Part II Clinical Application/clinical_app_EY_completed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Clinical Application\n",
    "\n",
    "### Contents\n",
    "Fill out this notebook as part 2 of your final project submission.\n",
    "\n",
    "**You will have to complete the Code (Load Metadata & Compute Resting Heart Rate) and Project Write-up sections.**  \n",
    "\n",
    "- [Code](#Code) is where you will implement some parts of the **pulse rate algorithm** you created and tested in Part 1 and already includes the starter code.\n",
    "  - [Imports](#Imports) - These are the imports needed for Part 2 of the final project. \n",
    "    - [glob](https://docs.python.org/3/library/glob.html)\n",
    "    - [os](https://docs.python.org/3/library/os.html)\n",
    "    - [numpy](https://numpy.org/)\n",
    "    - [pandas](https://pandas.pydata.org/)\n",
    "  - [Load the Dataset](#Load-the-dataset)  \n",
    "  - [Load Metadata](#Load-Metadata)\n",
    "  - [Compute Resting Heart Rate](#Compute-Resting-Heart-Rate)\n",
    "  - [Plot Resting Heart Rate vs. Age Group](#Plot-Resting-Heart-Rate-vs.-Age-Group)\n",
    "- [Project Write-up](#Project-Write-Up) to describe the clinical significance you observe from the **pulse rate algorithm** applied to this dataset, what ways/information that could improve your results, and if we validated a trend known in the science community. \n",
    "\n",
    "### Dataset (CAST)\n",
    "\n",
    "The data from this project comes from the [Cardiac Arrhythmia Suppression Trial (CAST)](https://physionet.org/content/crisdb/1.0.0/), which was sponsored by the National Heart, Lung, and Blood Institute (NHLBI). CAST collected 24 hours of heart rate data from ECGs from people who have had a myocardial infarction (MI) within the past two years.[1] This data has been smoothed and resampled to more closely resemble PPG-derived pulse rate data from a wrist wearable.[2]\n",
    "\n",
    "1. **CAST RR Interval Sub-Study Database Citation** - Stein PK, Domitrovich PP, Kleiger RE, Schechtman KB, Rottman JN. Clinical and demographic determinants of heart rate variability in patients post myocardial infarction: insights from the Cardiac Arrhythmia Suppression Trial (CAST). Clin Cardiol 23(3):187-94; 2000 (Mar)\n",
    "2. **Physionet Citation** - Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals (2003). Circulation. 101(23):e215-e220.\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code\n",
    "#### Imports\n",
    "\n",
    "When you implement the functions, you'll only need to you use the packages you've used in the classroom, like [Pandas](https://pandas.pydata.org/) and [Numpy](http://www.numpy.org/). These packages are imported for you here. We recommend you don't import other packages outside of the [Standard Library](https://docs.python.org/3/library/) , otherwise the grader might not be able to run your code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.11.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the dataset\n",
    "\n",
    "The dataset is stored as [.npz](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html) files. Each file contains roughly 24 hours of heart rate data in the 'hr' array sampled at 1Hz. The subject ID is the name of the file. You will use these files to compute resting heart rate.\n",
    "\n",
    "Demographics metadata is stored in a file called 'metadata.csv'. This CSV has three columns, one for subject ID, age group, and sex. You will use this file to make the association between resting heart rate and age group for each gender.\n",
    "\n",
    "Find the dataset in `../data/datasets/crisdb/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hr_filenames = glob.glob(os.path.join('..','data','datasets', 'crisdb', '*.npz'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1537"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hr_filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Metadata\n",
    "Load the metadata file into a datastructure that allows for easy lookups from subject ID to age group and sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_filename = os.path.join('..', 'data', 'datasets', 'crisdb', 'metadata.csv')\n",
    "\n",
    "# Load the metadata file into this variable.\n",
    "with open(metadata_filename, 'r') as f:\n",
    "    metadata = pd.read_csv(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1543, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>subject</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e198a</td>\n",
       "      <td>20-24</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e198b</td>\n",
       "      <td>20-24</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e028b</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e028a</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e061b</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>e061a</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>e265b</td>\n",
       "      <td>35-39</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>e033b</td>\n",
       "      <td>35-39</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>e265a</td>\n",
       "      <td>35-39</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>e033a</td>\n",
       "      <td>35-39</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject    age   sex\n",
       "0   e198a  20-24  Male\n",
       "1   e198b  20-24  Male\n",
       "2   e028b  30-34  Male\n",
       "3   e028a  30-34  Male\n",
       "4   e061b  30-34  Male\n",
       "5   e061a  30-34  Male\n",
       "6   e265b  35-39  Male\n",
       "7   e033b  35-39  Male\n",
       "8   e265a  35-39  Male\n",
       "9   e033a  35-39  Male"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      1266\n",
       "Female     277\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60-64    313\n",
       "65-69    291\n",
       "55-59    261\n",
       "70-74    196\n",
       "50-54    164\n",
       "45-49    124\n",
       "75-79     98\n",
       "40-44     62\n",
       "35-39     28\n",
       "30-34      4\n",
       "20-24      2\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60-64    67\n",
       "65-69    61\n",
       "55-59    46\n",
       "70-74    39\n",
       "75-79    19\n",
       "50-54    18\n",
       "45-49    15\n",
       "40-44     8\n",
       "35-39     4\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata[metadata['sex']=='Female'].age.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute Resting Heart Rate\n",
    "For each subject we want to compute the resting heart rate while keeping track of which age group this subject belongs to. An easy, robust way to compute the resting heart rate is to use the lowest 5th percentile value in the heart rate timeseries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "hr_file1 = np.load(hr_filenames[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hr']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_file1.files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "hr_data = hr_file1['hr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86143,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.08474576271186"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(hr_data, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.08474576271186"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(hr_data[hr_data == np.percentile(hr_data, 5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'e001a'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name1 = os.path.split(hr_filenames[0])[1][0:5]\n",
    "name1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'60-64'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata[metadata['subject']==name1]['age'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def AgeAndRHR(metadata, filename):\n",
    "\n",
    "    # Load the heart rate timeseries\n",
    "    hr_data = np.load(filename)['hr']\n",
    "    \n",
    "    # Compute the resting heart rate from the timeseries by finding the lowest 5th percentile value in hr_data\n",
    "    rhr = np.percentile(hr_data, 5)\n",
    "\n",
    "    # Find the subject ID from the filename.\n",
    "    subject = os.path.split(filename)[1][0:5]\n",
    "\n",
    "    # Find the age group for this subject in metadata.\n",
    "    age_group = metadata[metadata['subject']==subject]['age'].values[0]\n",
    "    \n",
    "    # Find the sex for this subject in metadata.\n",
    "    sex = metadata[metadata['subject']==subject]['sex'].values[0]\n",
    "\n",
    "    return age_group, sex, rhr\n",
    "\n",
    "df = pd.DataFrame(data=[AgeAndRHR(metadata, filename) for filename in hr_filenames],\n",
    "                  columns=['age_group', 'sex', 'rhr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_group</th>\n",
       "      <th>sex</th>\n",
       "      <th>rhr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60-64</td>\n",
       "      <td>Male</td>\n",
       "      <td>65.084746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60-64</td>\n",
       "      <td>Male</td>\n",
       "      <td>69.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65-69</td>\n",
       "      <td>Male</td>\n",
       "      <td>77.591753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65-69</td>\n",
       "      <td>Male</td>\n",
       "      <td>72.024615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55-59</td>\n",
       "      <td>Male</td>\n",
       "      <td>54.111052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55-59</td>\n",
       "      <td>Male</td>\n",
       "      <td>57.047334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>45-49</td>\n",
       "      <td>Male</td>\n",
       "      <td>81.702128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>45-49</td>\n",
       "      <td>Male</td>\n",
       "      <td>69.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70-74</td>\n",
       "      <td>Male</td>\n",
       "      <td>47.116564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70-74</td>\n",
       "      <td>Male</td>\n",
       "      <td>40.580956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50-54</td>\n",
       "      <td>Male</td>\n",
       "      <td>70.458716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50-54</td>\n",
       "      <td>Male</td>\n",
       "      <td>65.831428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>70-74</td>\n",
       "      <td>Male</td>\n",
       "      <td>76.013821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>70-74</td>\n",
       "      <td>Male</td>\n",
       "      <td>89.302326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>70-74</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.935484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>70-74</td>\n",
       "      <td>Female</td>\n",
       "      <td>57.101161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>70-74</td>\n",
       "      <td>Female</td>\n",
       "      <td>64.444593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>70-74</td>\n",
       "      <td>Female</td>\n",
       "      <td>65.084746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>45-49</td>\n",
       "      <td>Male</td>\n",
       "      <td>47.988994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>45-49</td>\n",
       "      <td>Male</td>\n",
       "      <td>54.276296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_group     sex        rhr\n",
       "0      60-64    Male  65.084746\n",
       "1      60-64    Male  69.818182\n",
       "2      65-69    Male  77.591753\n",
       "3      65-69    Male  72.024615\n",
       "4      55-59    Male  54.111052\n",
       "5      55-59    Male  57.047334\n",
       "6      45-49    Male  81.702128\n",
       "7      45-49    Male  69.818182\n",
       "8      70-74    Male  47.116564\n",
       "9      70-74    Male  40.580956\n",
       "10     50-54    Male  70.458716\n",
       "11     50-54    Male  65.831428\n",
       "12     70-74    Male  76.013821\n",
       "13     70-74    Male  89.302326\n",
       "14     70-74  Female  61.935484\n",
       "15     70-74  Female  57.101161\n",
       "16     70-74  Female  64.444593\n",
       "17     70-74  Female  65.084746\n",
       "18     45-49    Male  47.988994\n",
       "19     45-49    Male  54.276296"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Resting Heart Rate vs. Age Group\n",
    "We'll use [seaborn](https://seaborn.pydata.org/) to plot the relationship. Seaborn is a thin wrapper around matplotlib, which we've used extensively in this class, that enables higher-level statistical plots.\n",
    "\n",
    "We will use [lineplot](https://seaborn.pydata.org/generated/seaborn.lineplot.html#seaborn.lineplot) to plot the mean of the resting heart rates for each age group along with the 95% confidence interval around the mean. Learn more about making plots that show uncertainty [here](https://seaborn.pydata.org/tutorial/relational.html#aggregation-and-representing-uncertainty)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "labels = sorted(np.unique(df.age_group))\n",
    "df['xaxis'] = df.age_group.map(lambda x: labels.index(x)).astype('float')\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.lineplot(x='xaxis', y='rhr', hue='sex', data=df, palette={'Male':'orange', 'Female':'purple'})\n",
    "plt.title('Resting Heart Rates by Age Groups')\n",
    "plt.xlabel('age group')\n",
    "plt.ylabel('resting heart rate')\n",
    "_ = plt.xticks(np.arange(len(labels)), labels)\n",
    "plt.savefig('out/Resting_HR_by_Age_Groups')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clinical Conclusion\n",
    "Answer the following prompts to draw a conclusion about the data.\n",
    "> 1. For women, we see .... \n",
    "> 2. For men, we see ... \n",
    "> 3. In comparison to men, women's heart rate is .... \n",
    "> 4. What are some possible reasons for what we see in our data?\n",
    "> 5. What else can we do or go and find to figure out what is really happening? How would that improve the results?\n",
    "> 6. Did we validate the trend that average resting heart rate increases up until middle age and then decreases into old age? How?\n",
    "\n",
    "Your write-up will go here..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.  For women, we see from 95% Confidence Intervals that the BPM range is 55 - 85 BPM.  We see that the heart rate changes with age, with heart rates on-average at the highest peak in the age range of 40-44 years old.\n",
    "\n",
    "2.  For men, we see from 95% Confidence Intervals that the BPM range is 60 - 70 BPM.  We also see that the heart rate changes with age, with heart rates on-average at the highest peak in the age range of 45-49 years old.\n",
    "\n",
    "3.  We see the following differences between men's and women's heart rates:  \n",
    "-In comparison to men, women's heart rates show a wider range within each age group.  For the 35-39, 40-44, and 45-49 age groups, the range of women's heart rates in the 95% CI is nearly 20BPM.  On the other hand, the range of men's heart rates in the 95%CI is less than 10BPM.  \n",
    "-The mean of women's heart rates show more variability across age groups than men.  The range of the mean of women's heart rates is 60 BPM - 70 BPM.  For men, the rate of their mean heart rates is 62.5 BPM - 65 BPM.  \n",
    "\n",
    "4.  The following are possible reasons for the variability in women's heart rates data:  \n",
    "-The amount of data for men and women are not balanced in our dataset.  There are 1,266 Males and 277 Female in the dataset.  Obtaining more data from Females will reduce variability and standard deviation, thus the 95% CI as well.  \n",
    "-The amount of data in each age group also affects the variability.  For women, the age group 35-39 only has 4 data points, the 40-44 age group only has 8 data points.  These groups have ranges of approximately 20 BPM in the 95% CI.  In comparison, for women in the age group 60-64 there are 67 data points.  This group has a smaller range of approximately 5 BPM in the 95% CI.  \n",
    "\n",
    "5.   The data in the project comes from CAST.  The data is 24 hours of ECG heart rate data from people who have had a myocardial infarction (MI) within the past two years.  \n",
    "To prove the generalizability of these results, we need data from the general population that has not had a myocardial infarction.  \n",
    "We can look for more heart rate data from other studies and papers.  \n",
    "We may also conduct our own trials to collect heart rate information for men and women.\n",
    "\n",
    "6.  Using the CAST data, we see the overall trend that human resting heart rates decrease from middle age until old age.  We did not have data to show that heart rates increase from young ages to middle age.\n"
   ]
  }
 ],
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