517 lines (516 with data), 108.5 kB

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3a5f6fd",
   "metadata": {},
   "source": [
    "# Script to extract data from Apple Watch PDFs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcf02b0",
   "metadata": {},
   "source": [
    "By: Joske van der Zande\n",
    "\n",
    "Recently, it has become possible to save ECGs generated by an Apple Watch in two formats: as a PDF and raw data as a CSV. This script enables the extraction of data from the PDFs and allows for comparison with the raw data from the CSV files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5acf9c12",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T11:47:52.417788Z",
     "start_time": "2023-10-10T11:47:52.020689Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import necesarry utilities from packages\n",
    "import pandas as pd\n",
    "import PyPDF2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from PIL import Image, ImageDraw\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6360aa2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T11:47:52.421103Z",
     "start_time": "2023-10-10T11:47:52.419412Z"
    }
   },
   "outputs": [],
   "source": [
    "# Source file name\n",
    "PDF = 'examplePDF.pdf'\n",
    "CSV = 'exampleCSV.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ad932a2",
   "metadata": {},
   "source": [
    "## Extracing data from PDF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "626706fd",
   "metadata": {},
   "source": [
    "This script is designed to extract ECG data from PDF files generated by Apple's ECG app. It uses the PyPDF2 library to parse the PDFs and extract the raw data points for further analysis. \n",
    "\n",
    "Script Functions:\n",
    "- `pdf_to_image(pdf_path)`: Converts a PDF page to an image and extracts drawing operations.\n",
    "- `extract_values(op_list)`: Processes the extracted drawing operations to obtain the amplitude ('y') values corresponding to ECG data.\n",
    "\n",
    "In the PDF, the ECG signal is represented in the color red ['0.8', '0.039', '0.13']. This color-coding is used to distinguish and identify the ECG data within the document. Therefore, the script utilizes the sc_correct check to verify if the drawing operations contain the color red. This  step, is denoted by sc_correct.\n",
    "\n",
    "Additionally, it's observed that there is a consistent loss of a data point when transitioning between the three data sections (from section 1 to section 2 and from section 2 to section 3) in the PDF. As a result, the script performs interpolation twice to bridge these gaps and maintain the integrity of the ECG data.\n",
    "\n",
    "Note: This script assumes specific format and structure in the PDF files generated by Apple's ECG app and may require adjustments for different versions or formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0351135a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T11:47:52.428338Z",
     "start_time": "2023-10-10T11:47:52.422412Z"
    }
   },
   "outputs": [],
   "source": [
    "def pdf_to_image(pdf_path):\n",
    "    op_list = []\n",
    "    pdf = PyPDF2.PdfReader(open(pdf_path, 'rb'))\n",
    "    page = pdf.pages[0]  # Assuming you want to process the first page\n",
    "\n",
    "    # Extract page dimensions\n",
    "    page_width = int(page.mediabox.width)\n",
    "    page_height = int(page.mediabox.height)\n",
    "\n",
    "    # Create a blank image\n",
    "    image = Image.new('RGB', (page_width, page_height), 'white')\n",
    "    draw = ImageDraw.Draw(image)\n",
    "    \n",
    "    def visitor_svg(op, args, cm, tm):\n",
    "        op_list.append((op, args, cm, tm))\n",
    "    \n",
    "    page.extract_text(visitor_operand_before=visitor_svg)\n",
    "    \n",
    "    return op_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ee6ebff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T12:21:39.981355Z",
     "start_time": "2023-10-10T12:21:39.561103Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f46ad5cc1e94e2e8952cf482f8735f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of 'PDF': 15169\n"
     ]
    }
   ],
   "source": [
    "def extract_values(op_list):\n",
    "    \n",
    "    cs_correct, sc_correct = False, False  # Flag to indicate if 'cs' (color space) and 'sc' (color encoding) are correct\n",
    "\n",
    "    last_move = 0    \n",
    "    y_list, y_lists = [], []\n",
    "\n",
    "    # Iterate over the list of operations\n",
    "    for idx, (op, args, cm, tm) in tqdm(enumerate(op_list)):\n",
    "        \n",
    "        if op == b'm':  # Move to\n",
    "            if cs_correct and sc_correct:\n",
    "                if idx - last_move != 132 and len(y_list): # Skip lines that are not part of the three long lines of red points\n",
    "                    y_lists.append(y_list)\n",
    "                    y_list = []\n",
    "                last_move = idx\n",
    "\n",
    "        elif op == b'l':  # Line to\n",
    "            end_x, end_y = args\n",
    "            if cs_correct and sc_correct:\n",
    "                y_list.append(float(end_y))\n",
    "\n",
    "        elif op == b'sc' or op == b'cs' or op == b'SC' or op == b'CS':\n",
    "            if op == b'cs' or op == b'CS':\n",
    "                cs_correct = '/Cs3' in args\n",
    "            if op == b'sc' or op == b'SC':\n",
    "                sc_correct = all([f'{float(x):.2}' in ['0.8', '0.039', '0.13'] for x in args if isinstance(x, PyPDF2.generic._base.FloatObject)])\n",
    "        \n",
    "    y_lists.append(y_list)\n",
    "    \n",
    "    inter_val_1 = (y_lists[0][-1] + y_lists[1][0]) / 2  \n",
    "    inter_val_2 = (y_lists[1][-1] + y_lists[2][0]) / 2  \n",
    "    \n",
    "    y_combined = np.concatenate([y_lists[0], [inter_val_1], y_lists[1], [inter_val_2], y_lists[2]])  # Combined list of y values\n",
    "    y = y_combined / -28.3465 + 2 # Convert to mV and apply a 1.5 mV offset\n",
    "    y = y - np.mean(y)\n",
    "    return y\n",
    "\n",
    "op_list = pdf_to_image(PDF)\n",
    "y = extract_values(op_list)\n",
    "# Print the resulting 'y' values\n",
    "print(\"Length of 'PDF':\", len(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "be206eb7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T12:21:40.322071Z",
     "start_time": "2023-10-10T12:21:39.982697Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06ab0fed497a496380ada7691d9ac8d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of 'PDF': 15169\n"
     ]
    }
   ],
   "source": [
    "op_list = pdf_to_image(PDF)\n",
    "y = extract_values(op_list)\n",
    "\n",
    "# Print the resulting 'y' values\n",
    "print(\"Length of 'PDF':\", len(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de69f3a4",
   "metadata": {},
   "source": [
    "## Raw Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f97dda0",
   "metadata": {},
   "source": [
    "This Python script is designed to process data from a CSV file. It uses the Pandas library to read the CSV file and perform data manipulation operations. The processed data is returned as a NumPy array.\n",
    "\n",
    "Script Functions:\n",
    "- `process_data(fname)`: Reads a CSV file, skips header rows, and processes the data.\n",
    "\n",
    "Note: This script assumes a specific CSV file format and data processing requirements. You may need to customize it for your specific dataset and analysis needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0cc8ad96",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T12:21:41.249481Z",
     "start_time": "2023-10-10T12:21:41.245436Z"
    }
   },
   "outputs": [],
   "source": [
    "def process_data(fname):\n",
    "    df_raw_original = pd.read_csv(fname, delimiter='.', decimal=',', skiprows=13, header=None)\n",
    "    df_raw_original = df_raw_original.iloc[:, 0].values\n",
    "    print(\"Length of 'Raw:\", len(df_raw_original))\n",
    "    df_raw_new = df_raw_original[186:]\n",
    "    df_raw_new = ((df_raw_new/1000))\n",
    "    return df_raw_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "590c0598",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T12:21:41.534846Z",
     "start_time": "2023-10-10T12:21:41.528453Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of 'Raw: 15355\n",
      "Length of 'Raw' without calibration block: 15169\n"
     ]
    }
   ],
   "source": [
    "raw = process_data(CSV)\n",
    "# Print the resulting 'y' values\n",
    "print(\"Length of 'Raw' without calibration block:\", len(raw))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c4f160a",
   "metadata": {},
   "source": [
    "## Visual Comparison PDF and CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104e6947",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-25T11:50:35.313614Z",
     "start_time": "2023-09-25T11:50:35.309957Z"
    }
   },
   "source": [
    "This Python script is used for visualizing data using Matplotlib. It consists of two parts:\n",
    "\n",
    "1. Plot 'Raw' and 'PDF' signals against time.\n",
    "2. Calculates and plots the difference between 'PDF' and 'Raw' signals against time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e225b52b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T12:21:42.702245Z",
     "start_time": "2023-10-10T12:21:42.568239Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 5))\n",
    "\n",
    "sampling_frequency = 511.844\n",
    "time = np.arange(len(y)) / sampling_frequency\n",
    "\n",
    "plt.plot(time, raw, label='Raw')\n",
    "plt.plot(time, y, label = 'PDF')\n",
    "\n",
    "plt.xlabel('Time', fontsize=20)\n",
    "plt.ylabel('Amplitude', fontsize=20)\n",
    "\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.xticks(fontsize=20)  # Adjust font size for x-axis tick labels\n",
    "plt.yticks(fontsize=20)  # Adjust font size for y-axis tick labels\n",
    "\n",
    "# Add x-label\n",
    "plt.xlabel('Seconds')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "66cbb4ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T12:21:43.731254Z",
     "start_time": "2023-10-10T12:21:43.609000Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "difference = (y - raw)\n",
    "plt.figure(figsize=(30, 5))\n",
    "\n",
    "sampling_frequency = 511.844\n",
    "time = np.arange(len(y)) / sampling_frequency\n",
    "\n",
    "plt.plot(time, difference, label='difference per sample')\n",
    "\n",
    "plt.xlabel('Time (s)', fontsize=20)\n",
    "plt.ylabel('Amplitude (mv)', fontsize=20)\n",
    "\n",
    "plt.legend(fontsize=20)\n",
    "\n",
    "plt.xticks(fontsize=20)  # Adjust font size for x-axis tick labels\n",
    "plt.yticks(fontsize=20)  # Adjust font size for y-axis tick labels\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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