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Empirical Mode Decomposition Based on ECG Analysis Design

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The electrocardiogram (ECG) has been widely used for diagnosis purposes of heart diseases. Good quality ECG is utilized by the physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. One prominent artifact is the high frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes. Noise severely limits the utility of the recorded ECG and thus need to be removed for better clinical evaluation. A new ECG denoising method based on the recently developed Empirical Mode Decomposition (EMD) is proposed to diagnosis the ECG abnormality on System on Chip Architecture. The Empirical Mode Decomposition (EMD) is becoming increasingly popular for the multi-scale analysis of signals. However, the data-driven and adaptive nature of the EMD raises concerns regarding the uniqueness of the decomposition as well as the extent to which oscillatory modes can be mixed across different IMFs. The method is validated through experiments on the MIT-BIH database. Both quantitative and Qualitative results are given. The results show that the proposed method provides very good results for de-noising. The proposed architecture is designed and synthesized with the Matlab.
Keywords:Empirical mode decomposition, Electro cardio graph.
Electrocardiogram (ECG) signal can be used to count the heart rate beat for various diagnostic purposes in medicine. There are many research are related to the ECG signal such as fatigue driver detection using ECG signal. However, most of the captured ECG signal will be distorted by the noise that cause by the measurement instrument. Sometimes, the noise will totally mask the ECG signal. The signal is hardly to be processed for further analysis. Therefore, it is essential that the ECG signal must be filtered to avoid the failure detection of the signal. The output of ECG signal is compared with the ECG signal before filtering by plotting the signal in time domain and frequency domain using MATLAB.
ECG signal can be applied in the detection of a fatigue driver whether he is sleepy. However, the captured ECG signal is distorted by noise caused by measurement equipment. The presence of noise will lead to failure detection of the ECG signal in further analysis. Moreover, it is essential to minimize the hardware resources used in FPGA. Having a lot of multiply units is not area efficient. Therefore, it is essential to filter the noise that exists in ECG signal and implement an optimized hardware in FPGA.Empirical Mode Decomposition is a recent development which provides a powerful tool for decomposing a signal into a finite number of IMFs (Intrinsic Mode Functions). Empirical Mode Decomposition (EMD) has been used in a number of literature for R-peak detection as well as enhancement. The hilbrt-huang transform (hht) is widely adopted in analyzing biomedical signal, including electrocardiograph electro encephalography (eeg), etc the empirical mode decomposition (EMD) are the key component of HHT. EEMD ensembles the results of multiple EMD with different noise aids to solve mode mixing. In home care application the portable device requires low latency and low energy consumption for HHT computation. The EMD is key component of HHT to decompose data into intrinsic mode function (IMF) and a residue therefore an efficient VLSI design of the EMD engine is describe for future application..


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