--- a
+++ b/preprocessOfApneaECG/denoising.py
@@ -0,0 +1,67 @@
+"""
+	This file is to solve the problem of baseline drift of ECG signal.
+	
+	The methods come from Yildiz et al. paper, "An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings".
+	
+	Firstly, use wavelet decomposition on ECG signal to six level and get cD1 to cD6, cA6. Secondly, set the cD6 to zero.
+	Lastly, use wavelet reconstruction on cD1 to cD6(zeros) and cA6, and achieve denoising ECG signal.
+	
+	I summarize frequency band of some waves in ECG signals.
+	P wave: atrial depolarization, 心房除极.
+	QRS complexes: ventricular depolarization，心室去极.
+	T wave: ventricular repolarization, 心室复极.
+	P and T waves range: 0.5hz-10hz.
+	QRS complex range: 10hz-25hz.
+	Detail signal D2 is used as a reference signal for the detection of QRS fiducial location.
+
+	cD1: 25-50hz
+	cD2: 12.5-25hz
+	cD3: 6.25-12.5hz
+	cD4: 3.125-6.25hz
+	cD5: 1.5625-3.125hz
+	cD6: 0.78125-1.5625hz
+	cD7: 0.390625-0.78125hz
+	cA7: 0-0.390625hz
+	
+"""
+
+__version__ = '0.1'
+__time__ = "2019.06.22"
+__author__ = "zzklove3344"
+
+
+import pywt
+import numpy as np
+
+
+def denoise_ecg(ecg_segment):
+	"""
+	Remove baseline drafts from ECG signal.
+	:param ecg_segment: ecg record, a numpy array.
+	:return: denoised ecg record, a numpy array.
+	
+	Example:
+	denoising_ecg = denoise_ecg(raw_ecg)
+	"""
+	
+	denoising_wd_level = 6
+	denoising_wd_wavelet = "db6"
+	coffes_set = []
+	cA_signal = np.reshape(ecg_segment, len(ecg_segment))
+	for index_dec in range(denoising_wd_level):
+		cA, cD = pywt.dwt(cA_signal, denoising_wd_wavelet)
+		coffes_set.append(cD)
+		cA_signal = cA
+	coffes_set.append(cA_signal)
+	coffes_set[denoising_wd_level] = np.zeros(len(coffes_set[denoising_wd_level]))
+	
+	cA_signal = coffes_set[denoising_wd_level]
+	for index_dec in range(denoising_wd_level):
+		cD_signal = coffes_set[denoising_wd_level - 1 - index_dec]
+		if len(cD_signal) != len(cA_signal):
+			cA_signal = np.delete(cA_signal, len(cA_signal) - 1, axis=0)
+		cA_signal = pywt.idwt(cA_signal, cD_signal, denoising_wd_wavelet)
+	cA_signal = np.reshape(cA_signal, (len(ecg_segment), 1))
+	return cA_signal
+
+