| Summary: | 碩士 === 國立中山大學 === 醫學科技研究所 === 106 === Electrocardiography (ECG) has been widely used in medicine for the detection of the electrical signal of cardiac. For example: the doctors can diagnose the cardiac diseases via the electrical conduction system of ECG. For first aid and emergency medical help, automated external defibrillator (AED) is applied to automatically diagnose the life-threatening cardiac arrhythmias of ventricular fibrillation and is able to treat them through defibrillation if the patient’s cardiac rhythm is fatal ventricular arrhythmia. However, several noises, such as baseline wander, muscle artifact, and power-line interference (PLI), are also mixed within ECG signal. Removing these noises before diagnosis plays the most important key. Many researchers proposed the Least Mean Square (LMS)-based adaptive filtering algorithm to reduce these noises. In the proposed simulation results, it revealed that all of the LMS-based algorithms would have the capability to reduce the PLI noise, but caused higher errors in Mean Square Error (MSE), which is a kind of signal quality criterial.
This paper proposed the transfer-domain (TD) adaptive filtering algorithm called FFT-based Normalized Least Mean Square (FFT NLMS). It combined the advantage of Normalized Least Mean Square (NLMS) and TD Least Mean Square (i.e. FFT LMS). The FFT NLMS not only can tack a specific frequency signal but also can adjust the step size to improve the convergence time. For hardware implementation, we adopt a FPGA and ARM co-operation platform for FFT NLMS to realize a real-time PLI canceler. The PLI cancelling process can be divided into three steps: 1) time domain signals are first transferred to frequency domain signals via a 1D-to-2D structure of 12-point FFT operation (i.e. Radix-3 × Radix-4) in FPGA. Compared with the 16-point Cooley-Tukey FFT with 4-point zero padding scheme (totally takes 32 complex multiplications and 64 complex additions), the proposed 12-point FFT only requires 94 complex additions. The result shows that it greatly reduces a lot of computational complexity. 2) the frequency domain signals converted from the proposed FFT processor are further transferred to ARM through AXI bus from FPGA. 3) ARM processor executes FFT NLMS to eliminate the PLI noise in ECG signal and remove the noise.
After tens of thousands of simulations and comparisons, the results demonstrate that the proposed FFT NLMS has better performance than NLMS in suppression capability of PLI in spectrum. For the comparison of MSE, FFT NLMS has the lowest error, which can reduce 18 dB and 20 dB less than FFT LMS, and LMS, respectively. Because the signal after FFT NLMS noise suppression (i.e. the reconstructed signal) is highly closed to the original ECG, the Heart Rate Variability (HRV) analytic result of the reconstructed signal is quite similar to that of original ECG signal. This implies that the proposed FFT NLMS algorithm has higher suppression capability and higher computational accuracy so that it would be very useful and helpful for future applications.
|
|---|
