Fetal ECG Extraction Using Independent Components and Characteristics Matching
| Autor: | Nai-Shyong Yeh, Mohanad Alkhodari, Meera Alex, Abdelrahman Rashed |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Matching (statistics)
Fetus medicine.diagnostic_test Computer science business.industry Noise (signal processing) Pattern recognition 02 engineering and technology Fetal health 021001 nanoscience & nanotechnology Independent component analysis Signal Fetal ecg 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence 0210 nano-technology business Electrocardiography |
| Zdroj: | 2018 International Conference on Signal Processing and Information Security (ICSPIS). |
| DOI: | 10.1109/cspis.2018.8642725 |
| Popis: | In this paper, further investigations into a simpler automated use of Independent Component Analysis (ICA) in the process of Fetal ECG (FECG) extraction are performed. Extracting FECG signals through abdominal electrodes helps clinicians in diagnosing the overall health of the fetus non-invasively. In the ICA technique, FECG signals are separated from Abdominal ECG (AECG) mixtures containing maternal and noise signals. 300,000 Data samples of three AECG recordings are obtained from PhysioNet database at 1 kHz sampling frequency. Data are pre-processed through MATLAB software by centering, whitening, and filtering techniques. Then, a simpler Fast ICA algorithm is developed and used to smoothly distinguish between AECG components through automatic signal characteristics matching. Moreover, further analysis of the extracted FECG signal is performed to determine the fetus heart rate. Results successfully show efficient automatic separation between the FECG, Maternal ECG (MECG), and noise from the AECG recordings. In addition, the developed characteristics matching algorithm automatically identified the fetus signal and smoothed it to be ready for further fetal health observations. The integration of AECG signal characteristics as a prior information into the ICA algorithm promises to assist clinicians in decision making when diagnosing fetal health conditions non-invasively. |
| Databáze: | OpenAIRE |
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