Adaptive denoising technique for robust analysis of functional magnetic resonance imaging data

Ouda, Samiha; Tawfik, Bassel M; Youssef, A.-B.M.; Kadah, Yasser M.

doi:10.1109/iembs.2001.1017280

Cited by 1 publication

(2 citation statements)

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“…Examples of such applications include separating fetal and maternal ECG signals (ZARZOSO et at., 1997;DE LATHAUWER et at., 2000), analysis of the ST segment for ischaemia detection (STAMKOPOULOS et al, 1998) and identification of humans using ECG (BIEL et at., 2001). They have been also used in related areas to diagnose peripheral vascular disease from multiple blood flow measurements (PANERAI et at., 1988). in all these techniques, the measurements were either acquired simultaneously or gated to a certain reference point in the signal (e.g.…”

Section: Introductionmentioning

confidence: 97%

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Characterisation of electrocardiogram signals based on blind source separation

Owis

Youssef

Kadah

2002

Med. Biol. Eng. Comput.

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Blind source separation assumes that the acquired signal is composed of a weighted sum of a number of basic components corresponding to a number of limited sources. This work poses the problem of ECG signal diagnosis in the form of a blind source separation problem. In particular, a large number of ECG signals undergo two of the most commonly used blind source separation techniques, namely, principal component analysis (PCA) and independent component analysis (ICA), so that the basic components underlying this complex signal can be identified. Given that such techniques are sensitive to signal shift, a simple transformation is used that computes the magnitude of the Fourier transformation of ECG signals. This allows the phase components corresponding to such shifts to be removed. Using the magnitude of the projection of a given ECG signal onto these basic components as features, it was shown that accurate arrhythmia detection and classification were possible. The proposed strategies were applied to a large number of independent 3 s intervals of ECG signals consisting of 320 training samples and 160 test samples from the MIT-BIH database. The samples equally represent five different ECG signal types, including normal, ventricular couplet, ventricular tachycardia, ventricular bigeminy and ventricular fibrillation. The intervals analysed were windowed using either a rectangular or a Hamming window. The methods demonstrated a detection rate of sensitivity 98% at specificity of 100% using nearest neighbour classification of features from ICA and a rectangular window. Lower classification rates were obtained using the same classifier with features from either PCA or ICA and a rectangular window. The results demonstrate the potential of the new method for clinical use.

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Section: Introductionmentioning

confidence: 97%

“…The advantages of using such components, rather than any other arbitrary choice for the basis functions, include the ability to reduce the number of features significantly, in addition to separating the components that correspond to noise. This makes the use of such techniques desirable for biomedical signals (OUDA et at., 2001).…”

Section: Introductionmentioning

confidence: 99%