DSA_SS24/notebooks/first_signal_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Signal Processing\n",
    "\n",
    "The notebook goes into more detail on the processing of the 12 ECG data per patient. First, the signal itself is considered and then the ECG signal processing methods integrated in the 'wfdb' module are explored. Various approaches are then used to normalize the data and reduce noise in order to make the signals as comparable as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from matplotlib import pyplot as plt\n",
    "import wfdb.processing\n",
    "import scipy.signal\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"C:/Studium/dsa/data\"\n",
    "#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading SB\n",
      "Length of SB: 16559\n",
      "Reading AFIB\n",
      "Length of AFIB: 9839\n",
      "Reading GSVT\n",
      "Length of GSVT: 948\n",
      "Reading SR\n",
      "Length of SR: 9720\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# read pickle files and check len and print first record and first record keys\n",
    "\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)}\")"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys of first record of SB: dict_keys(['record_name', 'n_sig', 'fs', 'counter_freq', 'base_counter', 'sig_len', 'base_time', 'base_date', 'comments', 'sig_name', 'p_signal', 'd_signal', 'e_p_signal', 'e_d_signal', 'file_name', 'fmt', 'samps_per_frame', 'skew', 'byte_offset', 'adc_gain', 'baseline', 'units', 'adc_res', 'adc_zero', 'init_value', 'checksum', 'block_size'])\n",
      "{'record_name': 'JS00002', 'n_sig': 12, 'fs': 500, 'counter_freq': None, 'base_counter': None, 'sig_len': 5000, 'base_time': None, 'base_date': None, 'comments': ['Age: 59', 'Sex: Female', 'Dx: 426177001,164934002', 'Rx: Unknown', 'Hx: Unknown', 'Sx: Unknown'], 'sig_name': ['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6'], 'p_signal': array([[-0.01 ,  0.01 ,  0.02 , ...,  0.054,  0.049,  0.   ],\n",
      "       [-0.024, -0.02 ,  0.005, ...,  0.034,  0.034, -0.015],\n",
      "       [-0.02 , -0.02 ,  0.   , ...,  0.034,  0.034, -0.01 ],\n",
      "       ...,\n",
      "       [ 0.015,  0.01 , -0.005, ..., -0.015, -0.02 ,  0.005],\n",
      "       [ 0.01 ,  0.01 ,  0.   , ..., -0.02 , -0.024,  0.   ],\n",
      "       [ 0.01 ,  0.01 ,  0.   , ..., -0.024, -0.029, -0.005]]), 'd_signal': None, 'e_p_signal': None, 'e_d_signal': None, 'file_name': ['JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat', 'JS00002.mat'], 'fmt': ['16', '16', '16', '16', '16', '16', '16', '16', '16', '16', '16', '16'], 'samps_per_frame': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'skew': [None, None, None, None, None, None, None, None, None, None, None, None], 'byte_offset': [24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24], 'adc_gain': [1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0], 'baseline': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'units': ['mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV', 'mV'], 'adc_res': [16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16], 'adc_zero': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'init_value': [-10, 10, 20, 0, -15, 15, -10, -54, 63, 54, 49, 0], 'checksum': [12346, 26962, 14528, -13537, -6991, 14569, -383, 16468, -29512, -7548, -22267, 31542], 'block_size': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}\n",
      "(5000, 12)\n"
     ]
    }
   ],
   "source": [
    "# print keys of first record\n",
    "print(f\"Keys of first record of SB: {data['SB'][0].__dict__.keys()}\")\n",
    "print(data['SB'][0].__dict__)\n",
    "print(data['SB'][0].__dict__['p_signal'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records with p_signal: 9720\n",
      "Number of records with e_p_signal: 0\n",
      "Number of records with d_signal: 0\n",
      "Number of records with e_d_signal: 0\n"
     ]
    }
   ],
   "source": [
    "# count how many different signals are available\n",
    "counter_p = 0\n",
    "counter_e_p = 0\n",
    "counter_d = 0\n",
    "counter_e_d = 0\n",
    "for record in data['SR']:\n",
    "    if record.p_signal is not None:\n",
    "        counter_p += 1\n",
    "    if record.e_p_signal is not None:\n",
    "        counter_e_p += 1\n",
    "    if record.d_signal is not None:\n",
    "        counter_d += 1\n",
    "\n",
    "print(f\"Number of records with p_signal: {counter_p}\")\n",
    "print(f\"Number of records with e_p_signal: {counter_e_p}\")\n",
    "print(f\"Number of records with d_signal: {counter_d}\")\n",
    "print(f\"Number of records with e_d_signal: {counter_e_d}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a complete Record\n",
    "wfdb.plot_wfdb(data['SB'][24],title=\"Example for all Channels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot specific Channel of Record\n",
    "fig = wfdb.plot_items(data['SB'][24].p_signal[:,0],title=\"Example for Channel 7\",figsize=(15,4),return_fig=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find peaks in Channel 6 of Record 25\n",
    "hard = wfdb.processing.find_local_peaks(data['SB'][24].p_signal[:,6],radius=3)\n",
    "record = data['SB'][24].p_signal[:,6]\n",
    "wholeRecord = data['SB'][24]\n",
    "peak_points_y = [record[i] for i in hard] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(record)\n",
    "plt.plot(hard, peak_points_y, 'ro', label='Peaks')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Signal with Peaks')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning initial signal parameters...\n",
      "Failed to find 8 beats during learning.\n",
      "Initializing using default parameters\n",
      "Running QRS detection...\n",
      "QRS detection complete.\n",
      "[ 484 1038 1590 2131 2675 3219 3758 4300 4838]\n"
     ]
    }
   ],
   "source": [
    "# Normalize data by setting qrs peaks to index position 0 (“X-axis normalization”)\n",
    "indicies = wfdb.processing.xqrs_detect(wholeRecord.p_signal[:,6],wholeRecord.fs,0,5000)\n",
    "print(indicies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For normalization:    [0.059 0.068 0.083 ... 0.068 0.063 0.049]\n",
      "After normalization:  [0.27931363 0.28789323 0.30219256 ... 0.28789323 0.28312679 0.26978074]\n"
     ]
    }
   ],
   "source": [
    "# Normalization through upper and lower bounds (“Y-axis normalization”)\n",
    "print(\"For normalization:   \", wholeRecord.p_signal[:,0])\n",
    "wholeRecord.p_signal[:,0] = wfdb.processing.normalize_bound(wholeRecord.p_signal[:,0],0,1)\n",
    "print(\"After normalization: \",wholeRecord.p_signal[:,0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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(wholeRecord,title=\"Example for all Channels\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wfdb.plot_items(data['SB'][24].p_signal[:,0],title=\"Example for Channel 1\",figsize=(15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAHWCAYAAACFeEMXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3gU1frHv7ubzaZXUiCUQJDepEoJHaIoCCoq+pMqKoqoUVSuV4qoIE3EgkoRxXrxKnoRqYKAICAdqdJbKIH0sm1+f8zu7LRNdncm2dnk/TxPnuz0s2fPnHPe8zYdwzAMCIIgCIIgCIIgCFXQ+7sABEEQBEEQBEEQVQkSsgiCIAiCIAiCIFSEhCyCIAiCIAiCIAgVISGLIAiCIAiCIAhCRUjIIgiCIAiCIAiCUBESsgiCIAiCIAiCIFSEhCyCIAiCIAiCIAgVISGLIAiCIAiCIAhCRUjIIgiCIAiCIAiCUBESsgiCKJPU1FSMHDnS38UAAJw9exY6nQ7Lli2rkPuPHDkSqampHp8bERHh87NSU1Nxzz33+Hy91lm2bBl0Oh3Onj3r76JoluXLl6NJkyYwGo2IiYkBAPTs2RM9e/bkzqnoNq81qtI7riXk3kdxWyMIQl1IyCKIasqhQ4fwwAMPoF69eggJCUFKSgr69euH999/399F0wxFRUWYOnUqNm/e7O+ieMRHH30kOzk9cuQIpk6dSgKPhjh27BhGjhyJtLQ0LFq0CJ9++qnH165evRpTp06tuMJVI7T6jm/fvh1Tp05FTk6OR+ePHDkSOp1O9m/NmjUe3ePy5cuYOnUq9u/f73vBCYLgCPJ3AQiCqHy2b9+OXr16oW7duhg7diySk5Nx4cIF/Pnnn3jvvffw7LPPcuceP34cen31WI9ZtGgR7HY7t11UVIRp06YBQECs+H700UeoUaOGRPN45MgRTJs2DT179vR4FZ+oWDZv3gy73Y733nsPDRs25PavW7eu3GtXr16NDz/8kAQtHwiUd3z79u2YNm0aRo4cyWk5y8NkMmHx4sWS/a1bt0a/fv3w8MMPw2Qyub3+8uXLmDZtGlJTU9GmTRsfS04QhBMSsgiiGvLWW28hOjoau3fvlgzg165dE2yXNShXNYxGo7+LEFAUFhYiPDzc38UISJzvmfj9Cw4O9kNpAIZhUFJSgtDQUL88v7Koyu94UFAQ/u///s/tcYPBUImlcUH9BFFdqR7L0wRBCDh16hSaN28uu0KamJgo2JbzyTp48CB69OiB0NBQ1K5dG2+++SY+++wzic2/0+9o27Zt6NixI0JCQtCgQQN88cUXgvvdvHkTL730Elq2bImIiAhERUXhrrvuwoEDB7z+bjk5OTAYDFiwYAG378aNG9Dr9YiPjwfDMNz+cePGITk5mdvm+2ucPXsWCQkJAIBp06Zxpjdi7cGlS5cwePBgREREICEhAS+99BJsNpvH5V23bh3atGmDkJAQNGvWDD/88IPg+NSpU6HT6STXiX0sUlNT8ffff+P333/nytqzZ08sW7YMQ4cOBQD06tWLO8Y3j/r111+Rnp6O8PBwREZG4u6778bff/8teJ7TP+XUqVMYMGAAIiMj8eijj3r8PZ189NFHaN68OUwmE2rVqoVnnnlGYBK1YMECGAwGwb65c+dCp9MhMzOT22ez2RAZGYlXXnkFgMuXZ86cOXj33XdRr149hIaGokePHjh8+LCgDAcPHsTIkSPRoEEDhISEIDk5GaNHj0Z2drbgvPz8fDz//PNITU2FyWRCYmIi+vXrh71793LnnDx5Evfffz+Sk5MREhKC2rVr4+GHH0Zubq7bOkhNTcWUKVMAAAkJCYJ2VZ6fzMiRI/Hhhx8CgMAkzIndbsf8+fPRvHlzhISEICkpCU8++SRu3bolKcM999yDtWvXon379ggNDcUnn3zi9rlbt27F0KFDUbduXZhMJtSpUwcvvPACiouLJeWLiIjw6L3IycnByJEjER0djZiYGIwYMcIj8zgtvuOFhYV48cUXUadOHZhMJjRu3Bhz5swRlKUsfzP+c6dOnYqJEycCAOrXr8+VS4m5b3k+kps3b0aHDh0AAKNGjeKeyS/rzp07ceeddyI6OhphYWHo0aMH/vjjD8F9nP3VkSNH8MgjjyA2NhbdunXzudwEEciQJosgqiH16tXDjh07cPjwYbRo0cKray9dusRN1idNmoTw8HAsXrzYrcbrn3/+wQMPPIAxY8ZgxIgRWLp0KUaOHIl27dqhefPmAIDTp09j5cqVGDp0KOrXr4+rV6/ik08+QY8ePXDkyBHUqlXL4/LFxMSgRYsW2LJlCyZMmAAA2LZtG3Q6HW7evIkjR45wz926dSvS09Nl75OQkICFCxdi3LhxGDJkCO677z4AQKtWrbhzbDYbMjIy0KlTJ8yZMwcbNmzA3LlzkZaWhnHjxpVb1pMnT+Khhx7CU089hREjRuCzzz7D0KFDsWbNGvTr18/j7wwA8+fPx7PPPouIiAi89tprAICkpCSkpaVhwoQJWLBgAf71r3+hadOmAMD9X758OUaMGIGMjAy88847KCoqwsKFC9GtWzfs27dPYF5otVqRkZGBbt26Yc6cOQgLC/OqjFOnTsW0adPQt29fjBs3DsePH8fChQuxe/du/PHHHzAajUhPT4fdbse2bdu4wCBbt26FXq/H1q1buXvt27cPBQUF6N69u+AZX3zxBfLz8/HMM8+gpKQE7733Hnr37o1Dhw4hKSkJALB+/XqcPn0ao0aNQnJyMv7++298+umn+Pvvv/Hnn39yQstTTz2F77//HuPHj0ezZs2QnZ2Nbdu24ejRo2jbti3MZjMyMjJQWlqKZ599FsnJybh06RJWrVqFnJwcREdHu/2tvvjiC/z4449YuHAhIiIiBO2qLJ588klcvnwZ69evx/Lly2WPL1u2DKNGjcKECRNw5swZfPDBB9i3bx9Xx06OHz+OYcOG4cknn8TYsWPRuHFjt89dsWIFioqKMG7cOMTHx2PXrl14//33cfHiRaxYsUJwrifvBcMwuPfee7Ft2zY89dRTaNq0KX788UeMGDGi3DrQ2jvOMAwGDRqETZs2YcyYMWjTpg3Wrl2LiRMn4tKlS3j33XfL/U587rvvPpw4cQLffPMN3n33XdSoUYMrb3ncuHFDsG00Gt22Qz5NmzbFG2+8gcmTJ+OJJ57g6qxLly4AgN9++w133XUX2rVrhylTpkCv1+Ozzz5D7969sXXrVnTs2FFwv6FDh+K2227D22+/LRA0CaJawRAEUe1Yt24dYzAYGIPBwHTu3Jl5+eWXmbVr1zJms1lybr169ZgRI0Zw288++yyj0+mYffv2cfuys7OZuLg4BgBz5swZwbUAmC1btnD7rl27xphMJubFF1/k9pWUlDA2m03w3DNnzjAmk4l54403BPsAMJ999lmZ3++ZZ55hkpKSuO3MzEyme/fuTGJiIrNw4UKuzDqdjnnvvfe480aMGMHUq1eP275+/ToDgJkyZYrkGSNGjGAACMrHMAxz++23M+3atSuzfAzjqpv//ve/3L7c3FymZs2azO23387tmzJlCiPXVX/22WeS+m7evDnTo0cPybkrVqxgADCbNm0S7M/Pz2diYmKYsWPHCvZnZWUx0dHRgv3O7/vqq6+W+93kynft2jUmODiY6d+/v+C3/uCDDxgAzNKlSxmGYRibzcZERUUxL7/8MsMwDGO325n4+Hhm6NChjMFgYPLz8xmGYZh58+Yxer2euXXrFsMwrrYRGhrKXLx4kbv/zp07GQDMCy+8wO0rKiqSlPebb76RtNXo6GjmmWeecfsd9+3bxwBgVqxY4VGd8HH+rtevXxfs79Gjh+A3lGvzzzzzjGyb2Lp1KwOA+eqrrwT716xZI9nvbH9r1qzxqLxydTZjxgxGp9Mx586d4/Z5+l6sXLmSAcDMmjWL22e1Wpn09PSAe8ed3+XNN98UnPfAAw8wOp2O+eeffxiGKbv/Epdh9uzZkve7LJxlFf8525JcfyFua7t375Ytn91uZ2677TYmIyODsdvt3P6ioiKmfv36TL9+/bh9znY9bNgwj8p
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data = data['SB'][0].p_signal[:,0]\n",
    "\n",
    "\n",
    "from scipy.signal import butter, lfilter\n",
    "\n",
    "def butter_lowpass(cutoff, fs, order=5):\n",
    "    nyq = 0.5 * fs\n",
    "    normal_cutoff = cutoff / nyq\n",
    "    b, a = butter(order, normal_cutoff, btype='low', analog=False)\n",
    "    return b, a\n",
    "\n",
    "def butter_lowpass_filter(data, cutoff, fs, order=5):\n",
    "    b, a = butter_lowpass(cutoff, fs, order=order)\n",
    "    y = lfilter(b, a, data)\n",
    "    return y\n",
    "\n",
    "# Filter requirements.\n",
    "\n",
    "order = 1\n",
    "fs = 500.0\n",
    "cutoff = 25#25\n",
    "\n",
    "# Get the filter coefficients so we can check its frequency response.\n",
    "b, a = butter_lowpass(cutoff, fs, order)\n",
    "\n",
    "# Apply filter to the signal\n",
    "butter_filtered_sig = butter_lowpass_filter(test_data, cutoff, fs, order)\n",
    "\n",
    "# Plot the signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(test_data, 'b-', label='data')\n",
    "plt.plot(butter_filtered_sig, 'g-', linewidth=2, label='filtered data')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Signal with butter lowpass filter and without Filter')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Find peaks in Channel 6 of Record 25\n",
    "hard = wfdb.processing.find_local_peaks(butter_filtered_sig,radius=3)\n",
    "record = butter_filtered_sig\n",
    "peak_points_y = [record[i] for i in hard] \n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(record)\n",
    "plt.plot(hard, peak_points_y, 'ro', label='Peaks')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Filtered Signal with Peaks')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning initial signal parameters...\n",
      "Failed to find 8 beats during learning.\n",
      "Initializing using default parameters\n",
      "Running QRS detection...\n",
      "QRS detection complete.\n",
      "[ 544 1114 1683 2281 2855 3452 4016 4606]\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalize data by setting qrs peaks to index position 0 (“X-axis normalization”)\n",
    "indicies = wfdb.processing.xqrs_detect(butter_filtered_sig,500,0,5000)\n",
    "print(indicies)\n",
    "\n",
    "# detected = [ 530 1076 1631 2174 2725 3281 3828 4378 4938]\n",
    "cut_signal = butter_filtered_sig[indicies[0]:indicies[2]]\n",
    "\n",
    "# plot the cut signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(cut_signal)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Cut Signal based on R to get one QRS complex')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.03577630855309041, -0.16337948001854852]\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(butter_filtered_sig)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Cut Signal with Minimas Q, S detection')\n",
    "plt.grid(True)\n",
    "\n",
    "for peak in indicies:\n",
    "    start_idx = 20\n",
    "    end_idx = 25\n",
    "    signal_section = butter_filtered_sig[peak-start_idx:peak+end_idx]\n",
    "    minimas = scipy.signal.argrelextrema(signal_section, comparator=np.less, order=5)\n",
    "    \n",
    "    # map the minimas to the original signal\n",
    "    minimas = minimas[0] + peak - start_idx\n",
    "    minimas_y = [butter_filtered_sig[i] for i in minimas]\n",
    "    # if minimas more than 2 take those with biggest difference in y\n",
    "    if len(minimas) > 2:\n",
    "        minimas_y = [butter_filtered_sig[i] for i in minimas]\n",
    "        minimas_y = sorted(minimas_y)\n",
    "        minimas_y = minimas_y[:2]\n",
    "        minimas = [minimas[i] for i in range(0,2)]\n",
    "\n",
    "    # plot the minimas\n",
    "    plt.plot(minimas, minimas_y, 'ro')\n",
    "\n",
    "plt.legend(['Signal', 'Minimas'])\n",
    "plt.show()\n",
    "\n",
    "#print y values of minimas\n",
    "print(minimas_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find peaks in the cut signal\n",
    "hard = wfdb.processing.find_local_peaks(cut_signal,radius=3)\n",
    "#peak_points_y = [cut_signal[i] for i in hard]\n",
    "\n",
    "tolerance = 1\n",
    "# get standard deviation of the hard peaks\n",
    "std = cut_signal[hard].std() * tolerance\n",
    "# filter peaks with std\n",
    "hard = [i for i in hard if cut_signal[i] > std]\n",
    "# get the y values of the peaks\n",
    "peak_points_y = [cut_signal[i] for i in hard]\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(cut_signal)\n",
    "plt.plot(hard, peak_points_y, 'ro', label='Peaks')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Cuted Signal with Peaks (STD)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.nonparametric.smoothers_lowess import lowess\n",
    "\n",
    "org_signal = butter_filtered_sig \n",
    "#org_signal = test_data\n",
    "x = org_signal\n",
    "y = np.arange(len(org_signal))\n",
    "\n",
    "# Apply lowess function\n",
    "frac = 0.003#0.01\n",
    "lowess_filtered_sig = lowess(x, y, is_sorted=True, frac=frac, it=1)\n",
    "print(lowess_filtered_sig.shape)\n",
    "# Plot the signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(org_signal, 'b-', linewidth=2, label='filtered data')\n",
    "plt.plot(lowess_filtered_sig[:, 0], lowess_filtered_sig[:, 1], 'g-', linewidth=2, label='loess data')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Signal with and without LOESS Filter')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2 as cv\n",
    "\n",
    "\n",
    "org_signal = lowess_filtered_sig[:, 1]\n",
    "#org_signal = test_data\n",
    "\n",
    "# Reshape the signal to a 2D array for OpenCV (camera image processing library)\n",
    "org_signal_2d = np.reshape(org_signal, (-1, 1))\n",
    "\n",
    "# Scale the signal to the range [0, 255] (normal pixel range for images)\n",
    "org_signal_2d_scaled = np.uint8((org_signal_2d - np.min(org_signal_2d)) / (np.max(org_signal_2d) - np.min(org_signal_2d)) * 255)\n",
    "\n",
    "# Apply Non-Local Means denoising\n",
    "\n",
    "filter_strength = 50\n",
    "template_window_size = 7\n",
    "search_window_size = 21\n",
    "\n",
    "nlm_filtered_sig_2d = cv.fastNlMeansDenoising(org_signal_2d_scaled, None, filter_strength, template_window_size, search_window_size)\n",
    "\n",
    "# Rescale the denoised signal back to the original range\n",
    "nlm_filtered_sig = np.reshape(nlm_filtered_sig_2d, -1) * (np.max(org_signal) - np.min(org_signal)) / 255 + np.min(org_signal)\n",
    "\n",
    "\n",
    "# Plot the original and denoised signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(org_signal, label='Original signal')\n",
    "plt.plot(nlm_filtered_sig, label='Denoised 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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the original and denoised signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(test_data, label='Original signal')\n",
    "plt.plot(nlm_filtered_sig, label='Denoised 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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Find peaks in Channel 6 of Record 25\n",
    "hard = wfdb.processing.find_local_peaks(nlm_filtered_sig,radius=50)\n",
    "record = nlm_filtered_sig\n",
    "peak_points_y = [record[i] for i in hard] \n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(record)\n",
    "plt.plot(hard, peak_points_y, 'ro', label='Peaks')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Filtered Signal with Peaks')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Find peaks in Channel 6 of Record 25\n",
    "hard, soft = wfdb.processing.find_peaks(nlm_filtered_sig)\n",
    "record = nlm_filtered_sig\n",
    "peak_points_y = [record[i] for i in hard] \n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(record)\n",
    "plt.plot(hard, peak_points_y, 'ro', label='Peaks')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.title('Filtered Signal with Peaks')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAHWCAYAAACFeEMXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3wU1drHf7Mlm04oIaEHASnSpApSFQRRERRFXxtYsCFoLMj1SlMEsXEtgBdFFBQrliuIFAk1CtIRCDX0FAjpybY57x+TnZ3Z2ZCdnQlb8nw/HzQ7Oztz9tkz55znPI1jjDEQBEEQBEEQBEEQumAIdAMIgiAIgiAIgiDCCVKyCIIgCIIgCIIgdISULIIgCIIgCIIgCB0hJYsgCIIgCIIgCEJHSMkiCIIgCIIgCILQEVKyCIIgCIIgCIIgdISULIIgCIIgCIIgCB0hJYsgCIIgCIIgCEJHSMkiCIIgCIIgCILQEVKyCIKQkZKSgjFjxgS6GQCAzMxMcByHxYsXV8v1x4wZg5SUFJ/PjY2NrZZ26ElaWho4jkNaWlq1XH/AgAEYMGCAz+e2b9++WtpBXB41v1MgWbx4MTiOQ2ZmZqCbAkDdmBBKcByHadOmqfpMuMqCIK4UpGQRRA1h3759GDVqFJo1a4bIyEg0atQIgwcPxgcffBDopgUNpaWlmDZtWrUpKOHIuXPnMG3aNOzevTvQTZGxcuVKVYvKAQMGgOM48V9ERASaN2+OcePG4fTp036344033sBPP/2kOL5161ZMmzYN+fn5fl+bCBzS/mIwGBAfH4/WrVvjgQcewJo1awLdPIIgggBToBtAEET1s3XrVgwcOBBNmzbFY489huTkZJw+fRp//vkn/vOf/+CZZ54Rz83IyIDBUDP2XxYuXAie58XXpaWlmD59OgCEhBUgEKxevVr2+ty5c5g+fTpSUlLQuXPnwDTKCytXrsRHH32kStFq3LgxZs2aBQCw2Ww4cOAAFixYgN9//x0HDx5EdHS06na88cYbGDVqFEaMGCE7vnXrVkyfPh1jxoxBQkKC6uuGCw888ADuueceWCyWQDdFNdL+UlJSgqNHj2L58uVYunQp7r77bixduhRmsznArRQoKyuDyaRuyec5PhIEoQ5SsgiiBjBz5kzUqlUL27dvVyzocnJyZK9DcbHjL8GyAAolIiIiAt2EaqNWrVq4//77ZceaN2+O8ePHY8uWLRg8eHCAWuY7paWlfimDgcJoNMJoNAa6GX7hrb/Mnj0bEyZMwLx585CSkoI333wzQK2TExkZqfozND4ShDZqxnY1QdRwjh07hmuuucbrjnn9+vVlr73FZO3duxf9+/dHVFQUGjdujNdffx2fffaZIpYiJSUFt956KzZv3owePXogMjISV111Fb744gvZ9fLy8vDCCy+gQ4cOiI2NRXx8PG6++Wbs2bNH9XfLz8+H0WjE+++/Lx67cOECDAYD6tatC8aYePzJJ59EcnKy+Foac5CZmYnExEQAwPTp00VXIE9LyNmzZzFixAjExsYiMTERL7zwApxOZ5Xt/Pnnn3HLLbegYcOGsFgsaNGiBV577TXFZ11xTAcOHMDAgQMRHR2NRo0aYc6cOYprnjlzBiNGjEBMTAzq16+P5557Dlartcq27N27FxzH4ZdffhGP7dixAxzHoUuXLrJzb775ZvTs2VPWPpeVLy0tDd27dwcAjB07VpSZZwydL98lJycHjzzyCJKSkhAZGYlOnTrh888/l51TWbyZZ+zemDFj8NFHHwGAzAXQH1z9RWoFqCxWZdq0abL7cByHkpISfP7552IbxowZg2nTpuHFF18EIChxrvekz9LSpUvRtWtXREVFoU6dOrjnnnsUbouuvrJjxw7069cP0dHR+Ne//lXpd/FFxl26dMEdd9whO9ahQwdwHIe9e/eKx7755htwHIeDBw9Wej8A+OCDD3DNNdcgOjoatWvXRrdu3fDVV1+J73uLyeJ5HtOmTUPDhg0RHR2NgQMH4sCBA4qxyfXZLVu2IDU1FYmJiYiJicHIkSORm5sra4evz59WXGNRu3bt8OGHH6KgoED2vprfVa/nBlDGZBUVFeHZZ59FSkoKLBYL6tevj8GDB2Pnzp3iOZ793PWcvf322/jvf/+LFi1awGKxoHv37ti+fbvint999x3atWuHyMhItG/fHj/++CPFeRE1CrJkEUQNoFmzZkhPT8f+/ftVJyI4e/YsBg4cCI7jMHnyZMTExOCTTz6p1OJ19OhRjBo1Co888ggeeughLFq0CGPGjEHXrl1xzTXXAACOHz+On376CXfddReaN2+O7OxsfPzxx+jfvz8OHDiAhg0b+ty+hIQEtG/fHhs3bsSECRMAAJs3bwbHccjLy8OBAwfE+27atAl9+/b1ep3ExETMnz8fTz75JEaOHCkuNDt27Cie43Q6MWTIEPTs2RNvv/021q5di3feeQctWrTAk08+edl2Ll68GLGxsUhNTUVsbCz++OMPTJkyBYWFhXjrrbdk5166dAlDhw7FHXfcgbvvvhvff/89Jk2ahA4dOuDmm28GILj/3HjjjTh16hQmTJiAhg0bYsmSJfjjjz+qlFn79u2RkJCAjRs3Yvjw4aJsDAYD9uzZg8LCQsTHx4PneWzduhXjxo3zep22bdtixowZmDJlCsaNGyfKtnfv3qq/y4ABA3D06FGMHz8ezZs3x3fffYcxY8YgPz8fEydOrPI7SXn88cdx7tw5rFmzBkuWLPH5c06nExcuXAAA2O12HDx4EFOnTkXLli1x/fXXq2oDACxZsgSPPvooevToIcqwRYsWiImJweHDh7Fs2TK89957qFevHgCISv7MmTPx6quv4u6778ajjz6K3NxcfPDBB+jXrx927dol2yy5ePEibr75Ztxzzz24//77kZSU5LUtvsq4b9++WLZsmfi5vLw8/PPPPzAYDNi0aZP4PGzatAmJiYlo27Ztpd9/4cKFmDBhAkaNGoWJEyeivLwce/fuxV9//YX/+7//q/RzkydPxpw5c3DbbbdhyJAh2LNnD4YMGYLy8nKv5z/zzDOoXbs2pk6diszMTMydOxfjx4/HN998I56j5vnTitFoxL333otXX30Vmzdvxi233AJA3e9a3c/NE088ge+//x7jx49Hu3btcPHiRWzevBkHDx5UbLR48tVXX6GoqAiPP/44OI7DnDlzcMcdd+D48eOi9WvFihUYPXo0OnTogFmzZuHSpUt45JFH0KhRI43SJYgQghEEEfasXr2aGY1GZjQaWa9evdhLL73Efv/9d2az2RTnNmvWjD300EPi62eeeYZxHMd27dolHrt48SKrU6cOA8BOnDgh+ywAtnHjRvFYTk4Os1gs7PnnnxePlZeXM6fTKbvviRMnmMViYTNmzJAdA8A+++yzy36/p59+miUlJYmvU1NTWb9+/Vj9+vXZ/PnzxTZzHMf+85//iOc99NBDrFmzZuLr3NxcBoBNnTpVcY+HHnqIAZC1jzHGrr32Wta1a9fLto8xxkpLSxXHHn/8cRYdHc3Ky8vFY/3792cA2BdffCEes1qtLDk5md15553isblz5zIA7NtvvxWPlZSUsJYtWzIAbP369Zdtzy233MJ69Oghvr7jjjvYHXfcwYxGI/vtt98YY4zt3LmTAWA///yzrH39+/cXX2/fvr3S30jtd1m6dKl4zGazsV69erHY2FhWWFjIGGNs/fr1Xr+bt37y9NNPMzVTnKutnv/atm3Ljh8/LjvXs9+4mDp1quKeMTExsufJxVtvvaV4fhhjLDMzkxmNRjZz5kzZ8X379jGTySQ77mrzggULvH4f6e/kq4y/++47BoAdOHCAMcbYL7/8wiwWCxs+fDgbPXq0+NmOHTuykSNHKu4r5fbbb2fXXHPNZc/57LPPZHLIyspiJpOJjRgxQnbetGnTGACZLF2fHTRoEON5Xjz+3HPPMaPRyPLz88Vjvj5/lf22nvTv3/+y3+3HH39kAMTxxp/fVa/nhjGmGNdq1arFnn766ct+R09ZuJ6zunXrsry8PPH4zz//zACw//3vf+KxDh06sMaNG7OioiL
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "butter_filtered_sig = data_helper.butterlowpass_filter(data['SB'][0], 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(data['SB'][0].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": 28,
   "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(butter_filtered_sig, filter_params['frac'], filter_params['it'])\n",
    "# Plot the original and denoised signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(data['SB'][0].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": 29,
   "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(lowess_filtered_sig, 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(data['SB'][0].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": 30,
   "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(data['SB'][0], filter_params)\n",
    "# Plot the original and denoised signal\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(data['SB'][0].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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 41.5 s\n",
      "Wall time: 55.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "all_filtered_signals = data_helper.filter_data(data['SB'][:10], filter_params)"
   ]
  }
 ],
 "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
}