preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import wfdb\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_context(\"notebook\")\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./data/mit-bih-arrhythmia-database-1.0.0/RECORDS', \"r\") as f:\n",
    "    records = f.read().split(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ansi_map = {\n",
    "    \".\": \"N\",\n",
    "    \"N\": \"N\",\n",
    "    \"L\": \"N\",\n",
    "    \"R\": \"N\",\n",
    "    \"e\": \"N\",\n",
    "    \"j\": \"N\",\n",
    "    \"A\": \"S\",\n",
    "    \"J\": \"S\",\n",
    "    \"a\": \"S\",\n",
    "    \"S\": \"S\",\n",
    "    \"E\": \"V\",\n",
    "    \"V\": \"V\",\n",
    "    \"F\": \"F\",\n",
    "    \"/\": \"Q\",\n",
    "    \"f\": \"Q\",\n",
    "    \"Q\": \"Q\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca60a9558fd848a5acd1c5d8e1f8ab08",
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       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heartbeat_data = [] # list of lists (by record) of heartbeats\n",
    "heartbeat_labels = [] # list of lists (by record) of labels\n",
    "for record_id in tqdm(records):\n",
    "    record = wfdb.rdrecord('./data/mit-bih-arrhythmia-database-1.0.0/' + record_id)\n",
    "    annotation = wfdb.rdann('./data/mit-bih-arrhythmia-database-1.0.0/' + record_id, 'atr')\n",
    "    heartbeats = []\n",
    "    labels = []\n",
    "    for (idx, hb_class) in zip(annotation.sample, annotation.symbol):\n",
    "        # Get +- 180 samples around the heartbeat\n",
    "        if hb_class not in ansi_map:\n",
    "            continue\n",
    "        try:\n",
    "            heartbeat = record.p_signal[idx - 180:idx + 180, 0] # only use first channel\n",
    "        except Exception:\n",
    "            continue\n",
    "        if len(heartbeat) != 360:\n",
    "            continue\n",
    "        heartbeats.append(heartbeat)\n",
    "        labels.append(ansi_map[hb_class])\n",
    "    heartbeat_data.append(heartbeats)\n",
    "    heartbeat_labels.append(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (record_id, heartbeats, labels) in zip(records, heartbeat_data, heartbeat_labels):\n",
    "    np.savez_compressed('./data/heartbeats/' + record_id, heartbeats=heartbeats, labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['heartbeats', 'labels']\n"
     ]
    }
   ],
   "source": [
    "npzfile = np.load('./data/heartbeats/100.npz')\n",
    "print(npzfile.files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2271, 360)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npzfile[\"heartbeats\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eee094574f9d4bee95c3dda5c2b99748",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'N': 90582, 'S': 2781, 'V': 7235, 'Q': 8038, 'F': 802}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the most average record by proportion of labels\n",
    "# (i.e. the record with the closest distribution of labels to the average)\n",
    "\n",
    "heartbeat_labels = {}\n",
    "for _, _, files in os.walk(\"./data/heartbeats/\"):\n",
    "    for file in tqdm(files):\n",
    "        npzfile = np.load('./data/heartbeats/' + file)\n",
    "        heartbeat_labels[file] = npzfile[\"labels\"]\n",
    "\n",
    "label_counts = {}\n",
    "for record_id in tqdm(heartbeat_labels):\n",
    "    for label in heartbeat_labels[record_id]:\n",
    "        if label not in label_counts:\n",
    "            label_counts[label] = 0\n",
    "        label_counts[label] += 1\n",
    "\n",
    "label_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N: 0.83\n",
      "S: 0.03\n",
      "V: 0.07\n",
      "Q: 0.07\n",
      "F: 0.01\n"
     ]
    }
   ],
   "source": [
    "for label, count in label_counts.items():\n",
    "    print(f\"{label}: {count / sum(label_counts.values()):.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_proportions = {label: count / sum(label_counts.values()) for label, count in label_counts.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weighted_distance_from_ratio(ratio, label_counts):\n",
    "    # ratio: target dict of label to proportion\n",
    "    # label_counts: dict of label in record to count\n",
    "    distance = 0\n",
    "    length = sum(label_counts.values())\n",
    "    label_ratios = {label: 0 for label in ratio.keys()}\n",
    "    label_ratios = {label: count / length for label, count in label_counts.items()}\n",
    "    for (label, prop) in ratio.items():\n",
    "        distance += abs(ratio[label] - label_ratios[label]) * prop\n",
    "    return distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2271"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(heartbeat_labels[\"100.npz\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.argwhere(heartbeat_labels[\"100.npz\"] == \"Q\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e09f4ea9971e4457b9f902323795541a",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.npz: 0.14\n",
      "101.npz: 0.15\n",
      "102.npz: 0.72\n",
      "103.npz: 0.15\n",
      "104.npz: 0.69\n",
      "105.npz: 0.14\n",
      "106.npz: 0.09\n",
      "107.npz: 0.75\n",
      "108.npz: 0.14\n",
      "109.npz: 0.14\n",
      "111.npz: 0.15\n",
      "112.npz: 0.15\n",
      "113.npz: 0.15\n",
      "114.npz: 0.13\n",
      "115.npz: 0.15\n",
      "116.npz: 0.11\n",
      "117.npz: 0.15\n",
      "118.npz: 0.11\n",
      "119.npz: 0.06\n",
      "121.npz: 0.15\n",
      "122.npz: 0.15\n",
      "123.npz: 0.15\n",
      "124.npz: 0.11\n",
      "200.npz: 0.15\n",
      "201.npz: 0.01\n",
      "202.npz: 0.12\n",
      "203.npz: 0.03\n",
      "205.npz: 0.12\n",
      "207.npz: 0.01\n",
      "208.npz: 0.27\n",
      "209.npz: 0.05\n",
      "210.npz: 0.08\n",
      "212.npz: 0.15\n",
      "213.npz: 0.02\n",
      "214.npz: 0.06\n",
      "215.npz: 0.11\n",
      "217.npz: 0.65\n",
      "219.npz: 0.12\n",
      "220.npz: 0.11\n",
      "221.npz: 0.02\n",
      "222.npz: 0.08\n",
      "223.npz: 0.05\n",
      "228.npz: 0.02\n",
      "230.npz: 0.15\n",
      "231.npz: 0.15\n",
      "232.npz: 0.53\n",
      "233.npz: 0.10\n",
      "234.npz: 0.14\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('207.npz', 0.010833268033605542),\n",
       " ('201.npz', 0.012993623019520626),\n",
       " ('228.npz', 0.01796498452300128),\n",
       " ('213.npz', 0.019479441519031762),\n",
       " ('221.npz', 0.020070627561252528),\n",
       " ('203.npz', 0.02876206925701876),\n",
       " ('223.npz', 0.04850211748994977),\n",
       " ('209.npz', 0.04919803344005147),\n",
       " ('214.npz', 0.056899493652572984),\n",
       " ('119.npz', 0.05883623558255227),\n",
       " ('210.npz', 0.07801000609475524),\n",
       " ('222.npz', 0.08419978248516223),\n",
       " ('106.npz', 0.08840739222456871),\n",
       " ('233.npz', 0.10496944765240229),\n",
       " ('124.npz', 0.10818710676626729),\n",
       " ('215.npz', 0.10845287958851486),\n",
       " ('118.npz', 0.11167910078004502),\n",
       " ('116.npz', 0.11231376903180557),\n",
       " ('220.npz', 0.11492501524572529),\n",
       " ('202.npz', 0.1227715602278828),\n",
       " ('219.npz', 0.12335919352425531),\n",
       " ('205.npz', 0.1247504453645146),\n",
       " ('114.npz', 0.12539600791194525),\n",
       " ('234.npz', 0.13660213182875514),\n",
       " ('105.npz', 0.1370763512046033),\n",
       " ('109.npz', 0.1389914196594744),\n",
       " ('100.npz', 0.14028616573148997),\n",
       " ('108.npz', 0.1415682415887285),\n",
       " ('113.npz', 0.15022155427853845),\n",
       " ('101.npz', 0.15073643207562282),\n",
       " ('123.npz', 0.1513087755808605),\n",
       " ('231.npz', 0.15139436869184916),\n",
       " ('121.npz', 0.152137664503963),\n",
       " ('103.npz', 0.15225724721157116),\n",
       " ('112.npz', 0.1524038299267698),\n",
       " ('117.npz', 0.15252023056053074),\n",
       " ('111.npz', 0.152655551469188),\n",
       " ('230.npz', 0.15267999795446352),\n",
       " ('200.npz', 0.15295720867586055),\n",
       " ('115.npz', 0.15307636692183318),\n",
       " ('122.npz', 0.15307636692183318),\n",
       " ('212.npz', 0.15307636692183318),\n",
       " ('208.npz', 0.2655081503320216),\n",
       " ('232.npz', 0.528922354000559),\n",
       " ('217.npz', 0.6493501564509243),\n",
       " ('104.npz', 0.6921304088192923),\n",
       " ('102.npz', 0.7171276501602437),\n",
       " ('107.npz', 0.7543584044237523)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances = {}\n",
    "for record_id in tqdm(heartbeat_labels):\n",
    "    record_counts = {label: len(np.argwhere(heartbeat_labels[record_id] == label)) for label in label_counts.keys()}\n",
    "    distance = weighted_distance_from_ratio(label_proportions, record_counts)\n",
    "    print(f\"{record_id}: {distance:.2f}\")\n",
    "    distances[record_id] = distance\n",
    "\n",
    "# sort by distance\n",
    "sorted(distances.items(), key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we manually select a test dataset with samples that have a similar distribution to the mean distribution. We refer to [https://archive.physionet.org/physiobank/database/html/mitdbdir/records.htm](https://archive.physionet.org/physiobank/database/html/mitdbdir/records.htm) to determine whether the record is suitable. 10% of the dataset would be 4.8 samples, so we select 5 samples for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.800000000000001"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(heartbeat_labels) * 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 207: Not suitable\n",
    "- 201: Suitable\n",
    "- 228: Suitable\n",
    "- 213: Suitable\n",
    "- 221: Suitable\n",
    "- 203: Not suitable\n",
    "- 223: Suitable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['100.npz',\n",
       " '101.npz',\n",
       " '102.npz',\n",
       " '103.npz',\n",
       " '104.npz',\n",
       " '105.npz',\n",
       " '106.npz',\n",
       " '107.npz',\n",
       " '108.npz',\n",
       " '109.npz',\n",
       " '111.npz',\n",
       " '112.npz',\n",
       " '113.npz',\n",
       " '114.npz',\n",
       " '115.npz',\n",
       " '116.npz',\n",
       " '117.npz',\n",
       " '118.npz',\n",
       " '119.npz',\n",
       " '121.npz',\n",
       " '122.npz',\n",
       " '123.npz',\n",
       " '124.npz',\n",
       " '200.npz',\n",
       " '201.npz',\n",
       " '202.npz',\n",
       " '203.npz',\n",
       " '205.npz',\n",
       " '207.npz',\n",
       " '208.npz',\n",
       " '209.npz',\n",
       " '210.npz',\n",
       " '212.npz',\n",
       " '213.npz',\n",
       " '214.npz',\n",
       " '215.npz',\n",
       " '217.npz',\n",
       " '219.npz',\n",
       " '220.npz',\n",
       " '221.npz',\n",
       " '222.npz',\n",
       " '223.npz',\n",
       " '228.npz',\n",
       " '230.npz',\n",
       " '231.npz',\n",
       " '232.npz',\n",
       " '233.npz',\n",
       " '234.npz']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[record_id for record_id in distances.keys()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee1e924e678c44189d8e0561982d26ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_records = [\"201\", \"228\", \"213\", \"221\", \"223\"]\n",
    "test_records = [record + \".npz\" for record in test_records]\n",
    "if not os.path.exists(\"./data/heartbeats_train/\"):\n",
    "    os.makedirs(\"./data/heartbeats_train/\")\n",
    "if not os.path.exists(\"./data/heartbeats_test/\"):\n",
    "    os.makedirs(\"./data/heartbeats_test/\")\n",
    "for record_id in tqdm(distances.keys()):\n",
    "    if record_id in test_records:\n",
    "        # copy to test\n",
    "        shutil.copy(\"./data/heartbeats/\" + record_id, \"./data/heartbeats_test/\" + record_id)\n",
    "    else:\n",
    "        # copy to train\n",
    "        shutil.copy(\"./data/heartbeats/\" + record_id, \"./data/heartbeats_train/\" + record_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_records, test_records = train_test_split(records, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d68dcddc1ce49eb807f0751814a928f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if not os.path.exists(\"./data/heartbeats_evensplit_train/\"):\n",
    "    os.makedirs(\"./data/heartbeats_evensplit_train/\")\n",
    "    \n",
    "if not os.path.exists(\"./data/heartbeats_evensplit_test/\"):\n",
    "    os.makedirs(\"./data/heartbeats_evensplit_test/\")\n",
    "\n",
    "for record_id in tqdm(records):\n",
    "    if record_id in test_records:\n",
    "        # copy to test\n",
    "        shutil.copy(\"./data/heartbeats/\" + record_id + \".npz\", \"./data/heartbeats_evensplit_test/\" + record_id + \".npz\")\n",
    "    else:\n",
    "        # copy to train\n",
    "        shutil.copy(\"./data/heartbeats/\" + record_id + \".npz\", \"./data/heartbeats_evensplit_train/\" + record_id + \".npz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset + Dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ECGDataset(Dataset):\n",
    "    def __init__(self, data_dir, item_transform=None, target_transform=None) -> None:\n",
    "        self.data_dir = data_dir\n",
    "        self.lengths = {}\n",
    "        self.item_transform = item_transform\n",
    "        self.target_transform = target_transform\n",
    "        for _, _, files in os.walk(data_dir):\n",
    "            for file in files:\n",
    "                npzfile = np.load(data_dir + file)\n",
    "                self.lengths[file] = len(npzfile[\"labels\"])\n",
    "        \n",
    "    def __len__(self) -> int:\n",
    "        return sum(self.lengths.values())\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        running_count = 0\n",
    "        assert idx < self.__len__() # make sure we're not out of bounds\n",
    "        for file, length in self.lengths.items():\n",
    "            if idx < running_count + length:\n",
    "                npzfile = np.load(self.data_dir + file)\n",
    "                hb = npzfile[\"heartbeats\"][idx - running_count]\n",
    "                label = npzfile[\"labels\"][idx - running_count]\n",
    "                if self.item_transform:\n",
    "                    hb = self.item_transform(hb)\n",
    "                if self.target_transform:\n",
    "                    label = self.target_transform(label)\n",
    "                return hb, label\n",
    "            else:\n",
    "                running_count += length\n",
    "        # should never get here\n",
    "        raise IndexError(\"Index out of bounds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fft_hb = np.fft.fft(npzfile[\"heartbeats\"][0])\n",
    "fft_hb = np.fft.fftshift(fft_hb)\n",
    "sns.lineplot(x=np.arange(-180, 180), y=np.abs(fft_hb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def one_hot_encode(label):\n",
    "    possible_labels = [\"N\", \"S\", \"V\", \"F\", \"Q\"]\n",
    "    # one hot encoding\n",
    "    return torch.zeros(len(possible_labels)).scatter_(\n",
    "        dim=0,\n",
    "        index=torch.tensor(possible_labels.index(label)),\n",
    "        value=1\n",
    "    )\n",
    "\n",
    "noise_frequencies = {\n",
    "    \"breathing\": ([1/18, 1/12], [1/5, 1/3]) # breathing rate and impact\n",
    "}\n",
    "\n",
    "def noise_at_frequencies(hb, frequencies_distribution, fs=360):\n",
    "    # hb: input heartbeat\n",
    "    # frequencies_distribution: distribution of frequencies to add noise at\n",
    "    # returns: hb with noise added at frequency\n",
    "    \n",
    "    # generate sinusoid noise with random phase and amplitude from the distribution\n",
    "    noise = np.zeros(hb.shape)\n",
    "    for (source, (freq_range, amp_range)) in frequencies_distribution.items():\n",
    "        freq = np.random.uniform(freq_range[0], freq_range[1])\n",
    "        amp = np.random.uniform(amp_range[0], amp_range[1])\n",
    "        phase = np.random.uniform(0, 2 * np.pi)\n",
    "        noise += amp * np.sin(2 * np.pi * freq * 1/fs + phase)\n",
    "    return hb + noise\n",
    "\n",
    "ax = sns.lineplot(x=np.arange(0, 360), y=noise_at_frequencies(npzfile[\"heartbeats\"][0], noise_frequencies))\n",
    "sns.lineplot(x=np.arange(0, 360), y=npzfile[\"heartbeats\"][0], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def noise_at_frequencies_tensor(hb, frequencies_distribution, fs=360):\n",
    "    # hb: input heartbeat\n",
    "    # frequencies_distribution: distribution of frequencies to add noise at\n",
    "    # returns: hb with noise added at frequency\n",
    "    \n",
    "    # generate sinusoid noise with random phase and amplitude from the distribution\n",
    "    noise = torch.zeros(hb.shape)\n",
    "    for (source, (freq_range, amp_range)) in frequencies_distribution.items():\n",
    "        freq = torch.distributions.uniform.Uniform(freq_range[0], freq_range[1]).sample()\n",
    "        amp = torch.distributions.uniform.Uniform(amp_range[0], amp_range[1]).sample()\n",
    "        phase = torch.distributions.uniform.Uniform(0, 2 * torch.pi).sample()\n",
    "        noise += amp * torch.sin(2 * torch.pi * freq * 1/fs + phase)\n",
    "    return hb.add(noise)\n",
    "\n",
    "def z_normalise(hb):\n",
    "    return (hb - hb.mean()) / hb.std()\n",
    "\n",
    "def hb_transform(hb):\n",
    "    hb = torch.from_numpy(hb)\n",
    "    hb = noise_at_frequencies_tensor(hb, noise_frequencies)\n",
    "    hb = z_normalise(hb)\n",
    "    return hb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = ECGDataset(\"./data/heartbeats_train/\", item_transform=hb_transform, target_transform=one_hot_encode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-1.4884e-01, -1.7836e-01, -3.0752e-02,  5.7814e-02,  5.7814e-02,\n",
       "          1.1686e-01,  8.7335e-02,  1.1686e-01,  1.4638e-01,  2.0542e-01,\n",
       "          3.2351e-01,  3.5303e-01,  3.5303e-01,  2.6447e-01,  3.2351e-01,\n",
       "          3.8255e-01,  4.7112e-01,  3.8255e-01,  3.8255e-01,  3.2351e-01,\n",
       "          3.2351e-01,  4.1207e-01,  3.5303e-01,  3.2351e-01,  3.8255e-01,\n",
       "          3.2351e-01,  3.5303e-01,  3.5303e-01,  2.6447e-01,  2.9399e-01,\n",
       "          2.9399e-01,  2.6447e-01,  2.3494e-01,  2.6447e-01,  2.9399e-01,\n",
       "          2.0542e-01,  1.7590e-01,  1.1686e-01,  2.0542e-01,  2.3494e-01,\n",
       "          2.9399e-01,  1.7590e-01,  1.7590e-01,  1.4638e-01,  2.0542e-01,\n",
       "          2.0542e-01,  2.6447e-01,  2.3494e-01,  1.4638e-01,  1.1686e-01,\n",
       "          1.7590e-01,  2.0542e-01,  1.4638e-01,  1.7590e-01,  5.7814e-02,\n",
       "          5.7814e-02,  2.8292e-02,  8.7335e-02,  5.7814e-02, -1.2301e-03,\n",
       "         -1.2301e-03,  2.8292e-02,  8.7335e-02,  1.4638e-01,  1.4638e-01,\n",
       "          1.1686e-01,  8.7335e-02, -1.2301e-03,  1.1686e-01,  8.7335e-02,\n",
       "          5.7814e-02,  2.8292e-02, -1.2301e-03, -8.9795e-02, -1.2301e-03,\n",
       "          5.7814e-02,  1.1686e-01, -1.2301e-03, -8.9795e-02, -1.1932e-01,\n",
       "         -3.0752e-02,  5.7814e-02,  2.8292e-02, -1.2301e-03, -6.0274e-02,\n",
       "         -1.1932e-01, -8.9795e-02, -3.0752e-02, -1.2301e-03,  2.8292e-02,\n",
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       "         -8.2784e-01, -8.5736e-01, -9.4593e-01, -8.8688e-01, -8.5736e-01,\n",
       "         -8.2784e-01, -8.5736e-01, -9.4593e-01, -9.7545e-01, -8.8688e-01,\n",
       "         -8.2784e-01, -7.9832e-01, -7.6880e-01, -7.3927e-01, -7.6880e-01,\n",
       "         -5.9167e-01, -5.0310e-01, -3.5549e-01, -3.2597e-01, -2.9645e-01,\n",
       "         -3.2597e-01, -1.7836e-01, -8.9795e-02, -3.0752e-02, -6.0274e-02,\n",
       "         -1.1932e-01, -1.1932e-01, -6.0274e-02, -1.2301e-03,  5.7814e-02,\n",
       "          5.7814e-02, -6.0274e-02, -6.0274e-02, -3.0752e-02,  2.8292e-02,\n",
       "          2.8292e-02,  8.7335e-02, -6.0274e-02, -1.1932e-01,  2.8292e-02,\n",
       "         -1.2301e-03,  5.7814e-02,  5.7814e-02, -6.0274e-02, -8.9795e-02,\n",
       "         -1.2301e-03,  2.8292e-02, -3.0752e-02,  2.8292e-02, -3.0752e-02,\n",
       "         -3.0752e-02, -1.2301e-03,  8.7335e-02,  1.1686e-01,  2.8292e-02,\n",
       "         -1.2301e-03, -6.0274e-02, -3.0752e-02,  5.7814e-02, -3.0752e-02,\n",
       "         -3.0752e-02, -3.0752e-02, -1.1932e-01, -3.0752e-02, -6.0274e-02,\n",
       "         -3.0752e-02, -8.9795e-02, -1.4884e-01, -1.7836e-01, -1.4884e-01,\n",
       "         -1.4884e-01, -1.1932e-01, -8.9795e-02, -2.0788e-01, -2.3740e-01,\n",
       "         -2.0788e-01, -1.4884e-01, -1.1932e-01, -1.1932e-01, -2.0788e-01,\n",
       "         -2.0788e-01, -2.0788e-01, -6.0274e-02, -1.1932e-01, -1.4884e-01],\n",
       "        dtype=torch.float64),\n",
       " tensor([1., 0., 0., 0., 0.]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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