We presents a refined multiscale Shannon entropy for analyzing electroencephalogram (EEG), which reflects the underlying dynamics of EEG over multiple scales. The rationale behind this method is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposi...

Introduction Automatic human emotion recognition is one of the most interesting topics in the field of affective computing. However, development of a reliable approach with a reasonable recognition rate is a challenging task. The main objective of the present study was to propose a robust method for discrimination of emotional responses thorough examination of heart rate variability (HRV). In t...

This work is based on the comparative study of different decomposition methods used to de-noise an ECG signal. There are several signal processing techniques available like Fourier Transform method, Short Term Fourier Transform method, Wavelet analysis, Empirical Mode of Decomposition method etc. Though Fourier Transform method is predominantly used for decomposition purpose but it is not suita...

This paper presents a new approach for human cataract automatical detection based on ultrasound signal processing. Two signal decomposition techniques, empirical mode decomposition and discrete wavelet transform are used in the presented method. Performance comparison of these two decomposition methods when applied to this specific ultrasound signal is given. Described method includes ultrasoni...

A new algorithm for pitch extraction based on the Ensemble Empirical Mode Decomposition (EEMD) is presented. Applications to normal and pathological voices are considered. EEMD is a completely data-driven method for signal decomposition into a sum of AM FM components, called Intrinsic Mode Functions (IMFs) or modes, which can be written as ( ) cos( ( )) A t t φ . The voice fundamental frequency...

The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decom...

Empirical mode decomposition is investigated as a tool to determine the phase and frequency and to study phase synchronization between complex chaotic oscillators. Within this approach, the oscillator is characterized by a spectrum of frequencies corresponding to the empirical modes. First, the phase and frequency of the oscillators resulting from two well-known methods, based on modified varia...