def test_filter(self):
s = SleepSet(self.patients)
f = lambda x: 'u2' in x._folder
a = s.filter(f)
self.assertTrue(len(a) == 1)
self.assertTrue(isinstance(a, SleepSet))
self.assertTrue('u2' in a[0]._folder)
from sklearn.metrics import f1_score, classification_report
from scipy.signal import resample
from sklearn.utils import shuffle
from scipy.stats import zscore
from functions import interpolate_nans
from tqdm import tqdm
import misc
np.random.seed(0)
tf.random.set_seed(0)
os.environ['PYTHONHASHSEED'] = '0'
rn.seed(0)
#%%
ss = SleepSet(cfg.folder_unisens)
ss = ss.filter(lambda x: x.duration < 60 * 60 * 11) # only less than 14 hours
ss = ss.filter(
lambda x: x.group in ['control', 'nt1']) # only less than 14 hours
ss = ss.filter(lambda x: np.mean(x.get_artefacts(only_sleeptime=True)) < 0.25
) #only take patients with artefact percentage <25%
#%% Load data
length = 2 * 60 * 3 # first 3 hours
downsample = 2 # downsample factor
feat_names = [
'mean_HR', 'rrHRV', 'SDNN', 'RMSSD', 'RR_range', 'SDSD', 'LF_power',
'HF_power', 'LF_HF', 'SD2_SD1', 'SD1', 'SD2', 'SampEn', 'pNN50',
'PetrosianFract', 'SDSD'
]
feats = []
descriptors = [
'gender', 'age', 'Number of epochs (only sleeptime)', 'Artefact ratio'
]
table_general = {name: {'nt1': {}, 'control': {}} for name in descriptors}
total_number_of_epochs = sum(
[len(p.get_hypno(only_sleeptime=True)) for p in ss])
total_number_discarded = sum(
[sum(p.get_artefacts(only_sleeptime=True)) for p in ss])
print(
f'{total_number_of_epochs} are available, of which {total_number_discarded} are discarded ({total_number_discarded/total_number_of_epochs*100:.1f}%)'
)
for group in ['nt1', 'control']:
subset = ss.filter(lambda x: x.group == group)
# gender
table_general['gender'][group]['female'] = np.sum(
[x.gender == 'female' for x in subset])
table_general['gender'][group]['male'] = np.sum(
[x.gender == 'male' for x in subset])
table_general['gender'][group]['values'] = [
x.gender == 'male' for x in subset
]
table_general['gender'][group]['mean'] = 5
table_general['gender'][group]['std'] = None
table_general['gender'][group]['p'] = 2
# number of epochs
values = list([len(p.get_hypno(only_sleeptime=True)) for p in subset])
import config as cfg
import numpy as np
import pandas as pd
import seaborn as sns
from tqdm import tqdm
from sleep import SleepSet
from datetime import timedelta
import matplotlib.pyplot as plt
from joblib.parallel import Parallel, delayed
#%% settings
length = int(2 * 60 * 3.50) # first four hours
ss = SleepSet(cfg.folder_unisens)
ss = ss.filter(lambda x: x.duration < 60 * 60 * 11) # only less than 14 hours
ss = ss.filter(lambda x: x.group in ['control', 'nt1']) # no hypersomnikers
ss = ss.filter(
lambda x: np.mean(x.get_artefacts(only_sleeptime=True)[:length]
) < 0.25) # only low artefacts count <25%
p = ss[1]
data_y = np.array([p.group == 'nt1' for p in ss], dtype=bool)
stop
#%% average hypnograms to see importances
hypnos = np.array([p.get_hypno(only_sleeptime=True)[:length + 1] for p in ss])
cmap = matplotlib.cm.get_cmap("magma", 6)
hypno_nt1 = hypnos[data_y]
hypno_cnt = hypnos[~data_y]
hypno_nt1[hypno_nt1 == 5] = -1 # replace artefact with 0
hypno_cnt[hypno_cnt == 5] = -1 # replace artefact with 0
y_tree = KDTree(np.vstack([r_true, np.zeros_like(r_true)]).T, p=1)
dist, idxs = y_tree.query(np.vstack([r_pred, np.zeros_like(r_pred)]).T,
k = 1, return_distance=True)
def compare(r_true, r_pred):
"""
R positions are annotated in seconds
"""
y_tree = KDTree(np.vstack([r_true, np.zeros_like(r_true)]).T, p=1)
dist, idxs = y_tree.query(np.vstack([r_pred, np.zeros_like(r_pred)]).T,
k = 1, return_distance=True)
return dist, idxs
ss = SleepSet(cfg.folder_unisens)
ss = ss.filter(lambda x:x.ecg.sampleRate==256)
p = ss[0]
r_true = p.get_RR(offset=False)[0]
ecg = p.ecg.get_data('ecg').squeeze()
fs = p.ecg.sampleRate
detectors = Detectors(fs)
detector_names = ['engzee_detector', 'hamilton_detector', 'christov_detector',
'swt_detector', 'pan_tompkins_detector', 'two_average_detector']
res = {}
for name in detector_names: