{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "from numpy import NaN, Inf, arange, isscalar, asarray, array\n", "\n", "def peakdet(v, delta, x = None):\n", " \"\"\"\n", " Converted from MATLAB script at http://billauer.co.il/peakdet.html\n", " \n", " Returns two arrays\n", " \n", " function [maxtab, mintab]=peakdet(v, delta, x)\n", " %PEAKDET Detect peaks in a vector\n", " % [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local\n", " % maxima and minima (\"peaks\") in the vector V.\n", " % MAXTAB and MINTAB consists of two columns. Column 1\n", " % contains indices in V, and column 2 the found values.\n", " % \n", " % With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices\n", " % in MAXTAB and MINTAB are replaced with the corresponding\n", " % X-values.\n", " %\n", " % A point is considered a maximum peak if it has the maximal\n", " % value, and was preceded (to the left) by a value lower by\n", " % DELTA.\n", " \n", " % Eli Billauer, 3.4.05 (Explicitly not copyrighted).\n", " % This function is released to the public domain; Any use is allowed.\n", " \n", " \"\"\"\n", " maxtab = []\n", " mintab = []\n", " \n", " if x is None:\n", " x = arange(len(v))\n", " \n", " v = asarray(v)\n", " \n", " if len(v) != len(x):\n", " sys.exit('Input vectors v and x must have same length')\n", " \n", " if not isscalar(delta):\n", " sys.exit('Input argument delta must be a scalar')\n", " \n", " if delta <= 0:\n", " sys.exit('Input argument delta must be positive')\n", " \n", " mn, mx = Inf, -Inf\n", " mnpos, mxpos = NaN, NaN\n", " \n", " lookformax = True\n", " \n", " for i in arange(len(v)):\n", " this = v[i]\n", " if this > mx:\n", " mx = this\n", " mxpos = x[i]\n", " if this < mn:\n", " mn = this\n", " mnpos = x[i]\n", " \n", " if lookformax:\n", " if this < mx-delta:\n", " maxtab.append((mxpos, mx))\n", " mn = this\n", " mnpos = x[i]\n", " lookformax = False\n", " else:\n", " if this > mn+delta:\n", " mintab.append((mnpos, mn))\n", " mx = this\n", " mxpos = x[i]\n", " lookformax = True\n", "\n", " return array(maxtab), array(mintab)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ekg = pd.read_csv('surgery.txt',sep='\\t', index_col=0, header=0, usecols=(0,1))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMS value: 10.0136885799\n", "mean BPM: 701.785008448\n" ] }, { "data": { "image/png": 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Tnsv+wmZSLsqyAYsMF5TV67r9U6xVMps+pGrciF3m2OqLi4mVDz2ydHV1QVdX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\n37GFb2EJ02rnvULYwkewhF6WlVdXVxd0dXUBALcpaxiWGrg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RKtk33qj/St5YuDjto2mD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\nLo0HhrVrgUsvZedQH7e/+ipwxx1MkLdsieSsX8+GrVc6AV78o4+OjKS1F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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start = 73000\n", "width = 10000\n", "a = ekg.iloc[start:start+width]\n", "b = a.diff()\n", "c = b*b\n", "d = c.copy()\n", "d.EKG = pd.np.convolve(c.EKG.values, np.ones(10), 'same')\n", "rms = np.sqrt(np.mean(np.square(d)))\n", "print 'RMS value:', rms.EKG\n", "e_max,e_min = peakdet(d, rms.EKG)\n", "\n", "\n", "fig = plt.figure(figsize=(10, 10))\n", "ax = fig.add_subplot(211)\n", "a.plot(ax=ax)\n", "\n", "ax = fig.add_subplot(212)\n", "ax.hold(True)\n", "plt.plot(d)\n", "plt.plot(e_max[:,0], e_max[:,1], 'rx')\n", "\n", "fig.tight_layout()\n", "\n", "freq = 1./np.diff(e_max[:,0]*1e-3)\n", "e = np.mean(freq)\n", "print 'mean BPM:', e*60." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_HR(inData, **kwargs):\n", " b = np.diff(inData) # Differentiate\n", " c = np.square(b) # square\n", " d = np.convolve(c, np.ones(10), 'same') # smooth\n", " # get RMS value to use in the peak detection algorithm\n", " rms = np.sqrt(np.mean(np.square(d)))\n", " # print 'RMS value:', rms.EKG\n", " e_max, e_min = peakdet(d, rms)\n", "\n", " freq = 1./np.diff(e_max[:, 0] * 1e-3)\n", " e = np.mean(freq)\n", " return e * 60." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(999,)\n", "(999,)\n", "(10,)\n", "711.993888464\n" ] } ], "source": [ "data = ekg.iloc[0:1000].values\n", "data = data.flatten()\n", "print get_HR(data)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "object too deep for desired array", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mekg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mekg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mget_HR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m\u001b[0m in \u001b[0;36mget_HR\u001b[0;34m(inData, **kwargs)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minData\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Differentiate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# square\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m 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2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }