Extraction of foetal ECG by combination of singular value decomposition and neuro-fuzzy inference system

Al-Zaben, Awad; Al-Smadi, Adnan

doi:10.1088/0031-9155/51/1/010

Cited by 41 publications

(24 citation statements)

References 12 publications

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“…Polynomial networks technique for the FECG extraction presented in (Assaleh and Al-Nashash, 2005). Hybridisation of Singular Value Decomposition (SVD) and ANFIS is discussed in (Al-Zaben and Al-Smadi, 2006).…”

Section: Related Workmentioning

confidence: 99%

Fetal Electrocardiogram Extraction Using Let Techniques

L.¹

2012

Journal of Computer Science

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Fetal Electrocardiogram Extraction (FECG) identifies the congenital heart problems at the earlier stage. The major problem in the non invasive procedure is the extraction of FECG from Maternal ECG (MECG) and many interferences. The proposed methods (i) Combination of Adaptive Neuro Fuzzy Inference (ANFIS) and Fractional spline wavelet (ii) Combination of Fractional spline wavelet and ANFIS (iii) Combination of ANFIS and SURE-LET and (iv) Combination of SURE-LET and ANFIS remove the unwanted noises present in the FECG more effectively. This new approach extracts FECG by removing the noisy Abdominal ECG (AECG) and subsequently cancels the MECG. The pure thoracic ECG (TECG) or maternal ECG was used to remove noisy MECG present in the signal from abdomen signal and thereby the required noiseless FECG is extracted by means of the new approach. The excellence of the LET techniques are evaluated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The result of combination of ANFIS and SURELET gives the best result and the closest match to the simulated FECG with high PSNR and low MSE among all the proposed methods.

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Section: Related Workmentioning

confidence: 99%

Fetal Electrocardiogram Extraction Using Let Techniques

L.¹

2012

Journal of Computer Science

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“…Such as the faint signal extraction, for example, by applying SVD to the complicated maternal abdominal electrocardiogram (ECG), the fetal ECG component can be detected. 16 Cong et al used SVD to decompose the short-time matrix series for bearing transient signal extraction. 17 In image noise suppression, the freedom or possible independent pixel values in the image are considered as the image dimensionality, and SVD is regarded as an effective dimensionality reduction approach for fringe image denoising by image dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition

Wang

Liú

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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The fast kurtogram, a faint signal extraction method, has been regarded as an effective approach to detect and characterize faint transient features in vibration signals. However, the fast kurtogram, a band-pass filtering method, which extracts transient signals by optimal frequency band selection and leaves the noise in the selected frequency band unprocessed. Therefore, to overcome the shortcoming of the fast kurtogram method, a method which can wipe off the noise in the whole frequency band is necessary. This paper proposes a novel faint signal extraction method by timefrequency distribution image dimensionality reduction. Since time-frequency distribution image can reveal intrinsic feature of nonstationary signals and can make the weak impulses feature prominent, and besides, the transient impulse feature and the noise component lie in different dimensions, so using the dimensionality reduction method based on singular value decomposition to suppress the background noise in the raw time-frequency distribution image is motivated. A bearing outer race fault signal obtained from a test-to-failure experiment and a bearing inner race fault signal obtained from an experimental motor are employed to demonstrate the enhanced performance of the proposed method in faint signal extraction. The results indicate that the proposed method outperforms the fast kurtogram method and is effective in faint signal extraction.

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“…SVD traditionally has been applied for de-noising [15,16] and the dimensionality reduction [17,18,19], in various problems [20,21,22].…”

Section: Introductionmentioning

confidence: 99%

Forecast of Traffic Accidents Based on Components Extraction and an Autoregressive Neural Network with Levenberg-Marquardt

Barba

Rodríguez

2014

Mining Intelligence and Knowledge Exploration

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Abstract. In this paper is proposed an improved one-step-ahead strategy for traffic accidents and injured forecast in Concepción, Chile, from year 2000 to 2012 with a weekly sample period. This strategy is based on the extraction and estimation of components of a time series, the Hankel matrix is used to map the time series, the Singular Value Decomposition(SVD) extracts the singular values and the orthogonal matrix, and the components are forecasted with an Autoregressive Neural Network (ANN) based on Levenberg-Marquardt (LM) algorithm. The forecast accuracy of this proposed strategy are compared with the conventional process, SVD-ANN-LM achieved a MAPE of 1.9% for the time series Accidents, and a MAPE of 2.8% for the time series Injured, in front of 14.3% and 21.1% that were obtained with the conventional process.

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Extraction of foetal ECG by combination of singular value decomposition and neuro-fuzzy inference system

Cited by 41 publications

References 12 publications

Fetal Electrocardiogram Extraction Using Let Techniques

Fetal Electrocardiogram Extraction Using Let Techniques

An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition

Forecast of Traffic Accidents Based on Components Extraction and an Autoregressive Neural Network with Levenberg-Marquardt

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