The effectiveness of the choice of criteria on the stationary and non-stationary noise removal in the phonocardiogram (PCG) signal using discrete wavelet transform

被引:8
|
作者
Rouis, Mohamed [1 ,2 ]
Sbaa, Salim [1 ,2 ]
Benhassine, Nasser Edinne [3 ,4 ]
机构
[1] Univ Biskra, Dept Elect Engn, Biskra 07020, Algeria
[2] Univ Biskra, Lab LESIA, Biskra, Algeria
[3] Univ 8 Mai 1945 Guelma, Adv Control Lab LABCAV, Guelma, Algeria
[4] Univ Zian Achour, Dept Exact Sci & Informat, Djelfa 17000, Algeria
来源
关键词
decomposition level; mother wavelet; PCG signal de-noising; test criteria; QUALITY ASSESSMENT; TIME-SERIES;
D O I
10.1515/bmt-2019-0197
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The greatest problem with recording heart sounds is parasitic noise effects. A reasonable solution to reduce noise can be carried out by minimization of extraneous noises in the vicinity of the patient during recording, in addition to the methods of signal processing that must be effective in noisy environments. Wavelet transform has become an essential tool for many applications, but its effectiveness is influenced by main parameters. Determination of mother wavelet function and decomposition level (DL) are important key factors to demonstrate the advantages of wavelet denoising. So, selection of optimal mother wavelet with DL is a main challenge to current algorithms. The principal aim of this study was the choice of an appropriate criterion for finding the optimal DL and the optimal mother wavelet function according to four criteria which are: signal-to-noise ratio (SNR), mean square error (MSE), percentage root-mean-square difference (PRD) and the structure similarity index measure (SSIM) for testing the robustness of the proposed algorithm. The proposed method is applied to the PCG signal contaminated with four colored noise types, in addition to the Gaussian noise. The obtained results show the effectiveness of the proposed method in reducing noise from the noisy PCG signals, especially at a low SNR.
引用
收藏
页码:353 / 366
页数:14
相关论文
共 50 条
  • [11] An Improved Empirical Wavelet Transform for Noisy and Non-Stationary Signal Processing
    Zhuang, Cuifang
    Liao, Ping
    IEEE ACCESS, 2020, 8 : 24484 - 24494
  • [12] A Simulation of Non-stationary Signal Analysis Using Wavelet Transform Based on LabVIEW and Matlab
    Jaber, Alaa Abdulhady
    Bicker, Robert
    UKSIM-AMSS EIGHTH EUROPEAN MODELLING SYMPOSIUM ON COMPUTER MODELLING AND SIMULATION (EMS 2014), 2014, : 138 - 144
  • [13] Automated Detection of Heart Murmurs From the PCG Signal Using Stationary Wavelet Transform
    Das, Samarjeet
    Dandapat, Samarendra
    2022 IEEE 19TH INDIA COUNCIL INTERNATIONAL CONFERENCE, INDICON, 2022,
  • [14] DETECTION OF A NON-STATIONARY SIGNAL IN NOISE
    MCNEIL, DR
    AUSTRALIAN JOURNAL OF PHYSICS, 1967, 20 (03): : 325 - +
  • [15] Ambiguity Function of Non-Stationary Signals Using Wavelet Transform
    Shokouh, Reza Keyvan
    Alaee, Mohammad
    Okhovvat, Majid
    Amiri, Reza
    PROCEEDINGS OF THE 2010 IEEE ASIA PACIFIC CONFERENCE ON CIRCUIT AND SYSTEM (APCCAS), 2010, : 328 - 331
  • [16] Stationary and Non-Stationary Noise Removal from Cardiac Signals using CSLMS
    Rao, Y. Raghavender
    DevadasNaik, N.
    2017 IEEE INTERNATIONAL CONFERENCE ON POWER, CONTROL, SIGNALS AND INSTRUMENTATION ENGINEERING (ICPCSI), 2017, : 1208 - 1212
  • [17] Noise Elimination and Finding R Peaks of ECG Signal by Using Discrete Stationary Wavelet Transform
    Kucukgoz, Nuray
    Karaboga, Nurhan
    2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2017,
  • [18] Wavelet transforms for non-stationary signal processing
    Weiss, LG
    WAVELET APPLICATIONS IN SIGNAL AND IMAGE PROCESSING VII, 1999, 3813 : 540 - 550
  • [19] Non-stationary wavelet for ECG signal classification
    Boussaad, Abdelmalik
    Melkemi, Khaled
    Melgani, Farid
    Mokhtari, Zouhir
    JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES, 2023, 44 (04): : 607 - 623
  • [20] Ordering analysis of non-stationary exhaust noise signals based on wavelet transform
    Liu H.
    Zhendong yu Chongji/Journal of Vibration and Shock, 2019, 38 (22): : 29 - 35and51