - np.min(rsp_troughs_in_ms))/ 1000 * interpolation_rate))\n",
"x_new = np.linspace(rsp_troughs_in_ms[0], rsp_troughs_in_ms[-1], n_samples)\n",
"interp_rsp_rate_in_bpm = nk.signal_interpolate(rsp_troughs_in_ms[1:], rsp_rate_in_bpm, x_new, method=\"linear\")\n",
"\n",
"# Create array of 1s and 0s where 1 is where the troughs are closest to the timestamps\n",
"troughs = np.zeros(len(x_new))\n",
"for i in range(len(rsp_troughs_in_ms)):\n",
" troughs[np.argmin(np.abs(x_new - rsp_troughs_in_ms[i]))] = 1\n",
"\n",
"rsp_rate = pd.DataFrame({\"RSP_Rate\": interp_rsp_rate_in_bpm, \"RSP_Troughs\": troughs})\n",
"\n",
"# Extract features\n",
"rsp_rrv = nk.rsp_rrv(rsp_rate, troughs)\n",
"\n",
"# Export to CSV\n",
"rsp_rrv.to_csv(\"rsp_rrv.csv\")\n",
"rsp_rrv.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 240
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"id": "Mh5mQBtJS0QB",
"outputId": "b3ff9f4a-eb85-47e7-990b-fa51ba9d9623"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/neurokit2/rsp/rsp_rrv.py:141: RuntimeWarning: divide by zero encountered in scalar divide\n",
" out[\"CVBB\"] = out[\"SDBB\"] / out[\"MeanBB\"]\n",
"/usr/local/lib/python3.10/dist-packages/neurokit2/rsp/rsp_rrv.py:142: RuntimeWarning: divide by zero encountered in scalar divide\n",
" out[\"CVSD\"] = out[\"RMSSD\"] / out[\"MeanBB\"]\n",
"/usr/local/lib/python3.10/dist-packages/neurokit2/rsp/rsp_rrv.py:147: RuntimeWarning: invalid value encountered in scalar divide\n",
" out[\"MCVBB\"] = out[\"MadBB\"] / out[\"MedianBB\"]\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" RRV_RMSSD RRV_MeanBB RRV_SDBB RRV_SDSD RRV_CVBB RRV_CVSD \\\n",
"0 0.039353 0.0 0.022821 0.039353 inf inf \n",
"\n",
" RRV_MedianBB RRV_MadBB RRV_MCVBB RRV_VLF ... \\\n",
"0 0.0 0.0 NaN 0.004875 ... \n",
"\n",
" RRV_MFDFA_alpha1_Increment RRV_DFA_alpha2 RRV_MFDFA_alpha2_Width \\\n",
"0 0.001322 -0.467051 0.004688 \n",
"\n",
" RRV_MFDFA_alpha2_Peak RRV_MFDFA_alpha2_Mean RRV_MFDFA_alpha2_Max \\\n",
"0 -0.466533 -0.466451 0.994555 \n",
"\n",
" RRV_MFDFA_alpha2_Delta RRV_MFDFA_alpha2_Asymmetry \\\n",
"0 -0.000486 -0.482711 \n",
"\n",
" RRV_MFDFA_alpha2_Fluctuation RRV_MFDFA_alpha2_Increment \n",
"0 5.283983e-12 7.482260e-07 \n",
"\n",
"[1 rows x 38 columns]"
],
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" <th>RRV_RMSSD</th>\n",
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" <th>RRV_SDBB</th>\n",
" <th>RRV_SDSD</th>\n",
" <th>RRV_CVBB</th>\n",
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "rsp_rrv"
}
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"source": [
"## Respiratory sinus arrhythmia features"
],
"metadata": {
"id": "x7AAIYV2aBWI"
}
},
{
"cell_type": "code",
"source": [
"rsp_troughs_in_ms = pd.read_csv(\"rsp_troughs_in_ms.csv\")[\"rspTroughsInMilliseconds\"].values\n",
"rsp_peaks_in_ms = pd.read_csv(\"rsp_peaks_in_ms.csv\")[\"rspPeaksInMilliseconds\"].values\n",
"cardiac_peaks_in_ms = pd.read_csv(\"cardiac_peaks_in_ms.csv\")[\"cardiacPeaksInMilliseconds\"].values\n",
"\n",
"# Create array of 1s and 0s where 1 is where the troughs are closest to the timestamps\n",
"troughs = np.zeros(len(x_new))\n",
"for i in range(len(rsp_troughs_in_ms)):\n",
" troughs[np.argmin(np.abs(x_new - rsp_troughs_in_ms[i]))] = 1\n",
"\n",
"# Create array of 1s and 0s where 1 is where the peaks are closest to the timestamps\n",
"rsp_peaks = np.zeros(len(x_new))\n",
"for i in range(len(rsp_peaks_in_ms)):\n",
" rsp_peaks[np.argmin(np.abs(x_new - rsp_peaks_in_ms[i]))] = 1\n",
"\n",
"# Create array of 1s and 0s where 1 is where the peaks are closest to the timestamps\n",
"cardiac_peaks = np.zeros(len(x_new))\n",
"for i in range(len(cardiac_peaks_in_ms)):\n",
" cardiac_peaks[np.argmin(np.abs(x_new - cardiac_peaks_in_ms[i]))] = 1\n",
"\n",
"rsp_signals = pd.DataFrame({\"RSP_Troughs\": troughs, \"RSP_Peaks\": rsp_peaks})\n",
"ecg_signals = pd.DataFrame({\"ECG_R_Peaks\": cardiac_peaks})\n",
"rsp_phase = nk.rsp_phase(rsp_signals, desired_length=len(x_new))\n",
"rsp_signals = pd.concat([rsp_signals, rsp_phase], axis=1)\n",
"\n",
"# Extract features\n",
"hrv_rsa = pd.DataFrame(nk.hrv_rsa(ecg_signals, rsp_signals, sampling_rate=1000), index=[0])\n",
"hrv_rsa.to_csv(\"hrv_rsa.csv\")\n",
"hrv_rsa.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 100
},
"id": "9T9ZNOPHXRp4",
"outputId": "30fb99e6-9ed0-4170-c841-1ddde05d9953"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" RSA_P2T_Mean RSA_P2T_Mean_log RSA_P2T_SD RSA_P2T_NoRSA \\\n",
"0 78.671053 4.365275 58.356008 0 \n",
"\n",
" RSA_PorgesBohrer RSA_Gates_Mean RSA_Gates_Mean_log RSA_Gates_SD \n",
"0 -4.331271 7.732986 2.045495 0.155214 "
],
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"metadata": {},
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{
"cell_type": "markdown",
"source": [
"## Signal quality"
],
"metadata": {
"id": "rOXlj7dnaKlz"
}
},
{
"cell_type": "code",
"source": [
"cardioresp_waveform_df = pd.read_csv(\"cardioresp_waveform.csv\")\n",
"cardiac_waveform = cardioresp_waveform_df[\"ECG\"].values\n",
"sampling_rate = 1/np.median(np.diff(cardioresp_waveform_df[\"timestampInMilliseconds\"].values)/1000)\n",
"\n",
"cardiac_signal_quality = nk.ecg_quality(cardiac_waveform, sampling_rate=sampling_rate)\n",
"\n",
"# Export to CSV\n",
"cardiac_signal_quality_df = pd.DataFrame({\"timestampInMilliseconds\": cardioresp_waveform_df[\"timestampInMilliseconds\"].values,\n",
" \"cardiacSignalQuality\": cardiac_signal_quality})\n",
"cardiac_signal_quality_df.to_csv(\"cardiac_signal_quality.csv\")\n",
"cardiac_signal_quality_df.head()\n"
],
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"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" timestampInMilliseconds cardiacSignalQuality\n",
"0 0.0 0.984252\n",
"1 10.0 0.984252\n",
"2 20.0 0.984252\n",
"3 30.0 0.984252\n",
"4 40.0 0.984252"
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"summary": "{\n \"name\": \"cardiac_signal_quality_df\",\n \"rows\": 30000,\n \"fields\": [\n {\n \"column\": \"timestampInMilliseconds\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 86603.98374208891,\n \"min\": 0.0,\n \"max\": 299990.0,\n \"num_unique_values\": 30000,\n \"samples\": [\n 23080.0,\n 224040.0,\n 233970.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cardiacSignalQuality\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12505349383076148,\n \"min\": -0.1564240356458834,\n \"max\": 1.0500466788142102,\n \"num_unique_values\": 29946,\n \"samples\": [\n 0.9614134865964491,\n 0.9763897676332483,\n 0.9681018442459197\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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"metadata": {},
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{
"cell_type": "code",
"source": [
"# Plot X seconds of raw signal along with cardiac signal quality\n",
"X_SECONDS = 60\n",
"\n",
"# Calculate the start and end times for the window\n",
"start_time = 250000\n",
"end_time = start_time + X_SECONDS*1000\n",
"window_data = cardioresp_waveform_df[(cardioresp_waveform_df[\"timestampInMilliseconds\"] >= start_time) & (cardioresp_waveform_df[\"timestampInMilliseconds\"] <= end_time)]\n",
"\n",
"# Plot the raw ECG signal for the window\n",
"fig, axs = plt.subplots(2, 1, sharex=True, figsize=(15, 6))\n",
"axs[0].plot(window_data[\"timestampInMilliseconds\"], window_data[\"ECG\"])\n",
"axs[0].set_ylabel(\"ECG Signal\")\n",
"axs[1].plot(cardiac_signal_quality_df[\"timestampInMilliseconds\"], cardiac_signal_quality_df[\"cardiacSignalQuality\"])\n",
"axs[1].set_xlabel(\"Time (ms)\")\n",
"axs[1].set_ylabel(\"Cardiac signal quality\")\n",
"axs[1].set_xlim(np.min(window_data[\"timestampInMilliseconds\"]), np.max(window_data[\"timestampInMilliseconds\"]))\n",
"plt.show()\n"
],
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"id": "lMhxacvpbGue",
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"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x600 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
}
]
}
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