Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median

نویسندگان

  • Li Su
  • Hau-Tieng Wu
چکیده

The multiple fundamental frequency detection problem and the source separation problem from a single-channel signal containing multiple oscillatory components and a nonstationary noise are both challenging tasks. To extract the fetal electrocardiogram (ECG) from a single-leadmaternal abdominal ECG, we need to solve both challenges. We propose a novel method to extract the fetal ECG from a single-lead maternal abdominal ECG, without any additional measurement. The algorithm is composed of three components. First, the maternal and fetal heart rates are estimated by the de-shape short time Fourier transform (STFT), which is a recently proposed nonlinear time-frequency analysis technique. The beat tracking technique is the second component which is applied to accurately obtain the maternal and fetal R peaks. The third component consists of establishing the maternal and fetal ECG waveforms by the nonlocal median. The algorithm is tested on two real databases with the annotation provided by experts (adfecgdb database and CinC2013 database) and a simulated database (fecgsym), and provides the state-of-the-art results. We conclude that with the proposed algorithm, the fetal ECG waveform and the fetal heart rate could be accurately obtained from the single-lead maternal abdominal ECG.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A PCA/ICA based Fetal ECG Extraction from Mother Abdominal Recordings by Means of a Novel Data-driven Approach to Fetal ECG Quality Assessment

Background: Fetal electrocardiography is a developing field that provides valuable information on the fetal health during pregnancy. By early diagnosis and treatment of fetal heart problems, more survival chance is given to the infant.Objective: Here, we extract fetal ECG from maternal abdominal recordings and detect R-peaks in order to recognize fetal heart rate. On the next step, we find a be...

متن کامل

Person Identification System Based on Electrocardiogram Signal Using LabVIEW

This paper presents a method to analyze electrocardiogram (ECG) signal, extract the features, for the real time human identification. Data were obtained from short-term Lead-I ECG records (only one lead) of forty students at Paris Est University (UPEC). Signal averaging was applied to generate ECG databases and templates for reducing the noise recorded with palm ECG signals. Time domain signal ...

متن کامل

Cardiology knowledge free ECG feature extraction using generalized tensor rank one discriminant analysis

Applications based on electrocardiogram (ECG) signal feature extraction and classification are of major importance to the autodiagnosis of heart diseases. Most studies on ECG classification methods have targeted only 1or 2-lead ECG signals. This limitation results from the unavailability of real clinical 12-lead ECG data, which would help train the classification models. In this study, we propo...

متن کامل

Extraction of Fetal QRS Complex from Abdominal ECG Signals

Background: Extraction of Fetal ECG signal from non-invasive abdominal ECG signal is an important clinical application. Fetal ECG signal provides significant and valuable information about the fetal heart growth and health condition. Objective: Abdominal signals are usually corrupted by high amplitude maternal ECG signals and often found superimposed with the Fetal ECG signal. Suppression of ma...

متن کامل

Nonparametric Modelling of ECG: Applications to Denoising and to Single Sensor Fetal ECG Extraction

In this work, we tackle the problem of fetal electrocardiogram (ECG) extraction from a single sensor. The proposed method is based on non-parametric modelling of the ECG signal described thanks to its second order statistics. Each assumed source in the mixture is thus modelled as a second order process thanks to its covariance function. This modelling allows to reconstruct each source by maximi...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017