Parkinson Disease is a neurological syndrome, characterized by resting tremor, reduced body movement and difficulty in maintaining proper posture. In this article I demonstrate a new method of parkinsonism diagnosis using Empirical Mode Decomposition (EMD) based on body movement synchronization analysis. EMD is a new method of signal decomposition into its monocomponents, so called Intrinsic Mo...

Electrocardiogram (ECG) is the most extensively applied diagnostic approach for heart diseases. However, an ECG signal a weak bioelectrical and easily disturbed by baseline wander, powerline interference, muscle artefacts, which make detection of diseases more difficult. Therefore, it very important to denoise contaminated in practical application. In this article, effective segments denoising ...

Empirical mode decomposition is a method to decompose signals proposed by N.E.Huang et. al in 1998. It can extract adaptively the oscillatory modes at each time from a complex signal, namely it can decompose the signal into a finite (often less) number of intrinsic mode functions (IMFs). With Hilbert transform, the IMFs yield instantaneous frequencies as functions of time, that give sharp ident...

A novel algorithm for 2D object orientation and scaling factor estimation, is proposed in this paper. The proposed method is accurate, effective, computationally efficient and fullyautomated. The object orientation is calculated by using object principal axes estimation. The main contribution of the proposed approach is the utilization of a 2D empirical mode like decomposition (EMD-like) as a n...