Electrocardiogram (ECG) is a primary signal utilized in the medical field for the identification and interpretation of pathological and physiological phenomenon. In different real conditions, the ECG is corrupted by many artifacts out of them is power-line interference (PLI). The PLI sternly limits the effectiveness of ECG recordings, and therefore, it is vital to remove PLI for better clinical judgment. In this paper, we have compared discrete wavelet transform (DWT), empirical mode decomposition (EMD), Kalman filter (KF), and KF smoother (KFS) for the elimination of PLI from ECG. These methodologies have experimented on different ECG recordings taken from the MIT-BIH arrhythmia database in the input signal to noise ratio (SNR) range of -10 to 10dB. The simulation results calculated using reconstructed ECG, magnitude spectrum, output SNR, and computational cost indicate that the KFS framework gives better denoising performance compared to KF, DWT, and EMD.

The localization of neural sources is a crucial issue in medical, scientific and technical applications. However, the dipole sources in the brain may vary in nature and change constantly with time, adding to the difficulty of source localization. In this paper, the dynamic neural sources in the brain are simulated by sequential Monte-Carlo (MC) method, based on electroencephalography (EEG) data. Firstly, the EEG data were considered as a state space model. Considering the nonlinearity of the EEG data, the sequential M-C method was introduced as a particle filter, and the Metropolis-Hastings (M-H) resampling was employed to alleviate the particle impoverishment of general particle filter. The accuracy of our method in source localization was verified through two experiments, using both synthetic and real data. The research results shed important new light on the research of brain neurology.

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Tracking and detection of neural activity has numerous applications in the medical research field. By considering neural sources, it can be monitored by electroencephalography (EEG). In this paper, we focus primarily on developing advanced signal processing methods for locating neural sources. Due to its high performance in state estimation and tracking, particle filter was used to locate neural sources. However, particle degeneracy limits the performance of particle filters in the most utmost situations. A few resampling methods were subsequently proposed to ease this issue. These resampling methods, however, take on heavy computational costs. In this article, we aim to investigate the Partial Stratified Resampling algorithm which is time-efficient that can be used to locate neural sources and compare them to conventional resampling algorithms. This work is aimed at reflecting on the capabilities of various resampling algorithms and estimating the performance of locating neural sources. Simulated data and real EEG data are used to conduct evaluation and comparison experiments.

To recover the neural activity from Magnetoencephalography (MEG) and Electroencephalography (EEG) measurements, we need to solve the inverse problem by utilizing the relation between dipole sources and the data generated by dipolar sources. In this study, we propose a new approach based on the implementation of a particle filter (PF) that uses minimum sampling variance resampling methodology to track the neural dipole sources of cerebral activity. We use this approach for the EEG data and demonstrate that it can naturally estimate the sources more precisely than the traditional systematic resampling scheme in PFs.