155 lines
4.2 KiB
Plaintext
155 lines
4.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Data Quality\n",
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"\n",
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"This notebook is used to ensure data quality for further evaluations. For this reason, it is examined how much of the data is incomplete. It is important that this only affects a small part of the data in order to avoid any distortion of the data in further analyses."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pickle"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"path = \"C:/Studium/dsa/data\"\n",
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"#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Reading SB\n",
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"Length of SB: 50\n",
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"Reading AFIB\n",
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"Length of AFIB: 27\n",
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"Reading GSVT\n",
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"Length of GSVT: 0\n",
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"Reading SR\n",
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"Length of SR: 13\n"
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]
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}
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],
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"source": [
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"import pickle\n",
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"from matplotlib import pyplot as plt\n",
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"import wfdb\n",
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"# read pickle files and check len and print first record and first record keys\n",
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"\n",
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"#path = \"C:/Studium/dsa/data\"\n",
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"#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\n",
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"path = \"C:/Users/klara/projects/DSA/data\"\n",
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"\n",
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"\n",
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"categories_dict = {\n",
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"'SB': [426177001],\n",
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"'AFIB': [164889003, 164890007],\n",
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"'GSVT': [426761007, 713422000, 233896004, 233897008, 713422000],\n",
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"'SR': [426783006, 427393009]\n",
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"}\n",
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"\n",
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"\n",
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"data = {}\n",
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"for cat_name in categories_dict.keys():\n",
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" print(f\"Reading {cat_name}\")\n",
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" with open(f'{path}/{cat_name}.pkl', 'rb') as f:\n",
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" records = pickle.load(f)\n",
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" data[cat_name] = records\n",
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" print(f\"Length of {cat_name}: {len(records)}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Check for missing data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"First record in SB: ['Age: 59', 'Sex: Female', 'Dx: 426177001,164934002', 'Rx: Unknown', 'Hx: Unknown', 'Sx: Unknown']\n",
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"Missing timeseries in 0 records\n",
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"Missing age in 55 records\n",
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"Missing sex in 21 records\n"
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]
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}
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],
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"source": [
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"# print first record and first record keys\n",
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"print(f\"First record in SB: {data['SB'][0].comments}\")\n",
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"\n",
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"missing_timeseries = []\n",
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"missing_age = []\n",
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"missing_sex = []\n",
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"for cat_name, records in data.items():\n",
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" for record in records:\n",
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" if len(record.p_signal) != 5000:\n",
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" missing_timeseries.append(record)\n",
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" print(f\"Missing timeseries in {record.record_name}\")\n",
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" #if record.comments[2]== '':\n",
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" if 'Age: ' not in record.comments[0] or record.comments[0] == 'Age: NaN':\n",
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" missing_age.append(record)\n",
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" if record.comments[1] == 'Sex: Unknown' or record.comments[1] == '':\n",
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" missing_sex.append(record)\n",
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" \n",
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"print(f\"Missing timeseries in {len(missing_timeseries)} records\")\n",
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"print(f\"Missing age in {len(missing_age)} records\")\n",
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"print(f\"Missing sex in {len(missing_sex)} records\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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