DTCWT与形态滤波：PPG信号降噪新策略

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本文是一篇研究论文，针对脉搏波信号(PPG)中存在的各种噪声，特别是其非线性和非平稳特性，提出了一种基于双树复小波变换(DTCWT)和形态滤波的降噪方法。DTCWT作为一种改进的多分辨率分析工具，具有构造简单、数学解释清晰以及计算复杂度低的优势，有助于克服离散小波变换中的位移敏感性和频率混叠问题。传统的降噪方法可能在处理这些复杂信号时效果有限，而DTCWT能够更好地捕捉信号的局部特征，通过其分解层次结构，可以有效地分离信号的高频噪声和低频成分。形态滤波则是一种基于图像处理的概念，用于信号处理中，它利用形状保持性和边缘保留性，能够有效地去除信号中的噪声，特别是椒盐噪声和脉冲噪声。在该算法中，首先对PPG信号进行DTCWT分解，将信号从时域转换到多尺度分析的频域，然后利用形态滤波器对各频带的系数进行处理，去除噪声成分。由于形态滤波器的适应性和选择性，它可以精确地定位并消除干扰，如电源线噪声和肌肉电活动(EMG)噪声，而不影响有用信号的信息。为了验证算法的有效性，研究者进行了模拟实验，结果显示该方法能有效抑制噪声，提高了PPG信号的质量。通过定量指数的测量，比如信噪比(SNR)或峰值信噪比(PSNR)，可以量化降噪前后信号的改善程度。此外，文中还可能讨论了与其他常见降噪技术的比较结果，以证明新方法在实际应用中的优越性。总结来说，这篇论文不仅探讨了基于DTCWT和形态滤波的PPG信号降噪策略，而且强调了这种方法在实际信号处理中的实用价值，为脉搏波信号的精确测量和分析提供了新的技术手段。这对于健康监测、生理信号分析等领域具有重要意义。

A PPG Signal De-noising Method Based on The DTCWT and The Morphological

Filtering

Tong Bai

, Dan Li, Huiqian Wang*

,Yu Pang

, Guoquan Li

,Jinzhao Lin

, Qianneng Zhou

, Gwanggil Jeon

1: Chongqing University of Posts and Telecommunications, Chongqing, China

2: Department of Embedded Systems Engineering, College of Information and Technology, Incheon National University,

119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea

*Corresponding author: wanghq@cqupt.edu.cn

Abstract—The pulse wave signal contains a variety of noises

and has strong nonlinear and non-stationary. According to

the previous wavelet transformation method, this paper

proposes a PPG signal de-noising algorithm based on dual-

tree complex wavelet transform (DTCWT) and

morphological filtering. With the advantage of simple

construction, clear mathematical implications and low

computational complexity, this method overcomes the shift

sensitive and the frequency aliasing in the discrete wavelet

transform. The simulation results show that this algorithm

could remove the power line interference and EMG

interference, and the quantitative index of SNR and mean

square error is superior to the traditional threshold de-

noising algorithm. Therefore, the DTCWT and

morphological filtering de-noising algorithm would obtain a

clear pulse wave signal.

Keywords-pulse wave signal; DTCWT; morphological

filtering

I. INTRODUCTION

Photoplethysmography

[1]

(PPG) is used to optically

obtain a volumetric measurement of an organ. Many

clinical diseases especially heart disease may cause pulse

changes, thus, the pulse wave signal of human body

contains abundant physiology and pathology information.

The changes of circulatory system cause different shapes

of the pulse wave, therefore, the changes of pulse wave

shapes could be used for diagnosing the aortic valve, the

rhythm of heart and the elasticity of artery. The frequency

range of pulse signal of a healthy normal people

[2]

is 0 to

20Hz and almost 99 percent of the energy is distributed on

0 to 10Hz. Affected by the instruments in the extracting

process, pules wave signal tend to be multiple noise

interference. The low-frequency interference caused by the

human breathing, less than 1Hz, will generate the drift

phenomenon. Therefore, the extracted pulse wave signal is

not in a horizontal line. The high-frequency interference,

power frequency interference and EMG interference, cause

the PPG signal exists thorns and slight oscillation. These

interferences generate significantly impeding the judgment

of heart function changes. Therefore, the pulse wave signal

is supposed to extract efficiently from the noise to obtain

the accurate and pure PPG signal.

In recent years, the researchers propose a variety of

methods for removing PPG signal noise. The most

common method, Adapting filtering, needs the additional

hardware to acquire the reference signal and uses the

synthesis techniques for reference signal such as Fourier

transform

[3]

, wavelet transform

[4][5]

, support vector

machine(SVM) decomposition

[6]

and independent

component analysis(ICA)

[7][8]

to eliminate the noise. Based

on the resolution characteristics, wavelet transform could

be used to the PPG signal de-noising. However, in the

traditional wavelet transform, the tiny drift of the input

signal causes a significant change of wavelet coefficient,

the missing of information. Thus, the false results generate

a strong effect on the experiment.

II. D

E-NOISING METHED BASED ON THE DTCWT AND

MORPHPLOGICAL FILTERING

TYPE STYLE AND

FONTS

[9]

A. Morphological filtering

[10][11]

Morphological filtering is a kind of nonlinear filter

technique from Mathematical morphology (MM). MM is

most commonly applied to digital images, but it can be

employed as well on graphs, signal processing and some

other fields. The basic idea in binary morphology is to

probe an image with a simple, pre-defined shape, drawing

conclusions on how this shape fits or misses the shapes in

the image. This simple "probe" is called the structuring

element, and is itself a binary image.

The MM includes four basic operators - erosion,

dilation, opening and closing. Assume the signal

sequence

1}-N,{0,1,=Ff "

, the structuring element

1}-M,{0,1,=Kk "

. The dilation of A by the

structuring element B is defined by:

1)N,M,1,M(n

,k(m)-m)-f(nmaxk)(n)(f

1M,0,m







The erosion of A by the structuring element B is

defined by:

M)NM0,(n

,k(m)-m)f(nmink)(n)(f

1M,0,m







Ɨ

The opening of A by B is obtained by the erosion

of A by B, followed by dilation of the resulting image

by B:

k)(n)k(fk)(n)(f  ƗD

The closing of A by B is obtained by the dilation

of A by B, followed by dilation of the resulting image

by B:

k)(n)k(fk)(n)(f Ɨ 

Morphological filtering correction the positive pulse

and negative pulse of the signal based on the opening

operator and closing operator.

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems

DOI 10.1109/SITIS.2016.85

503

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems

DOI 10.1109/SITIS.2016.85

503

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