statistical_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:36:27.150666100Z",
     "start_time": "2023-10-30T10:36:23.564735900Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "from scipy.stats import ttest_ind\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "DATA_FILE_NAME = 'osasud_numpy_dict.pkl'\n",
    "DATA_DIR = 'data'"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:06:43.716419200Z",
     "start_time": "2023-10-30T10:06:43.703484900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "with open(os.path.join(DATA_DIR, DATA_FILE_NAME), 'rb') as f:  # read preprocessing result\n",
    "    data_dict = pickle.load(f)\n",
    "derived_data = data_dict['derived']\n",
    "waveform_data = data_dict['waveform']\n",
    "psg_data = data_dict['psg']\n",
    "binary_labels = data_dict['binary_labels']\n",
    "multi_class_labels = data_dict['multi_label']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:06:46.023958200Z",
     "start_time": "2023-10-30T10:06:43.715416100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "psg_data_disease = psg_data[:int(len(psg_data)/2)]\n",
    "psg_data_normal = psg_data[int(len(psg_data)/2):]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:12:25.186771Z",
     "start_time": "2023-10-30T10:12:25.157481100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "waveform_data_disease = waveform_data[:int(len(waveform_data)/2),:,1:]\n",
    "waveform_data_normal = waveform_data[int(len(waveform_data)/2):,:,1:]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:30:18.645605200Z",
     "start_time": "2023-10-30T10:30:18.638602500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [],
   "source": [
    "derived_data_disease = derived_data[:int(len(derived_data)/2)]\n",
    "derived_data_normal = derived_data[int(len(derived_data)/2):]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:31:03.948656Z",
     "start_time": "2023-10-30T10:31:03.920057300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "array([-0.08316733, -0.00143888, -0.07738928,  0.2023377 , -0.06219007])"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.mean(psg_data_disease, axis=1), axis=0)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:13:26.091029200Z",
     "start_time": "2023-10-30T10:13:26.051452200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "array([ 0.02361762, -0.00635807,  0.07137443,  0.44395985,  0.016598  ])"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.mean(psg_data_normal, axis=1), axis=0)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:13:10.597877600Z",
     "start_time": "2023-10-30T10:13:10.564309300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.10223265, 0.21384938, 0.00636434, 0.12133027, 0.07652145])"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.std(psg_data_disease, axis=1), axis=0)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:14:12.688611500Z",
     "start_time": "2023-10-30T10:14:12.607033800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.10223265, 0.21384938, 0.00636434, 0.12133027, 0.07652145])"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.std(psg_data_disease, axis=1), axis=0)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:14:18.356081300Z",
     "start_time": "2023-10-30T10:14:18.278492900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "array([-0.42420276,  0.68673739, -0.19948616, ...,  0.51316725,\n       -0.74733348, -0.0668411 ])"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(psg_data_disease[:,:,0], axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:27:08.535169700Z",
     "start_time": "2023-10-30T10:27:08.523168600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TtestResult(statistic=-45.972121727202435, pvalue=0.0, df=290654.0)\n",
      "TtestResult(statistic=1.483617996983169, pvalue=0.13791137647883933, df=290654.0)\n",
      "TtestResult(statistic=-48.43012335664294, pvalue=0.0, df=290654.0)\n",
      "TtestResult(statistic=-138.95237120449684, pvalue=0.0, df=290654.0)\n",
      "TtestResult(statistic=-31.12236857863712, pvalue=2.6891289048044436e-212, df=290654.0)\n"
     ]
    }
   ],
   "source": [
    "for i in range(psg_data.shape[2]):\n",
    "    print(ttest_ind(np.mean(psg_data_disease[:,:,i], axis=1), np.mean(psg_data_normal[:,:,i], axis=1)))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:28:04.670357100Z",
     "start_time": "2023-10-30T10:28:04.598776500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TtestResult(statistic=nan, pvalue=nan, df=nan)\n",
      "TtestResult(statistic=nan, pvalue=nan, df=nan)\n",
      "TtestResult(statistic=nan, pvalue=nan, df=nan)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 3 is out of bounds for axis 2 with size 3",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mIndexError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[49], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(waveform_data\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m2\u001B[39m]):\n\u001B[1;32m----> 2\u001B[0m     \u001B[38;5;28mprint\u001B[39m(ttest_ind(np\u001B[38;5;241m.\u001B[39mmean(\u001B[43mwaveform_data_disease\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m), np\u001B[38;5;241m.\u001B[39mmean(waveform_data_normal[:,:,i], axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)))\n",
      "\u001B[1;31mIndexError\u001B[0m: index 3 is out of bounds for axis 2 with size 3"
     ]
    }
   ],
   "source": [
    "for i in range(waveform_data.shape[2]):\n",
    "    print(ttest_ind(np.mean(waveform_data_disease[:,:,i], axis=1), np.mean(waveform_data_normal[:,:,i], axis=1)))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:30:24.041326600Z",
     "start_time": "2023-10-30T10:30:23.900730700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TtestResult(statistic=nan, pvalue=nan, df=nan)\n",
      "TtestResult(statistic=nan, pvalue=nan, df=nan)\n",
      "TtestResult(statistic=nan, pvalue=nan, df=nan)\n",
      "TtestResult(statistic=nan, pvalue=nan, df=nan)\n",
      "TtestResult(statistic=nan, pvalue=nan, df=nan)\n"
     ]
    }
   ],
   "source": [
    "for i in range(derived_data.shape[1]):\n",
    "    print(ttest_ind(derived_data_disease[:,i], derived_data_normal[:,i]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:32:20.216589Z",
     "start_time": "2023-10-30T10:32:20.202985700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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fCCsA4GGsSTMXa9LOHyPWrAAAAIyFmRUA8DDWpJmLNWnnD2EFADyMNWkoBnwMBAAAjEZYAQAARiOsAAAAoxFWAACA0QgrAADAaIQVAABgNMIKAAAwGmEFAAAYjbACAACMRlgBAABGI6wAAACjEVYAAIDRCCsAAMBohBUAAGA0wgoAADAaYQUAABiNsAIAAIxGWIHxgsGgozoAwNsIKzBeOp12VAcAeBthBcYrKRn/1/RcdQCAt/EsD+NVV1c7qgMAvI2wAuN1dXU5qgMAvI2wAuOxwBYAihthBcYrLS11VAcAeBthBcY7e/asozoAc02ZMsVRHcWBsALjhcNhR3UA5hoaGnJUR3EgrMB4F1xwgaM6AHNNnTrVUR3FgbAC482fP99RHYC5LrroIkd1FAfCCoz3/vvvO6oDMBebPmIi+C2A8Y4dO+aoDsBciUTCUR3FgbAC4918882O6gDMVVZW5qiO4uBqWJk9e7Ysy8o4vvWtb7nZEgz0ox/9yFEdgLkWLlzoqI7i4PoF7Nu2bdPatWtHHl988cUudgMT2bbtqA7AXKdOnXJUR3FwPaxMnz5d5eXlbrcBg91yyy06fPjwuHUA3nThhRc6qqM4uL5m5eGHH9bMmTN13XXXafv27RocHBz3/FQqpUQikXGgsL3yyiuO6gDMZVmWozqKg6szK83NzaqurtYll1yiAwcOaNOmTTp+/Li++93vjvk1ra2tevDBB/PYJQAgV7h0GRNx3n8Ltm7dOmrR7CePgwcPSpI2btyoxYsXa/78+frqV7+qnTt36tlnn1U8Hh/z+2/atEkDAwMjR09Pz/n+EWAYbmQIFC4uXcZEnPeZlQ0bNmjVqlXjnjN79uys49dff72kj/fNmDlzZtZz/H6//H6/ox7hLTU1Ners7By3DsCb5s2bp1/84hfj1oHzHlZCoZBCodCn+trhRZSXX375+WwJHvezn/3snPWbbropT90AOJ9isZijOoqDax8Gvv766/r2t7+tI0eO6Pjx4/qnf/onfe1rX9Mf//EfcxddZGhoaHBUB2Cuc13Nx9V+kFwMK36/X7t379aSJUt0zTXXaPPmzVq7dq3+8R//0a2WYKhnn33WUR2Aucb7iHcidRQHy/b4jlqJRELBYFADAwMstCxQS5cuHXfjN8uyuHwZ8KhkMqnly5ePWf/Rj36kQCCQx46QL5N5/eaaMBhv165djuoAzMXtNDARhBUY78UXX3RUB2CuuXPnOqqjOBBWYLxgMOioDsBcfX19juooDoQVGI/tuAGguBFWYLzLLrvMUR2AucrKyhzVURwIKzDe/v37HdUBmOu1115zVEdxIKzAeDNmzHBUB2Cuz372s47qKA6EFRjvL//yLx3VAZiLNWmYCMIKjNfb2+uoDsBcJSXjvwydq47iwG8BjFdVVTXm7oalpaWqqqrKc0cAzpeFCxc6qqM4EFZgvJ6eHiUSiay1RCKhnp6ePHcE4Hx58sknHdVRHAgrMB4zK0DhCofDjuooDoQVGI+ZFaBwlZeXO6qjOBBWYLxwOKza2tpRVwVYlqVFixbxzgvwsIqKCkd1FAfCCoxnWZaam5tHXRXg8/nU3NzMpY2Ah3G1HyaCsAJPqKysVFNTU8ZYU1OTZs2a5VJHAM6HU6dOOaqjOBBW4Blf+cpXRmZXSkpKdNttt7ncEQCnPvOZzziqozgQVuAZP/jBD2TbtiTJtm398z//s8sdAXCqsrLSUR3FgbACT4jFYopEIhlhJRKJKBaLudwZACdmz5495v1/Pve5z2n27Nn5bQhGIqzAeLZtq729fczx4QADwHssy9K6deuy1tatW8cCekgirMADotGouru7lU6nM8bT6bS6u7sVjUZd6gyAU7Zta/fu3Vlru3bt4s0IJBFW4AHsswIUruE3I9nwZgTDCCswnmVZamxsHPUOy7ZtNTY2Mk0MeFg4HNa8efOy1ubPn8+bEUgirMADhqeJs82sME0MFC7+tjGMsALjDU8TZ5tZYZoY8LZoNKqjR49mrR09epS/b0girMADmCYGCldlZeWoW2kMKykpYZ8VSCKswOOYJga8bf/+/RoaGspaGxoa0v79+/PcEUxEWIHxmCYGCtfll1/uqI7iQFiB8YYvXf7kVHFJSQmXLgMeN94Otr//+7/PDraQRFiBB1iWpebm5lFXA5WUlGQdB+Ad4+1gu3btWv6+IYmwAo+orKxUU1PTyBOXZVlqamrSrFmzXO4MgBPsYIuJIKzAM1avXq2ZM2dKkkKhkJqamlzuCIBT7GCLiSCswDMCgYBWrFihkpISLV++XIFAwO2WADjEmjRMBGEFnpFMJtXZ2amhoSF1dnYqmUy63RIAh4bXpGXb9JE1aRhGWIFndHR0KB6PS5Li8bgikYjLHQE4X7KFFdarYBhhBZ4Qi8UUiURGnrxs21YkElEsFnO5MwBO2Lat9vb2rLX29nYCCyQRVuABYz2ZDY/zZAZ4FwtsMRE5DSvbt29XQ0ODLrzwQs2YMSPrOdFoVLfeeqsuuugihUIh3XPPPRocHMxlW/CY4SezdDqdMZ5Op3kyAzyusrJSPp8va83n83FvIEjKcVgZHBzU7bffrr/+67/OWk+n07rlllt09uxZ/fznP9euXbv0gx/8QPfdd18u24LHDF8t8MknNJ/Px9UCgMcdOHBg1BuRYel0WgcOHMhzRzBRTsPKgw8+qI0bN455x9y9e/fqrbfe0gsvvKAFCxbopptu0uOPP67vfOc7SiQSuWwNHjJ8tcBY41wtAHhXXV2dSktLs9aCwaDq6ury3BFM5Oqalddff11z585VRUXFyNjNN9+sVCqlQ4cOZf2aVCqlRCKRcaDwsYMtUJhKSkq0fv36rLX169eP2n8FxcnV34K+vj6VlZVljF1yySWaOnWq+vr6sn5Na2urgsHgyFFVVZWPVmEAdrAFCo9t23r55Zez1n784x+zgB6SPkVY2bp1qyzLGvc4ePDghL9ftil827bHnNrftGmTBgYGRo6enp7J/gjwqEAgoJaWFpWVlWnjxo3sYAsUAK4GwkRMmewXbNiwQatWrRr3nIne0ru8vFz79+/PGPvggw/00UcfjZpxGeb3++X3+yf0/QEAZhteQH/w4MGMWRTLslRbW8sCekj6FGElFAopFAqdl/95fX29tm/frhMnTujyyy+X9PGiW7/fr5qamvPy/0DhSCaTamtrU39/v9ra2lRdXc3sCuBxlmWpsbFx1OyKbdtqbGxkAT0k5XjNSjQa1ZEjRxSNRpVOp3XkyBEdOXJEZ86ckSQtW7ZM11xzje644w4dPnxYL7/8sr7xjW9o7dq1Y64OR/Fiu32g8Ni2rd27d48KJZZladeuXaxZgaQch5XNmzdrwYIF2rJli86cOaMFCxZowYIFI2tafD6fXnrpJQUCAd1www36sz/7M61cuVKPPfZYLtuCB7HdPlCYhtesZLs3EGtWMMyyPR5bE4mEgsGgBgYGmI0pULZt65vf/Kb+4z/+I2PzKJ/Pp+rqaj3yyCNMFQMeNd7fd01NjR5++GH+vgvUZF6/uYAdxmO7faBwsekjJoKwAuOx3T5Q2Nj0EedCWIHxeOcFFL7Vq1dr+vTpkqTp06ez6SMyEFbgCbzzAgrf7y6gB34XYQWewXb7QOHq6OgY2dbizJkzbE2ADIQVeAbb7QOFia0JcC6EFXhKQ0ODdu/erYaGBrdbAXAe2Lat9vb2Mcf5SAgSYQUA4CK2JsBEEFYAAK5hawJMBGEFAOAatibARBBWAACuYmsCnAthBQDgOrYmwHgIKwAA17E1AcYzxe0GAACQPt6agG0JkA0zKwAAwGiEFQAAYDTCCgAAMBphBQAAGI2wAgAAjEZYAQAARiOsAAAAoxFW4CldXV1qbGxUV1eX260AAPKEsALPSCaTamtr08mTJ9XW1qZkMul2SwCAPCCswDM6OjoUj8clSfF4XJFIxOWOAAD5QFiBJ8RiMUUiEdm2LUmybVuRSESxWMzlzgAAuUZYgfFs21Z7e/uY48MBBgBQmAgrMF40GlV3d7fS6XTGeDqdVnd3t6LRqEudAQDygbAC44XDYdXW1srn82WM+3w+LVq0SOFw2KXOAAD5QFiB8SzLUnNz85jjlmW50BUAIF8IK/CEyspKNTU1jQQTy7LU1NSkWbNmudwZACDXCCvwjNWrV2vmzJmSpFAopKamJpc7AgDkA2EFnhEIBNTS0qKysjJt3LhRgUDA7ZYAAHkwxe0GgMloaGhQQ0OD220AAPKImRUAAGA0wgoAADAaYQUAABiNsAIAAIxGWAEAAEbLaVjZvn27GhoadOGFF2rGjBlZz7Esa9Sxc+fOXLYFAAA8JKeXLg8ODur2229XfX29nn322THPe+6557R8+fKRx8FgMJdtAQAAD8lpWHnwwQclSc8///y4582YMUPl5eW5bAUAAHiUEWtWNmzYoFAopNraWu3cuVNDQ0NjnptKpZRIJDIOAABQuFzfwfahhx7Sl770JU2bNk0vv/yy7rvvPvX39+uBBx7Ien5ra+vIjA0AACh8k55Z2bp1a9ZFsb97HDx4cMLf74EHHlB9fb2uu+463Xfffdq2bZseffTRMc/ftGmTBgYGRo6enp7J/ggAAMBDJj2zsmHDBq1atWrcc2bPnv1p+9H111+vRCKhkydPqqysbFTd7/fL7/d/6u8PAAC8ZdJhJRQKKRQK5aIXSdLhw4cVCATGvNQZAAAUl5yuWYlGo3r//fcVjUaVTqd15MgRSdLVV1+tiy++WC+++KL6+vpUX1+vadOm6ZVXXtH999+vdevWMXsCAAAk5TisbN68Wd/73vdGHi9YsECS9Morr2jJkiW64IILtGPHDrW0tGhoaEhXXnmltm3bpq9//eu5bAsAAHiIZdu27XYTTiQSCQWDQQ0MDKi0tNTtdpBjXV1dam9vV3NzsxoaGtxuBwDwKU3m9duIfVaAiUgmk2pra9PJkyfV1tamZDLpdksAgDwgrMAzOjo6FI/HJUnxeFyRSMTljgAA+UBYgSfEYjFFIhENf2pp27YikYhisZjLnQEAco2wAuPZtq329vYxxz2+7AoAcA6EFRgvGo2qu7tb6XQ6YzydTqu7u1vRaNSlzgAA+UBYgfHC4bBqa2vl8/kyxn0+nxYtWqRwOOxSZwCAfCCswHiWZam5uXnMccuyXOgKAJAvhBV4QmVlpZqamkaCiWVZampq0qxZs1zuDACQa4QVeMbq1as1c+ZMSR/fo6qpqcnljgAA+UBYgWcEAgG1tLSorKxMGzduVCAQcLslAEAe5PTeQMD51tDQwDb7AFBkmFkBAABGI6wAAACjEVYAAIDRCCsAAMBohBUAAGA0wgoAADAaYQUAABiNsAIAAIxGWAEAAEYjrAAAAKMRVgAAgNEIKwAAwGiEFQAAYDTCCgAAMBphBQAAGI2wAgAAjEZYAQAARiOsAAAAoxFWAACA0QgrAADAaIQVeEpXV5caGxvV1dXldisAgDwhrMAzksmk2tradPLkSbW1tSmZTLrdEgAgDwgr8IyOjg7F43FJUjweVyQScbkjAEA+EFbgCbFYTJFIRLZtS5Js21YkElEsFnO5MwBArhFWYDzbttXe3j7m+HCAAQAUJsIKjBeNRtXd3a10Op0xnk6n1d3drWg06lJnAIB8yFlYeffdd3XXXXdpzpw5mjZtmq666ipt2bJFg4ODGedFo1HdeuutuuiiixQKhXTPPfeMOgfFLRwOq7a2Vj6fL2Pc5/Np0aJFCofDLnUGAMiHKbn6xr/85S81NDSkp59+WldffbXeeOMNrV27VmfPntVjjz0m6eN3xrfccosuu+wy/fznP1c8HteaNWtk27aeeOKJXLUGj7EsS83NzVqzZk3WccuyXOoMAJAPlp3HD/wfffRRPfXUU/r1r38tSers7NQf/dEfqaenRxUVFZKkXbt26c4779SpU6dUWlp6zu+ZSCQUDAY1MDAwofPhXc8++6xeeOEF2bYty7J0xx136K/+6q/cbgsA8ClM5vU7r2tWBgYGdOmll448fv311zV37tyRoCJJN998s1KplA4dOpT1e6RSKSUSiYwDxWH16tWaOXOmJCkUCqmpqcnljgAA+ZC3sPLOO+/oiSee0N133z0y1tfXp7KysozzLrnkEk2dOlV9fX1Zv09ra6uCweDIUVVVldO+YY5AIKCWlhaVlZVp48aNCgQCbrcEAMiDSYeVrVu3yrKscY+DBw9mfE1vb6+WL1+u22+/XV/96lczatnWGwxP82ezadMmDQwMjBw9PT2T/RHgYQ0NDdq9e7caGhrcbgUAkCeTXmC7YcMGrVq1atxzZs+ePfLfvb29Wrp0qerr6/XMM89knFdeXq79+/dnjH3wwQf66KOPRs24DPP7/fL7/ZNtGwAAeNSkw0ooFFIoFJrQub/5zW+0dOlS1dTU6LnnnlNJSeZETn19vbZv364TJ07o8ssvlyTt3btXfr9fNTU1k20NAAAUoJxdDdTb26vFixcrHA7r+9//fsYeGeXl5ZI+vnT5uuuuU1lZmR599FG9//77uvPOO7Vy5coJX7rM1UAAAHjPZF6/c7bPyt69e3Xs2DEdO3ZMlZWVGbXhfOTz+fTSSy9p/fr1uuGGGzRt2jQ1NTWN7MMCAACQ131WcoGZFQAAvMfYfVYAAAAmi7ACAACMRlgBAABGI6wAAACjEVYAAIDRCCsAAMBohBUAAGA0wgoAADAaYQUAABiNsAIAAIxGWAEAAEYjrAAAAKMRVgAAgNEIKwAAwGiEFQAAYDTCCgAAMBphBQAAGI2wAgAAjEZYAQAARiOswFO6urrU2Niorq4ut1sBAOQJYQWekUwm1dbWppMnT6qtrU3JZNLtlgAAeUBYgWd0dHQoHo9LkuLxuCKRiMsdAQDygbACT4jFYopEIrJtW5Jk27YikYhisZjLnQEAco2wAuPZtq329vYxx4cDDACgMBFWYLxoNKru7m6l0+mM8XQ6re7ubkWjUZc6AwDkA2EFxguHw6qtrZXP58sY9/l8WrRokcLhsEudAQDygbAC41mWpebm5jHHLctyoSsAQL4QVuAJlZWVuu222zLGbrvtNs2aNculjgAA+UJYgWe89dZb4z4GABQmwgo84eDBg3rzzTczxt544w0dPHjQpY4AAPlCWIHxhoaGtG3btqy1bdu2aWhoKM8dAQDyibAC4+3fv1+JRCJrLZFIaP/+/XnuCACQT4QVGK+urk6lpaVZa8FgUHV1dXnuCACQT4QVGK+kpESbN2/OWtuyZYtKSvg1BoBCxrM8PGHhwoWaN29extj8+fNVXV3tUkcAgHwhrMAzHnrooZFZlJKSkjEX3QIACgthBZ4xY8YMrV69WiUlJVq9erVmzJjhdksAgDzIWVh59913ddddd2nOnDmaNm2arrrqKm3ZskWDg4MZ51mWNerYuXNnrtqCx9111136yU9+orvuusvtVgAAeTIlV9/4l7/8pYaGhvT000/r6quv1htvvKG1a9fq7NmzeuyxxzLOfe6557R8+fKRx8FgMFdtAQAAj8lZWFm+fHlGALnyyiv1q1/9Sk899dSosDJjxgyVl5fnqhUAAOBheV2zMjAwoEsvvXTU+IYNGxQKhVRbW6udO3eOuyNpKpVSIpHIOAAAQOHK2czKJ73zzjt64okn9Pjjj2eMP/TQQ/rSl76kadOm6eWXX9Z9992n/v5+PfDAA1m/T2trqx588MF8tAwAAAxg2bZtT+YLtm7des6w0N3drYULF4487u3t1eLFi7V48WJ997vfHfdrH3/8cW3btk0DAwNZ66lUSqlUauRxIpFQVVWVBgYGxtzlFAAAmCWRSCgYDE7o9XvSYaW/v1/9/f3jnjN79mwFAgFJHweVpUuXqq6uTs8///w5dxv9xS9+oT/4gz9QX1+fysrKztnPZH5YAABghsm8fk/6Y6BQKKRQKDShc3/zm99o6dKlqqmp0XPPPTehbdEPHz6sQCDAHhoAAEBSDtes9Pb2asmSJQqHw3rsscf0P//zPyO14St/XnzxRfX19am+vl7Tpk3TK6+8ovvvv1/r1q2T3+/PVWsAAMBDchZW9u7dq2PHjunYsWOqrKzMqA1/8nTBBRdox44damlp0dDQkK688kpt27ZNX//613PVFgAA8JhJr1kxzcDAgGbMmKGenh7WrAAA4BHDF8j89re/PedmsHm7dDlXTp8+LUmqqqpyuRMAADBZp0+fPmdY8fzMytDQkHp7ezV9+nRZluV2O8ix4STOTBpQePj7Li62bev06dOqqKg45wU4np9ZKSkpGbUmBoWvtLSUJzOgQPH3XTwmei/AvG63DwAAMFmEFQAAYDTCCjzF7/dry5Yt7MMDFCD+vjEWzy+wBQAAhY2ZFQAAYDTCCgAAMBphBQAAGI2wAgAAjEZYgafs2LFDc+bMUSAQUE1NjX72s5+53RIAh37605/q1ltvVUVFhSzL0g9/+EO3W4JhCCvwjN27d+vee+/V/fffr8OHD+vGG2/UihUrFI1G3W4NgANnz57VtddeqyeffNLtVmAoLl2GZ9TV1am6ulpPPfXUyNjnP/95rVy5Uq2trS52BuB8sSxLe/bs0cqVK91uBQZhZgWeMDg4qEOHDmnZsmUZ48uWLVNXV5dLXQEA8oGwAk/o7+9XOp1WWVlZxnhZWZn6+vpc6goAkA+EFXiKZVkZj23bHjUGACgshBV4QigUks/nGzWLcurUqVGzLQCAwkJYgSdMnTpVNTU12rdvX8b4vn371NDQ4FJXAIB8mOJ2A8BEtbS06I477tDChQtVX1+vZ555RtFoVHfffbfbrQFw4MyZMzp27NjI4+PHj+vIkSO69NJLFQ6HXewMpuDSZXjKjh079Mgjj+jEiROaO3euvv3tb+uLX/yi220BcODVV1/V0qVLR42vWbNGzz//fP4bgnEIKwAAwGisWQEAAEYjrAAAAKMRVgAAgNEIKwAAwGiEFQAAYDTCCgAAMBphBQAAGI2wAgAAjEZYAQAARiOsAAAAoxFWAACA0QgrAADAaP8P+tKlmJN1zU0AAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot([np.mean(psg_data_disease[:,:,0], axis=1), np.mean(psg_data_normal[:,:,0], axis=1)])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:36:32.190642700Z",
     "start_time": "2023-10-30T10:36:31.735221800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot([np.mean(psg_data_disease[:,:,1], axis=1), np.mean(psg_data_normal[:,:,1], axis=1)])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:36:35.638869200Z",
     "start_time": "2023-10-30T10:36:35.505730600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot([np.mean(psg_data_disease[:,:,2], axis=1), np.mean(psg_data_normal[:,:,2], axis=1)])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:36:38.190063200Z",
     "start_time": "2023-10-30T10:36:38.078475800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot([np.mean(psg_data_disease[:,:,3], axis=1), np.mean(psg_data_normal[:,:,3], axis=1)])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:36:39.534552500Z",
     "start_time": "2023-10-30T10:36:39.405956Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot([np.mean(psg_data_disease[:,:,4], axis=1), np.mean(psg_data_normal[:,:,4], axis=1)])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T10:36:40.783222600Z",
     "start_time": "2023-10-30T10:36:40.590782800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}