Exemple #1
1
    IATT = 0
    filtered_signal = ourFilters.filterSignal(downsampled_data, SAMP_F, passFr = F_PASS, stopFr = F_STOP, LOSS = ILOSS, ATTENUATION = IATT, filterType = filterType)
    '''
    #filter 2
    #the user selects the parameters, with default suggested
    start_good_beats = 1290 #this parameter hasn't a default, this number is only for the example used in this algorithm
    end_good_beats = 1360 #this parameter hasn't a default, this number is only for the example used in this algorithm
    plen_bef = 0.35
    plen_aft = 1
    filtered_signal = ourFilters.matched_filter(downsampled_data, SAMP_F, start_good_beats, end_good_beats, plen_bef, plen_aft)
    print 'filtered' '''
    
    #extraction peaks from the signal
    #the user selects the parameters, with default suggested
    delta = 0.2
    peaks = IBI.getPeaksIBI(filtered_signal,SAMP_F, delta)
    print 'plotting...'
    plt.plot(filtered_signal[:,0],filtered_signal[:,1])
    plt.plot(peaks[:,0], peaks[:,1], 'o')
    plt.show()
    
    #calculation of the IBI
    #the user selects the parameters, with default suggested
    minFr = 40
    maxFr = 200
    ibi = IBI.max2interval(peaks, minFr, maxFr)
    
    ourTools.array_labels_to_csv(ibi, np.array(["timestamp", "IBI", "labels"]), "./output/preproc_"+fileName[7:-4]+".csv")

    #-----FEATURES EXTRACTION-----
Exemple #2
0
    IATT = 40
    filtered_signal = ourFilters.filterSignal(rawdata,
                                              SAMP_F,
                                              passFr=F_PASS,
                                              stopFr=F_STOP,
                                              LOSS=ILOSS,
                                              ATTENUATION=IATT,
                                              filterType=filterType)

    #compact timestamp, signal and labels for the next processes
    total_signal = filtered_signal

    #extraction peaks from the signal
    #the user selects the parameters, with default suggested
    delta = 1
    peaks = IBI.getPeaksIBI(total_signal, SAMP_F, delta)
    #calculation of the IBI
    #the user selects the parameters, with default suggested
    minFr = 40
    maxFr = 200
    ibi = IBI.max2interval(peaks, minFr, maxFr)

    tools.array_labels_to_csv(ibi, np.array(["timestamp", "IBI", "lables"]),
                              "./output/preproc_" + fileName[7:-4] + ".csv")

    #-----FEATURES EXTRACTION-----
    timestamp = ibi[:, 0]
    timed_vals = ibi[:, [0, 1]]
    lbls = ibi[:, 2]
    winds, lbls = windowing.get_windows_contiguos(timestamp, lbls, 100, 50)
Exemple #3
0
    #filter the signal
    #the user selects the parameters, with default suggested
    filterType = 'butter'
    F_PASS = 2
    F_STOP = 6
    ILOSS = 0.1
    IATT = 40
    filtered_signal = ourFilters.filterSignal(rawdata, SAMP_F, passFr = F_PASS, stopFr = F_STOP, LOSS = ILOSS, ATTENUATION = IATT, filterType = filterType)

    #compact timestamp, signal and labels for the next processes
    total_signal = filtered_signal
    
    #extraction peaks from the signal
    #the user selects the parameters, with default suggested
    delta = 1
    peaks = IBI.getPeaksIBI(total_signal,SAMP_F, delta)
    #calculation of the IBI
    #the user selects the parameters, with default suggested
    minFr = 40
    maxFr = 200
    ibi = IBI.max2interval(peaks, minFr, maxFr)

    tools.array_labels_to_csv(ibi, np.array(["timestamp", "IBI", "lables"]), "./output/preproc_"+fileName[7:-4]+".csv")


    #-----FEATURES EXTRACTION-----
    timestamp = ibi[:,0]
    timed_vals = ibi[:,[0,1]]
    lbls = ibi[:,2]
    winds, lbls = windowing.get_windows_contiguos(timestamp, lbls, 100, 50)