afdb_segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import functools\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lengthInSeconds = 5\n",
    "strideInSeconds = 1\n",
    "fs = 250\n",
    "length = lengthInSeconds * fs\n",
    "stride = strideInSeconds * fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "onemindf = pd.read_csv('hugedfrpeak.csv', index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "     af  0  1  2  3  4  5  6  7  8  ...  14990  14991  14992  14993  14994  \\\n",
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onemindf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strided_app(a, L, S ):  \n",
    "    nrows = ((a.size-L)//S)+1\n",
    "#     print(nrows)\n",
    "    n = a.strides[0]\n",
    "    return pd.DataFrame(np.lib.stride_tricks.as_strided(a, shape=(nrows,L), strides=(S*n,n)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "L_arr = [length]*len(onemindf)\n",
    "S_arr = [stride]*len(onemindf)\n",
    "labels = onemindf['af'].copy()\n",
    "del onemindf['af']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_ecg_list = list(map(strided_app, onemindf.to_numpy(), L_arr, S_arr))\n",
    "final_label_list = list(map(lambda x: [x]*56, labels.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.concat(final_ecg_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>787528 rows × 1250 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0     1     2     3     4     5     6     7     8     9     ...  1240  \\\n",
       "0    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "1    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "2    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "3    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "4    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "..   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "51   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
       "52   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
       "53   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
       "54   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
       "55   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   NaN   \n",
       "\n",
       "    1241  1242  1243  1244  1245  1246  1247  1248  1249  \n",
       "0    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "1    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "3    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "4    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "..   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "51   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "52   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "53   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "54   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "55   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[787528 rows x 1250 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_labels_list_flat = functools.reduce(operator.iconcat, final_label_list, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.insert(0,'af', final_labels_list_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.reset_index(inplace=True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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    "final_df[final_df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv('hugedfrpeak5sec.csv')"
   ]
  }
 ],
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