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Generally, P and T waves in an electrocardiogram (ECG) signal are lower in amplitude compared to amplitude of QRS complex and contaminated with noises from various sources. Due to these problems and lack of universal delineation rule, the automated detection and delineation of T and P waves (on, off, and peak position of T and P wave) in the ECG signal are challenging task. The effectiveness for detection of on, off, and peak position of T and P wave by using differential evolution (DE) algorithm with the denoising technique has been verified in this manuscript. The denoising operation of the ECG signal has been performed by extended Kalman smoother (EKS) framework. DE algorithm is used for selection of optimized width and phase of five waves of the ECG signal. These parameters are used in EKS for initialization of the process noise covariance matrix and also development of the state equation. The new algorithm (an intelligent process of searching and subtraction) for detection of on, off and peak location of P and T waves without using amplitude threshold is developed by using the optimized parameters computed by the DE algorithm and denoised ECG signal with the help of the EKS framework. The effectiveness of the proposed technique has been validated using real-time QT database. Our proposed method shows better sensitivity, predicitvity and accuracy compared to other well-known methods for detection of on, off, peak location of P and T wave.

Fetal electrocardiogram (ECG) gives information about the health status of fetus and so, an early diagnosis of any cardiac defect before delivery increases the effectiveness of appropriate treatment. In this paper, authors investigate the use of adaptive neuro-fuzzy inference system (ANFIS) with extended Kalman filter for fetal ECG extraction from one ECG signal recorded at the abdominal areas of the mother's skin. The abdominal ECG is considered to be composite as it contains both mother's and fetus' ECG signals. We use extended Kalman filter framework to estimate the maternal component from abdominal ECG. The maternal component in the abdominal ECG signal is a nonlinear transformed version of maternal ECG. ANFIS network has been used to identify this nonlinear relationship, and to align the estimated maternal ECG signal with the maternal component in the abdominal ECG signal. Thus, we extract the fetal ECG component by subtracting the aligned version of the estimated maternal ECG from the abdominal signal. Our results demonstrate the effectiveness of the proposed technique in extracting the fetal ECG component from abdominal signal at different noise levels. The proposed technique is also validated on the extraction of fetal ECG from both actual abdominal recordings and synthetic abdominal recording.

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