reproducing_fitzpatrick_2001.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import wfdb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.signal import butter, lfilter, freqz, hilbert\n",
    "import peakutils\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Reproducing-Fitzpatrick-et-al-2001\" data-toc-modified-id=\"Reproducing-Fitzpatrick-et-al-2001-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Reproducing Fitzpatrick et al 2001</a></span><ul class=\"toc-item\"><li><span><a href=\"#Loading-the-data\" data-toc-modified-id=\"Loading-the-data-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Loading the data</a></span></li></ul></li><li><span><a href=\"#Processing\" data-toc-modified-id=\"Processing-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Processing</a></span><ul class=\"toc-item\"><li><span><a href=\"#Doing-the-subsetting-of-the-data\" data-toc-modified-id=\"Doing-the-subsetting-of-the-data-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Doing the subsetting of the data</a></span></li><li><span><a href=\"#Visualizing-the-processing-steps\" data-toc-modified-id=\"Visualizing-the-processing-steps-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Visualizing the processing steps</a></span></li></ul></li><li><span><a href=\"#Evaluating\" data-toc-modified-id=\"Evaluating-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Evaluating</a></span><ul class=\"toc-item\"><li><span><a href=\"#Testing-on-the-same-records-as-the-paper\" data-toc-modified-id=\"Testing-on-the-same-records-as-the-paper-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Testing on the same records as the paper</a></span></li><li><span><a href=\"#Testing-on-the-entire-MIT-BIH-database\" data-toc-modified-id=\"Testing-on-the-entire-MIT-BIH-database-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Testing on the entire MIT-BIH database</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproducing Fitzpatrick et al 2001\n",
    "The aim of this notebook is to reproduce the QRS detection algorithm described in _\"The use of the Hilbert transform in ECG signal analysis\"_ by D. Benitez, P. A. Gaydeckia, A. Zaidi, A.P. Fitzpatrick."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-0.145, -0.065],\n",
       "        [-0.145, -0.065],\n",
       "        [-0.145, -0.065],\n",
       "        ...,\n",
       "        [-0.475, -0.45 ],\n",
       "        [-0.48 , -0.465],\n",
       "        [-0.485, -0.475]]),\n",
       " {'fs': 360,\n",
       "  'sig_len': 8050,\n",
       "  'n_sig': 2,\n",
       "  'base_date': None,\n",
       "  'base_time': None,\n",
       "  'units': ['mV', 'mV'],\n",
       "  'sig_name': ['MLII', 'V5'],\n",
       "  'comments': ['69 M 1085 1629 x1', 'Aldomet, Inderal']})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record = wfdb.rdsamp('mitdb/100', sampto=8050)\n",
    "annotation = wfdb.rdann('mitdb/100', 'atr', sampto=8050)\n",
    "record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lead_I = record[0][:, 0]\n",
    "fs = record[1]['fs']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_ecg(data, threshold=0.15):\n",
    "    transformed = np.abs(hilbert(np.gradient(data)))\n",
    "    return peakutils.indexes(transformed, thres=0.1,  min_dist=100)\n",
    "\n",
    "def calculate_metrics(pred, test):\n",
    "    # Counting the number of hits and misses. \n",
    "    # This needs to be developed more rigorous development\n",
    "    fp, fn = 0, 0\n",
    "    for p in pred:\n",
    "        if np.sum(np.abs(p-test)<150)==0:\n",
    "            fp += 1\n",
    "    \n",
    "    for t in test:\n",
    "        if np.sum(np.abs(t-pred)<150)==0:\n",
    "            fn += 1\n",
    "    return fp, fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_into_subsets(data):\n",
    "    subset_len = 1024\n",
    "    return splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_threshols_for_subset(subset):\n",
    "    h = np.abs(hilbert(np.gradient(subset)))\n",
    "    h = h/np.max(h)\n",
    "    max_amp = np.max(h)\n",
    "    rms = np.sqrt(np.mean(np.square(h)))\n",
    "    if rms>=0.18*max_amp:\n",
    "        threshold = 0.39*max_amp\n",
    "    else:\n",
    "        threshold = 1.6*rms\n",
    "    \n",
    "    return threshold, process_ecg(h, threshold=threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Doing the subsetting of the data\n",
    "In the paper they state that they subset the dataset into groups of 1024 points, because this should improve the performance of the Hilbert Transform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2502921983859773 [ 80 374 484 666 951]\n"
     ]
    }
   ],
   "source": [
    "subset = lead_I[:1025]\n",
    "threshold, indxes = set_threshols_for_subset(subset)\n",
    "print(threshold, indxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 80, 374, 484, 666, 951])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1091dd710>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = np.abs(hilbert(np.gradient(subset)))\n",
    "h = h/np.max(h)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2)\n",
    "ax[0].plot(h)\n",
    "ax[0].plot([0, 1025], [threshold, threshold], '--r')\n",
    "ax[1].plot(np.arange(0, 1025), subset)\n",
    "\n",
    "for pos, sym in zip(annotation.sample, annotation.symbol):\n",
    "    if pos>len(h):\n",
    "        break\n",
    "    else:\n",
    "        ax[1].plot(pos, lead_I[pos], 'go', markersize=4, markerfacecolor='white')\n",
    "        ax[1].text(pos+10, lead_I[pos], sym, color='orange')\n",
    "        \n",
    "ax[1].plot(indxes, subset[indxes], 'or')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the processing steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hartmann/Documents/projects/ecg-analysis-mit-bih/env/lib/python3.6/site-packages/numpy/core/numeric.py:492: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = np.arange(0, len(lead_I))/fs\n",
    "\n",
    "# The bandpass is different from the one used in the paper, and  \n",
    "# generally does not produce the wished results.\n",
    "bandpass =  lead_I #butter_bandpass_filter(lead_I, 6, 14, 360, order=6)\n",
    "\n",
    "differentiator = np.gradient(bandpass)\n",
    "hil = hilbert(differentiator)\n",
    "abs_hil = np.abs(hil)\n",
    "\n",
    "# Currently I am using the peakutils here, this should be replaced\n",
    "pulse = peakutils.indexes(abs_hil, thres=0.1,  min_dist=100)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=7, figsize=(12,10))\n",
    "ax[0].plot(time, lead_I)\n",
    "ax[1].plot(time, bandpass, 'r')\n",
    "ax[2].plot(time, differentiator, 'green')\n",
    "ax[3].plot(time, hil, 'orange')\n",
    "ax[4].plot(time, abs_hil, 'cyan');\n",
    "ax[4].plot(time[pulse], abs_hil[pulse], 'or')\n",
    "ax[5].plot(time, lead_I)\n",
    "ax[6].plot(time[pulse], np.arange(len(pulse))*0+1, 'ob')\n",
    "\n",
    "ax[0].set_ylabel('Original signal')\n",
    "ax[1].set_ylabel('Bandpass')\n",
    "ax[2].set_ylabel('Differntiator')\n",
    "ax[3].set_ylabel('Hilbert')\n",
    "ax[4].set_ylabel('Abs Hilbert')\n",
    "ax[5].set_ylabel('Shifted original')\n",
    "ax[6].set_ylabel('Pulse')\n",
    "\n",
    "for x in ax:\n",
    "    x.set_xlim([0, time[-1]])\n",
    "\n",
    "for pos, sym in zip(annotation.sample, annotation.symbol):\n",
    "    ax[0].plot(time[pos], lead_I[pos], 'ro', markersize=4)\n",
    "    ax[0].text(time[pos], lead_I[pos], sym)\n",
    "\n",
    "    \n",
    "for pos, sym in zip(annotation.sample, annotation.symbol):\n",
    "    ax[5].plot(time[pos], lead_I[pos], 'ro', markersize=4)\n",
    "    ax[5].text(time[pos]+0.2, lead_I[pos], sym)\n",
    "    \n",
    "ax[0].set_title('Analyzing Lead I ({0:} beats)'.format(len(annotation.sample)))\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating\n",
    "In the paper they do not evaluate the performance on the entire MIT-BIH database, so I will do the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing on the same records as the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "recnums = ['100', '101', '102', '103', '104', '105', '106', '107',\n",
    "           '109', '111', '112', '113', '114', '115', '116', '117', '118',\n",
    "           '119', '121', '122', '123', '124']\n",
    "data = np.zeros( (len(recnums), 6) )\n",
    "for idx, recno in enumerate(recnums):\n",
    "    entire_sample = wfdb.rdsamp('mitdb/'+str(recno)) \n",
    "    entire_sample_anon = wfdb.rdann('mitdb/'+str(recno), 'atr')\n",
    "    \n",
    "    total_beats = np.sum([(sym!='+')&(sym!='~')&(sym!='\"') for sym in entire_sample_anon.symbol])\n",
    "    test = entire_sample_anon.sample\n",
    "    \n",
    "    dat = entire_sample[0][:, 0]\n",
    "    pred = process_ecg(dat, threshold=0.01)\n",
    "    \n",
    "    fp, fn = calculate_metrics(pred, test)\n",
    "    \n",
    "    # Adding the results to the data matrix\n",
    "    data[idx, :] = np.array([recno, total_beats, fp, fn, fp+fn, 100*(fp+fn)/total_beats])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>tape</th>\n",
       "      <th>total_beats</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>failed_detection_beats</th>\n",
       "      <th>failed_detection_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2273.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101.0</td>\n",
       "      <td>1869.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.374532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.0</td>\n",
       "      <td>2187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.045725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103.0</td>\n",
       "      <td>2084.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.143954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104.0</td>\n",
       "      <td>2229.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.269179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>105.0</td>\n",
       "      <td>2602.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.230592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>106.0</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.960039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>107.0</td>\n",
       "      <td>2137.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.795508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>109.0</td>\n",
       "      <td>2532.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>111.0</td>\n",
       "      <td>2124.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.188324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>112.0</td>\n",
       "      <td>2539.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>113.0</td>\n",
       "      <td>1795.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>114.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.117021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>115.0</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.051046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>116.0</td>\n",
       "      <td>2412.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.704809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>117.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.130293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>118.0</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.393357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>119.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.214394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>121.0</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.322061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>122.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>123.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.131752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>124.0</td>\n",
       "      <td>1619.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.555899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tape  total_beats    FP    FN  failed_detection_beats  \\\n",
       "0   100.0       2273.0   0.0   0.0                     0.0   \n",
       "1   101.0       1869.0   7.0   0.0                     7.0   \n",
       "2   102.0       2187.0   0.0   1.0                     1.0   \n",
       "3   103.0       2084.0   0.0   3.0                     3.0   \n",
       "4   104.0       2229.0   3.0   3.0                     6.0   \n",
       "5   105.0       2602.0   6.0   0.0                     6.0   \n",
       "6   106.0       2027.0  30.0  30.0                    60.0   \n",
       "7   107.0       2137.0  17.0   0.0                    17.0   \n",
       "8   109.0       2532.0   0.0   0.0                     0.0   \n",
       "9   111.0       2124.0   2.0   2.0                     4.0   \n",
       "10  112.0       2539.0   0.0   0.0                     0.0   \n",
       "11  113.0       1795.0   0.0   0.0                     0.0   \n",
       "12  114.0       1880.0  18.0   3.0                    21.0   \n",
       "13  115.0       1959.0   0.0   1.0                     1.0   \n",
       "14  116.0       2412.0   1.0  16.0                    17.0   \n",
       "15  117.0       1535.0   2.0   0.0                     2.0   \n",
       "16  118.0       2288.0   9.0   0.0                     9.0   \n",
       "17  119.0       1987.0   1.0  43.0                    44.0   \n",
       "18  121.0       1863.0   5.0   1.0                     6.0   \n",
       "19  122.0       2478.0   0.0   0.0                     0.0   \n",
       "20  123.0       1518.0   2.0   0.0                     2.0   \n",
       "21  124.0       1619.0   7.0   2.0                     9.0   \n",
       "\n",
       "    failed_detection_percent  \n",
       "0                   0.000000  \n",
       "1                   0.374532  \n",
       "2                   0.045725  \n",
       "3                   0.143954  \n",
       "4                   0.269179  \n",
       "5                   0.230592  \n",
       "6                   2.960039  \n",
       "7                   0.795508  \n",
       "8                   0.000000  \n",
       "9                   0.188324  \n",
       "10                  0.000000  \n",
       "11                  0.000000  \n",
       "12                  1.117021  \n",
       "13                  0.051046  \n",
       "14                  0.704809  \n",
       "15                  0.130293  \n",
       "16                  0.393357  \n",
       "17                  2.214394  \n",
       "18                  0.322061  \n",
       "19                  0.000000  \n",
       "20                  0.131752  \n",
       "21                  0.555899  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(\n",
    "    data=data,\n",
    "    columns=['tape', 'total_beats', 'FP', 'FN', 'failed_detection_beats', 'failed_detection_percent']\n",
    ")\n",
    "results.head(48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beats: 45937, FP: 110, FN: 105, Failed detections: 215 (0.48‰)\n"
     ]
    }
   ],
   "source": [
    "print('Beats: {0:.0f}, FP: {1:.0f}, FN: {2:.0f}, Failed detections: {3:.0f} ({4:.2f}‰)'.format(\n",
    "            results.total_beats.sum(),\n",
    "            results.FP.sum(),\n",
    "            results.FN.sum(),\n",
    "            results.failed_detection_beats.sum(),\n",
    "            results.failed_detection_percent.mean()\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall detection error percent: 0.47%\n"
     ]
    }
   ],
   "source": [
    "print('Overall detection error percent: {0:.2f}%'.format(results.failed_detection_beats.sum()/results.total_beats.sum()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing on the entire MIT-BIH database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_record_list():\n",
    "    filenames = []\n",
    "    all_files = [files for (path, dirs, files) in os.walk('mitdb')][0]\n",
    "    for file in all_files:\n",
    "        if file.endswith(\".dat\"):\n",
    "            filenames.append(file[:3])\n",
    "    return filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "recnums = get_record_list()\n",
    "data = np.zeros( (len(recnums), 6) )\n",
    "for idx, recno in enumerate(recnums):\n",
    "    entire_sample = wfdb.rdsamp('mitdb/'+str(recno)) \n",
    "    entire_sample_anon = wfdb.rdann('mitdb/'+str(recno), 'atr')\n",
    "    \n",
    "    total_beats = np.sum([(sym!='+')&(sym!='~')&(sym!='\"') for sym in entire_sample_anon.symbol])\n",
    "    test = entire_sample_anon.sample\n",
    "    \n",
    "    dat = entire_sample[0][:, 0]\n",
    "    pred = process_ecg(dat, threshold=0.01)\n",
    "    \n",
    "    fp, fn = calculate_metrics(pred, test)\n",
    "    \n",
    "    # Adding the results to the data matrix\n",
    "    data[idx, :] = np.array([recno, total_beats, fp, fn, fp+fn, 100*(fp+fn)/total_beats])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>tape</th>\n",
       "      <th>total_beats</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>failed_detection_beats</th>\n",
       "      <th>failed_detection_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>213.0</td>\n",
       "      <td>3251.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>207.0</td>\n",
       "      <td>2346.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>6.436488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>212.0</td>\n",
       "      <td>2749.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>210.0</td>\n",
       "      <td>2651.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.075443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>205.0</td>\n",
       "      <td>2657.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.037636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>215.0</td>\n",
       "      <td>3363.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>214.0</td>\n",
       "      <td>2267.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.749890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>200.0</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.960784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>228.0</td>\n",
       "      <td>2077.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>4.429466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>202.0</td>\n",
       "      <td>2138.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.608045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>203.0</td>\n",
       "      <td>3006.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.565536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>217.0</td>\n",
       "      <td>2209.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.814848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>106.0</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.960039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>112.0</td>\n",
       "      <td>2539.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>113.0</td>\n",
       "      <td>1795.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>107.0</td>\n",
       "      <td>2137.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.795508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>111.0</td>\n",
       "      <td>2124.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.188324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>105.0</td>\n",
       "      <td>2602.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.230592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>104.0</td>\n",
       "      <td>2229.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.269179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>114.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.117021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2273.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>101.0</td>\n",
       "      <td>1869.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.374532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>115.0</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.051046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>103.0</td>\n",
       "      <td>2084.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.143954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>117.0</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.130293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>116.0</td>\n",
       "      <td>2412.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.704809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>102.0</td>\n",
       "      <td>2187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.045725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>124.0</td>\n",
       "      <td>1619.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.555899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>118.0</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.393357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>119.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.214394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>109.0</td>\n",
       "      <td>2532.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>121.0</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.322061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>108.0</td>\n",
       "      <td>1782.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>16.105499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>122.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>123.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.131752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>232.0</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.595506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>233.0</td>\n",
       "      <td>3081.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>231.0</td>\n",
       "      <td>1573.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>27.654164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>219.0</td>\n",
       "      <td>2287.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.393529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>230.0</td>\n",
       "      <td>2257.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.019052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>208.0</td>\n",
       "      <td>2963.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.438745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>220.0</td>\n",
       "      <td>2048.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>234.0</td>\n",
       "      <td>2753.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.036324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>221.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.412031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>209.0</td>\n",
       "      <td>3012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>223.0</td>\n",
       "      <td>2605.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.191939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>222.0</td>\n",
       "      <td>2483.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.248490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tape  total_beats     FP     FN  failed_detection_beats  \\\n",
       "0   213.0       3251.0    0.0    0.0                     0.0   \n",
       "1   207.0       2346.0    5.0  146.0                   151.0   \n",
       "2   212.0       2749.0    0.0    0.0                     0.0   \n",
       "3   210.0       2651.0    0.0    2.0                     2.0   \n",
       "4   205.0       2657.0    0.0    1.0                     1.0   \n",
       "5   201.0       2000.0    1.0   20.0                    21.0   \n",
       "6   215.0       3363.0    0.0    0.0                     0.0   \n",
       "7   214.0       2267.0    5.0   12.0                    17.0   \n",
       "8   200.0       2601.0   25.0   26.0                    51.0   \n",
       "9   228.0       2077.0   66.0   26.0                    92.0   \n",
       "10  202.0       2138.0   12.0    1.0                    13.0   \n",
       "11  203.0       3006.0   10.0    7.0                    17.0   \n",
       "12  217.0       2209.0    0.0   18.0                    18.0   \n",
       "13  106.0       2027.0   30.0   30.0                    60.0   \n",
       "14  112.0       2539.0    0.0    0.0                     0.0   \n",
       "15  113.0       1795.0    0.0    0.0                     0.0   \n",
       "16  107.0       2137.0   17.0    0.0                    17.0   \n",
       "17  111.0       2124.0    2.0    2.0                     4.0   \n",
       "18  105.0       2602.0    6.0    0.0                     6.0   \n",
       "19  104.0       2229.0    3.0    3.0                     6.0   \n",
       "20  114.0       1880.0   18.0    3.0                    21.0   \n",
       "21  100.0       2273.0    0.0    0.0                     0.0   \n",
       "22  101.0       1869.0    7.0    0.0                     7.0   \n",
       "23  115.0       1959.0    0.0    1.0                     1.0   \n",
       "24  103.0       2084.0    0.0    3.0                     3.0   \n",
       "25  117.0       1535.0    2.0    0.0                     2.0   \n",
       "26  116.0       2412.0    1.0   16.0                    17.0   \n",
       "27  102.0       2187.0    0.0    1.0                     1.0   \n",
       "28  124.0       1619.0    7.0    2.0                     9.0   \n",
       "29  118.0       2288.0    9.0    0.0                     9.0   \n",
       "30  119.0       1987.0    1.0   43.0                    44.0   \n",
       "31  109.0       2532.0    0.0    0.0                     0.0   \n",
       "32  121.0       1863.0    5.0    1.0                     6.0   \n",
       "33  108.0       1782.0  285.0    2.0                   287.0   \n",
       "34  122.0       2478.0    0.0    0.0                     0.0   \n",
       "35  123.0       1518.0    2.0    0.0                     2.0   \n",
       "36  232.0       1780.0   59.0    5.0                    64.0   \n",
       "37  233.0       3081.0    0.0    0.0                     0.0   \n",
       "38  231.0       1573.0    0.0  435.0                   435.0   \n",
       "39  219.0       2287.0    1.0    8.0                     9.0   \n",
       "40  230.0       2257.0    1.0   22.0                    23.0   \n",
       "41  208.0       2963.0    0.0   13.0                    13.0   \n",
       "42  220.0       2048.0    0.0    0.0                     0.0   \n",
       "43  234.0       2753.0    1.0    0.0                     1.0   \n",
       "44  221.0       2427.0    7.0    3.0                    10.0   \n",
       "45  209.0       3012.0    0.0    0.0                     0.0   \n",
       "46  223.0       2605.0    0.0    5.0                     5.0   \n",
       "47  222.0       2483.0   12.0   19.0                    31.0   \n",
       "\n",
       "    failed_detection_percent  \n",
       "0                   0.000000  \n",
       "1                   6.436488  \n",
       "2                   0.000000  \n",
       "3                   0.075443  \n",
       "4                   0.037636  \n",
       "5                   1.050000  \n",
       "6                   0.000000  \n",
       "7                   0.749890  \n",
       "8                   1.960784  \n",
       "9                   4.429466  \n",
       "10                  0.608045  \n",
       "11                  0.565536  \n",
       "12                  0.814848  \n",
       "13                  2.960039  \n",
       "14                  0.000000  \n",
       "15                  0.000000  \n",
       "16                  0.795508  \n",
       "17                  0.188324  \n",
       "18                  0.230592  \n",
       "19                  0.269179  \n",
       "20                  1.117021  \n",
       "21                  0.000000  \n",
       "22                  0.374532  \n",
       "23                  0.051046  \n",
       "24                  0.143954  \n",
       "25                  0.130293  \n",
       "26                  0.704809  \n",
       "27                  0.045725  \n",
       "28                  0.555899  \n",
       "29                  0.393357  \n",
       "30                  2.214394  \n",
       "31                  0.000000  \n",
       "32                  0.322061  \n",
       "33                 16.105499  \n",
       "34                  0.000000  \n",
       "35                  0.131752  \n",
       "36                  3.595506  \n",
       "37                  0.000000  \n",
       "38                 27.654164  \n",
       "39                  0.393529  \n",
       "40                  1.019052  \n",
       "41                  0.438745  \n",
       "42                  0.000000  \n",
       "43                  0.036324  \n",
       "44                  0.412031  \n",
       "45                  0.000000  \n",
       "46                  0.191939  \n",
       "47                  1.248490  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(\n",
    "    data=data,\n",
    "    columns=['tape', 'total_beats', 'FP', 'FN', 'failed_detection_beats', 'failed_detection_percent']\n",
    ")\n",
    "results.head(48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beats: 110303, FP: 600, FN: 876, Failed detections: 1476 (1.63‰)\n"
     ]
    }
   ],
   "source": [
    "print('Beats: {0:.0f}, FP: {1:.0f}, FN: {2:.0f}, Failed detections: {3:.0f} ({4:.2f}‰)'.format(\n",
    "            results.total_beats.sum(),\n",
    "            results.FP.sum(),\n",
    "            results.FN.sum(),\n",
    "            results.failed_detection_beats.sum(),\n",
    "            results.failed_detection_percent.mean()\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall detection error percent: 1.34%\n"
     ]
    }
   ],
   "source": [
    "print('Overall detection error percent: {0:.2f}%'.format(results.failed_detection_beats.sum()/results.total_beats.sum()*100))"
   ]
  }
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