Fetal activity parameters classification from multidimensional Doppler signals

被引:0
|
作者
Ribes, S. [2 ]
Voicu, I. [1 ]
Quinsac, C. [2 ]
Girault, J. -M. [1 ]
Fournier-Massignan, M. [3 ]
Perrotin, F. [3 ]
Kouame, D. [2 ]
机构
[1] Univ Tours, INSERM, UMR U930, F-37032 Tours, France
[2] Univ Toulouse, IRIT, UMR 5505, F-31062 Toulouse, France
[3] CHRU Tours, CIC IT, F-37044 Tours, France
关键词
Fetal Doppler signals; Fetal heart rate; Fetal monitoring; Supervised classification; Support Vector Machines (SVM);
D O I
10.1016/j.irbm.2011.01.028
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
This paper shows preliminary feasibility to separate normal and pathological fetuses using a purposed built multi-transducer-multi-gate Doppler system and developed dedicated signal processing techniques via fetal activity parameters extraction. A dataset consisting of two groups of fetal signals (normal and pathological) has been established and provided by physicians. From fetal activity estimated parameters, an instantaneous Manning-like score, referred to as ultrasonic score was introduced and was used together with movements, heart rate and the associated parameters (variability, accelerations) in a classification process using Support Vector Machines (SVM) method. The influence of the fetal activity parameters and the performance of the SVM were evaluated using the computation of sensibility, specificity, percentage of support vectors and total classification accuracy. We showed the ability of our system to separate the data into two sets: normal fetuses and pathological fetuses and obtained an excellent matching with the clinical classification performed by physicians. (C) 2011 Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:152 / 154
页数:3
相关论文
共 50 条
  • [21] Identifying Fetal Heart Anomalies using Fetal ECG and Doppler Cardiogram Signals
    Khandoker, Ahsan H.
    Kimura, Yoshitaka
    Palaniswami, Marimuthu
    Marusic, Slaven
    COMPUTING IN CARDIOLOGY 2010, VOL 37, 2010, 37 : 891 - 894
  • [22] INFLUENCE OF DOPPLER ULTRASOUND ON FETAL ACTIVITY
    MURRILLS, AJ
    BARRINGTON, P
    HARRIS, PD
    WHEELER, T
    BRITISH MEDICAL JOURNAL, 1983, 286 (6370): : 1009 - 1012
  • [23] Modified multidimensional scaling on EEG signals for emotion classification
    Garima, Nidhi
    Goel, Nidhi
    Rathee, Neeru
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (18) : 28547 - 28568
  • [24] Modified multidimensional scaling on EEG signals for emotion classification
    Nidhi Garima
    Neeru Goel
    Multimedia Tools and Applications, 2023, 82 : 28547 - 28568
  • [25] An Adaptive S-Method to Analyze Micro-Doppler Signals for Human Activity Classification
    Li, Fangmin
    Yang, Chao
    Xia, Yuqing
    Ma, Xiaolin
    Zhang, Tao
    Zhou, Zhou
    SENSORS, 2017, 17 (12)
  • [26] Extraction of Fetal Electrocardiogram from Maternal Electrocardiogram and Classification of Normal and Abnormal Signals
    Veenadevi, S. V.
    Padmavathi, C.
    Shanthamma, B.
    Abbigeri, Balaji Govindraj
    Pavithra, K. M.
    2017 IEEE 2ND INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP), 2017, : 396 - 401
  • [27] Pattern Classification for Doppler Ultrasonic Wrist Pulse Signals
    Chen, Yinghui
    Zhang, Lei
    Zhang, David
    Zhang, Dongyu
    2009 3RD INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING, VOLS 1-11, 2009, : 2411 - 2414
  • [28] Research on the Classification of Audio Doppler Signals Based on SVM
    Luo, Donghua
    2018 4TH INTERNATIONAL CONFERENCE ON EDUCATION, MANAGEMENT AND INFORMATION TECHNOLOGY (ICEMIT 2018), 2018, : 36 - 41
  • [29] Intraplacental spectral Doppler scanning: Fetal growth classification based on Doppler velocimetry
    Haberman, S
    Friedman, ZM
    GYNECOLOGIC AND OBSTETRIC INVESTIGATION, 1997, 43 (01) : 11 - 19
  • [30] Blind Doppler parameters estimation of satellite communication signals
    Peng, Geng
    Huang, Zhi-Tao
    Jiang, Wen-Li
    Zhou, Yi-Yu
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2010, 32 (04): : 674 - 677