DSA_SS24/notebooks/data_quality.ipynb

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      "Reading SB\n",
      "Length of SB: 50\n",
      "Reading AFIB\n",
      "Length of AFIB: 27\n",
      "Reading GSVT\n",
      "Length of GSVT: 0\n",
      "Reading SR\n",
      "Length of SR: 13\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "from matplotlib import pyplot as plt\n",
    "import wfdb\n",
    "# read pickle files and check len and print first record and first record keys\n",
    "\n",
    "#path = \"C:/Studium/dsa/data\"\n",
    "#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\n",
    "path = \"C:/Users/klara/projects/DSA/data\"\n",
    "\n",
    "\n",
    "categories_dict = {\n",
    "'SB':    [426177001],\n",
    "'AFIB':  [164889003, 164890007],\n",
    "'GSVT':  [426761007, 713422000, 233896004, 233897008, 713422000],\n",
    "'SR':    [426783006, 427393009]\n",
    "}\n",
    "\n",
    "\n",
    "data = {}\n",
    "for cat_name in categories_dict.keys():\n",
    "    print(f\"Reading {cat_name}\")\n",
    "    with open(f'{path}/{cat_name}.pkl', 'rb') as f:\n",
    "        records = pickle.load(f)\n",
    "        data[cat_name] = records\n",
    "        print(f\"Length of {cat_name}: {len(records)}\")"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for missing data in timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing timeseries in 0 records\n"
     ]
    }
   ],
   "source": [
    "missing_timeseries = []\n",
    "for cat_name, records in data.items():\n",
    "    for record in records:\n",
    "        if len(record.p_signal) != 5000:\n",
    "            missing_timeseries.append(record)\n",
    "            print(f\"Missing timeseries in {record.record_name}\")\n",
    "        #if record.comments[2]== '':\n",
    "print(f\"Missing timeseries in {len(missing_timeseries)} records\")"
   ]
  }
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