On-line empirical mode decomposition biomedical microprocessor for Hilbert Huang transform

Abstract

Abstract-On-chip implementation of Hilbert-Huang transform (HHT) has great impact to analyze the non-linear and nonstationary biomedical signals on wearable or implantable sensors for the real-time applications. Empirical mode decomposition (EMD) is the key component for the HHT processor. In tradition, EMD is usually performed after the collection of a large window of signals, and the long latency may not be feasible for the real-time applications. In this work, the architecture of on-line EMD for biomedical signals is proposed. The on-line interpolation method with data reuse as well as component and iteration loop decomposition is applied to obtain low latency and low hardware cost. The first chip of EMD processor is fabricated in UMC 90nm LL process and consumes 57.3µW.

Multiple stopping criteria and high-precision EMD architecture implementation for Hilbert-Huang transform

“…In the same way, the lower envelope of the local minima is obtained by a set of local minima. The cubic polynomial equation s(t) is represented as 1 and 2 1 0 for ) ( ) ( (4) where s i (t) denotes the piecewise cubic polynomial equation and is represented as…”

“…The overlapped window concept [2], [4] is adopted to decrease the window length such that the computation effort in hardware can be reduced. The proposed EMD architecture supports multiple stopping criteria including the constant criterion, the SD criterion and the ratio criterion.…”

“…Recently, HHT has been further involved in the biomedical signal processing [2]- [9] for EEG analysis [2], [4], [9], EKG analysis [3], [5]- [6], breathing signal analysis [6], biological sound analysis [7] and ultrasound signal analysis [8]. Since most biomedical signals have non-linear and non-stationary property, HHT becomes favorable in biomedical signal processing and can provide different information compared to the conventional analysis methods including fast Fourier transform (FFT) or discrete wavelet transform (DWT).…”

On-line Local Mean Decomposition and its application to ECG signal denoising

“…Empirical Mode Decomposition (EMD) was proposed by Huang et al [1] for dealing with nonlinear and non-stationary time series. However, the key operation of the EMD algorithm involves using cubic spline interpolation (CSI), which causes high computation delay, large computation loading and heavy power consumption when solving the sequential tridiagonal matrix algorithm for CSI [2] [3]. Thus, EMD is often computed in off-line fashion.…”

Nonconvulsive epileptic seizures detection using multiway data analysis

“…By definition, an IMF meets two conditions: (i) for the entire data set, the number of extrema and the number of zero crossings must be either equal or differ at most by one and (ii) the mean at any point of the contour defined by interpolating the local maxima (upper envelope) and the contour defined by interpolating the local minima (low envelope) is zero [9]. The EMD can be described with the following equation,…”