neuroQWERTY MIT-CSXPD Dataset 1.0.0

File: <base>/readme.ipynb (78,518 bytes)
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This IPython Notebook describes the two datasets presented in: \n",
    "\n",
    "> L. Giancardo, A. Sánchez-Ferro, T. Arroyo-Gallego, I. Butterworth, C. S. Mendoza, P. Montero, M. Matarazzo, J. A. Obeso, M. L. Gray & R. San José Estépar. Computer keyboard interaction as an indicator of early Parkinson's disease. Sci. Rep. 6, 34468; doi: 10.1038/srep34468 (2016)\n",
    "\n",
    "The Early Parkinson's Disease dataset is referred as MIT-CS1PD; the De-Novo Parkinson's Disease dataset is  referred as MIT-CS2PD. It also shows how to load timepress data from the datasets.\n",
    "\n",
    "These datasets have been collected thanks to the financial support by the Comunidad de Madrid, Fundacion Ramon Areces and The Michael J Fox Foundation for Parkinson's research (grant number 10860). We thank the M + Vision faculty for their guidance in developing this project. We also thank our many clinical collaborators at MGH in Boston, at “12 de Octubre”, Hospital Clinico and Centro Integral en Neurociencias HM CINAC in Madrid for their insightful contributions.\n",
    "\n",
    "This notebook requires:\n",
    "- Python 2.7\n",
    "- IPython\n",
    "- Pandas\n",
    "- Numpy\n",
    "- Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PD_MIT-CS1PD dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MIT_CS1PD dataset includes the typing information collected on a population sample of 31 subjects,\n",
    "13 healthy controls and 18 Parkinson’s disease (PD) sufferers. The subjects were recruited from two\n",
    "movement disorder units in Madrid (Spain) following the institutional protocols approved by the\n",
    "Massachusetts Institute of Technology, USA (Committee on the Use of Humans as Experimental Subjects\n",
    "approval no. 1402006203), Hospital 12 de Octubre, Spain (no. CEIC:14/090) and Hospital Clinico San\n",
    "Carlos, Spain (no. 14/136-E).\n",
    "\n",
    "Each data file includes the timing information collected during two sessions of typing activity using a\n",
    "standard word processor on a Lenovo G50-70 i3-4005U with 4MB of memory and a 15 inches screen\n",
    "running Manjaro Linux. Subjects were instructed to type as they normally would do at home and they\n",
    "were left free to correct typing mistakes only if they wanted to. The key acquisition software presented\n",
    "a temporal resolution of 3/0.28 (mean/std) milliseconds.\n",
    "\n",
    "\n",
    "Subjects were asked to visit a movement disorder unit twice to complete the study. Clinical evaluation\n",
    "includes UPDRS and finger tapping tests.\n",
    "\n",
    "Usage policy\n",
    "---------\n",
    "---------\n",
    "\n",
    "This dataset is free to use for research purposes only. We encourage researchers to try\n",
    "new computational algorithms on these data. Any time MIT_CS1PD is used for publishing or presenting the following publication needs to be referenced \n",
    "\n",
    "> L. Giancardo, A. Sánchez-Ferro, T. Arroyo-Gallego, I. Butterworth, C. S. Mendoza, P. Montero, M. Matarazzo, J. A. Obeso, M. L. Gray & R. San José Estépar. Computer keyboard interaction as an indicator of early Parkinson's disease. Sci. Rep. 6, 34468; doi: 10.1038/srep34468 (2016)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ground truth loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ground truth used to describe motor impairment on each participant. The GT value discriminate the two classes in the sample population (CNT-False and PD-True). The results of the clinical evaluation, UPDRS-\n",
    "III, alternating finger tapping, single key tapping, neuroQWERTY score and typing speed are\n",
    "included in updrs108, afTap, sTap, nqScore, typingSpeed, respectively.\n",
    "\n",
    "Columns file_1 and file_2 correspond to the first and second repetition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import nqDataLoader as nq #data loading library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gt</th>\n",
       "      <th>updrs108</th>\n",
       "      <th>afTap</th>\n",
       "      <th>sTap</th>\n",
       "      <th>nqScore</th>\n",
       "      <th>typingSpeed</th>\n",
       "      <th>file_1</th>\n",
       "      <th>file_2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pID</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>11</th>\n",
       "      <td>True</td>\n",
       "      <td>14.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.25</td>\n",
       "      <td>0.117543</td>\n",
       "      <td>189.372549</td>\n",
       "      <td>1402930351.011_001_014.csv</td>\n",
       "      <td>1403706430.011_003_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>False</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.25</td>\n",
       "      <td>0.070350</td>\n",
       "      <td>60.533333</td>\n",
       "      <td>1402932300.060_001_014.csv</td>\n",
       "      <td>1403708258.060_003_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>True</td>\n",
       "      <td>25.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.75</td>\n",
       "      <td>0.223411</td>\n",
       "      <td>54.333333</td>\n",
       "      <td>1401117235.067_001_014.csv</td>\n",
       "      <td>1401978395.067_003_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>False</td>\n",
       "      <td>6.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.00</td>\n",
       "      <td>0.074973</td>\n",
       "      <td>71.800000</td>\n",
       "      <td>1401114972.068_001_014.csv</td>\n",
       "      <td>1401980765.068_003_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>True</td>\n",
       "      <td>26.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.50</td>\n",
       "      <td>0.175751</td>\n",
       "      <td>39.614035</td>\n",
       "      <td>1404311419.070_001_014.csv</td>\n",
       "      <td>1404743687.070_003_014.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gt  updrs108  afTap    sTap   nqScore  typingSpeed  \\\n",
       "pID                                                          \n",
       "11    True     14.25    NaN  162.25  0.117543   189.372549   \n",
       "60   False      2.00    NaN  162.25  0.070350    60.533333   \n",
       "67    True     25.25    NaN  133.75  0.223411    54.333333   \n",
       "68   False      6.00    NaN  159.00  0.074973    71.800000   \n",
       "70    True     26.25    NaN  113.50  0.175751    39.614035   \n",
       "\n",
       "                         file_1                      file_2  \n",
       "pID                                                          \n",
       "11   1402930351.011_001_014.csv  1403706430.011_003_014.csv  \n",
       "60   1402932300.060_001_014.csv  1403708258.060_003_014.csv  \n",
       "67   1401117235.067_001_014.csv  1401978395.067_003_014.csv  \n",
       "68   1401114972.068_001_014.csv  1401980765.068_003_014.csv  \n",
       "70   1404311419.070_001_014.csv  1404743687.070_003_014.csv  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load ground Ground Truth \n",
    "cs1PdFr = pd.read_csv( 'MIT-CS1PD/GT_DataPD_MIT-CS1PD.csv' )\n",
    "# set Patient ID as index\n",
    "cs1PdFr = cs1PdFr.set_index('pID')\n",
    "# show part of Data Frame\n",
    "cs1PdFr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In here it is shown how to load the typing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load typing data for patient 60 (first repetition)\n",
    "# this method automatically filters out meta keys, mouse presses and backspace\n",
    "keyPressed, htArr, pressArr, releaseArr = \\\n",
    "            nq.getDataFiltHelper( 'MIT-CS1PD/data_MIT-CS1PD/' + cs1PdFr.loc[60]['file_1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we diplay the raw hold time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAAGFCAYAAAAxTsNsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb2b1898310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotRawHT(pressArrIn, htArrIn, maxWinID=3):\n",
    "    winSize = 90\n",
    "    lblfSize = 24\n",
    "    plt.figure()\n",
    "    plt.ion()\n",
    "    plt.plot( pressArrIn, htArrIn, '.' )\n",
    "    plt.ylim(0,0.49)\n",
    "    plt.xticks( range( 0, np.int(np.max(pressArrIn)), winSize ) )\n",
    "#     rmBorder( plt.gca() )\n",
    "    plt.xlabel( '$t$', fontsize=lblfSize )\n",
    "    plt.ylabel( 'Hold time, $a[t]$', fontsize=lblfSize )\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "plotRawHT( pressArr, htArr  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PD_MIT-CS2PD dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MIT_CS2PD dataset includes the typing information collected on a population sample of 54 subjects,\n",
    "30 healthy controls and 24 Parkinson’s disease (PD) sufferers. The subjects were recruited from two\n",
    "movement disorder units in Madrid (Spain) following the institutional protocols approved by the\n",
    "Massachusetts Institute of Technology, USA (Committee on the Use of Humans as Experimental Subjects\n",
    "approval no. 1402006203), Hospital 12 de Octubre, Spain (no. CEIC:14/090) and Hospital Clinico San\n",
    "Carlos, Spain (no. 14/136-E).\n",
    "\n",
    "Each data file includes the timing information collected during a session of typing activity using a\n",
    "standard word processor on a Lenovo G50-70 i3-4005U with 4MB of memory and a 15 inches screen\n",
    "running Manjaro Linux. Subjects were instructed to type as they normally would do at home and they\n",
    "were left free to correct typing mistakes only if they wanted to. The key acquisition software presented\n",
    "a temporal resolution of 3/0.28 (mean/std) milliseconds. Clinical evaluation includes UPDRS and finger tap\n",
    "\n",
    "\n",
    "Subjects were asked to visit a movement disorder unit once to complete the study. Clinical evaluation\n",
    "includes UPDRS and finger tapping tests.\n",
    "\n",
    "Usage policy\n",
    "---------\n",
    "---------\n",
    "\n",
    "This dataset is free to use for research purposes only. We encourage researchers to try\n",
    "new computational algorithms on these data. Any time MIT_CS1PD is used for publishing or presenting the following publication needs to be referenced \n",
    "\n",
    "> L. Giancardo, A. Sánchez-Ferro, T. Arroyo-Gallego, I. Butterworth, C. S. Mendoza, P. Montero, M. Matarazzo, J. A. Obeso, M. L. Gray & R. San José Estépar. Computer keyboard interaction as an indicator of early Parkinson's disease. Sci. Rep. 6, 34468; doi: 10.1038/srep34468 (2016)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ground truth loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ground truth used to describe motor impairment on each participant. The GT value discriminate the two classes in the sample population (CNT-False and PD-True). The results of the clinical evaluation, UPDRS-\n",
    "III, alternating finger tapping, single key tapping, neuroQWERTY score and typing speed are\n",
    "included in updrs108, afTap, sTap, nqScore, typingSpeed, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gt</th>\n",
       "      <th>updrs108</th>\n",
       "      <th>afTap</th>\n",
       "      <th>sTap</th>\n",
       "      <th>nqScore</th>\n",
       "      <th>typingSpeed</th>\n",
       "      <th>file_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pID</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>1000</th>\n",
       "      <td>True</td>\n",
       "      <td>27</td>\n",
       "      <td>79.0</td>\n",
       "      <td>184.5</td>\n",
       "      <td>0.107179</td>\n",
       "      <td>56.866667</td>\n",
       "      <td>1424946827.1000_001_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>96.5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>0.056286</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>1427279751.1001_001_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>140.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.039519</td>\n",
       "      <td>119.037037</td>\n",
       "      <td>1426676689.1002_001_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>True</td>\n",
       "      <td>22</td>\n",
       "      <td>83.5</td>\n",
       "      <td>191.5</td>\n",
       "      <td>0.034853</td>\n",
       "      <td>74.266667</td>\n",
       "      <td>1429866367.1004_001_014.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>True</td>\n",
       "      <td>17</td>\n",
       "      <td>68.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.048307</td>\n",
       "      <td>74.969697</td>\n",
       "      <td>1430134526.1005_001_014.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         gt  updrs108  afTap   sTap   nqScore  typingSpeed  \\\n",
       "pID                                                          \n",
       "1000   True        27   79.0  184.5  0.107179    56.866667   \n",
       "1001   True        16   96.5  189.0  0.056286   118.000000   \n",
       "1002  False         5  140.0  158.0  0.039519   119.037037   \n",
       "1004   True        22   83.5  191.5  0.034853    74.266667   \n",
       "1005   True        17   68.0  150.0  0.048307    74.969697   \n",
       "\n",
       "                           file_1  \n",
       "pID                                \n",
       "1000  1424946827.1000_001_014.csv  \n",
       "1001  1427279751.1001_001_014.csv  \n",
       "1002  1426676689.1002_001_014.csv  \n",
       "1004  1429866367.1004_001_014.csv  \n",
       "1005  1430134526.1005_001_014.csv  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load ground Ground Truth \n",
    "cs2PdFr = pd.read_csv( 'MIT-CS2PD/GT_DataPD_MIT-CS2PD.csv' )\n",
    "# set Patient ID as index\n",
    "cs2PdFr = cs2PdFr.set_index('pID')\n",
    "# show part of Data Frame\n",
    "cs2PdFr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In here it is shown how to load the typing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load typing data for patient 1000\n",
    "# this method automatically filters out meta keys, mouse presses and backspace\n",
    "keyPressed, htArr, pressArr, releaseArr = \\\n",
    "            nq.getDataFiltHelper( 'MIT-CS2PD/data_MIT-CS2PD/' + cs2PdFr.loc[1000]['file_1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we diplay the raw hold time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAAGFCAYAAAAxTsNsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBgFUlqEEEIIMQr+fz4OyfNWkob+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb2b19253d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRawHT( pressArr, htArr  )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}