Hardware Implementation of EMD Using DSP and FPGA for Online Signal Processing

Lee, Ming-Huan; Shyu, Kuo-Kai; Lee, Po-Lei; Huang, Chien‐Cheng; Chiu, Yun-Jen

doi:10.1109/tie.2010.2060454

Cited by 88 publications

(48 citation statements)

References 19 publications

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Lu¹,

Chen²,

Yeh³

et al. 2014

2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings

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In this work, a multiple stopping criteria and highprecision empirical mode decomposition (EMD) hardware architecture implementation is proposed for Hilbert-Huang transform (HHT) in biomedical signal processing. The proposed architecture can support multiple stopping criteria including the constant criteria, the SD criteria and the ratio criteria. The 38-bit floating point precision is adopted in this work to support 10 IMF components with enough accuracy. The off-chip memory architecture is adopted to increase the processing capacity. By the pipelined cubic spline coefficient unit (PCSCU), the computation time can be reduced. The proposed EMD hardware architecture is implemented in TSMC 90 nm CMOS process with the core area of 4.47 mm 2 at the operating frequency of 40 MHz. The post-layout simulation result shows that our work with the constant criterion can speed up the performance 50.4 times compared to the software computation on a single core of ARM11 for 2K data size breathing signals.

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Section: Introductionmentioning

confidence: 99%

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

Lu¹,

Chen²,

Yeh³

et al. 2014

2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings

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“…Wavelet transform has the advantage that the windows will adapt, and thus, it is possible to generate an infinite set of possible basis functions [4]. This paper concentrates on the implementation of Empirical mode decomposition (EMD) using field-programmable gate array (FPGA) for denoising of ECG [5,6]. EMD is widely used for non-stationary and non-linear signal analysis procedures.…”

Section: Introductionmentioning

confidence: 99%

Field-Programmable Gate Array Implementation of Empirical Mode Decomposition Algorithm for Electrocardiogram Processing

Fernandes

Suchetha

2017

Asian J Pharm Clin Res

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The electrocardiogram (ECG) signal contains important information that is utilized by physicians for the diagnosis and analysis of heart diseases. Therefore, good quality ECG signal is required. Hilbert-Huang transform (HHT) is a method to analyze non-stationary and non-linear signals. Empirical mode decomposition (EMD) is the core of HHT. EMD breaks down signals into smaller number of components. These components form a complete and nearly orthogonal basis for the original signal. This algorithm is implemented on field-programmable gate array using the process of extrema generation, envelope generation, and stopping criterion.Keywords: Hilbert-Huang transform, Electrocardiogram, Empirical mode decomposition, Field-programmable gate array. INTRODUCTIONElectrocardiogram (ECG) is the recording of electrical activity of the heart. Fig. 1 shows the normal ECG without any kind of interferences. In clinical practice, ECG is the most commonly used cardiovascular signal because it gives us plenty of diagnostic information. Doctors use ECG to find heart-related problems. ECG from healthy hearts has a characteristic shape. Any damage to heart muscles can change the electrical activity of heart causing change in the characteristic shape of ECG. While recording, ECG contains different types of noises such as power-line interferences, baseline wandering, motion artifacts, electrosurgical noise, and EMG noise which need to be filtered. An ECG contains three main parts: p-wave which denotes atrial depolarization, the QRS complex which denotes ventricular depolarization, and T-wave that represents ventricular repolarization [1]. The most important part in ECG processing is the detection of QRS complex which has an essential role in the diagnosis of heart rhythm irregularities [1,2].Many methods exist to detect QRS complex that are based on standardized filtering and wavelet transform. Each technique has its own advantages and drawbacks. One of the influential signal detection technique considered in literature is adaptive filtering method. One of the most widely used algorithms is least mean square algorithm introduced by Widrow and Hoff in adaptive filtering. It is very simple in structure and is easy to compute [3]. Geophysicist Morlet put forward the concept of wavelet transform in analysis and processing geophysical data in France in 1984. Wavelet transform has the advantage that the windows will adapt, and thus, it is possible to generate an infinite set of possible basis functions [4]. This paper concentrates on the implementation of Empirical mode decomposition (EMD) using field-programmable gate array (FPGA) for denoising of ECG [5,6]. EMD is widely used for non-stationary and non-linear signal analysis procedures. The decomposition method that is used in the EMD algorithm is called shifting process. It has proved versatile in a wide range of applications for signal extraction from nonlinear and non-stationary processes. It is an iterative algorithm which computes the maximum and minimum extreme. Main advantage ...

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“…The Field programmable gate array (FPGA)-based systems gain particular flexibility for verifying hardware implementations. Due to the advantages of FPGA prototyping based verification, a large number of systems have been implemented in FPGAs in different fields, such as radio communication protocol [6], robotics [7], [8], power system [9] and signal processing [10].…”

Section: Introductionmentioning

confidence: 99%

Implementation of XOR Based Pad Generation Mutual Authentication Protocol for RF Link

Ramya¹

2013

IOSR-JECE

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Hardware Implementation of EMD Using DSP and FPGA for Online Signal Processing

Cited by 88 publications

References 19 publications

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

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

Field-Programmable Gate Array Implementation of Empirical Mode Decomposition Algorithm for Electrocardiogram Processing

Implementation of XOR Based Pad Generation Mutual Authentication Protocol for RF Link

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