def test_ecg_peaks():
sampling_rate = 1000
noise = 0.15
ecg = nk.ecg_simulate(duration=120,
sampling_rate=sampling_rate,
noise=noise,
random_state=42)
ecg_cleaned_nk = nk.ecg_clean(ecg,
sampling_rate=sampling_rate,
method="neurokit")
# Test without request to correct artifacts.
signals, info = nk.ecg_peaks(ecg_cleaned_nk,
correct_artifacts=False,
method="neurokit")
assert signals.shape == (120000, 1)
assert np.allclose(signals["ECG_R_Peaks"].values.sum(dtype=np.int64),
139,
atol=1)
# Test with request to correct artifacts.
signals, info = nk.ecg_peaks(ecg_cleaned_nk,
correct_artifacts=True,
method="neurokit")
assert signals.shape == (120000, 1)
assert np.allclose(signals["ECG_R_Peaks"].values.sum(dtype=np.int64),
139,
atol=1)
def extract_rpeak_features(row, signal):
"""
Extract the R peak features.
:param row: a `BaseDataset` row to calculate the features from
:param signal: the raw ECG signal
:return: `row` with the added features
"""
ecg_cleaned = nk.ecg_clean(signal, sampling_rate=row.Fs)
peaks, info = nk.ecg_peaks(ecg_cleaned, sampling_rate=row.Fs)
r_peaks_sec = np.where(peaks['ECG_R_Peaks'].to_numpy() == 1)[0].astype(
np.float32)
r_peaks_sec /= row.Fs # get R-peak times in seconds
num_peaks = len(r_peaks_sec)
if num_peaks > 2:
hrv = nk.hrv(peaks, sampling_rate=row.Fs, show=False).iloc[0]
row = row.append(hrv)
row['N_QRS'] = num_peaks
rr = np.diff(r_peaks_sec)
row = row.append(get_statistics(rr, 'RR'))
row = row.append(get_statistics(signal, 'signal'))
return row, info
def get_HRVs_values(data, header_data):
filter_lowcut = 0.001
filter_highcut = 15.0
filter_order = 1
tmp_hea = header_data[0].split(' ')
ptID = tmp_hea[0]
num_leads = int(tmp_hea[1])
sample_Fs = int(tmp_hea[2])
gain_lead = np.zeros(num_leads)
for ii in range(num_leads):
tmp_hea = header_data[ii + 1].split(' ')
gain_lead[ii] = int(tmp_hea[2].split('/')[0])
# for testing, we included the mean age of 57 if the age is a NaN
# This value will change as more data is being released
for iline in header_data:
if iline.startswith('#Age'):
tmp_age = iline.split(': ')[1].strip()
age = int(tmp_age if tmp_age != 'NaN' else 57)
elif iline.startswith('#Sex'):
tmp_sex = iline.split(': ')[1]
if tmp_sex.strip() == 'Female':
sex = 1
else:
sex = 0
elif iline.startswith('#Dx'):
label = iline.split(': ')[1].split(',')[0]
signal = data[1]
gain = gain_lead[1]
ecg_signal = nk.ecg_clean(signal * gain,
sampling_rate=sample_Fs,
method="biosppy")
_, rpeaks = nk.ecg_peaks(ecg_signal, sampling_rate=sample_Fs)
hrv_time = nk.hrv_time(rpeaks, sampling_rate=sample_Fs)
# hrv_non = nk.hrv_nonlinear(rpeaks, sampling_rate=sample_Fs)
try:
signal_peak, waves_peak = nk.ecg_delineate(ecg_signal,
rpeaks,
sampling_rate=sample_Fs)
p_peaks = waves_peak['ECG_P_Peaks']
except ValueError:
print('Exception raised!')
pass
p_peaks = np.asarray(p_peaks, dtype=float)
p_peaks = p_peaks[~np.isnan(p_peaks)]
p_peaks = [int(a) for a in p_peaks]
mean_P_Peaks = np.mean([signal[w] for w in p_peaks])
hrv_time['mean_P_Peaks'] = mean_P_Peaks
hrv_time['age'] = age
hrv_time['label'] = label
# df = pd.concat([hrv_time, hrv_non], axis=1)
return hrv_time
def apply_pan_tompkins_old(x,
n_beats=110,
fs=460,
left_data=0.25,
right_data=0.5):
_, rpeaks = nk.ecg_peaks(x, sampling_rate=fs)
# Select 0.25 seconds left of peak and 0.5 seconds right of beat
cleaned_signal_dict = {}
for peak in rpeaks['ECG_R_Peaks']:
left_index = int(left_data * fs)
right_index = int(right_data * fs)
cleaned_signal_dict.update(
{peak: x[peak - left_index:peak + right_index]})
# If n_beats is defined, returned those beats
if n_beats and n_beats <= len(cleaned_signal_dict):
# Get index of middle beat
i = int(len(cleaned_signal_dict) / 2)
half_n_beats = int(n_beats / 2)
valid_keys = list(cleaned_signal_dict.keys())[i - half_n_beats:i +
half_n_beats]
return {
key: val
for key, val in cleaned_signal_dict.items() if key in valid_keys
}
# Otherwise return all beats except first and last
valid_keys = list(cleaned_signal_dict.keys())[1:-1]
return {
key: val
for key, val in cleaned_signal_dict.items() if key in valid_keys
}
def test_ecg_rate():
sampling_rate = 1000
noise = 0.15
ecg = nk.ecg_simulate(duration=120,
sampling_rate=sampling_rate,
noise=noise,
random_state=42)
ecg_cleaned_nk = nk.ecg_clean(ecg,
sampling_rate=sampling_rate,
method="neurokit")
signals, info = nk.ecg_peaks(ecg_cleaned_nk, method="neurokit")
# Test without desired length.
rate = nk.ecg_rate(rpeaks=info, sampling_rate=sampling_rate)
assert rate.shape == (info["ECG_R_Peaks"].size, )
assert np.allclose(rate.mean(), 70, atol=2)
# Test with desired length.
test_length = 1200
rate = nk.ecg_rate(rpeaks=info,
sampling_rate=sampling_rate,
desired_length=test_length)
assert rate.shape == (test_length, )
assert np.allclose(rate.mean(), 70, atol=2)
def test_ecg_delineate():
sampling_rate = 1000
# test with simulated signals
ecg = nk.ecg_simulate(duration=20,
sampling_rate=sampling_rate,
random_state=42)
_, rpeaks = nk.ecg_peaks(ecg, sampling_rate=sampling_rate)
number_rpeaks = len(rpeaks['ECG_R_Peaks'])
# Method 1: derivative
_, waves_derivative = nk.ecg_delineate(ecg,
rpeaks,
sampling_rate=sampling_rate)
assert len(waves_derivative['ECG_P_Peaks']) == number_rpeaks
assert len(waves_derivative['ECG_Q_Peaks']) == number_rpeaks
assert len(waves_derivative['ECG_S_Peaks']) == number_rpeaks
assert len(waves_derivative['ECG_T_Peaks']) == number_rpeaks
assert len(waves_derivative['ECG_P_Onsets']) == number_rpeaks
assert len(waves_derivative['ECG_T_Offsets']) == number_rpeaks
# Method 2: CWT
_, waves_cwt = nk.ecg_delineate(ecg,
rpeaks,
sampling_rate=sampling_rate,
method='cwt')
assert np.allclose(len(waves_cwt['ECG_P_Peaks']), 22, atol=1)
assert np.allclose(len(waves_cwt['ECG_T_Peaks']), 22, atol=1)
assert np.allclose(len(waves_cwt['ECG_R_Onsets']), 23, atol=1)
assert np.allclose(len(waves_cwt['ECG_R_Offsets']), 23, atol=1)
assert np.allclose(len(waves_cwt['ECG_P_Onsets']), 22, atol=1)
assert np.allclose(len(waves_cwt['ECG_P_Offsets']), 22, atol=1)
assert np.allclose(len(waves_cwt['ECG_T_Onsets']), 22, atol=1)
assert np.allclose(len(waves_cwt['ECG_T_Offsets']), 22, atol=1)
def qrs_detection_pantompkins_vs_neurokit(self, dataset):
row = dataset.data.iloc[50]
signal = dataset.read_record(row.Record)[:row.Fs * 20 + 1]
method_names = {'pantompkins': 'Pan–Tompkins', 'neurokit': 'Neurokit'}
for method in ['pantompkins', 'neurokit']:
ecg_cleaned = nk.ecg_clean(signal,
sampling_rate=row.Fs,
method=method)
peaks, info = nk.ecg_peaks(ecg_cleaned,
sampling_rate=row.Fs,
method=method)
r_peaks = np.where(peaks['ECG_R_Peaks'].to_numpy() == 1)[0]
fig = go.Figure()
fig.add_trace(
go.Scatter(x=np.arange(len(signal)) / row.Fs, y=signal))
fig.add_trace(
go.Scatter(mode='markers',
x=r_peaks / row.Fs,
y=signal[r_peaks]))
fig.update_traces(marker=dict(size=8))
self.set_ecg_layout(
fig,
title=f'{row.Record} - R peaks ({method_names[method]} method)',
showlegend=False,
xaxis=dict(range=[0, 20]),
yaxis=dict(range=[-5000, 5000]))
self.save_image(fig, f'qrs_{method}.png', width=900, height=300)
def find_R_peaks(ecg_data, samplefreq):
try:
_, rpeaks = nk.ecg_peaks(ecg_data, sampling_rate=samplefreq)
r_peaks = rpeaks['ECG_R_Peaks']
r_peaks = np.delete(r_peaks,
np.where(np.isnan(r_peaks))[0]).astype(int)
except:
print("cleaning data")
cleaned_ecg = nk.ecg_clean(ecg_data,
sampling_rate=samplefreq,
method="neurokit")
try:
_, rpeaks = nk.ecg_peaks(cleaned_ecg, sampling_rate=samplefreq)
r_peaks = rpeaks['ECG_R_Peaks']
r_peaks = np.delete(r_peaks,
np.where(np.isnan(r_peaks))[0]).astype(int)
except:
print("could not analyse cleaned ECG")
#Midlertidig løsning:
r_peaks = np.array([0, 1, 2, 3])
return r_peaks
def compute_qrs_ratio(x, fs=460, length_of_beat=0.75):
# Find Q and S peaks
_, rpeaks = nk.ecg_peaks(x, sampling_rate=fs)
_, waves_peak = nk.ecg_delineate(x,
rpeaks,
sampling_rate=fs,
method="peak")
# Compute diff across Q and S peaks
Q_waves = waves_peak['ECG_Q_Peaks']
S_waves = waves_peak['ECG_S_Peaks']
diff = [s - q for s, q in zip(S_waves, Q_waves)]
# Divide the diff by 0.75 seconds (length of 1 beat in samples)
duration_samples = int(length_of_beat * fs)
ratio = [i / duration_samples for i in diff if not np.isnan(i)]
return sum(ratio) / len(ratio)
def generate_features(ecg, header):
#input: 12-lead ecg and its header
fs = 500
features = {}
lead_names = []
for iline in header:
if '.mat' in iline:
name = iline.split(' 0 ')[2].strip()
lead_names.append(name)
for ecg_signal, lead in zip(ecg, lead_names):
ecg_cleaned = nk.ecg_clean(ecg_signal, sampling_rate=fs)
if np.all((ecg_cleaned == 0)):
return None
else:
_, rpeaks = nk.ecg_peaks(ecg_cleaned, sampling_rate=fs)
if rpeaks['ECG_R_Peaks'].size == 0:
return None
else:
try:
signal_dwt, waves_dwt = nk.ecg_delineate(
ecg_cleaned,
rpeaks['ECG_R_Peaks'],
sampling_rate=fs,
method="dwt")
biphase, areas, t_till_peaks, ampls, dur, idxs, pq_intervals = p_peak_features(
ecg_cleaned, waves_dwt)
features_for_single_lead = {
'PQ_int': calculate_features(pq_intervals),
'P_dur': calculate_features(dur),
'Area/Dur_P': calculate_features(idxs),
'Area_under_P': calculate_features(areas),
'P_amp': calculate_features(ampls),
'Time_till_P': calculate_features(t_till_peaks),
'Biphase_P': calculate_features(biphase)
}
except IndexError:
return None
features[lead] = features_for_single_lead
return features
def my_processing(ecg_signal):
# Try processing
ecg_cleaned = nk.ecg_clean(ecg_signal, sampling_rate=300, method="biosppy")
instant_peaks, rpeaks = nk.ecg_peaks(ecg_cleaned,
sampling_rate=300,
method='hamilton2002')
info = rpeaks
try:
# Additional info of the ecg signal
delineate_signal, delineate_waves = nk.ecg_delineate(
ecg_cleaned=ecg_cleaned,
rpeaks=rpeaks,
sampling_rate=300,
method='cwt')
except:
delineate_signal = np.NaN
delineate_waves = np.NaN
return ecg_cleaned, delineate_signal, delineate_waves, info
def Get_R_Peaks(ecg_signal):
# Extract R-peak locations from ecg signal file
print(
"======== Retrieving R Peaks of QRS complex ========")
_, rpeaks = nk.ecg_peaks(ecg_signal, sampling_rate=3000)
#Print to console
for key in rpeaks:
print(key, ' : ', rpeaks[key])
print()
# Visualize R-peaks in ECG signal **for Jupyter Notebook
plot_Rpeak_Signal = nk.events_plot(rpeaks['ECG_R_Peaks'], ecg_signal)
plot_Rpeak_Signal.savefig("rpeaks_signal", dpi=300)
# Visual R-peak locations for first 5 5 R-peaks **for Jupyter Notebook
plot_Rpeak_Head = nk.events_plot(rpeaks['ECG_R_Peaks'][:5],
ecg_signal[:20000])
plot_Rpeak_Head.savefig("rpeaks_head", dpi=300)
return rpeaks
def find_hr(signal, t):
_, r_peaks = nk.ecg_peaks(signal, sampling_rate=500)
r_peaks = r_peaks['ECG_R_Peaks']
num_beats = len(r_peaks)
window_len = (t[r_peaks[-1]] - t[r_peaks[0]])
hr = (num_beats - 1) * 60 / window_len
# Update ECG plot
plt.clf()
plt.plot(t, signal)
plt.plot(t[r_peaks], signal[r_peaks], "x")
plt.title("Electrocardiogram (ECG), Heart Rate: %.1f bpm" % hr)
plt.xlabel("Time (seconds)")
plt.ylabel("Amplitude (arbitrary)")
plt.pause(0.0001)
plt.show()
return hr
from wettbewerb import load_references
from imblearn.over_sampling import SMOTE
import warnings
warnings.filterwarnings("ignore")
ecg_leads, ecg_labels, fs, ecg_names = load_references(
) # Importiere EKG-Dateien, zugehörige Diagnose, Sampling-Frequenz (Hz) und Name # Sampling-Frequenz 300 Hz
features = np.array([])
detectors = Detectors(fs) # Initialisierung des QRS-Detektors
labels = np.array([])
for idx, ecg_lead in enumerate(ecg_leads):
if (ecg_labels[idx] == "N") or (ecg_labels[idx] == "A"):
peaks, info = nk.ecg_peaks(ecg_lead, sampling_rate=fs)
peaks = peaks.astype('float64')
hrv = nk.hrv_time(peaks, sampling_rate=fs)
hrv = hrv.astype('float64')
features = np.append(features, [
hrv['HRV_CVNN'], hrv['HRV_CVSD'], hrv['HRV_HTI'], hrv['HRV_IQRNN'],
hrv['HRV_MCVNN'], hrv['HRV_MadNN'], hrv['HRV_MeanNN'],
hrv['HRV_MedianNN'], hrv['HRV_RMSSD'], hrv['HRV_SDNN'],
hrv['HRV_SDSD'], hrv['HRV_TINN'], hrv['HRV_pNN20'],
hrv['HRV_pNN50']
])
features = features.astype('float64')
labels = np.append(labels, ecg_labels[idx])
features = features.reshape(int(len(features) / 14), 14)
x = np.isnan(features)
def _find_R_peaks(ecg_data, samplefreq):
_, rpeaks = nk.ecg_peaks(ecg_data[1], sampling_rate=samplefreq)
r_peaks = rpeaks['ECG_R_Peaks']
r_peaks = np.delete(r_peaks,
np.where(np.isnan(r_peaks))[0]).astype(int)
return r_peaks
import pandas as pd
import csv
data = pd.read_csv('/content/drive/My Drive/Anxiety Spider Dataset/ECG/ECG_Combined.csv',header=None)
data.head
len(data)
ecg=data.iloc[256]
nk.signal_plot(ecg)
signals, info = nk.ecg_process(ecg,sampling_rate=100)
signals
peaks, info = nk.ecg_peaks(ecg, sampling_rate=100)
nk.hrv(peaks, sampling_rate=100, show=True)
ecg_features=nk.hrv(peaks, sampling_rate=100)
# X=ecg_features[["HRV_RMSSD","HRV_MeanNN","HRV_SDNN", "HRV_SDSD", "HRV_CVNN", "HRV_CVSD", "HRV_MedianNN", "HRV_MadNN", "HRV_MCVNN", "HRV_IQRNN", "HRV_pNN50", "HRV_pNN20"]]
# X
data_features=[]
for i in range(0,len(data)):
ecg=data.iloc[i]
peaks, info = nk.ecg_peaks(ecg, sampling_rate=100)
ecg_features=nk.hrv(peaks, sampling_rate=100)
X=ecg_features[["HRV_RMSSD","HRV_MeanNN","HRV_SDNN", "HRV_SDSD", "HRV_CVNN", "HRV_CVSD", "HRV_MedianNN", "HRV_MadNN", "HRV_MCVNN", "HRV_IQRNN", "HRV_pNN50", "HRV_pNN20", "HRV_TINN", "HRV_HTI", "HRV_ULF", "HRV_VLF", "HRV_LF", "HRV_HF", "HRV_VHF", "HRV_LFHF", "HRV_LFn", "HRV_HFn", "HRV_LnHF", "HRV_SD1", "HRV_SD2", "HRV_SD1SD2", "HRV_S", "HRV_CSI", "HRV_CVI", "HRV_CSI_Modified", "HRV_PIP", "HRV_IALS", "HRV_PSS", "HRV_PAS", "HRV_GI", "HRV_SI", "HRV_AI", "HRV_PI", "HRV_C1d", "HRV_C1a", "HRV_SD1d", "HRV_SD1a", "HRV_C2d", "HRV_C2a", "HRV_SD2d", "HRV_SD2a", "HRV_Cd", "HRV_Ca", "HRV_SDNNd", "HRV_SDNNa", "HRV_ApEn", "HRV_SampEn"]]
data_features.append(X)
def kalidas2017(ecg, sampling_rate):
signal, info = nk.ecg_peaks(ecg,
sampling_rate=sampling_rate,
method="kalidas2017")
return info["ECG_R_Peaks"]
def engzeemod2012(ecg, sampling_rate):
signal, info = nk.ecg_peaks(ecg,
sampling_rate=sampling_rate,
method="engzeemod2012")
return info["ECG_R_Peaks"]
plot = nk.signal_plot([original, distorted, cleaned])
# Save plot
fig = plt.gcf()
fig.set_size_inches(10, 6)
fig.savefig("README_signalprocessing.png", dpi=300, h_pad=3)
# =============================================================================
# Heart Rate Variability
# =============================================================================
# Download data
data = nk.data("bio_resting_8min_100hz")
# Find peaks
peaks, info = nk.ecg_peaks(data["ECG"], sampling_rate=100)
# Compute HRV indices
hrv = nk.hrv(peaks, sampling_rate=100, show=True)
hrv
# Save plot
fig = plt.gcf()
fig.set_size_inches(10 * 1.5, 6 * 1.5, forward=True)
fig.savefig("README_hrv.png", dpi=300, h_pad=3)
# =============================================================================
# ECG Delineation
# =============================================================================
# Download data
def predict_labels(ecg_leads, fs, ecg_names, use_pretrained=False):
'''
Parameters
----------
model_name : str
Dateiname des Models. In Code-Pfad
ecg_leads : list of numpy-Arrays
EKG-Signale.
fs : float
Sampling-Frequenz der Signale.
ecg_names : list of str
eindeutige Bezeichnung für jedes EKG-Signal.
Returns
-------
predictions : list of tuples
ecg_name und eure Diagnose
'''
#------------------------------------------------------------------------------
# Euer Code ab hier
# model_name = "model.npy"
# if use_pretrained:
# model_name = "model_pretrained.npy"
# with open(model_name, 'rb') as f:
# th_opt = np.load(f) # Lade simples Model (1 Parameter)
#
# detectors = Detectors(fs) # Initialisierung des QRS-Detektors
#
# predictions = list()
#
# for idx,ecg_lead in enumerate(ecg_leads):
# r_peaks = detectors.hamilton_detector(ecg_lead) # Detektion der QRS-Komplexe
# sdnn = np.std(np.diff(r_peaks)/fs*1000)
# if sdnn < th_opt:
# predictions.append((ecg_names[idx], 'N'))
# else:
# predictions.append((ecg_names[idx], 'A'))
# if ((idx+1) % 100)==0:
# print(str(idx+1) + "\t Dateien wurden verarbeitet.")
#
#
# #------------------------------------------------------------------------------
# return predictions # Liste von Tupels im Format (ecg_name,label) - Muss unverändert bleiben!
# Load test-dataset
# from tqdm import tqdm
# import os
# from os import listdir
# from os.path import isfile, join
# import cv2
#####
# THRESHOLD = 0.5
# def precision(y_true, y_pred, threshold_shift=0.5 - THRESHOLD):
# # just in case
# y_pred = K.clip(y_pred, 0, 1)
# # shifting the prediction threshold from .5 if needed
# y_pred_bin = K.round(y_pred + threshold_shift)
# tp = K.sum(K.round(y_true * y_pred_bin)) + K.epsilon()
# fp = K.sum(K.round(K.clip(y_pred_bin - y_true, 0, 1)))
# precision = tp / (tp + fp)
# return precision
# def recall(y_true, y_pred, threshold_shift=0.5 - THRESHOLD):
# # just in case
# y_pred = K.clip(y_pred, 0, 1)
# # shifting the prediction threshold from .5 if needed
# y_pred_bin = K.round(y_pred + threshold_shift)
# tp = K.sum(K.round(y_true * y_pred_bin)) + K.epsilon()
# fn = K.sum(K.round(K.clip(y_true - y_pred_bin, 0, 1)))
# recall = tp / (tp + fn)
# return recall
# def fbeta(y_true, y_pred, threshold_shift=0.5 - THRESHOLD):
# beta = 1
# # just in case
# y_pred = K.clip(y_pred, 0, 1)
# # shifting the prediction threshold from .5 if needed
# y_pred_bin = K.round(y_pred + threshold_shift)
# tp = K.sum(K.round(y_true * y_pred_bin)) + K.epsilon()
# fp = K.sum(K.round(K.clip(y_pred_bin - y_true, 0, 1)))
# fn = K.sum(K.round(K.clip(y_true - y_pred, 0, 1)))
# precision = tp / (tp + fp)
# recall = tp / (tp + fn)
# beta_squared = beta ** 2
# return (beta_squared + 1) * (precision * recall) / (beta_squared * precision + recall)
#####
# ecg_leads,ecg_labels,fs,ecg_names = load_references('../test/') # Importiere EKG-Dateien, zugehörige Diagnose, Sampling-Frequenz (Hz) und Name # Sampling-Frequenz 300 Hz
# test_set = ecg_leads
# if os.path.exists("../test/ecg_images"):
# print("File exists.")
# else:
# os.mkdir("../test/ecg_images")
# for i in tqdm(range(len(test_set))):
# data = ecg_leads[i].reshape(len(ecg_leads[i]), 1)
# plt.figure(figsize=(60, 5))
# plt.xlim(0, len(ecg_leads[i]))
# # plt.plot(data, color='black', linewidth=0.1)
# plt.savefig('../test/ecg_images/{}.png'.format(ecg_names[i]))
# onlyfiles = [f for f in listdir("../test/ecg_images") if isfile(join("../test/ecg_images", f))]
# df = pd.read_csv("../test/REFERENCE.csv", header=None)
# x = []
# y = []
# name_test =[]
# for i in range(len(onlyfiles)):
# if (df.iloc[i][1] == "N") or (df.iloc[i][1] == "A"):
# image = cv2.imread("../test/ecg_images/{}".format(onlyfiles[i]))
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# resize_x = cv2.resize(gray, (128, 1024))
# reshape_x = np.asarray(resize_x).reshape(resize_x.shape[0], resize_x.shape[1], 1)
# x.append(reshape_x)
# y.append(df.iloc[i][1])
# name_test.append(df.iloc[i])
# for n, i in enumerate(y):
# if i == "N":
# y[n] = 0
# elif i == "A":
# y[n] = 1
# x = np.asarray(x).astype(int)
# y = np.asarray(y).astype(int)
# test_images, test_labels = x, y
# # Normalize pixel values to be between 0 and 1
# test_images = test_images / 255.0
# import keras
# import tensorflow as tf
# from keras.models import load_model
# model = load_model('./pred_model.h5', custom_objects={'fbeta': fbeta})
# model = tf.keras.models.load_model('./pred_model.h5', custom_objects={'fbeta': fbeta})
# model = tf.keras.models.load_model('./pred_model.h5', custom_objects={'fbeta': fbeta})
# converter = tf.lite.TFLiteConverter.from_keras_model(model) # .from_saved_model(saved_model_dir)
# tflite_model = converter.convert()
# open("model.tflite", "wb").write(tflite_model)
# pred_labels = model.predict_classes(test_images)
# pred_labels = np.asarray(pred_labels).astype('str')
# for n, i in enumerate(pred_labels):
# if i == '0':
# pred_labels[n] = "N"
# elif i == '1':
# pred_labels[n] = "A"
#-------------------------------------------------------------------------------
''' Gradient Boosting Classfier '''
# import warnings
# warnings.filterwarnings("ignore")
#
# test_features = np.array([])
#
# for idx, ecg_lead in enumerate(ecg_leads):
# peaks, info = nk.ecg_peaks(ecg_lead, sampling_rate= 200)
# peaks = peaks.astype('float64')
# hrv = nk.hrv_time(peaks, sampling_rate= fs)
# hrv = hrv.astype('float64')
# test_features = np.append(test_features, [hrv['HRV_CVNN'], hrv['HRV_CVSD'], hrv['HRV_HTI'], hrv['HRV_IQRNN'], hrv['HRV_MCVNN'], hrv['HRV_MadNN'], hrv['HRV_MeanNN'], hrv['HRV_MedianNN'], hrv['HRV_RMSSD'], hrv['HRV_SDNN'], hrv['HRV_SDSD'], hrv['HRV_TINN'], hrv['HRV_pNN20'],hrv['HRV_pNN50'] ])
# test_features = test_features.astype('float64')
#
# test_features= test_features.reshape(int(len(test_features)/14), 14)
# x = np.isnan(test_features)
# # replacing NaN values with 0
# test_features[x] = 0
#
# X_test = test_features
#
# # with trained model to predict
# loaded_model = joblib.load('GradientBoostingClassifier')
# pred_labels = loaded_model.predict(X_test)
# # a list of tuple
# predictions = list()
#
# for idx in range(len(X_test)):
# predictions.append((ecg_names[idx], pred_labels[idx]))
#------------------------------------------------------------------
''' Convolutional Neural Network '''
# import warnings
# warnings.filterwarnings("ignore")
# from tensorflow.keras.models import load_model
# from tensorflow.keras.optimizers import SGD
# import numpy as np
#
# ###
# def f1(y_true, y_pred):
# def recall(y_true, y_pred):
# """Recall metric.
#
# Only computes a batch-wise average of recall.
#
# Computes the recall, a metric for multi-label classification of
# how many relevant items are selected.
# """
# true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
# possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
# recall = true_positives / (possible_positives + K.epsilon())
# return recall
#
# def precision(y_true, y_pred):
# """Precision metric.
#
# Only computes a batch-wise average of precision.
#
# Computes the precision, a metric for multi-label classification of
# how many selected items are relevant.
# """
# true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
# predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
# precision = true_positives / (predicted_positives + K.epsilon())
# return precision
#
# precision = precision(y_true, y_pred)
# recall = recall(y_true, y_pred)
# return 2 * ((precision * recall) / (precision + recall + K.epsilon()))
#
# def f1_weighted(true, pred): # shapes (batch, 4)
#
# # for metrics include these two lines, for loss, don't include them
# # these are meant to round 'pred' to exactly zeros and ones
# # predLabels = K.argmax(pred, axis=-1)
# # pred = K.one_hot(predLabels, 4)
#
# ground_positives = K.sum(true, axis=0) + K.epsilon() # = TP + FN
# pred_positives = K.sum(pred, axis=0) + K.epsilon() # = TP + FP
# true_positives = K.sum(true * pred, axis=0) + K.epsilon() # = TP
# # all with shape (4,)
#
# precision = true_positives / pred_positives
# recall = true_positives / ground_positives
# # both = 1 if ground_positives == 0 or pred_positives == 0
# # shape (4,)
#
# f1 = 2 * (precision * recall) / (precision + recall + K.epsilon())
# # still with shape (4,)
#
# weighted_f1 = f1 * ground_positives / K.sum(ground_positives)
# weighted_f1 = K.sum(weighted_f1)
#
# return 1 - weighted_f1 # for metrics, return only 'weighted_f1'
# ###
#
# cnn_best_model = load_model('cnn_best_model.h5', custom_objects={'f1_weighted': f1_weighted, 'f1': f1})
#
# sgd = SGD(lr=0.00005, decay=1e-6, momentum=0.9, nesterov=True)
# cnn_best_model.compile(optimizer=sgd, # change: use SGD
# loss=f1_weighted, # 'binary_crossentropy' #'mean_squared_error' #categorical_crossentropy
# metrics=[f1, "binary_accuracy"])
#
# test_features = np.array([])
#
# for idx, ecg_lead in enumerate(ecg_leads):
# peaks, info = nk.ecg_peaks(ecg_lead, sampling_rate= 200)
# peaks = peaks.astype('float64')
# hrv = nk.hrv_time(peaks, sampling_rate= fs)
# hrv = hrv.astype('float64')
# test_features = np.append(test_features, [hrv['HRV_CVNN'], hrv['HRV_CVSD'], hrv['HRV_HTI'], hrv['HRV_IQRNN'], hrv['HRV_MCVNN'], hrv['HRV_MadNN'], hrv['HRV_MeanNN'], hrv['HRV_MedianNN'], hrv['HRV_RMSSD'], hrv['HRV_SDNN'], hrv['HRV_SDSD'], hrv['HRV_TINN'], hrv['HRV_pNN20'],hrv['HRV_pNN50'] ])
# test_features = test_features.astype('float64')
#
# test_features= test_features.reshape(int(len(test_features)/14), 14)
# x = np.isnan(test_features)
# # replacing NaN values with 0
# test_features[x] = 0
#
# X_test = test_features
# X_test_arr = np.array(X_test).reshape(np.array(X_test).shape[0], np.array(X_test).shape[1], 1)
# # with trained model to predict
# y_pred = cnn_best_model.predict(X_test_arr)
# pred_labels = [np.argmax(y, axis=None, out=None) for y in y_pred]
#
# for n, i in enumerate(pred_labels):
# if i == 0:
# pred_labels[n] = 'N'
# if i == 1:
# pred_labels[n] = 'A'
#
# predictions = list()
#
# for idx in range(len(X_test)):
# predictions.append((ecg_names[idx], pred_labels[idx]))
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------
''' AdaBoost Classifier '''
# import warnings
# warnings.filterwarnings("ignore")
#
# test_features = np.array([])
#
# for idx, ecg_lead in enumerate(ecg_leads):
# peaks, info = nk.ecg_peaks(ecg_lead, sampling_rate= 200)
# peaks = peaks.astype('float64')
# hrv = nk.hrv_time(peaks, sampling_rate= fs)
# hrv = hrv.astype('float64')
# test_features = np.append(test_features, [hrv['HRV_CVNN'], hrv['HRV_CVSD'], hrv['HRV_HTI'], hrv['HRV_IQRNN'], hrv['HRV_MCVNN'], hrv['HRV_MadNN'], hrv['HRV_MeanNN'], hrv['HRV_MedianNN'], hrv['HRV_RMSSD'], hrv['HRV_SDNN'], hrv['HRV_SDSD'], hrv['HRV_TINN'], hrv['HRV_pNN20'],hrv['HRV_pNN50'] ])
# test_features = test_features.astype('float64')
#
# test_features= test_features.reshape(int(len(test_features)/14), 14)
# x = np.isnan(test_features)
# # replacing NaN values with 0
# test_features[x] = 0
#
# X_test = test_features
#
# # with trained model to predict
# loaded_model = joblib.load('AdaBoostClassifier')
# pred_labels = loaded_model.predict(X_test)
#-----------------------------------------------------------------------------------------
''' XGBoost Classifier '''
import warnings
warnings.filterwarnings("ignore")
test_features = np.array([])
for idx, ecg_lead in enumerate(ecg_leads):
peaks, info = nk.ecg_peaks(ecg_lead, sampling_rate=fs)
peaks = peaks.astype('float64')
hrv = nk.hrv_time(peaks, sampling_rate=fs)
hrv = hrv.astype('float64')
test_features = np.append(test_features, [
hrv['HRV_CVNN'], hrv['HRV_CVSD'], hrv['HRV_HTI'], hrv['HRV_IQRNN'],
hrv['HRV_MCVNN'], hrv['HRV_MadNN'], hrv['HRV_MeanNN'],
hrv['HRV_MedianNN'], hrv['HRV_RMSSD'], hrv['HRV_SDNN'],
hrv['HRV_SDSD'], hrv['HRV_TINN'], hrv['HRV_pNN20'],
hrv['HRV_pNN50']
])
test_features = test_features.astype('float64')
test_features = test_features.reshape(int(len(test_features) / 14), 14)
x = np.isnan(test_features)
# replacing NaN values with 0
test_features[x] = 0
X_test = test_features
# with trained model to predict
loaded_model = joblib.load('XGBoostClassifier')
pred_labels = loaded_model.predict(X_test)
# a list of tuple
predictions = list()
for idx in range(len(X_test)):
predictions.append((ecg_names[idx], pred_labels[idx]))
return predictions # Liste von Tupels im Format (ecg_name,label) - Muss unverändert bleiben!
ecg_wave = np.load('test.npy') # 10초 ECG (sampling rate=500)
#ecg_wave.shape -> (5000,)
def bandpass(lowcut, highcut, order=5):
nyq = 0.5 * 500
low = lowcut / nyq
high = highcut / nyq
b, a = butter(order, [low, high], btype='band')
return b, a
b, a = bandpass(0.5, 45)
filtered_ecg = signal.filtfilt(b, a, ecg_wave[250:4750])
_, rpeaks = nk.ecg_peaks(filtered_ecg, sampling_rate=500)
_, waves_cwt = nk.ecg_delineate(filtered_ecg,
rpeaks,
sampling_rate=500,
method="dwt",
show=False,
show_type='peaks')
# 관심 ECG 설정
interest_ecg = filtered_ecg[rpeaks['ECG_R_Peaks'][0]:rpeaks['ECG_R_Peaks'][1]]
# template ECG 설정
template_ecg = []
import numpy as np
import pandas as pd
import neurokit2 as nk
sampling_rate = 1000
for heartrate in [80]:
# Simulate signal
ecg = nk.ecg_simulate(duration=60,
sampling_rate=sampling_rate,
heartrate=heartrate,
noise=0)
# Segment
_, rpeaks = nk.ecg_peaks(ecg, sampling_rate=sampling_rate)
# _, waves = nk.ecg_delineator(ecg, rpeaks=rpeaks["ECG_R_Peaks"])
def apply_pan_tompkins(x,
n_beats=10,
beat_length=512,
fs=460,
standardize=False):
_, rpeaks = nk.ecg_peaks(x, sampling_rate=fs)
# Standardize if necessary
if standardize:
scaler = MinMaxScaler()
x = np.reshape(x, [-1, 1])
x = scaler.fit_transform(x)
x = np.reshape(x, [-1])
# Create dict of peaks no longer than peak_length
cleaned_signal_dict = {}
peaks = rpeaks['ECG_R_Peaks']
# Loop through all the peaks
for i in range(len(peaks)):
# Get current peak
current_peak = rpeaks['ECG_R_Peaks'][i]
# If current peak is the last peak, set the last peak to be last sample of signal
if i == len(peaks) - 1:
next_peak = len(x) - 1
# Otherwise next peak is next peak in the peaks list
else:
# Get current and next peak
next_peak = rpeaks['ECG_R_Peaks'][i + 1]
# Check if distance to next peak is less than the required beat length
if (next_peak - current_peak) < beat_length:
# If so, compute additional zeros we need to add to fill the beat
additional_zeros = beat_length - (next_peak - current_peak)
additional_zeros_list = np.zeros(additional_zeros)
# Add the signal plus additional zeros to form entire beat
total_beat = np.concatenate(
(x[current_peak:next_peak], additional_zeros_list))
cleaned_signal_dict.update({current_peak: total_beat})
# Otherwise just return the signal within the required beat length
else:
total_beat = x[current_peak:current_peak + beat_length]
cleaned_signal_dict.update({current_peak: total_beat})
# If n_beats is defined, returned those beats
if n_beats and n_beats <= len(cleaned_signal_dict):
# Get index of middle beat
i = int(len(cleaned_signal_dict) / 2)
half_n_beats = int(n_beats / 2)
valid_keys = list(cleaned_signal_dict.keys())[i - half_n_beats:i +
half_n_beats]
return {
key: val
for key, val in cleaned_signal_dict.items() if key in valid_keys
}
# Otherwise return all beats except first and last
valid_keys = list(cleaned_signal_dict.keys())[1:-1]
return {
key: val
for key, val in cleaned_signal_dict.items() if key in valid_keys
}
def extract_features(
self,
clean_method: str = "neurokit",
r_method: str = "neurokit",
wave_method: str = "dwt",
min_peaks: int = 200,
size: int = 200000,
):
"""
Function to extract the ecg features using the neurokit2 package. That
is the P, Q, R, S and T peaks and the P, QRS and T waves onsets and
offsets. The result is saved internally.
:param clean_method: <str> The processing pipeline to apply. Can be one of
‘neurokit’ (default), ‘biosppy’, ‘pantompkins1985’,
‘hamilton2002’, ‘elgendi2010’, ‘engzeemod2012’.
:param r_method: <str> The algorithm to be used for R-peak detection. Can be one
of ‘neurokit’ (default), ‘pantompkins1985’, ‘hamilton2002’,
‘christov2004’, ‘gamboa2008’, ‘elgendi2010’, ‘engzeemod2012’
or ‘kalidas2017’.
:param wave_method: <str> Can be one of ‘dwt’ (default) for discrete
wavelet transform or ‘cwt’ for continuous wavelet transform.
:param min_peaks: <int> Minimum R peaks to be detected to proceed with
further calculations.
:param size: <int> ECG sample size to analyze per loop.
"""
if not self.lead:
return
for i, _ in enumerate(self.sampling_rate):
sampling_rate = self.sampling_rate[i][0]
init = self.sampling_rate[i][1]
if i == len(self.sampling_rate) - 1:
ecg_signal_size = (
ECG_TMAPS[f"{self.lead}_value"].tensor_from_file(
ECG_TMAPS[f"{self.lead}_value"],
self,
visit=self.visit,
)[0][init:].shape[0])
else:
ecg_signal_size = self.sampling_rate[i + 1][1] - init
if size < ecg_signal_size:
end = init + size
else:
end = init + ecg_signal_size
while init < ecg_signal_size + self.sampling_rate[i][1]:
ecg_signal = ECG_TMAPS[f"{self.lead}_value"].tensor_from_file(
ECG_TMAPS[f"{self.lead}_value"],
self,
visit=self.visit,
)[0][init:end]
ecg_signal = nk.ecg_clean(ecg_signal, sampling_rate,
clean_method)
try:
_, r_peaks = nk.ecg_peaks(ecg_signal, sampling_rate,
r_method)
except IndexError:
init = end
end = init + size
if end > ecg_signal_size + self.sampling_rate[i][1]:
end = ecg_signal_size + self.sampling_rate[i][1]
continue
if len(r_peaks["ECG_R_Peaks"]) < min_peaks:
init = end
end = init + size
if end > ecg_signal_size + self.sampling_rate[i][1]:
end = ecg_signal_size + self.sampling_rate[i][1]
continue
_, waves_peaks = nk.ecg_delineate(ecg_signal, r_peaks,
sampling_rate)
_, waves_peaks_2 = nk.ecg_delineate(
ecg_signal,
r_peaks,
sampling_rate,
wave_method,
)
waves_peaks.update(waves_peaks_2)
for peak_type in r_peaks:
if peak_type not in self.r_peaks:
self.r_peaks[peak_type] = r_peaks[peak_type]
else:
self.r_peaks[peak_type] = np.append(
self.r_peaks[peak_type],
r_peaks[peak_type],
)
for peak_type in waves_peaks:
if peak_type not in self.waves_peaks:
self.waves_peaks[peak_type] = waves_peaks[peak_type]
else:
self.waves_peaks[peak_type] = np.append(
self.waves_peaks[peak_type],
waves_peaks[peak_type],
)
init = end
end = init + size
if end > ecg_signal_size + self.sampling_rate[i][1]:
end = ecg_signal_size + self.sampling_rate[i][1]
for peak_type in self.r_peaks:
self.r_peaks[peak_type] = list(self.r_peaks[peak_type])
for peak_type in self.waves_peaks:
self.waves_peaks[peak_type] = list(self.waves_peaks[peak_type])
def rodrigues2020(ecg, sampling_rate):
signal, info = nk.ecg_peaks(ecg,
sampling_rate=sampling_rate,
method="rodrigues2020")
return info["ECG_R_Peaks"]
def extract_features_tmaps(
self,
signal_tm: TensorMap,
clean_method: str = "neurokit",
r_method: str = "neurokit",
wave_method: str = "dwt",
min_peaks: int = 200,
):
"""
Function to extract the ecg features using the neurokit2 package. That
is the P, Q, R, S and T peaks and the P, QRS and T waves onsets and
offsets. The result is saved internally.
:param signal_tm: <TensorMap>
:param clean_method: <str> The processing pipeline to apply. Can be one of
‘neurokit’ (default), ‘biosppy’, ‘pantompkins1985’,
‘hamilton2002’, ‘elgendi2010’, ‘engzeemod2012’.
:param r_method: <str> The algorithm to be used for R-peak detection. Can be one
of ‘neurokit’ (default), ‘pantompkins1985’, ‘hamilton2002’,
‘christov2004’, ‘gamboa2008’, ‘elgendi2010’, ‘engzeemod2012’
or ‘kalidas2017’.
:param wave_method: <str> Can be one of ‘dwt’ (default) for discrete
wavelet transform or ‘cwt’ for continuous wavelet transform.
:param min_peaks: <int> Minimum R peaks to be detected to proceed with
further calculations.
"""
for i, _ in enumerate(self.sampling_rate):
sampling_rate = self.sampling_rate[i][0]
init = self.sampling_rate[i][1]
if i == len(self.sampling_rate) - 1:
end = -1
else:
end = self.sampling_rate[i + 1][1]
ecg_signal = signal_tm.tensor_from_file(signal_tm,
self)[0][init:end]
ecg_signal = nk.ecg_clean(ecg_signal, sampling_rate, clean_method)
try:
_, r_peaks = nk.ecg_peaks(ecg_signal, sampling_rate, r_method)
except IndexError:
continue
if len(r_peaks["ECG_R_Peaks"]) < min_peaks:
continue
_, waves_peaks = nk.ecg_delineate(ecg_signal, r_peaks,
sampling_rate)
_, waves_peaks_2 = nk.ecg_delineate(
ecg_signal,
r_peaks,
sampling_rate,
wave_method,
)
waves_peaks.update(waves_peaks_2)
for peak_type in r_peaks:
if peak_type not in self.r_peaks:
self.r_peaks[peak_type] = r_peaks[peak_type]
else:
self.r_peaks[peak_type] = np.append(
self.r_peaks[peak_type],
r_peaks[peak_type],
)
for peak_type in waves_peaks:
if peak_type not in self.waves_peaks:
self.waves_peaks[peak_type] = waves_peaks[peak_type]
else:
self.waves_peaks[peak_type] = np.append(
self.waves_peaks[peak_type],
waves_peaks[peak_type],
)
for peak_type in self.r_peaks:
self.r_peaks[peak_type] = list(self.r_peaks[peak_type])
for peak_type in self.waves_peaks:
self.waves_peaks[peak_type] = list(self.waves_peaks[peak_type])
def create_df(dataframe: pd.DataFrame) -> pd.DataFrame:
# get lengths of signals for each sample
lengths = []
width = dataframe.shape[1]
for row in dataframe.index.tolist():
temp_width = width
for item in dataframe.loc[row][::-1]:
if not pd.isna(item) and isinstance(item, float):
temp_width -= 1
break
temp_width -= 1
lengths.append(temp_width)
"""
README
For the following features we measured: [mean, median, 5 % percentile, 95 % percentile, standard deviation]
R-peak location were retrieved by nk.ecg_peaks
Q-peak and S-location were retrieved by nk.ecg_delineate
?_ampl_* ?-Peak amplitude
?_nr_peaks number of ?-Peaks
?_diff_* Interval between ?-Peaks
QRS_diff_* QRS duration
len_* length of signal
Qual_* quality of signal measured with nk.ecg_quality
sign_* signal
Also the output from nk.hrv_time which contains different measurements for the heart rate variation (HRV*) was added
Additionally one 'typical' heartbeat was greated (all length 180):
MN_* mean signal
MD_* median signal
P5_* 5 % percentile signal
P95_* 95 % percentile signal
SD_* standard deviation of signal
"""
names = ['R_ampl_mean', 'R_ampl_median', 'R_ampl_perc5', 'R_ampl_perc95', 'R_ampl_sd', 'R_nr_peaks',
'len_mean', 'len_median', 'len_perc5', 'len_perc95', 'len_sd',
'sign_mean', 'sign_median', 'sign_perc5', 'sign_perc95', 'sign_sd',
'Qual_mean', 'Qual_median', 'Qual_perc5', 'Qual_perc95', 'Qual_sd',
'Q_ampl_mean', 'Q_ampl_median', 'Q_ampl_perc5', 'Q_ampl_perc95', 'Q_ampl_sd', 'Q_nr_peaks',
'Q_diff_mean', 'Q_diff_median', 'Q_diff_perc5', 'Q_diff_perc95', 'Q_diff_sd',
'S_ampl_mean', 'S_ampl_median', 'S_ampl_perc5', 'S_ampl_perc95', 'S_ampl_sd', 'S_nr_peaks',
'S_diff_mean', 'S_diff_median', 'S_diff_perc5', 'S_diff_perc95', 'S_diff_sd',
'P_ampl_mean', 'P_ampl_median', 'P_ampl_perc5', 'P_ampl_perc95', 'P_ampl_sd', 'P_nr_peaks',
'T_ampl_mean', 'T_ampl_median', 'T_ampl_perc5', 'T_ampl_perc95', 'T_ampl_sd', 'T_nr_peaks',
'QRS_diff_mean', 'QRS_diff_median', 'QRS_diff_perc5', 'QRS_diff_perc95', 'QRS_diff_sd',
'PR_diff_mean', 'PR_diff_median', 'PR_diff_perc5', 'PR_diff_perc95', 'PR_diff_sd',
'RT_diff_mean', 'RT_diff_median', 'RT_diff_perc5', 'RT_diff_perc95', 'RT_diff_sd',
'HRV_RMSSD', 'HRV_MeanNN', 'HRV_SDNN', 'HRV_SDSD', 'HRV_CVNN', 'HRV_CVSD', 'HRV_MedianNN',
'HRV_MadNN', 'HRV_MCVNN', 'HRV_IQRNN', 'HRV_pNN50', 'HRV_pNN20', 'HRV_TINN', 'HRV_HTI',
'HRV_ULF','HRV_VLF','HRV_LF','HRV_HF','HRV_VHF','HRV_LFHF','HRV_LFn','HRV_HFn', 'HRV_LnHF',
'HRV_SD1','HRV_SD2', 'HRV_SD1SD2','HRV_S','HRV_CSI','HRV_CVI','HRV_CSI_Modified', 'HRV_PIP',
'HRV_IALS','HRV_PSS','HRV_PAS','HRV_GI','HRV_SI','HRV_AI','HRV_PI','HRV_C1d','HRV_C1a','HRV_SD1d',
'HRV_SD1a','HRV_C2d','HRV_C2a','HRV_SD2d','HRV_SD2a','HRV_Cd','HRV_Ca','HRV_SDNNd','HRV_SDNNa','HRV_ApEn',
'HRV_SampEn','J_LF','J_HF','J_L/H']
template_len = 180
mean_names = ['MN_' + str(index) for index in range(template_len)]
median_names = ['MD_' + str(index) for index in range(template_len)]
perc5_names = ['P5_' + str(index) for index in range(template_len)]
perc95_names = ['P95_' + str(index) for index in range(template_len)]
sd_names = ['SD_' + str(index) for index in range(template_len)]
wavelet = 'db3'
wl_len = int(np.floor((template_len + pywt.Wavelet(wavelet).dec_len - 1) / 2))
wl_mean_names = ['WLMN_' + str(index) for index in range(2*wl_len)]
wl_median_names = ['WLMD_' + str(index) for index in range(2*wl_len)]
wl_perc5_names = ['WLP5_' + str(index) for index in range(2*wl_len)]
wl_perc95_names = ['WLP95_' + str(index) for index in range(2*wl_len)]
wl_sd_names = ['WLSD_' + str(index) for index in range(2*wl_len)]
typical_signal_names = mean_names + median_names + perc5_names + perc95_names + sd_names + wl_mean_names + \
wl_median_names + wl_perc5_names + wl_perc95_names + wl_sd_names
names += typical_signal_names
data = np.empty([dataframe.shape[0], len(names)])
iteration = 0
for row_index, row in dataframe.iterrows():
print(row_index)
# Retrieve ECG data
ecg_signal = row[:lengths[iteration] + 1]
ecg_signal = nk.ecg_clean(ecg_signal, sampling_rate=SAMPLING_RATE)
# Find R-peaks
peaks, info = nk.ecg_peaks(ecg_signal, sampling_rate=SAMPLING_RATE)
# R amplitude
R_amplitudes = ecg_signal[info['ECG_R_Peaks']]
# Check if the signal is flipped
# Check if we have enough peaks to retrieve more information
if len(R_amplitudes) > 4:
_, waves_peak = nk.ecg_delineate(ecg_signal, info, sampling_rate=300, show=False)
# Q amplitude
# remove nan values
Q_amplitudes = [ecg_signal[peak_index] if str(peak_index) != 'nan' else - np.infty for peak_index in
waves_peak['ECG_Q_Peaks']]
if np.sum([1 if np.abs(rpeak) > np.abs(Q_amplitudes[index]) else -1 for index, rpeak in
enumerate(R_amplitudes)]) < 0:
print("flip", row_index)
ecg_signal = -ecg_signal
peaks, info = nk.ecg_peaks(ecg_signal, sampling_rate=300)
# R amplitude
R_amplitudes = ecg_signal[info['ECG_R_Peaks']]
if len(R_amplitudes) > 4:
_, waves_peak = nk.ecg_delineate(ecg_signal, info, sampling_rate=300, show=False)
data_temp = []
if len(R_amplitudes) > 0:
data_temp = [np.mean(R_amplitudes),
np.median(R_amplitudes),
np.percentile(R_amplitudes, q=5),
np.percentile(R_amplitudes, q=95),
np.std(R_amplitudes),
len(R_amplitudes)]
else:
empty = np.empty([6])
empty[:] = np.NaN
data_temp += empty.tolist()
# length of signal
data_new = [np.mean(lengths[iteration] / SAMPLING_RATE),
np.median(lengths[iteration] / SAMPLING_RATE),
np.percentile(lengths[iteration] / SAMPLING_RATE, q=5),
np.percentile(lengths[iteration] / SAMPLING_RATE, q=95),
np.std(lengths[iteration] / SAMPLING_RATE)]
data_temp += data_new
# signal
data_new = [np.mean(ecg_signal),
np.median(ecg_signal),
np.percentile(ecg_signal, q=5),
np.percentile(ecg_signal, q=95),
np.std(ecg_signal)]
data_temp += data_new
# Check if we have enough peaks to retrieve more information
if len(R_amplitudes) > 4:
quality = nk.ecg_quality(ecg_signal, sampling_rate=SAMPLING_RATE)
data_new = [np.mean(quality),
np.median(quality),
np.percentile(quality, q=5),
np.percentile(quality, q=95),
np.std(quality)]
data_temp += data_new
# Delineate the ECG signal
# “ECG_P_Peaks”, “ECG_Q_Peaks”, “ECG_S_Peaks”, “ECG_T_Peaks”, “ECG_P_Onsets”, “ECG_T_Offsets”
# _, waves_peak = nk.ecg_delineate(ecg_signal, info, sampling_rate=SAMPLING_RATE, show=False)
# Q amplitude
# remove nan values
Q_peaks = [peak for peak in waves_peak['ECG_Q_Peaks'] if str(peak) != 'nan']
if len(Q_peaks) > 0:
Q_amplitudes = ecg_signal[Q_peaks]
data_new = [np.mean(Q_amplitudes),
np.median(Q_amplitudes),
np.percentile(Q_amplitudes, q=5),
np.percentile(Q_amplitudes, q=95),
np.std(Q_amplitudes),
len(Q_amplitudes)]
data_temp += data_new
else:
empty = np.empty([6])
empty[:] = np.NaN
empty[5] = 0
data_temp += empty.tolist()
# more than 1 Q-Peak => can build interval[s]
if len(Q_peaks) > 1:
Q_peaks_diff = [(Q_peaks[index + 1] - Q_peaks[index]) / SAMPLING_RATE
for index, item in enumerate(Q_peaks[:len(Q_peaks) - 1])]
# QQ interval
data_new = [np.mean(Q_peaks_diff),
np.median(Q_peaks_diff),
np.percentile(Q_peaks_diff, q=5),
np.percentile(Q_peaks_diff, q=95),
np.std(Q_peaks_diff)]
data_temp += data_new
# 0 or 1 Q-peak = no interval => return nan
else:
empty = np.empty([5])
empty[:] = np.NaN
data_temp += empty.tolist()
# S amplitude
# remove nan values
S_peaks = [peak for peak in waves_peak['ECG_S_Peaks'] if str(peak) != 'nan']
if len(S_peaks) > 0:
S_amplitudes = ecg_signal[S_peaks]
data_new = [np.mean(S_amplitudes),
np.median(S_amplitudes),
np.percentile(S_amplitudes, q=5),
np.percentile(S_amplitudes, q=95),
np.std(S_amplitudes),
len(S_amplitudes)]
data_temp += data_new
else:
empty = np.empty([6])
empty[:] = np.NaN
empty[5] = 0
data_temp += empty.tolist()
# more than one S-peak
if len(S_peaks) > 1:
S_peaks_diff = [(S_peaks[index + 1] - S_peaks[index]) / SAMPLING_RATE
for index, item in enumerate(S_peaks[:len(S_peaks) - 1])]
# SS interval
data_new = [np.mean(S_peaks_diff),
np.median(S_peaks_diff),
np.percentile(S_peaks_diff, q=5),
np.percentile(S_peaks_diff, q=95),
np.std(S_peaks_diff)]
data_temp += data_new
# 0 or 1 S-peak = no interval => return nan
else:
empty = np.empty([5])
empty[:] = np.NaN
data_temp += empty.tolist()
P_peaks = [peak for peak in waves_peak['ECG_P_Peaks'] if str(peak) != 'nan']
if len(P_peaks) > 0:
P_amplitudes = ecg_signal[P_peaks]
data_new = [np.mean(P_amplitudes),
np.median(P_amplitudes),
np.percentile(P_amplitudes, q=5),
np.percentile(P_amplitudes, q=95),
np.std(P_amplitudes),
len(P_amplitudes)]
data_temp += data_new
else:
empty = np.empty([6])
empty[:] = np.NaN
empty[5] = 0
data_temp += empty.tolist()
T_peaks = [peak for peak in waves_peak['ECG_T_Peaks'] if str(peak) != 'nan']
if len(T_peaks) > 0:
T_peaks = ecg_signal[T_peaks]
data_new = [np.mean(T_peaks),
np.median(T_peaks),
np.percentile(T_peaks, q=5),
np.percentile(T_peaks, q=95),
np.std(T_peaks),
len(T_peaks)]
data_temp += data_new
else:
empty = np.empty([6])
empty[:] = np.NaN
empty[5] = 0
data_temp += empty.tolist()
# QRS interval
QRS_peaks_diff = []
# compute difference between Q and S peak
for index in range(len(waves_peak['ECG_Q_Peaks'])):
if not (np.isnan(waves_peak['ECG_Q_Peaks'][index]) or np.isnan(waves_peak['ECG_S_Peaks'][index])):
QRS_peaks_diff.append(
(waves_peak['ECG_S_Peaks'][index] - waves_peak['ECG_Q_Peaks'][index]) / SAMPLING_RATE)
if len(QRS_peaks_diff) > 0:
data_new = [np.mean(QRS_peaks_diff),
np.median(QRS_peaks_diff),
np.percentile(QRS_peaks_diff, q=5),
np.percentile(QRS_peaks_diff, q=95),
np.std(QRS_peaks_diff)]
data_temp += data_new
else:
empty = np.empty([5])
empty[:] = np.NaN
data_temp += empty.tolist()
# PR interval
PR_peaks_diff = []
# compute difference between P and R peak
for index in range(len(waves_peak['ECG_P_Peaks'])):
if not np.isnan(waves_peak['ECG_P_Peaks'][index]):
PR_peaks_diff.append(
(info['ECG_R_Peaks'][index] - waves_peak['ECG_P_Peaks'][index]) / SAMPLING_RATE)
if len(PR_peaks_diff) > 0:
data_new = [np.mean(PR_peaks_diff),
np.median(PR_peaks_diff),
np.percentile(PR_peaks_diff, q=5),
np.percentile(PR_peaks_diff, q=95),
np.std(PR_peaks_diff)]
data_temp += data_new
else:
empty = np.empty([5])
empty[:] = np.NaN
data_temp += empty.tolist()
# RT interval
RT_peaks_diff = []
# compute difference between P and R peak
for index in range(len(waves_peak['ECG_T_Peaks'])):
if not np.isnan(waves_peak['ECG_T_Peaks'][index]):
RT_peaks_diff.append(
(waves_peak['ECG_T_Peaks'][index] - info['ECG_R_Peaks'][index]) / SAMPLING_RATE)
if len(RT_peaks_diff) > 0:
data_new = [np.mean(RT_peaks_diff),
np.median(PR_peaks_diff),
np.percentile(RT_peaks_diff, q=5),
np.percentile(RT_peaks_diff, q=95),
np.std(RT_peaks_diff)]
data_temp += data_new
else:
empty = np.empty([5])
empty[:] = np.NaN
data_temp += empty.tolist()
# Extract clean EDA and SCR features
# explanation of features:
# https://neurokit2.readthedocs.io/en/latest/functions.html?highlight=hrv%20time#neurokit2.hrv.hrv_time
hrv_time = nk.hrv(peaks, sampling_rate=SAMPLING_RATE, show=False)
data_new = hrv_time.values.tolist()[0]
data_temp += data_new
# Jannik
# http://www.paulvangent.com/2016/03/21/analyzing-a-discrete-heart-rate-signal-using-python-part-2/
rpeaks = info['ECG_R_Peaks']
r_interval = [rpeaks[index+1]-rpeaks[index] for index in range(len(rpeaks)-1)]
RR_x_new = np.linspace(rpeaks[0],rpeaks[-2],rpeaks[-2])
f = interp1d(rpeaks[:-1], r_interval, kind='cubic')
n = lengths[iteration] + 1 # Length of the signal
frq = np.fft.fftfreq(n, d=(1 / SAMPLING_RATE)) # divide the bins into frequency categories
frq = frq[range(int(n/2))] # Get single side of the frequency range
Y = np.fft.fft(f(RR_x_new))/n # Calculate FFT
try:
Y = Y[range(int(n / 2))]
lf = np.trapz(abs(Y[(frq >= 0.04) & (frq <= 0.15)]))
hf = np.trapz(abs(Y[(frq >= 0.16) & (frq <= 0.5)])) # Do the same for 0.16-0.5Hz (HF)
data_new = [lf, hf, lf / hf]
data_temp += data_new
except IndexError as err:
print(err)
data_temp += [None, None, None]
# if we don't have enough R peaks return vector of nan's
else:
empty = np.empty([len(names) - 16 - len(typical_signal_names)])
empty[:] = np.NaN
data_temp += empty.tolist()
# Create a 'typical' heartbeat
# Scaler = StandardScaler()
# ecg_signal = Scaler.fit_transform(X=ecg_signal.reshape(-1, 1)).reshape(1, -1)[0].tolist()
out = ecg.ecg(signal=ecg_signal, sampling_rate=SAMPLING_RATE, show=False)
mean = np.mean(out['templates'], axis=0)
median = np.median(out['templates'], axis=0)
perc5 = np.percentile(out['templates'].astype(np.float64), axis=0, q=5)
perc95 = np.percentile(out['templates'].astype(np.float64), axis=0, q=95)
std = np.std(out['templates'].astype(np.float64), axis=0)
data_new = np.concatenate((mean, median, perc5, perc95, std)).tolist()
data_temp += data_new
(wl_mean_cA, wl_mean_cD) = pywt.dwt(np.mean(out['templates'], axis=0),
'db3', 'periodic')
(wl_median_cA, wl_median_cD) = pywt.dwt(np.median(out['templates'], axis=0),
'db3', 'periodic')
(wl_perc5_cA, wl_perc5_cD) = pywt.dwt(np.percentile(out['templates'].astype(np.float64), axis=0, q=5),
'db3', 'periodic')
(wl_perc95_cA, wl_perc95_cD) = pywt.dwt(np.percentile(out['templates'].astype(np.float64), axis=0, q=95),
'db3', 'periodic')
(wl_sd_cA, wl_sd_cD) = pywt.dwt(np.std(out['templates'].astype(np.float64), axis=0),
'db3', 'periodic')
data_new = np.concatenate((wl_mean_cA, wl_mean_cD,
wl_median_cA, wl_median_cD,
wl_perc5_cA, wl_perc5_cD,
wl_perc95_cA, wl_perc95_cD,
wl_sd_cA, wl_sd_cD)).tolist()
data_temp += data_new
data[iteration] = data_temp
iteration += 1
features = pd.DataFrame(data, columns=names)
return features
plot = nk.signal_plot([original, distorted, cleaned])
# Save plot
fig = plt.gcf()
fig.set_size_inches(10, 6)
fig.savefig("README_signalprocessing.png", dpi=300, h_pad=3)
# =============================================================================
# Heart Rate Variability
# =============================================================================
# Download data
data = nk.data("bio_resting_8min_100hz")
# Find peaks
peaks, info = nk.ecg_peaks(data["ECG"], sampling_rate=100)
# Compute HRV indices
hrv = nk.hrv(peaks, sampling_rate=100, show=True)
hrv
# Save plot
fig = plt.gcf()
fig.set_size_inches(10 * 1.5, 6 * 1.5, forward=True)
fig.savefig("README_hrv.png", dpi=300, h_pad=3)
# =============================================================================
# Complexity
# =============================================================================
# Generate signal
def compute_features(data, condition, sampling_rate=700, window_size=60, window_shift=0.25):
index = 0
init = time.time()
# data cleaning
## ECG
ecg_cleaned = nk.ecg_clean(data["ECG"][condition].flatten(), sampling_rate=sampling_rate)
## == OLD
# ecg_rpeaks, _ = nk.ecg_peaks(ecg_cleaned, sampling_rate=sampling_rate)
# ecg_hr = nk.signal_rate(ecg_rpeaks, sampling_rate=sampling_rate)
## ==
## EDA
## 5Hz lowpass filter
eda_highcut = 5
eda_filtered = nk.signal_filter(data['EDA'][condition].flatten(), sampling_rate=sampling_rate, highcut=eda_highcut)
eda_cleaned = nk.standardize(eda_filtered)
# TODO: not sure about the approach. cvxeda takes longer periods
# phasic_tonic = nk.eda_phasic(cleaned, sampling_rate=700, method='cvxeda')
eda_phasic_tonic = nk.eda_phasic(eda_cleaned, sampling_rate=sampling_rate)
eda_phasic_tonic['t'] = [(1 / sampling_rate) * i for i in range(eda_phasic_tonic.shape[0])]
eda_scr_peaks, scr_info = nk.eda_peaks(eda_phasic_tonic['EDA_Phasic'], sampling_rate=sampling_rate)
## EMG
## For 5 sec window signal
## More on DC Bias https://www.c-motion.com/v3dwiki/index.php/EMG:_Removing_DC_Bias
emg_lowcut = 50
emg_filtered_dc = nk.signal_filter(data['EMG'][condition].flatten(), sampling_rate=sampling_rate, lowcut=emg_lowcut)
# OR 100 Hz highpass Butterworth filter followed by a constant detrending
# filtered_dc = nk.emg_clean(chest_data_dict['EMG'][baseline].flatten(), sampling_rate=700)
## For 60 sec window signal
# 50Hz lowpass filter
emg_highcut = 50
emg_filtered = nk.signal_filter(data['EMG'][condition].flatten(), sampling_rate=sampling_rate, highcut=emg_highcut)
## Resp
## Method biosppy important to appply bandpass filter 0.1 - 0.35 Hz
resp_processed, _ = nk.rsp_process(data['Resp'][condition].flatten(), sampling_rate=sampling_rate, method='biosppy')
print('Elapsed Preprocess', str(timedelta(seconds=time.time() - init)))
init = time.time()
chest_df_5 = pd.DataFrame() # For 5 sec window size
chest_df = pd.DataFrame()
window = int(sampling_rate * window_size)
for i in range(0, data['ACC'][condition].shape[0] - window, int(sampling_rate * window_shift)):
# ACC
w_acc_data = data['ACC'][condition][i: window + i]
acc_x_mean, acc_y_mean, acc_z_mean = np.mean(w_acc_data, axis=0) # Feature
acc_x_std, acc_y_std, acc_z_std = np.std(w_acc_data, axis=0) # Feature
acc_x_peak, acc_y_peak, acc_z_peak = np.amax(w_acc_data, axis=0) # Feature
acc_x_absint, acc_y_absint, acc_z_absint = np.abs(np.trapz(w_acc_data, axis=0)) # Feature
xyz = np.sum(w_acc_data, axis=0)
xyz_mean = np.mean(xyz) # Feature
xyz_std = np.std(xyz) # Feature
xyz_absint = np.abs(np.trapz(xyz)) # Feature
# == OLD
# ## ECG
# w_ecg_rpeaks = ecg_rpeaks[i: window + i]
# # HR
# w_ecg_hr = ecg_hr[i: window + i]
# hr_mean = np.mean(w_ecg_hr) # Feature
# hr_std = np.std(w_ecg_hr) # Feature
# # HRV Time-domain Indices
# # HRV_MeanNN
# # HRV_SDNN
# # HRV_pNN50
# # HRV_RMSSD -> Root mean square of the HRV
# # HRV_HTI -> Triangular interpolation index
# hrv_time = nk.hrv_time(w_ecg_rpeaks, sampling_rate=sampling_rate, show=False)
# hrv_mean = hrv_time.loc[0, 'HRV_MeanNN'] # Feature
# hrv_std = hrv_time.loc[0, 'HRV_SDNN'] # Feature
# # TODO: NN50
# # hrv_NN50 =
# hrv_pNN50 = hrv_time.loc[0, 'HRV_pNN50'] # Feature
# hrv_TINN = hrv_time.loc[0, 'HRV_HTI'] # Feature
# hrv_rms = hrv_time.loc[0, 'HRV_RMSSD'] # Feature
# # HRV Frequency-domain Indices
# # TODO: get NaN values within windows (*)
# # HRV_ULF *
# # HRV_LF *
# # HRV_HF
# # HRV_VHF
# # HRV_LFHF - Ratio LF/HF *
# # HRV_LFn *
# # HRV_HFn
# hrv_freq = nk.hrv_frequency(w_ecg_rpeaks, sampling_rate=sampling_rate, ulf=(0.01, 0.04), lf=(0.04, 0.15), hf=(0.15, 0.4), vhf=(0.4, 1.))
# hrv_ULF = hrv_freq.loc[0, 'HRV_ULF'] # Feature
# hrv_LF = hrv_freq.loc[0, 'HRV_LF'] # Feature
# hrv_HF = hrv_freq.loc[0, 'HRV_HF'] # Feature
# hrv_VHF = hrv_freq.loc[0, 'HRV_VHF'] # Feature
# hrv_lf_hf_ratio = hrv_freq.loc[0, 'HRV_LFHF'] # Feature
# hrv_f_sum = np.nansum(np.hstack((hrv_ULF, hrv_LF, hrv_HF, hrv_VHF)))
# # TODO: rel_f
# # hrv_rel_f =
# hrv_LFn = hrv_freq.loc[0, 'HRV_LFn'] # Feature
# hrv_HFn = hrv_freq.loc[0, 'HRV_HFn'] # Feature
# ==
## ECG
w_ecg_cleaned = ecg_cleaned[i: window + i]
_, ecg_info = nk.ecg_peaks(w_ecg_cleaned, sampling_rate=sampling_rate)
w_ecg_rpeaks = ecg_info['ECG_R_Peaks']
ecg_nni = pyhrv.tools.nn_intervals(w_ecg_rpeaks)
# HR
rs_hr = pyhrv.time_domain.hr_parameters(ecg_nni)
hr_mean = rs_hr['hr_mean'] # Feature
hr_std = rs_hr['hr_std'] # Feature
# HRV-time
rs_hrv = pyhrv.time_domain.nni_parameters(ecg_nni)
hrv_mean = rs_hrv['nni_mean'] # Feature
hrv_std = pyhrv.time_domain.sdnn(ecg_nni)['sdnn'] # Feature
rs_nn50 = pyhrv.time_domain.nn50(ecg_nni)
hrv_NN50 = rs_nn50['nn50'] # Feature
hrv_pNN50 = rs_nn50['pnn50'] # Feature
hrv_time = nk.hrv_time(w_ecg_rpeaks, sampling_rate=sampling_rate, show=False)
hrv_TINN = hrv_time.loc[0, 'HRV_TINN'] # Feature
hrv_rms = pyhrv.time_domain.rmssd(ecg_nni)['rmssd'] # Feature
# HRV-freq
hrv_freq = pyhrv.frequency_domain.welch_psd(ecg_nni, fbands={'ulf': (0.01, 0.04), 'vlf': (0.04, 0.15), 'lf': (0.15, 0.4), 'hf': (0.4, 1)}, mode='dev')
# hrv_freq = hrv_freq.as_dict()
hrv_freq = hrv_freq[0]
hrv_ULF = hrv_freq['fft_abs'][0] # Feature
hrv_LF = hrv_freq['fft_abs'][1] # Feature
hrv_HF = hrv_freq['fft_abs'][2] # Feature
hrv_VHF = hrv_freq['fft_abs'][3] # Feature
hrv_lf_hf_ratio = hrv_freq['fft_ratio'] # Feature
hrv_f_sum = hrv_freq['fft_total'] # Feature
hrv_rel_ULF = hrv_freq['fft_rel'][0] # Feature
hrv_rel_LF = hrv_freq['fft_rel'][1] # Feature
hrv_rel_HF = hrv_freq['fft_rel'][2] # Feature
hrv_rel_VHF = hrv_freq['fft_rel'][3] # Feature
hrv_LFn = hrv_freq['fft_norm'][0] # Feature
hrv_HFn = hrv_freq['fft_norm'][1] # Feature
# EDA
w_eda_data = eda_cleaned[i: window + i]
w_eda_phasic_tonic = eda_phasic_tonic[i: window + i]
eda_mean = np.mean(w_eda_data) # Feature
eda_std = np.std(w_eda_data) # Feature
eda_min = np.amin(w_eda_data) # Feature
eda_max = np.amax(w_eda_data) # Feature
# dynamic range: https://en.wikipedia.org/wiki/Dynamic_range
eda_slope = get_slope(w_eda_data) # Feature
eda_drange = eda_max / eda_min # Feature
eda_scl_mean = np.mean(w_eda_phasic_tonic['EDA_Tonic']) # Feature
eda_scl_std = np.std(w_eda_phasic_tonic['EDA_Tonic']) # Feature
eda_scr_mean = np.mean(w_eda_phasic_tonic['EDA_Phasic']) # Feature
eda_scr_std = np.std(w_eda_phasic_tonic['EDA_Phasic']) # Feature
eda_corr_scl_t = nk.cor(w_eda_phasic_tonic['EDA_Tonic'], w_eda_phasic_tonic['t'], show=False) # Feature
eda_scr_no = eda_scr_peaks['SCR_Peaks'][i: window + i].sum() # Feature
# Sum amplitudes in SCR signal
ampl = scr_info['SCR_Amplitude'][i: window + i]
eda_ampl_sum = np.sum(ampl[~np.isnan(ampl)]) # Feature
# TODO:
# eda_t_sum =
scr_peaks, scr_properties = scisig.find_peaks(w_eda_phasic_tonic['EDA_Phasic'], height=0)
width_scr = scisig.peak_widths(w_eda_phasic_tonic['EDA_Phasic'], scr_peaks, rel_height=0)
ht_scr = scr_properties['peak_heights']
eda_scr_area = 0.5 * np.matmul(ht_scr, width_scr[1]) # Feature
# EMG
## 5sec
w_emg_data = emg_filtered_dc[i: window + i]
emg_mean = np.mean(w_emg_data) # Feature
emg_std = np.std(w_emg_data) # Feature
emg_min = np.amin(w_emg_data)
emg_max = np.amax(w_emg_data)
emg_drange = emg_max / emg_min # Feature
emg_absint = np.abs(np.trapz(w_emg_data)) # Feature
emg_median = np.median(w_emg_data) # Feature
emg_perc_10 = np.percentile(w_emg_data, 10) # Feature
emg_perc_90 = np.percentile(w_emg_data, 90) # Feature
emg_peak_freq, emg_mean_freq, emg_median_freq = get_freq_features(w_emg_data) # Features
# TODO: PSD -> energy in seven bands
# emg_psd =
## 60 sec
peaks, properties = scisig.find_peaks(emg_filtered[i: window + i], height=0)
emg_peak_no = peaks.shape[0]
emg_peak_amp_mean = np.mean(properties['peak_heights']) # Feature
emg_peak_amp_std = np.std(properties['peak_heights']) # Feature
emg_peak_amp_sum = np.sum(properties['peak_heights']) # Feature
emg_peak_amp_max = np.abs(np.amax(properties['peak_heights']))
# https://www.researchgate.net/post/How_Period_Normalization_and_Amplitude_normalization_are_performed_in_ECG_Signal
emg_peak_amp_norm_sum = np.sum(properties['peak_heights'] / emg_peak_amp_max) # Feature
# Resp
w_resp_data = resp_processed[i: window + i]
## Inhalation / Exhalation duration analysis
idx = np.nan
count = 0
duration = dict()
first = True
for j in w_resp_data[~w_resp_data['RSP_Phase'].isnull()]['RSP_Phase'].to_numpy():
if j != idx:
if first:
idx = int(j)
duration[1] = []
duration [0] = []
first = False
continue
# print('New value', j, count)
duration[idx].append(count)
idx = int(j)
count = 0
count += 1
resp_inhal_mean = np.mean(duration[1]) # Feature
resp_inhal_std = np.std(duration[1]) # Feature
resp_exhal_mean = np.mean(duration[0]) # Feature
resp_exhal_std = np.std(duration[0]) # Feature
resp_inhal_duration = w_resp_data['RSP_Phase'][w_resp_data['RSP_Phase'] == 1].count()
resp_exhal_duration = w_resp_data['RSP_Phase'][w_resp_data['RSP_Phase'] == 0].count()
resp_ie_ratio = resp_inhal_duration / resp_exhal_duration # Feature
resp_duration = resp_inhal_duration + resp_exhal_duration # Feature
resp_stretch = w_resp_data['RSP_Amplitude'].max() - w_resp_data['RSP_Amplitude'].min() # Feature
resp_breath_rate = len(duration[1]) # Feature
## Volume: area under the curve of the inspiration phase on a respiratory cycle
resp_peaks, resp_properties = scisig.find_peaks(w_resp_data['RSP_Clean'], height=0)
resp_width = scisig.peak_widths(w_resp_data['RSP_Clean'], resp_peaks, rel_height=0)
resp_ht = resp_properties['peak_heights']
resp_volume = 0.5 * np.matmul(resp_ht, resp_width[1]) # Feature
# Temp
w_temp_data = data['Temp'][condition][i: window + i].flatten()
temp_mean = np.mean(w_temp_data) # Feature
temp_std = np.std(w_temp_data) # Feature
temp_min = np.amin(w_temp_data) # Feature
temp_max = np.amax(w_temp_data) # Feature
temp_drange = temp_max / temp_min # Feature
temp_slope = get_slope(w_temp_data.ravel()) # Feature
# chest_df_5 = chest_df_5.append({
# 'ACC_x_mean': acc_x_mean, 'ACC_y_mean': acc_y_mean, 'ACC_z_mean': acc_z_mean, 'ACC_xzy_mean': xyz_mean,
# 'ACC_x_std': acc_x_std, 'ACC_y_std': acc_y_std, 'ACC_z_std': acc_z_std, 'ACC_xyz_std': xyz_std,
# 'ACC_x_absint': acc_x_absint, 'ACC_y_absint': acc_y_absint, 'ACC_z_absint': acc_z_absint, 'ACC_xyz_absint': xyz_absint,
# 'ACC_x_peak': acc_x_peak, 'ACC_y_peak': acc_y_peak, 'ACC_z_peak': acc_z_peak,
# 'EMG_mean': emg_mean, 'EMG_std': emg_std, 'EMG_drange': emg_drange, 'EMG_absint': emg_absint, 'EMG_median': emg_median, 'EMG_perc_10': emg_perc_10,
# 'EMG_perc_90': emg_perc_90, 'EMG_peak_freq': emg_peak_freq, 'EMG_mean_freq': emg_mean_freq, 'EMG_median_freq': emg_median_freq
# }, ignore_index=True)
chest_df = chest_df.append({
'ACC_x_mean': acc_x_mean, 'ACC_y_mean': acc_y_mean, 'ACC_z_mean': acc_z_mean, 'ACC_xzy_mean': xyz_mean,
'ACC_x_std': acc_x_std, 'ACC_y_std': acc_y_std, 'ACC_z_std': acc_z_std, 'ACC_xyz_std': xyz_std,
'ACC_x_absint': acc_x_absint, 'ACC_y_absint': acc_y_absint, 'ACC_z_absint': acc_z_absint, 'ACC_xyz_absint': xyz_absint,
'ACC_x_peak': acc_x_peak, 'ACC_y_peak': acc_y_peak, 'ACC_z_peak': acc_z_peak,
'ECG_hr_mean': hr_mean, 'ECG_hr_std': hr_std, 'ECG_hrv_NN50': hrv_NN50, 'ECG_hrv_pNN50': hrv_pNN50, 'ECG_hrv_TINN': hrv_TINN, 'ECG_hrv_RMS': hrv_rms,
'ECG_hrv_ULF': hrv_ULF, 'ECG_hrv_LF': hrv_LF, 'ECG_hrv_HF': hrv_HF, 'ECG_hrv_VHF': hrv_VHF, 'ECG_hrv_LFHF_ratio': hrv_lf_hf_ratio, 'ECG_hrv_f_sum': hrv_f_sum,
'ECG_hrv_rel_ULF': hrv_rel_ULF, 'ECG_hrv_rel_LF': hrv_rel_LF, 'ECG_hrv_rel_HF': hrv_rel_HF, 'ECG_hrv_rel_VHF': hrv_rel_VHF, 'ECG_hrv_LFn': hrv_LFn, 'ECG_hrv_HFn': hrv_HFn,
'EDA_mean': eda_mean, 'EDA_std': eda_std, 'EDA_mean': eda_mean, 'EDA_min': eda_min, 'EDA_max': eda_max, 'EDA_slope': eda_slope,
'EDA_drange': eda_drange, 'EDA_SCL_mean': eda_scl_mean, 'EDA_SCL_std': eda_scl_mean, 'EDA_SCR_mean': eda_scr_mean, 'EDA_SCR_std': eda_scr_std,
'EDA_corr_SCL_t': eda_corr_scl_t, 'EDA_SCR_no': eda_scr_no, 'EDA_ampl_sum': eda_ampl_sum, 'EDA_scr_area': eda_scr_area,
'EMG_mean': emg_mean, 'EMG_std': emg_std, 'EMG_drange': emg_drange, 'EMG_absint': emg_absint, 'EMG_median': emg_median, 'EMG_perc_10': emg_perc_10,
'EMG_perc_90': emg_perc_90, 'EMG_peak_freq': emg_peak_freq, 'EMG_mean_freq': emg_mean_freq, 'EMG_median_freq': emg_median_freq,
'EMG_peak_no': emg_peak_no, 'EMG_peak_amp_mean': emg_peak_amp_mean, 'EMG_peak_amp_std': emg_peak_amp_std, 'EMG_peak_amp_sum': emg_peak_amp_sum,
'EMG_peak_amp_norm_sum': emg_peak_amp_norm_sum,
'RESP_inhal_mean': resp_inhal_mean, 'RESP_inhal_std': resp_inhal_std, 'RESP_exhal_mean': resp_exhal_mean, 'RESP_exhal_std': resp_exhal_std,
'RESP_ie_ratio': resp_ie_ratio, 'RESP_duration': resp_duration, 'RESP_stretch': resp_stretch, 'RESP_breath_rate': resp_breath_rate, 'RESP_volume': resp_volume,
'TEMP_mean': temp_mean, 'TEMP_std': temp_std, 'TEMP_min': temp_min, 'TEMP_max': temp_max, 'TEMP_drange': temp_drange, 'TEMP_slope': temp_slope
}, ignore_index=True)
# index += 1
# if index % 10 == 0:
# break
print('Elapsed Process', condition.shape[0], str(timedelta(seconds=time.time() - init)))
return chest_df, chest_df_5
def elgendi2010(ecg, sampling_rate):
signal, info = nk.ecg_peaks(ecg,
sampling_rate=sampling_rate,
method="elgendi2010")
return info["ECG_R_Peaks"]
