physionet/starter-ecg-dataset-physionet.ipynb

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   "source": [
    "**Access and use ECG Lead 2 Dataset PhysioNet**"
   ]
  },
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    "tags": []
   },
   "source": [
    "# Global Section"
   ]
  },
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     "text": [
      "Basic sampling rate(Hz): 128\n",
      "GLOBAL DIR :: db_npy\n",
      "STD ANN FILE :: db_npy\\annotations.txt\n",
      "\n",
      "Standard Labels::\n",
      "N\tb\tNormal beat\n",
      "L\tb\tLeft bundle branch block beat\n",
      "R\tb\tRight bundle branch block beat\n",
      "B\tb\tBundle branch block beat (unspecified)\n",
      "A\tb\tAtrial premature beat\n",
      "a\tb\tAberrated atrial premature beat\n",
      "J\tb\tNodal (junctional) premature beat\n",
      "S\tb\tSupraventricular premature or ectopic beat (atrial or nodal)\n",
      "V\tb\tPremature ventricular contraction\n",
      "r\tb\tR-on-T premature ventricular contraction\n",
      "F\tb\tFusion of ventricular and normal beat\n",
      "e\tb\tAtrial escape beat\n",
      "j\tb\tNodal (junctional) escape beat\n",
      "n\tb\tSupraventricular escape beat (atrial or nodal)\n",
      "E\tb\tVentricular escape beat\n",
      "/\tb\tPaced beat\n",
      "f\tb\tFusion of paced and normal beat\n",
      "Q\tb\tUnclassifiable \n",
      "?\tb\tBeat not classified during learning\n",
      "[\tn\tStart of ventricular flutter/fibrillation\n",
      "!\tn\tVentricular flutter wave\n",
      "]\tn\tEnd of ventricular flutter/fibrillation\n",
      "x\tn\tNon-conducted P-wave (blocked APC)\n",
      "(\tn\tWaveform onset\n",
      ")\tn\tWaveform end\n",
      "p\tn\tPeak of P-wave\n",
      "t\tn\tPeak of T-wave\n",
      "u\tn\tPeak of U-wave\n",
      "`\tn\tPQ junction\n",
      "'\tn\tJ-point\n",
      "^\tn\t(Non-captured) pacemaker artifact\n",
      "|\tn\tIsolated QRS-like artifact [1]\n",
      "~\tn\tChange in signal quality [1]\n",
      "+\tn\tRhythm change [2]\n",
      "s\tn\tST segment change [2]\n",
      "T\tn\tT-wave change [2]\n",
      "*\tn\tSystole\n",
      "D\tn\tDiastole\n",
      "=\tn\tMeasurement annotation [2]\n",
      "\"\tn\tComment annotation [2]\n",
      "@\tn\tLink to external data [3]\n",
      "\n",
      "beat-annotation: 19\n",
      "non-beat-annotation: 22\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "#<<----------------------------------- GLOBAL Variables\n",
    "\n",
    "# Resampled every signal to this rate for consistency\n",
    "BASIC_SRATE = 128 #Hz\n",
    "print('Basic sampling rate(Hz):',BASIC_SRATE)\n",
    "\n",
    "# global base directory\n",
    "global_dir = 'db_npy'\n",
    "print('GLOBAL DIR ::',global_dir)\n",
    "\n",
    "std_annotations = os.path.join(global_dir,'annotations.txt')\n",
    "print('STD ANN FILE ::',std_annotations)\n",
    "\n",
    "# ------ load standard labels ------------------------------------------\n",
    "std_ant = np.loadtxt(std_annotations, dtype='str',delimiter=\"\\t\")\n",
    "\n",
    "# Seperate out stand beat and non-beat annotations using 'annotations.txt'\n",
    "beat_ants = []\n",
    "non_beat_ants = []\n",
    "print('\\nStandard Labels::')\n",
    "for a in std_ant:\n",
    "    # a[0] =  # standard label (char)\n",
    "    # a[1] =  # type of label\n",
    "    # a[2]  = # description (str)\n",
    "    print(a[0]+'\\t'+a[1]+'\\t'+a[2])\n",
    "    if a[1]=='b': #<<--- is a beat-annotation\n",
    "        beat_ants.append(a[0])\n",
    "    else:\n",
    "        non_beat_ants.append(a[0])\n",
    "\n",
    "print('')\n",
    "print('beat-annotation:',len(beat_ants))\n",
    "print('non-beat-annotation:',len(non_beat_ants))\n",
    "#-----------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "# data file identifiers \n",
    "g_BEAT = '_BEAT.npy'         #<<--- beat annotations\n",
    "g_NBEAT = '_NBEAT.npy'       #<<--- non-beat annotations\n",
    "g_SIG2 = '_SIG_II.npy'     #<<--- Signal Data (Removed manual gain)\n",
    "\n"
   ]
  },
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    },
    "tags": []
   },
   "source": [
    "# Select a database and list its records"
   ]
  },
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     "text": [
      "Database@ db_npy\\mitdb_npy\n",
      "Total Records = 48\n",
      "['100' '101' '102' '103' '104' '105' '106' '107' '108' '109' '111' '112'\n",
      " '113' '114' '115' '116' '117' '118' '119' '121' '122' '123' '124' '200'\n",
      " '201' '202' '203' '205' '207' '208' '209' '210' '212' '213' '214' '215'\n",
      " '217' '219' '220' '221' '222' '223' '228' '230' '231' '232' '233' '234']\n"
     ]
    }
   ],
   "source": [
    "# Select a database name from ['mitdb', 'svdb', 'incartdb']\n",
    "iDatabase = 'mitdb'\n",
    "idbPath = os.path.join(global_dir,iDatabase + '_npy' )\n",
    "print('Database@',idbPath)\n",
    "\n",
    "# Read 'RECORDS' file for list of available records\n",
    "record_list = np.loadtxt(os.path.join(idbPath,'RECORDS'), dtype='str',delimiter=\"\\n\")\n",
    "print('Total Records =',len(record_list))\n",
    "print(record_list)"
   ]
  },
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   "cell_type": "markdown",
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    },
    "tags": []
   },
   "source": [
    "# Select a record within a database and read its data"
   ]
  },
  {
   "cell_type": "code",
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     "text": [
      "beats: (2955, 2)\n",
      "R-Peak\t [ 16  74 172 248 303]\n",
      "Labels\t ['F' 'V' 'N' 'F' 'V']\n",
      "\n",
      "non-beats: (85, 2)\n",
      "nR-Peak\t [   3 2305 2878 4633 7058]\n",
      "nLabels\t ['+' '~' '~' '~' '~']\n",
      "\n",
      "Signal Length = (231111,)\n",
      "Signal Duration = 1805.5546875 sec\n"
     ]
    }
   ],
   "source": [
    "# Select a Record name\n",
    "iRecord = '208' # (from database mitdb as selected in above cell)\n",
    "\n",
    "#<<---------------------------------------------\n",
    "# Load Beat-Annotation (Labels)\n",
    "#<<---------------------------------------------\n",
    "beat_file = os.path.join(idbPath,iRecord + g_BEAT)\n",
    "iBeat = np.load(beat_file)\n",
    "print('beats:',iBeat.shape)\n",
    "# seperate out R-peaks and Labels (note both are in string format)\n",
    "iRpeaks = iBeat[:,0].astype('int') #<<-- convert sampling locations to integer\n",
    "iLabels = iBeat[:,1]               #<<-- labels remain as string\n",
    "# print some values....\n",
    "print('R-Peak\\t', iRpeaks[0:5])\n",
    "print('Labels\\t', iLabels[0:5])\n",
    "print('')\n",
    "\n",
    "#<<---------------------------------------------\n",
    "# Load Non-Beat-Annotation\n",
    "#<<---------------------------------------------\n",
    "nbeat_file = os.path.join(idbPath,iRecord + g_NBEAT)\n",
    "iNbeat = np.load(nbeat_file)\n",
    "print('non-beats:',iNbeat.shape)\n",
    "# seperate out R-peaks and non-beats (note both are in string format)\n",
    "inRpeaks = iNbeat[:,0].astype('int') #<<-- convert sampling locations to integer\n",
    "inLabels = iNbeat[:,1] #<<-- labels remain as string\n",
    "# print some values....\n",
    "print('nR-Peak\\t', inRpeaks[0:5])\n",
    "print('nLabels\\t', inLabels[0:5])\n",
    "print('')\n",
    "\n",
    "#<<---------------------------------------------\n",
    "# Load Signal Data\n",
    "#<<---------------------------------------------\n",
    "signal_file = os.path.join(idbPath,iRecord + g_SIG2)\n",
    "iSignal = np.load(signal_file)\n",
    "print('Signal Length =',iSignal.shape)\n",
    "print('Signal Duration =',len(iSignal)/BASIC_SRATE, 'sec')"
   ]
  },
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   "source": [
    "# Plot the signal data (fixed duration)"
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select duration to be plotted\n",
    "plot_from, plot_to = 10, 15 # in sec, plot 5 sec signal\n",
    "\n",
    "# first convert to samples\n",
    "pf, pt = int(plot_from*BASIC_SRATE), int(plot_to*BASIC_SRATE)\n",
    "signal_slice = iSignal[pf:pt]\n",
    "\n",
    "plt.ylim(-2.5,3.5)\n",
    "_ = plt.plot(signal_slice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.019882,
     "end_time": "2021-10-16T23:35:49.004092",
     "exception": false,
     "start_time": "2021-10-16T23:35:48.984210",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Plot the signal data (for any beat/R-peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-16T23:35:49.081694Z",
     "iopub.status.busy": "2021-10-16T23:35:49.069305Z",
     "iopub.status.idle": "2021-10-16T23:35:49.232252Z",
     "shell.execute_reply": "2021-10-16T23:35:49.231709Z"
    },
    "papermill": {
     "duration": 0.207612,
     "end_time": "2021-10-16T23:35:49.232381",
     "exception": false,
     "start_time": "2021-10-16T23:35:49.024769",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected beat# 552 of 2955\n",
      "R-Peak Location: 41001\n",
      "Beat-Label: N\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find out total number of beats\n",
    "nos_beats = len(iRpeaks)\n",
    "\n",
    "# randomly select a beat to plot\n",
    "random_beat_index = random.randint(0,nos_beats-1)\n",
    "print('Selected beat#',random_beat_index,'of',nos_beats)\n",
    "\n",
    "# find location of R-peak of that beat ( a single point )\n",
    "Rpeak = iRpeaks[random_beat_index]\n",
    "print('R-Peak Location:',Rpeak)\n",
    "\n",
    "# select fixed duration of signal to be plotted on either side of the R-peak\n",
    "left_duration = 0.5 # sec\n",
    "right_duration = 1 # sec\n",
    "\n",
    "# convert to duration in sec to samples\n",
    "left_samples = int(left_duration*BASIC_SRATE)\n",
    "right_samples = int(right_duration*BASIC_SRATE)\n",
    "\n",
    "# calculate time interval\n",
    "pf, pt = Rpeak-left_samples, Rpeak+right_samples # in samples\n",
    "signal_slice = iSignal[pf:pt]\n",
    "\n",
    "# plot\n",
    "plt.figure('beat')\n",
    "plt.ylim(-2.5,3.5)\n",
    "plt.plot(signal_slice) #<<----- Plot signal slice\n",
    "plt.vlines(Rpeak-pf,-2.5,3.5,linewidth=0.4) #<<----- Plot location of R-peak\n",
    "print('Beat-Label:',iLabels[random_beat_index]) #<<----- Print Beat-Label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.019005,
     "end_time": "2021-10-16T23:35:49.271352",
     "exception": false,
     "start_time": "2021-10-16T23:35:49.252347",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Plot the signal data (fixed duration along with labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-16T23:35:49.333986Z",
     "iopub.status.busy": "2021-10-16T23:35:49.327870Z",
     "iopub.status.idle": "2021-10-16T23:35:49.468801Z",
     "shell.execute_reply": "2021-10-16T23:35:49.468155Z"
    },
    "papermill": {
     "duration": 0.178327,
     "end_time": "2021-10-16T23:35:49.468936",
     "exception": false,
     "start_time": "2021-10-16T23:35:49.290609",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heart-Rate: 96.0 bpm\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_from, plot_to = 10, 15 # in sec, plot 5 sec signal\n",
    "\n",
    "# first convert to samples\n",
    "pf, pt = int(plot_from*BASIC_SRATE), int(plot_to*BASIC_SRATE)\n",
    "signal_slice = iSignal[pf:pt]\n",
    "\n",
    "plt.plot(signal_slice)\n",
    "plt.ylim(-2.5,3.5)\n",
    "\n",
    "\n",
    "query_list = np.where((iRpeaks>=pf) & (iRpeaks<pt))[0]\n",
    "\n",
    "rPeaks = iRpeaks[query_list] - pf\n",
    "rLabels = iLabels[query_list]\n",
    "\n",
    "_ = plt.xticks(rPeaks,rLabels)\n",
    "plt.grid(axis='x')\n",
    "\n",
    "# also calculate the Heart-Rate in beats per minute(BPM) for given duration\n",
    "# if there are b beats in a in t sec duration then\n",
    "# beats per second = b/t\n",
    "# beats per minute = 60* (b/t)\n",
    "\n",
    "b,t = len(rPeaks), plot_to-plot_from\n",
    "bpm = 60*b/t\n",
    "print('Heart-Rate:',round(bpm,2),'bpm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020897,
     "end_time": "2021-10-16T23:35:49.510948",
     "exception": false,
     "start_time": "2021-10-16T23:35:49.490051",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Counting labels present in a record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-16T23:35:49.561149Z",
     "iopub.status.busy": "2021-10-16T23:35:49.559996Z",
     "iopub.status.idle": "2021-10-16T23:35:49.569751Z",
     "shell.execute_reply": "2021-10-16T23:35:49.569070Z"
    },
    "papermill": {
     "duration": 0.038362,
     "end_time": "2021-10-16T23:35:49.569888",
     "exception": false,
     "start_time": "2021-10-16T23:35:49.531526",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Record: mitdb 208\n",
      "Total\t 2955\n",
      "N \t 1586\n",
      "L \t 0\n",
      "R \t 0\n",
      "B \t 0\n",
      "A \t 0\n",
      "a \t 0\n",
      "J \t 0\n",
      "S \t 2\n",
      "V \t 992\n",
      "r \t 0\n",
      "F \t 373\n",
      "e \t 0\n",
      "j \t 0\n",
      "n \t 0\n",
      "E \t 0\n",
      "/ \t 0\n",
      "f \t 0\n",
      "Q \t 2\n",
      "? \t 0\n",
      "Sum\t 2955\n"
     ]
    }
   ],
   "source": [
    "#-------------------------------------------------------------------------\n",
    "# Find Count of Each Beat-Annotation in the record\n",
    "print('Record:',iDatabase,iRecord)\n",
    "beat_ant_count = {}\n",
    "print('Total\\t',len(iLabels))\n",
    "\n",
    "bsum = 0\n",
    "for b in beat_ants:\n",
    "    beat_ant_count[b]= len(np.where(iLabels==b)[0])\n",
    "    print(b,'\\t',beat_ant_count[b])\n",
    "    bsum+=beat_ant_count[b]\n",
    "print('Sum\\t',bsum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021329,
     "end_time": "2021-10-16T23:35:49.612913",
     "exception": false,
     "start_time": "2021-10-16T23:35:49.591584",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Find all beats of a particular lable in a given duration of the record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-16T23:35:49.678371Z",
     "iopub.status.busy": "2021-10-16T23:35:49.671012Z",
     "iopub.status.idle": "2021-10-16T23:35:49.833728Z",
     "shell.execute_reply": "2021-10-16T23:35:49.833078Z"
    },
    "papermill": {
     "duration": 0.199316,
     "end_time": "2021-10-16T23:35:49.833858",
     "exception": false,
     "start_time": "2021-10-16T23:35:49.634542",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total F type beats in given duration = 18\n",
      "\n",
      "Randomly select one beat...\n",
      "R-Peak Location: 12306\n",
      "Beat-Label: F\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the duration\n",
    "time_from, time_to = 10, 100 # in sec\n",
    "\n",
    "# Select type of label to find\n",
    "lable_to_find='F'\n",
    "\n",
    "# convert to samples\n",
    "pf,pt = int(time_from*BASIC_SRATE), int(time_to*BASIC_SRATE)\n",
    "\n",
    "# design query\n",
    "a_query = (\n",
    "          (iRpeaks >= pf) &\n",
    "          (iRpeaks < pt) &\n",
    "          (iLabels==lable_to_find)\n",
    "          )\n",
    "\n",
    "# retrive indices\n",
    "a_list = np.where(a_query)[0]\n",
    "\n",
    "print('Total',lable_to_find,'type beats in given duration =',len(a_list))\n",
    "\n",
    "\n",
    "# plot a random beat from retrived list\n",
    "print('\\nRandomly select one beat...')\n",
    "if len(a_list>0):\n",
    "    sel_index = np.random.choice(a_list)\n",
    "    \n",
    "    \n",
    "    # find location of R-peak of that beat ( a single point )\n",
    "    Rpeak = iRpeaks[sel_index]\n",
    "    print('R-Peak Location:',Rpeak)\n",
    "\n",
    "    # select fixed duration of signal to be plotted on either side of the R-peak\n",
    "    left_duration = 1 # sec\n",
    "    right_duration = 1.5 # sec\n",
    "\n",
    "    # convert to duration in sec to samples\n",
    "    left_samples = int(left_duration*BASIC_SRATE)\n",
    "    right_samples = int(right_duration*BASIC_SRATE)\n",
    "\n",
    "    # calculate time interval\n",
    "    pf, pt = Rpeak-left_samples, Rpeak+right_samples # in samples\n",
    "    signal_slice = iSignal[pf:pt]\n",
    "\n",
    "    plt.figure('beat')\n",
    "    plt.ylim(-2.5,3.5)\n",
    "    plt.plot(signal_slice) #<<----- Plot signal slice\n",
    "    plt.vlines(Rpeak-pf,-2.5,3.5,linewidth=0.4) #<<----- Plot location of R-peak\n",
    "    print('Beat-Label:',iLabels[sel_index]) #<<----- Print Beat-Label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.024524,
     "end_time": "2021-10-16T23:35:49.883082",
     "exception": false,
     "start_time": "2021-10-16T23:35:49.858558",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Creating a custom set of beat labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-16T23:35:49.949076Z",
     "iopub.status.busy": "2021-10-16T23:35:49.944407Z",
     "iopub.status.idle": "2021-10-16T23:35:49.958808Z",
     "shell.execute_reply": "2021-10-16T23:35:49.958014Z"
    },
    "papermill": {
     "duration": 0.051729,
     "end_time": "2021-10-16T23:35:49.958952",
     "exception": false,
     "start_time": "2021-10-16T23:35:49.907223",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Record: mitdb 208\n",
      "Total\t 2955\n",
      "_N \t 1586\n",
      "_S \t 2\n",
      "_V \t 992\n",
      "_F \t 373\n",
      "_Q \t 2\n",
      "Sum\t 2955\n"
     ]
    }
   ],
   "source": [
    "# mapping standard labels to AAMI or custom labels : NOTE - only beat-annotations are mapped\n",
    "\n",
    "# define a mapping dictionary\n",
    "aami_ants = [ '_N','_S','_V','_F','_Q']\n",
    "aami_mapping = {\n",
    "    \n",
    "    'N': '_N',\n",
    "    'L': '_N',\n",
    "    'R': '_N',\n",
    "    'B': '_N',\n",
    "    'A': '_S',\n",
    "    'a': '_S',\n",
    "    'J': '_S',\n",
    "    'S': '_S',\n",
    "    'V': '_V',\n",
    "    'r': '_V',\n",
    "    'F': '_F',\n",
    "    'e': '_N',\n",
    "    'j': '_N',\n",
    "    'n': '_N',\n",
    "    'E': '_N',\n",
    "    'f': '_Q',\n",
    "    '/': '_Q',\n",
    "    'Q': '_Q',\n",
    "    '?': '_Q',\n",
    "                } # <<-- aami labels have been prefixed by _ to avoid confusion\n",
    "\n",
    "\n",
    "# map all iLabels to aamiLabels\n",
    "aamiLabels = np.zeros(len(iLabels), dtype='U2')  #<<--- U2 since we use 2 chars for aami labels\n",
    "\n",
    "for i in range(0,len(iLabels)):\n",
    "    aamiLabels[i]=aami_mapping[iLabels[i]]\n",
    "\n",
    "#----- mapping done------------ \n",
    "# this new array can be used similar to iLabels\n",
    "\n",
    "#-------------------------------------------------------------------------\n",
    "# Find Count of Each AAMI Label in the record\n",
    "print('Record:',iDatabase,iRecord)\n",
    "aami_ant_count = {}\n",
    "print('Total\\t',len(aamiLabels))\n",
    "\n",
    "bsum = 0\n",
    "for b in aami_ants:\n",
    "    aami_ant_count[b]= len(np.where(aamiLabels==b)[0])\n",
    "    print(b,'\\t',aami_ant_count[b])\n",
    "    bsum+=aami_ant_count[b]\n",
    "print('Sum\\t',bsum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.02507,
     "end_time": "2021-10-16T23:35:50.008771",
     "exception": false,
     "start_time": "2021-10-16T23:35:49.983701",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Find all beats of a particular aami or Custom lable in a given duration of the record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-16T23:35:50.075208Z",
     "iopub.status.busy": "2021-10-16T23:35:50.069103Z",
     "iopub.status.idle": "2021-10-16T23:35:50.245915Z",
     "shell.execute_reply": "2021-10-16T23:35:50.244180Z"
    },
    "papermill": {
     "duration": 0.212078,
     "end_time": "2021-10-16T23:35:50.246094",
     "exception": false,
     "start_time": "2021-10-16T23:35:50.034016",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total _N type beats in given duration = 96\n",
      "\n",
      "Randomly select one beat...\n",
      "R-Peak Location: 3034\n",
      "Beat-Label: N\n",
      "AAMI-Label: _N\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select a duration\n",
    "time_from, time_to = 10, 100 # in sec\n",
    "lable_to_find='_N'\n",
    "\n",
    "# convert to samples\n",
    "pf,pt = int(time_from*BASIC_SRATE), int(time_to*BASIC_SRATE)\n",
    "\n",
    "# design query\n",
    "a_query = (\n",
    "          (iRpeaks >= pf) &\n",
    "          (iRpeaks < pt) &\n",
    "          (aamiLabels==lable_to_find)\n",
    "          )\n",
    "\n",
    "# retrive indices\n",
    "a_list = np.where(a_query)[0]\n",
    "\n",
    "print('Total',lable_to_find,'type beats in given duration =',len(a_list))\n",
    "\n",
    "\n",
    "# plot a random beat from retrived list\n",
    "print('\\nRandomly select one beat...')\n",
    "if len(a_list>0):\n",
    "    sel_index = np.random.choice(a_list)\n",
    "    \n",
    "    \n",
    "    # find location of R-peak of that beat ( a single point )\n",
    "    Rpeak = iRpeaks[sel_index]\n",
    "    print('R-Peak Location:',Rpeak)\n",
    "\n",
    "    # select fixed duration of signal to be plotted on either side of the R-peak\n",
    "    left_duration = 1 # sec\n",
    "    right_duration = 1.5 # sec\n",
    "\n",
    "    # convert to duration in sec to samples\n",
    "    left_samples = int(left_duration*BASIC_SRATE)\n",
    "    right_samples = int(right_duration*BASIC_SRATE)\n",
    "\n",
    "    # calculate time interval\n",
    "    pf, pt = Rpeak-left_samples, Rpeak+right_samples # in samples\n",
    "    signal_slice = iSignal[pf:pt]\n",
    "\n",
    "    plt.figure('beat')\n",
    "    plt.ylim(-2.5,3.5)\n",
    "    plt.plot(signal_slice) #<<----- Plot signal slice\n",
    "    plt.vlines(Rpeak-pf,-2.5,3.5,linewidth=0.4)  #<<----- Plot location of R-peak\n",
    "    print('Beat-Label:',iLabels[sel_index]) #<<----- Print Beat-Label\n",
    "    print('AAMI-Label:',aamiLabels[sel_index]) #<<----- Print Beat-Label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025849,
     "end_time": "2021-10-16T23:35:50.298046",
     "exception": false,
     "start_time": "2021-10-16T23:35:50.272197",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Displaying both standard and custom labels in a given duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-16T23:35:50.376140Z",
     "iopub.status.busy": "2021-10-16T23:35:50.374165Z",
     "iopub.status.idle": "2021-10-16T23:35:50.646642Z",
     "shell.execute_reply": "2021-10-16T23:35:50.647204Z"
    },
    "papermill": {
     "duration": 0.323879,
     "end_time": "2021-10-16T23:35:50.647369",
     "exception": false,
     "start_time": "2021-10-16T23:35:50.323490",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select time duration\n",
    "plot_from, plot_to = 1000, 1020 # in sec, plot 20 sec signal\n",
    "\n",
    "# first convert to samples\n",
    "pf, pt = int(plot_from*BASIC_SRATE), int(plot_to*BASIC_SRATE)\n",
    "signal_slice = iSignal[pf:pt]\n",
    "plt.figure('fancy',figsize=(20,6))\n",
    "plt.plot(signal_slice,linewidth=0.6,color='black')\n",
    "plt.ylim(-2.5,3.5)\n",
    "\n",
    "# display standard labels as ticks\n",
    "query_list = np.where((iRpeaks>=pf) & (iRpeaks<pt))[0]\n",
    "\n",
    "rPeaks = iRpeaks[query_list] - pf\n",
    "rLabels = iLabels[query_list]\n",
    "\n",
    "_ = plt.xticks(rPeaks,rLabels)\n",
    "plt.grid(axis='x')\n",
    "\n",
    "# display aami labels as scatter\n",
    "rLabels = aamiLabels[query_list]\n",
    "\n",
    "# need a color map for aami labels\n",
    "aami_color_dict = {'_N':'tab:green','_S':'tab:red','_V':'tab:blue','_F':'tab:purple','_Q':'tab:gray'}\n",
    "rLcol = np.zeros(len(rLabels), dtype='U10')\n",
    "for i in range(0,len(rLabels)):\n",
    "    rLcol[i]=aami_color_dict[rLabels[i]]\n",
    "_ = plt.scatter(rPeaks,np.zeros(len(rPeaks))-2,color=rLcol,marker='s')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.02582,
     "end_time": "2021-10-16T23:35:50.699310",
     "exception": false,
     "start_time": "2021-10-16T23:35:50.673490",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# plot RR-Intervals in a given duration\n",
    "RR-Interval might be import to determine the regularity of the rhythm. It can also be used to calculate heart rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-16T23:35:50.928332Z",
     "iopub.status.busy": "2021-10-16T23:35:50.764597Z",
     "iopub.status.idle": "2021-10-16T23:35:51.095666Z",
     "shell.execute_reply": "2021-10-16T23:35:51.094875Z"
    },
    "papermill": {
     "duration": 0.369541,
     "end_time": "2021-10-16T23:35:51.095799",
     "exception": false,
     "start_time": "2021-10-16T23:35:50.726258",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heart-Rate: 96.7 bpm\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select time duration\n",
    "plot_from, plot_to = 1000, 1020 # in sec, plot 20 sec signal\n",
    "\n",
    "# first convert to samples\n",
    "pf, pt = int(plot_from*BASIC_SRATE), int(plot_to*BASIC_SRATE)\n",
    "\n",
    "query_list = np.where((iRpeaks>=pf) & (iRpeaks<pt))[0]\n",
    "if len(query_list)>1 : #proceed only if at least 2 R peaks are present\n",
    "    \n",
    "    # calculate RR Intervals\n",
    "    rpeaks_range = iRpeaks[query_list]\n",
    "    rri_intervals = rpeaks_range[1:] - rpeaks_range[0:-1]\n",
    "    \n",
    "    # convert to seconds\n",
    "    rri_intervals=rri_intervals/BASIC_SRATE\n",
    "    \n",
    "    # plot\n",
    "    plt.plot(rri_intervals)\n",
    "    \n",
    "    # RR-Interval can also be considered as the duration of 1 beat\n",
    "    # to calculate Hear-Rate, find average RR Interval\n",
    "    avg_RRI = np.average(rri_intervals)\n",
    "    \n",
    "    # heart-rate\n",
    "    bpm = 60/avg_RRI\n",
    "    print('Heart-Rate:',round(bpm,2),'bpm')\n",
    "    \n",
    "else:\n",
    "    print('Not enough R-Peaks in the interval')\n",
    "        \n",
    "        \n",
    "\n"
   ]
  }
 ],
 "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": "3.8.10"
  },
  "papermill": {
   "duration": 16.359334,
   "end_time": "2021-10-16T23:35:51.234106",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-10-16T23:35:34.874772",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}