DSA_SS24/notebooks/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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from matplotlib import pyplot as plt\n",
    "import wfdb.processing\n",
    "import scipy.signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 38,
   "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": 39,
   "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": 40,
   "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": 41,
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABN0AAAGJCAYAAAC+dUXNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAADoxklEQVR4nOydd5wT1frGnyRb2QYssHSWKh0UBEHBhoIg9u5V8FquBcv1p1exgIqCvVzFy7VgV7z2AoKKYANEQZDel76N7btsS+b3RzKTmckkmzKT7Eye7+fjx+xkkjnL2TNzznue93ltgiAIIIQQQgghhBBCCCGE6IY91g0ghBBCCCGEEEIIIcRqMOhGCCGEEEIIIYQQQojOMOhGCCGEEEIIIYQQQojOMOhGCCGEEEIIIYQQQojOMOhGCCGEEEIIIYQQQojOMOhGCCGEEEIIIYQQQojOMOhGCCGEEEIIIYQQQojOMOhGCCGEEEIIIYQQQojOMOhGCCGEEEIIIYQQQojOMOhGCCGEENJMWb58OWw2G5YvX67bdxYUFOCiiy5CdnY2bDYbnn/+ed2+O1KmTp2K9PT0WDdDN6ZOnYrc3NxYN4MQQgghMYJBN0IIIYSYkjfffBM2m83vf6tWrYp1E5sl//znP7FkyRJMnz4d77zzDiZMmGD4NWtra/Hcc89h5MiRyMrKQkpKCvr06YNp06Zh+/bthl+/uRPo7/iMM86IdfMIIYQQEiYJsW4AIYQQQkgkPPLII+jevbvP8V69esWgNc2fH374Aeeeey7uuuuuqFyvuLgYEyZMwJo1a3D22WfjiiuuQHp6OrZt24YFCxbglVdeQX19fVTa0lx55513fI798ccfeOGFF3DmmWfGoEWEEEII0QMG3QghhBBias466ywMHz481s0wDYWFhWjZsqVu31dbW4ukpCTY7doJFFOnTsWff/6Jjz/+GBdeeKHivVmzZuH+++/XrS1m5W9/+5vPMTG1+PLLL49BiwghhBCiB0wvJYQQQoilmTlzJux2O5YuXao4fsMNNyApKQnr168HANTX12PGjBkYNmwYsrKykJaWhjFjxmDZsmWKz+Xl5cFms+Hpp5/G3Llz0aNHD7Ro0QJnnnkm9u/fD0EQMGvWLHTu3Bmpqak499xzUVJSoviO3NxcnH322fj2228xdOhQpKSkoH///vj000+D+p1+++03TJgwAVlZWWjRogVOPvlk/PrrrwE/I6bjCoKAuXPnSumLIrt378bFF1+M1q1bo0WLFjjhhBOwcOFCxXeIgaAFCxbggQceQKdOndCiRQtUVFT4befChQtx7bXX+gTcACA5ORlPP/20z/GDBw/ivPPOQ3p6Otq2bYu77roLTqdTcc7TTz+N0aNHIzs7G6mpqRg2bBg+/vhjn++y2WyYNm0aPv/8cwwcOBDJyckYMGAAFi9erDjvoYcegs1mw86dOzF16lS0bNkSWVlZuOaaa1BTU+Pzve+++y6GDRuG1NRUtG7dGpdddhn279+v+e8QKnV1dfjkk09w8skno3Pnzrp8JyGEEEKiD4NuhBBCCDE15eXlKC4uVvx35MgR6f0HHngAQ4cOxbXXXovKykoAwJIlS/Dqq69ixowZGDJkCACgoqICr732Gk455RQ88cQTeOihh1BUVITx48dj3bp1Ptd977338PLLL+PWW2/F//3f/+HHH3/EJZdcggceeACLFy/GPffcgxtuuAFfffWVZirnjh07cOmll+Kss87CnDlzkJCQgIsvvhjfffddwN/3hx9+wNixY1FRUYGZM2di9uzZKCsrw2mnnYbVq1f7/dzYsWOlNMYzzjgD77zzjvRzQUEBRo8ejSVLluDmm2/GY489htraWpxzzjn47LPPfL5r1qxZWLhwIe666y7Mnj0bSUlJmtf88ssvAQBXXXVVwN9JjtPpxPjx45GdnY2nn34aJ598Mp555hm88sorivNeeOEFHHvssXjkkUcwe/Zs6d9PHSgEgF9++QU333wzLrvsMjz55JOora3FhRdeqPg7EbnkkktQWVmJOXPm4JJLLsGbb76Jhx9+WHHOY489hquvvhq9e/fGs88+izvuuANLly7F2LFjUVZWFvTv6o9FixahrKwMV155ZcTfRQghhJAYIhBCCCGEmJA33nhDAKD5X3JysuLcDRs2CElJScJ1110nlJaWCp06dRKGDx8uNDQ0SOc0NjYKdXV1is+VlpYKOTk5wt///nfp2J49ewQAQtu2bYWysjLp+PTp0wUAwpAhQxTfe/nllwtJSUlCbW2tdKxbt24CAOGTTz6RjpWXlwsdOnQQjj32WOnYsmXLBADCsmXLBEEQBJfLJfTu3VsYP3684HK5pPNqamqE7t27C2eccUaT/24AhFtuuUVx7I477hAACD///LN0rLKyUujevbuQm5srOJ1ORXt69Ogh1NTUNHmt888/XwAglJaWNnmuIAjClClTBADCI488ojh+7LHHCsOGDVMcU1+/vr5eGDhwoHDaaacpjgMQkpKShJ07d0rH1q9fLwAQXnzxRenYzJkzBQCKvhZ/h+zsbOnnvLw8weFwCI899pjivA0bNggJCQmK41OmTBG6desWxG+u5MILLxSSk5OD/ncjhBBCSPOESjdCCCGEmJq5c+fiu+++U/z3zTffKM4ZOHAgHn74Ybz22msYP348iouL8dZbbyEhwWtv63A4JMWWy+VCSUkJGhsbMXz4cKxdu9bnuhdffDGysrKkn0eOHAnA7c8l/96RI0eivr4eBw8eVHy+Y8eOOP/886WfMzMzcfXVV+PPP/9Efn6+5u+6bt067NixA1dccQWOHDkiKfuqq6tx+umn46effoLL5Qr2n05i0aJFGDFiBE466STpWHp6Om644Qbk5eVh8+bNivOnTJmC1NTUJr9XTDvNyMgIqT033nij4ucxY8Zg9+7dimPy65eWlqK8vBxjxozR7Ktx48ahZ8+e0s+DBw9GZmamz3f6u/aRI0ek3+XTTz+Fy+XCJZdcolBXtm/fHr179/ZJRw6ViooKLFy4EBMnTtTVe48QQggh0YeFFAghhBBiakaMGBFUIYW7774bCxYswOrVqzF79mz079/f55y33noLzzzzDLZu3YqGhgbpuFZ11K5duyp+FgNwXbp00TxeWlqqON6rVy+FpxoA9OnTB4DbN659+/Y+19yxYwcAd9DLH+Xl5WjVqpXf97XYu3evFDSU069fP+n9gQMHSse1/j20yMzMBABUVlYGHUBKSUlB27ZtFcdatWrl8+/39ddf49FHH8W6detQV1cnHVf/mwK+feXvO7XOFf8tS0tLkZmZiR07dkAQBPTu3Vuz/YmJiX5+s+D45JNPUFtby9RSQgghxAIw6EYIIYSQuGD37t1S0GrDhg0+77/77ruYOnUqzjvvPNx9991o164dHA4H5syZg127dvmc73A4NK/j77ggCBG03o2oYnvqqacwdOhQzXPS09Mjvk5TBKNyA4C+ffsCcP97jxkzJqjP+Pv3k/Pzzz/jnHPOwdixY/Hyyy+jQ4cOSExMxBtvvIH3338/6O/U6pOmznW5XLDZbPjmm280z4303/+9995DVlYWzj777Ii+hxBCCCGxh0E3QgghhFgel8uFqVOnIjMzE3fccQdmz56Niy66CBdccIF0zscff4wePXrg008/VailZs6caUibdu7cCUEQFNfavn07AHd1Uy3EFMnMzEyMGzdOt7Z069YN27Zt8zm+detW6f1wmDx5MubMmYN333036KBbMHzyySdISUnBkiVLkJycLB1/4403dLuGP3r27AlBENC9e3dJmagXhw8fxrJlyzB16lTF70UIIYQQc0JPN0IIIYRYnmeffRYrVqzAK6+8glmzZmH06NG46aabUFxcLJ0jqpbk6qfffvsNK1euNKRNhw4dUlQGraiowNtvv42hQ4dqppYCwLBhw9CzZ088/fTTqKqq8nm/qKgorLZMnDgRq1evVvyu1dXVeOWVV5Cbm6uZihsMo0aNwoQJE/Daa6/h888/93m/vr5es7JrUzgcDthsNjidTulYXl6e5jX05oILLoDD4cDDDz/
      "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": 43,
   "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": 44,
   "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": 45,
   "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": 46,
   "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": 47,
   "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": 48,
   "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": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "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": 50,
   "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": 74,
   "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",
      "[ 530 1076 1631 2174 2725 3281 3828 4378 4938]\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": 73,
   "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": [
      "[-0.027759944001706754, -0.042024054853653535]\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": 72,
   "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": 102,
   "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 numpy as np\n",
    "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": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAHWCAYAAACFeEMXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd5gUVdaHf9VxcmIiaYYkQQQkKSpBAVEU1rRi+CSo4CooimnRXYKKoIKgqGBClDVgYBWVKGGJApJzHBjS5Jw61f3+qO7qUD0zXV01dPfMeZ+Hh67q29Wn79y6dc89iWOMMRAEQRAEQRAEQRCqoAm0AARBEARBEARBEA0JUrIIgiAIgiAIgiBUhJQsgiAIgiAIgiAIFSEliyAIgiAIgiAIQkVIySIIgiAIgiAIglARUrIIgiAIgiAIgiBUhJQsgiAIgiAIgiAIFSEliyAIgiAIgiAIQkVIySIIgiAIgiAIglARUrIIopGTkZGB0aNHB1oMAMDZs2fBcRwWL15cL9cfPXo0MjIyfG4bFRVVL3KoycaNG8FxHDZu3Fgv1x8wYAAGDBjgc9vOnTvXixxE4Fm8eDE4jsPZs2cDLQrhJ9OmTQPHcbI+U9/zMkE0VEjJIogGysGDB3HfffchPT0dYWFhaNasGQYPHoz58+cHWrSgobKyEtOmTas3BaUhcunSJUybNg379u0LtChurFixAtOmTfO5/YABA8BxHIYNGyZ5z7GonD17tooS+k6oK6uOhbxGo8H58+cl75eWliI8PBwcx2HChAkBkFA9HJscjn9GoxEpKSkYMGAA3nzzTeTl5QVaRIIgAgQpWQTRANm2bRt69uyJ/fv3Y+zYsfjggw/w+OOPQ6PR4L333nNre/z4cXz66acBkvTK8umnn+L48ePicWVlJaZPn05KVi2sWbMGa9asEY8vXbqE6dOnB6WSNX36dNmf++2337B79+56kIgwGo349ttvJeeXLVsWAGnql2eeeQZLlizBJ598ghdffBEJCQmYOnUqOnbsiPXr1wdaPJF//etfqKqqkvWZ9PR0VFVV4ZFHHqknqQiiYaILtAAEQajPjBkzEBsbi127diEuLs7tvdzcXLdjo9F4BSULLHq9PtAihBwGgyHQItQbLVu2RFlZGaZPn47ly5cHWpwGx9ChQ/Htt9/ipZdecjv/zTff4I477sBPP/0UIMnUp2/fvrjvvvvczu3fvx+33nor7r33Xhw5cgRpaWkBks6JTqeDTidv6cdxHMLCwupJIoJouJAliyAaIKdPn8bVV18tUbAAIDk52e3YW0zWgQMH0L9/f4SHh6N58+Z444038MUXX0jiMTIyMnDnnXdiy5Yt6N27N8LCwtC6dWt89dVXbtcrLCzECy+8gGuuuQZRUVGIiYnB7bffjv3798v+bcXFxdBqtXj//ffFc/n5+dBoNGjSpAkYY+L5J598EqmpqeKxa0zW2bNnkZSUBACYPn266O7j6XJ28eJF3HXXXYiKikJSUhJeeOEF2Gy2OuX85ZdfcMcdd6Bp06YwGo1o06YNXn/9dclnHa5hR44cwc0334yIiAg0a9YMb7/9tuSaFy5cwF133YXIyEgkJyfjueeeg8lkqlOWAwcOgOM4N0Vi9+7d4DgO3bt3d2t7++2347rrrnOTzxGTtXHjRvTq1QsAMGbMGLHPPGM1fPktubm5eOyxx5CSkoKwsDB07doVX375pVubmuLNPGNERo8ejQ8//BAA3Fy36iI6OhrPPfccfv31V+zZs6fO9mfOnMHf//53JCQkICIiAtdffz1+//13rzJ///33mDFjBpo3b46wsDAMHDgQp06dqvM75PDRRx/h6quvhtFoRNOmTTF+/HgUFxdL2u3YsQNDhw5FfHw8IiMj0aVLFzeL9oEDBzB69Gi0bt0aYWFhSE1NxaOPPoqCggJF8j300EPYt28fjh07Jp7Lzs7G+vXr8dBDD3n9jMlkwtSpU9G2bVsYjUa0aNECL730kmScf/HFF7jllluQnJwMo9GITp06YcGCBZLr+TpHWSwWTJ8+He3atUNYWBiaNGmCm266CWvXrvX793ft2hXz5s1DcXExPvjgA7f3Ll68iEcffRQpKSkwGo24+uqrsWjRIrc2csfSDz/8gB49eiA8PByJiYn4v//7P1y8eNGtjbeYrLVr1+Kmm25CXFwcoqKi0L59e7zyyivi+95ishwxq77MjwUFBXjkkUcQExODuLg4jBo1Cvv376c4L6LBQ0oWQTRA0tPTsXv3bhw6dEj2Zy9evIibb74Zhw8fxuTJk/Hcc8/h66+/lrgZOjh16hTuu+8+DB48GHPmzEF8fDxGjx6Nw4cPi23OnDmDn3/+GXfeeSfeffddvPjiizh48CD69++PS5cuyZIvLi4OnTt3xqZNm8RzW7ZsAcdxKCwsxJEjR8TzmzdvRt++fb1eJykpSVyU3X333ViyZAmWLFmCe+65R2xjs9kwZMgQNGnSBLNnz0b//v0xZ84cfPLJJ3XKuXjxYkRFRWHSpEl477330KNHD0yZMgX//Oc/JW2Liopw2223oWvXrpgzZw46dOiAl19+GStXrhTbVFVVYeDAgVi9ejUmTJiAV199FZs3b5ZYCbzRuXNnxMXFufXZ5s2bodFosH//fpSWlgIAeJ7Htm3b0K9fP6/X6dixI1577TUAwLhx48Q+c23v628ZMGAAlixZgocffhjvvPMOYmNjMXr06BrHWW088cQTGDx4MACIMi1ZssSnz06cOBHx8fF1xnPl5OTghhtuwOrVq/HUU09hxowZqK6uxvDhw/Hf//5X0n7WrFn473//ixdeeAGTJ0/Gn3/+iYcfflj2b6uJadOmYfz48WjatCnmzJmDe++9Fx9//DFuvfVWWCwWsd3atWvRr18/HDlyBBMnTsScOXNw880347fffnNrc+bMGYwZMwbz58/HAw88gO+++w5Dhw5127SQS79+/dC8eXN888034rmlS5ciKioKd9xxh6Q9z/MYPnw4Zs+ejWHDhmH+/Pm46667MHfuXIwYMcKt7YIFC5Ceno5XXnkFc+bMQYsWLfDUU0+JyrYrvsxR06ZNw/Tp03HzzTfjgw8+wKuvvoqWLVv6pHzXxn333Yfw8HA3l9ucnBxcf/31+OOPPzBhwgS89957aNu2LR577DHMmzdPcg1fxtLixYtx//33Q6vVYubMmRg7diyWLVuGm266yavi7eDw4cO48847YTKZ8Nprr2HOnDkYPnw4tm7dWudv82V+5Hkew4YNw7fffotRo0ZhxowZuHz5MkaNGlV35xFEqMMIgmhwrFmzhmm1WqbValmfPn3YSy+9xFavXs3MZrOkbXp6Ohs1apR4/PTTTzOO49jevXvFcwUFBSwhIYEBYJmZmW6fBcA2bdoknsvNzWVGo5E9//zz4rnq6mpms9ncvjczM5MZjUb22muvuZ0DwL744otaf9/48eNZSkqKeDxp0iTWr18/lpyczBYsWCDKzHEce++998R2o0aNYunp6eJxXl4eA8CmTp0q+Y5Ro0YxAG7yMcbYtddey3r06FGrfIwxVllZKTn3xBNPsIiICFZdXS2e69+/PwPAvvrqK/GcyWRiqamp7N577xXPzZs3jwFg33//vXiuoqKCtW3blgFgGzZsqFWeO+64g/Xu3Vs8vueee9g999zDtFotW7lyJWOMsT179jAA7JdffnGTr3///uLxrl27avwbyf0t//nPf8RzZrOZ9enTh0VFRbHS0lLGGGMbNmzw+tu8jZPx48czOY+0/v37s6uvvpoxxtj06dMZALZ7926367/zzjti+2effZYBYJs3bxbPlZWVsVatWrGMjAxxfDtk7tixIzOZTGLb9957jwFgBw8elCWbN3Jzc5nBYGC33nqr2331wQcfMABs0aJFjDHGrFYra9WqFUtPT2dFRUVu1+B5Xnztbax+++23knv7iy++kMwB3pg6dSoDwPLy8tgLL7zA2rZtK77Xq1cvNmbMGMYYYwDY+PHjxfeWLFnCNBqNWx8zxtjChQsZALZ169ZaZR4yZAhr3bq12zlf56iuXbuyO+64o9bf5Q3
      "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": 104,
   "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": 115,
   "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": 129,
   "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()"
   ]
  }
 ],
 "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
}