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visualize.py
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visualize.py
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import os
import matplotlib.pyplot as plt
import math
import operator
import time
from scipy import signal
from collections import OrderedDict
import requests
import random
import psutil
def getRemainingBatteryPower():
battery = psutil.sensors_battery()
plugged = battery.power_plugged
percent = str(battery.percent)
if plugged==False:
plugged = "Not Plugged In"
else:
plugged = "Plugged In"
print(percent+'% | '+plugged)
def butter_bandpass(lowcut, highcut, fs, order=5):
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = butter(order, [low, high], btype='band')
return b, a
def butter_bandpass_filter(data, lowcut, highcut, fs, order=3):
b, a = butter_bandpass(lowcut, highcut, fs, order=order)
y = lfilter(b, a, data)
return y
def bandPassFilter(originalSignal):
startFilter = time.time()
filterOrder = 4;
lowCutOffFreq = 0.25
highCutOffFreq = 0.01
B, A = signal.butter(filterOrder, highCutOffFreq, btype='high', output='ba')
filteredSignal = signal.filtfilt(B, A, originalSignal, axis=0);
B, A = signal.butter(filterOrder, lowCutOffFreq, btype='low', output='ba')
filteredSignal = signal.filtfilt(B, A, filteredSignal, axis=0);
endFilter = time.time()
print("Filter Elapsed time", endFilter - startFilter, "Filter order: ", filterOrder)
return filteredSignal
def get_change(current, previous):
if current == previous:
return 100.0
try:
measurement = abs(current - previous)/previous
percent = measurement*100
return percent
except ZeroDivisionError:
return 0
def getTrainingData():
ECGdata = OrderedDict()
patientCounter = 0
for filename in os.listdir("./mitdb"):
tempEcgData = OrderedDict()
if filename.startswith("data"):
print(filename)
patientCounter = patientCounter + 1
ecg_data_file = open("mitdb/" +filename, "r")
data = ecg_data_file.readlines()
for x in data:
x = x.strip().replace("\t","")
tempval = x.split(" ")
for x in tempval:
if x == "":
tempval.remove(x)
time = tempval[0]
mlIIvalue = tempval[1]
tempEcgData.update({float(time): float(mlIIvalue)})
tempEcgData.popitem(last=False)
mlIIAverageValue = math.sqrt(sum(tempEcgData.values())*sum(tempEcgData.values())) / len(tempEcgData)
averageHeartRates.append(mlIIAverageValue)
print("Finished with: " + filename)
ECGdata.update(tempEcgData)
return ECGdata
#get class for each beat
def getTrainingClassifications():
tempClassifications = []
for filename in os.listdir("./mitdb"):
if filename.startswith("annotation"):
annotation_file = open("mitdb/" + filename, "r")
annotations = annotation_file.readlines()
for x in annotations:
x = x.strip().replace("\t","")
tempval = x.split(" ")
for x in tempval:
if x == "":
tempval.remove(x)
tempClassifications.append(tempval[2])
del tempClassifications[0]
return tempClassifications
#Begin R Peak extraction from mlII
def RPeakExtraction(capture):
tempRPeaks = OrderedDict()
singleRPeakMap = OrderedDict()
for key, value in ecgData.items():
#Begin capturing values over the threshold
if(value > mlIIAverageValue):
singleRPeakMap.update({str(key): float(value)})
capture = True
#if the values go below threshold - store the largest value
#Clear the singleRPeakMap for the next RPeak, set capture to false
elif (capture == True):
RPeak = max(singleRPeakMap.items(), key=operator.itemgetter(1))
print(RPeak[0])
heartBeatTimes.append(float(RPeak[1]))
tempRPeaks.update({str(RPeak[0]): float(RPeak[1])})
singleRPeakMap.clear()
capture = False
#remove first beats from data - these are incomplete feature wise
for key in tempRPeaks.keys():
print("The key : " + key)
if float(key) < 0.6:
del tempRPeaks[key]
break
return tempRPeaks
# RR- Intervals
# now for each value in the list
# for each time in heartBeatTimes, subtract previous time from current time
def getRRIntervals():
tempRRIntervalList = []
for i in range(len(heartBeatTimes)- 1):
tempRRIntervalList.append(heartBeatTimes[i+1] - heartBeatTimes[i])
counter = 0
return tempRRIntervalList
def loadDataset(featureList, split, trainingSet=[] , testSet=[]):
dataset = list(featureList)
print(featureList[1])
for x in range(len(featureList)-1):
for y in range(3):
featureList[x][y] = float(featureList[x][y])
if random.random() < split:
trainingSet.append(featureList[x])
else:
testSet.append(featureList[x])
def euclideanDistance(instance1, instance2, length):
distance = 0
for x in range(length):
distance += pow((instance1[x] - instance2[x]), 2)
return math.sqrt(distance)
def getNeighbors(trainingSet, testInstance, k):
distances = []
length = len(testInstance)-1
for x in range(len(trainingSet)):
dist = euclideanDistance(testInstance, trainingSet[x], length)
distances.append((trainingSet[x], dist))
distances.sort(key=operator.itemgetter(1))
neighbors = []
for x in range(k):
neighbors.append(distances[x][0])
return neighbors
def getResponse(neighbors):
classVotes = {}
for x in range(len(neighbors)):
response = neighbors[x][-1]
if response in classVotes:
classVotes[response] += 1
else:
classVotes[response] = 1
sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)
return sortedVotes[0][0]
def getAccuracy(testSet, predictions):
correct = 0
for x in range(len(testSet)):
if testSet[x][-1] == predictions[x]:
correct += 1
return (correct/float(len(testSet))) * 100.0
def reportArrhytmia(data):
url = ""
req = requests.post(url, data)
print(req.status_code, r.reason)
featureList = []
def main():
averageHeartRates = []
ecgData = getTrainingData()
classifications = getTrainingClassifications()
# List to capture features of heartBeats
# Data sampled at 360/second
samplingFreq = 360
heartBeatTimes = []
#have r_peaks - get average heartbeats per min - feature
print("Average value: " + str(averageHeartRates))
RPeaksMap = RPeakExtraction(False)
RRIntervalList = getRRIntervals()
averageBeatsPerMin = len(RPeaksMap)//30
print("Beats per min: " + str(averageBeatsPerMin))
print (len(RPeaksMap))
print (len(RRIntervalList))
print (len(classifications))
counter = 0
#Get final feature map
for key, value in RPeaksMap.items():
time = key
RPeakValue = value
RRInterval = RRIntervalList[counter]
classification = classifications[counter]
featureList.append([time, RPeakValue, RRInterval, classification])
counter = counter + 1
# prepare data
trainingSet=[]
testSet=[]
split = 0.67
loadDataset(featureList, split, trainingSet, testSet)
# generate predictions
predictions=[]
k = 3
for x in range(len(testSet)):
neighbors = getNeighbors(trainingSet, testSet[x], k)
result = getResponse(neighbors)
predictions.append(result)
print('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))
accuracy = getAccuracy(testSet, predictions)
print('Accuracy: ' + repr(accuracy) + '%')
def plot():
#for time in dataArray["time"]:
#dataArray["mlII"] = bandPassFilter(dataArray["mlII"])
samplesToPlot = 1000
x = list(map(float, ecgData.keys()))[:samplesToPlot]
y = list(ecgData.values())[:samplesToPlot]
plt.plot(x, y)
plt.annotate(featureList[0][3], xy=(float(featureList[0][0]), featureList[0][1]))
plt.annotate(featureList[1][3], xy=(float(featureList[1][0]), featureList[1][1]))
plt.annotate(featureList[2][3], xy=(float(featureList[2][0]), featureList[2][1]))
plt.show()
getRemainingBatteryPower()
main()
plot()