以FPGA實現即時生理訊號處理晶片-以心率變異為一例

Implementation of Real-Time Physiological Signal Processing chip with FPGA for Heart Rate Variability

戴揚哲(Yang-Che Dai)

指導教授 ： 胡威志

中原大學/工學院/生物醫學工程研究所/碩士(2013年)

https://doi.org/10.6840/cycu201301114

摘要

生理訊號可提供豐富的生理資訊有利於醫療專業人員做診斷病情，但這些生理訊號都需要經過多層的處裡過後才可顯其特徵性為ㄧ般人了解。生理訊號處理不僅可在時域上分析，還可以頻域的角度來探討生理訊號的特徵，如心電圖之心率變異度頻譜可提供自主神經調控機制的資訊、腦電圖之頻譜用於輔助診斷腦部相關疾病等。
本研究提出了一套SoC系統可以用於完整分析心電訊號，其中訊號處理著重於頻譜分析的技術。訊號處理流程先將心電圖做微分取絕對值、moving average、R波偵測、重新取樣，以此獲得等距RR-Interval時間序列，接著輸入至FFT演算法把RR-Interval時間序列從時域轉換為頻域。系統開發內容重點在於1024點快速傅立葉轉換演算法(FFT)，採用Radix-2 DIF為基礎架構，每層運算中包含了資料緩衝區、蝶型運算、旋轉因子、記憶體位置控制以及正確控制輸出數據時脈等。
即時處理結果藉由USB傳輸至Borland C++ Builder視窗化軟體使用者介面顯示，以此觀察心率變異度的頻譜分析。實驗結果與討論中，設計了三種方法來進行驗證，(1)分別給予1Hz、10Hz、50Hz、125Hz的sin波進入自行建構的FFT演算法進行轉換，以1Hz為例來說，理論上頻譜圖應於1Hz的地方會出現高峰，觀察實際結果發現高峰位置剛好落在1Hz的位置，其餘sin波結果皆是如此，初步證明FFT演算法的正確性；(2)設計四種不同心跳頻率組合，作為心電訊號處理的驗證，事實證明實際結果全在理論計算的頻率範圍之內，說明本系統可完整處理生理訊號；(3)以兩種不同狀態實際量測十位受測者，即時觀察出在身心作較劇烈運動環境下低頻較高，代表交感神經作用，而在休息環境下高頻較高，代表副交感神經作用，經由心率變異度時頻域分析圖直接對自主神經的變化給予量化性的描述。
由上述各項驗證得知，本研究的即時生理訊號處理晶片不僅能完整的處理生理訊號達到即時量測即時分析的效果，而且還具有相當好的準確性。未來的發展上，希望能夠把本研究的FFT演算法應用在其他的生理訊號上，如腦波、血壓或肌電訊號，讓整個生理訊號處理更加完整。

關鍵字

生理訊號處理 ； 快速傅立葉轉換(FFT) ； 心率變異度(HRV) ； SoC

並列摘要

Biosignal can provide a lot of usefully physiological information to help the medical professionals to diagnose and identify variant diseases. However, deriving characteristics of these diseases from obtained biosignals need to calculate and signal process. Therefore, biosignal processing can be used to derive these characteristics of biosignal not only in time-domain analysis, but also in frequency-domain analysis. Such as HRV power spectrum density can provide important information for regulatory mechanisms of autonomic nervous system, and the EEG spectrum can be used to diagnose brain diseases.
This study proposed a SoC system to analyze ECG signals and focus on spectrum analysis technique. These signal processing produces included several steps: the differentiation, moving average, R-wave detection, and resampling. The equidistant RR-Interval time series were obtained from originally obtained ECG signals, subsequently, these derived signals were inputted into FFT algorithms to transform RR-Interval time series from time-domain to frequency-domain. A critical algorithm of 1024 points FFT was developed. The structure of this algorithm was based on the Radix-2 DIF, and each operator contained the buffer, butterfly algorithm, twiddle factor, memory control, and correct output data clock control.
The real-time results were transmitted to the computer by using USB interface, and HRV power spectrum density was presented by Borland C++ Builder program on a personal computer simultaneously. Experimental results presented three kinds of methods to valid. (1)Four kinds of sine wave with different frequencies (1Hz, 10Hz, 50Hz and 125Hz) were given into FFT algorithms, respectively. For example, the spectrum of a 1Hz sine wave should theoretically appear the peak at 1Hz. The practical result was found the peak at 1Hz as we supposed. The others sine wave presented the same results. Preliminary evidence of FFT algorithms was correct. (2) Four different heartbeat frequencies were combined for verification of ECG signal processing. The result showed peaks of the synthesized frequency were located in the theoretical range. (3)The validation of ECG signal in this study has been tested with 10 subjects in two different conditions. One was at resting condition and the other one was at mental athletic condition. At the resting condition, a relatively higher power was appeared at high-frequency range, indicating the parasympathetic was activated. At the mental athletic condition, a relatively higher power was showed at low-frequency range, indicating the sympathetic was activated. From observing the derived HRV power spectrum density, a quantified parameter was provided to descript the activation of the autonomic nervous system.
In conclusion, this study presented a real-time physiological signal processing algorithm to achieve real-time measurement and analysis, and results presented an excellent accuracy. In the future, this FFT algorithm can be used in other biosignal processing, such like EEG, blood pressure, and EMG.

並列關鍵字

System on Chip(SoC) ； biosignal processing ； Fast Fourier transform(FFT) ； Herat Rate Variability(HRV)

參考文獻

[1] M. R. Ilic, V. Z. Petrovic, and G. S. Jovanovic, "Simulation of synchronous network-on-chip router for system-on-chip communication," in Telecommunications Forum (TELFOR), 2012 20th, 2012, pp. 506-509.

[2] W.-C. Fang, C.-K. Chen, E. Chua, C.-C. Fu, S.-Y. Tseng, and S. Kang, "A low power biomedical signal processing system-on-chip design for portable brain-heart monitoring systems," in Green Circuits and Systems (ICGCS), 2010 International Conference on, 2010, pp. 18-23.

[3] 林永泰, "以 FPGA 實現即時 FFT 頻譜分析之心率變異系統," 中原大學醫學工程研究所.碩士學位論文, pp. 1-135, 2007.

[5] K. S. Saladin, Human anatomy: McGraw-Hill Higher Education, 2005.

[7] W. Einthoven, G. Fahr, and A. De Waart, "Über die Richtung und die manifeste Grösse der Potentialschwankungen im menschlichen Herzen und über den Einfluss der Herzlage auf die Form des Elektrokardiogramms," Pflügers Archiv European Journal of Physiology, vol. 150, pp. 275-315, 1913.