DSA_SS24/notebooks/noise_reduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Noise Reduction\n",
    "\n",
    "This Notebook is used to reduce the noise in the ECG signals.\n",
    "First a Butterworth filter is applied to the signals to remove the high frequency noise.\n",
    "Than a loess filter is applied to the signals to remove the low frequency noise.\n",
    "Last Non local means filter is applied to the signals to remove the remaining noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import wfdb.processing\n",
    "import sys\n",
    "import json\n",
    "\n",
    "sys.path.append('../scripts')\n",
    "import data_helper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a better understanding of the structure and dimensions of the individual data sets, there are printed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading GSVT\n",
      "Reading AFIB\n",
      "Reading SR\n",
      "Reading SB\n",
      "Number of patients per category:\n",
      "GSVT: 1027\n",
      "AFIB: 9787\n",
      "SR: 10426\n",
      "SB: 15826\n"
     ]
    }
   ],
   "source": [
    "data = data_helper.load_data(only_demographic=False)\n",
    "\n",
    "print(\"Number of patients per category:\")\n",
    "for cat_name in data.keys():\n",
    "    print(f\"{cat_name}: {len(data[cat_name])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_data = data['SB'][0]\n",
    "#Plot specific Channel of Record\n",
    "fig = wfdb.plot_items(example_data.p_signal[:,0],title=\"Example for Channel 0\",figsize=(15,4),return_fig=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_params = {\n",
    "    'names': ['butterlowpass', 'lowess', 'non_local_means'],\n",
    "    # params for butterworth filter\n",
    "    'order':1,\n",
    "    'fs':500.0,\n",
    "    'cutoff':25,\n",
    "    # params for lowess filter\n",
    "    'filter_strength':50,\n",
    "    'template_window_size':7,\n",
    "    'search_window_size':21,\n",
    "    # params for nlm filter\n",
    "    'frac':0.003,\n",
    "    'it':1\n",
    "}\n",
    "\n",
    "#save filter params to json file\n",
    "with open('../filter_params.json', 'w') as f:\n",
    "    json.dump(filter_params, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "butter_filtered_sig = data_helper.butterlowpass_filter(example_data, filter_params['cutoff'], filter_params['fs'], filter_params['order'])\n",
    "# Plot the original and denoised signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(example_data.p_signal[:,0], label='Original signal')\n",
    "plt.plot(butter_filtered_sig.p_signal[:,0], label='Butterlow signal')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Signal with and without Butterlow signal Denoising')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lowess_filtered_sig = data_helper.lowess_filter(example_data, filter_params['frac'], filter_params['it'])\n",
    "# Plot the original and denoised signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(example_data.p_signal[:,0], label='Original signal')\n",
    "plt.plot(lowess_filtered_sig.p_signal[:,0], label='Lowess signal')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Signal with and without Lowess Denoising')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nlm_filtered_sig = data_helper.non_local_means_filter(example_data, filter_params['filter_strength'], filter_params['template_window_size'], filter_params['search_window_size'])\n",
    "# Plot the original and denoised signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(example_data.p_signal[:,0], label='Original signal')\n",
    "plt.plot(nlm_filtered_sig.p_signal[:,0], label='NLM signal')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Signal with and without Non-Local Means Denoising')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_filtered_signals = data_helper.filter_data(example_data, filter_params)\n",
    "# Plot the original and denoised signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(example_data.p_signal[:,0], label='Original signal')\n",
    "plt.plot(all_filtered_signals.p_signal[:,0], label='filtered signal')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Signal with and without All Denoising')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wfdb.plot_wfdb(all_filtered_signals,title=\"Filtered Example for all Channels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wfdb.plot_wfdb(example_data,title=\"Raw Example for all Channels\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}