def plotSignal(file_path, sample_from, sample_to):
'''
plots the entire signal of specified file number
Args:
file_path (str): the location of file that needs to be displayed
sample_from (int): start index of sample
sample_to (int): end index of sample
'''
# make signal for file specified by user
signal, fields = getSignalInfo(file_path, sample_from, sample_to)
xqrs = getXQRS(signal, fields)
# plot waveforms
wfdb.plot_items(signal=signal, ann_samp=[
xqrs.qrs_inds], title='Signal ' + file_path + ' from MIT-BIH Arrhythmia Database')
def segmentation(records, typeBeats):
Normal = []
for e in records:
signals, fields = wfdb.rdsamp(e, channels=[0])
# plot result
wfdb.plot_items(signal=signals, fs=fields['fs'], title='')
ann = wfdb.rdann(e, 'q1c')
good = [typeBeats]
ids = np.in1d(ann.symbol, good)
imp_beats = ann.sample[ids]
beats = (ann.sample)
for i in imp_beats:
beats = list(beats)
j = beats.index(i)
if (j != 0 and j != (len(beats) - 1)):
x = beats[j - 1]
y = beats[j + 1]
diff1 = abs(x - beats[j]) // 2
diff2 = abs(y - beats[j]) // 2
Normal.append(signals[beats[j] - diff1:beats[j] + diff2, 0])
return Normal
def read_mit_fig(file):
def smooth_line(y, fd):
# fd - частота дискретизации
W = fftfreq(y.size, 1 / fd)
f_signal = rfft(y)
cut_f_signal = f_signal.copy()
cut_f_signal[(W < 0.25)] = 0
cut_f_signal[(W > 60)] = 0
cut_signal = irfft(cut_f_signal)
return cut_signal
plt.cla()
record = wfdb.rdrecord(file)
fig = wfdb.plot_items(signal=smooth_line(record.p_signal[:, 0], record.fs),
title='V1',
time_units='seconds',
fs=record.fs,
figsize=(10, 2),
return_fig=True) #, ecg_grids='all')
return fig
# awesome tutorial: https://www.youtube.com/watch?v=WyjGCEWU4zY&t=317s
file = tkinter.filedialog.askopenfilename()
file = file[:-4]
n_samples = tkinter.simpledialog.askinteger('Number of samples',
'Type in the number of samples you want to be displayed (example: 3000, 6000, 10000 etc.)')
#Define ecg
record = wfdb.rdrecord(file, sampto=n_samples)
ann = wfdb.rdann(file, 'dat', sampto=n_samples)
#Filerecord
file_record = record.__dict__
#print(file_record)
wfdb.plot_items(signal=record.p_signal, title='ECG Signal',ann_samp=[ann.sample, ann.sample], time_units='samples', figsize=(10,4))
#Detect R-Peaks
signal_slice = np.ndarray.flatten(record.p_signal[0:n_samples])
smooth_signal = signal.cspline1d(signal_slice, lamb=1000) #smoothing the signal (filtering)
#r_peak_index = peakutils.indexes(smooth_signal, thres = 0.45, min_dist = 0.1) # first peak detection option
peak_index = signal.find_peaks_cwt(smooth_signal, widths= np.arange(60,80)) # second peak detection option
fig, ax = plt.subplots()
ax.set_title('Detect R peak')
ax.plot(signal_slice)
p_min_distance = -20 # marking for p-wave example
p_max_distance = -60
from codes.python import ecg_waveform_extractor as waveform
import time as system_time
from scipy import stats
import warnings
import termcolor as colored
from math import*
mit100 = DB2.patient_records[0]
t_ecpu = np.asarray(mit100.annotated_t_waves_pos[0:7],dtype=int)
wfdb.plot_items(signal = mit100.filtered_MLII[0:2000], ann_samp = [t_ecpu])
pre_rr_interval, post_rr_interval = pre_pos_rr_interval(mit100)
rr_ten, rr_fifty, rr_all = rr_average_by_sample(pre_rr_interval, fifty = True)
columns = len(mit100.segmented_beat_time[0]) + len(mit100.segmented_beat_1[0])
rows = 0
for patient in DB2.patient_records:
rows += len(patient.segmented_beat_time)
DBn2 = np.zeros((rows,columns),dtype=object)
yn2 = np.zeros((rows,1), dtype=object)
qrs = extract(new_signal, rpeaks, d234, D3, fs, plot='false')
rpeak = qrs['rpeak']
q_wave = qrs['q_wave']
s_wave = qrs['s_wave']
qrs_on = qrs['qrs_onset']
qrs_off = qrs['qrs_offset']
ano = wfdb.rdann('/home/han/S.Kom/Database/qtdb/sel100', 'pu0')
ano_dictionary = ano.__dict__
ann_label = ano_dictionary['symbol']
ann = ano_dictionary['sample']
kamus = {'nilai': ann, 'label': ann_label}
df = pd.DataFrame(kamus)
df.to_csv(r'/home/han/S.Kom/Database/dataframe.csv')
figure1 = wfdb.plot_items(raw_data, [ann])
#################### BATAS KODINGAN LAMA #########################
#p = p_peak(new_signal, d45, rpeaks, qrs_on, fs)
t = t_peak(new_signal, d45, rpeak, qrs_on, qrs_off, fs)
tmax = t['index_max45']
batas_kiri = t['kiri']
batas_kanan = t['kanan']
tmin = t['index_min45']
tmula = t['t']
longqt = []
for i in range(len(rpeaks) - 1):
data_longqt = bazzet(qrs_on[i], tmin[i], rpeaks[i], rpeaks[i + 1], fs)