本論文提出一個基於PIC微控制器的心率變異度(Heart Rate Variability, HRV)訊號分析模組系統。心律變異度(HRV)是一項能夠評估自主神經系統的重要指標，而短時間的頻域心律變異度分析被廣泛的使用。交感神經旺盛意味著情緒的亢奮，而副交感旺盛代表著放鬆的狀態，分析交感神經與副交感神經的系統，不僅可以得知個人的情緒狀況，更能協助診斷疾病。本論文的HRV訊號分析系統包括三個部份:前段類比電路、通用非同步收發傳輸器（Universal Asynchronous Receiver/Transmitter, UART）模組與以可程式化邏輯閘陣列(Field Programmable Gate Array, FPGA)為平台，設計建構心率變異度訊號處理晶片。前端類比電路將收到的心電訊號進行放大與濾波，通過類比數位轉換器，以UART 8-N-1格式傳輸數位化心電訊號，輸入心率變異度訊號處理晶片，該晶片結合可程式化RISC之PIC微控制器，以及分析HRV訊號的數位訊號處理模組，達到完成HRV運算的目的。PIC微控制器是採用Harvard architecture的精簡指令集架構(RISC)，其命名來源來自周邊界面控制器(Peripheral Interface Controller)，少量的固定長度指令，使它相較於其他指令集而言具有更低面積的優勢。運用其指令集進行心電訊號R波偵測的演算法，之後交由其他數位訊號處理模組，進行重新取樣，以獲得等時距的RR-Interval時間序列，將等時距的資料依序輸入至512的離散傅立葉轉換，最後將HRV訊號中低頻的部分(LF)，與高頻的部分(HF)做比值，該值即代表一個人交感神經與副交感神經的平衡變化。
結合可程式化的微控制器與生醫應用模組相比於傳統特殊用途積體電路架構的生醫系統，具備更高的可調性以及功能擴充性。結果顯示，本系統具備即時量測與分析HRV訊號的功能，比起一般現行生醫訊號處理系統更具有可程式化與擴充性。

This paper presents a heart rate variability (HRV) analysis system that is composed of an electrocardiographic (ECG) acquisition circuit and a field programmable gate array (FPGA) based HRV signal processing chip. The digital part can be separated to three blocks. The first block is an 8-bit PIC microcontroller (MCU), the implementation of ECG wave QRS detection is accomplished by the instructions of PIC, RR- Interval (RRI) signals are extracted from every R wave. The second part performs resampling, to get evenly-sampled signals. The third part is the 512-point discrete Fourier transform (DFT) module, converting the time series of RRI signals to frequency-domain. The spectrum of HRV can be separated to three parts: high frequency (HF), low frequency (LF) and very low frequency (VLF), the ratio of LF and HF can be applied to analyze human’s mental and emotional conditions. Although the biomedical system-on-chip (SOC) can help people monitoring their health, developing application-specific integrated circuit (ASIC) for every biomedical application costs too much. Comparing with ASIC, the biomedical SOC combined with com a MCU is flexible and programmable, having more potential to increase more applications in the future. An MCU can be used to control other units or compute data, if there is a need to increase another application, a part of the calculation can be accomplished by the PIC assembly code. An HRV analysis system based on the PIC MCU is realized according to this concept, the PIC MCU is chosen because of its simplicity. According to the measurement result, the LF/HF ratio is higher during standing, while the ratio of LF/HF ratio are lower during sitting. The system can implement the real-time frequency-domain analysis of HRV, and have more potential to be applied to other biomedical applications.

[1] J. M. Dekker, R. S. Crow, A. R. Folsom, P.J. Hannan, D. Liao,C.A Swenne, et al.,
“Low heart rate variability in a 2-minute rhythm strip predicts risk of coronary heart disease and mortality from several causes: the ARIC Study. Atherosclerosis Risk In Communities,” Circulation, vol. 102, pp. 1239-1244, Sep. 2000.
[2] P. Palatini and S. Julius, “The role of cardiac autonomic function in hypertension and cardiovascular disease,” Current Hypertension Reports, vol.11, pp 199-205, June. 2009.
[3] Chen WL and Kuo CD, “Characteristics of heart rate variability can predict impending septic shock in emergency department patients with sepsis,” Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, vol. 14 5, pp 392-7, 2007.
[4] S. Akselrod, D. Gordon, F.A. Ubel, Shannon DC, Berger AC, Cohen RJ.” Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control.” Science, vol.213, pp.220-222, Jul. 1981.
[5] M. Kumar, M. Weippert, R. Vilbrandt, S. Kreuzfeld and R. Stoll, "Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment," IEEE Transactions on Fuzzy Systems, vol. 15, no. 5, pp. 791-808, Oct. 2007.
[6] 謝良地、陳達光, ”高血壓左心室肥厚的發生機制, ” 福建醫學院學報, 1992年02期
[7] M. Malik, J. Bigger, A. Camm, R. Kleiger, A. Malliani, A. Moss, and P. Schwartz, “Heart rate variability: Standards of measurement, physiological interpretation, and clinical use,” European Heart Journal, vol. 17, no. 3, pp. 354-381, 1996.
[8] H. H. So and K. L. Chan, "Development of QRS detection method for real-time ambulatory cardiac monitor," Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, Chicago, IL, USA, 1997, pp. 289-292.
[9] G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. Quint and H. T. Nagle, "A comparison of the noise sensitivity of nine QRS detection algorithms," in IEEE Transactions on Biomedical Engineering, vol. 37, no. 1, pp. 85-98, Jan. 1990.
[10] R. D. Berger, S. Akselrod, D. Gordon and R. J. Cohen, "An Efficient Algorithm for Spectral Analysis of Heart Rate Variability," IEEE Transactions on Biomedical Engineering, vol. BME-33, no. 9, pp. 900-904, Sept. 1986.
[11] PIC16C5X Data Sheet [Online].
Available: http://ww1.microchip.com/downloads/en/DeviceDoc/30453d.pdf
[12] 鍾富昭, “8051/8052 系列原理介紹與產品設計,” 全華科技圖書股份有限公司, 台北, 民國80年10月
[13] The RISC-V Instruction Set Manual [Online].
Available: https://riscv.org/specifications/
[14] J. W. Cooley and J. W. Tukey, “An Algorithm for the Machine Calculation of Complex Fourier Series,” Mathematics of Computation, Vol. 19, No. 90, pp. 297-301, Apr. 1965.
[15] A.V. Oppenheim and Ronald W. Schafer, Discrete-Time Signal Processing,
New Jersy: Prentice-hall, Inc., 1989.
[16] S. Winograd, “ On computing the Discrete Fourier Transform,” Proceedings of the National Academy of Sciences of the United States of America, vol. 73, p.1005, 1976.
[17] S.L. Gay, J. Hartung and G.L. Smith, “Algorithms for multi-channel DTMF detection for the WE DSP32 family,” in Acoustics, Speech, and Signal Processing, 1989. ICASSP-89., 1989 International Conference on, 1989, pp. 1134-1137
[18] D. Gajski, I. Tadatoshi, V. Chaiyakul, H. Juan and T. Hadley, “A Design Methodology and Environment for Interactive Behavioral Synthesis,” Technical Report 96-29, 1996.
[19] 8051 MCU Specification [Online].
Available: https://www.silabs.com/products/mcu/8-bit/c8051f98x
[20] ARM Cortex-M4 MCU Specification [Online].
Available: https://www.silabs.com/products/mcu/32-bit/efm32-giant-gecko-gg11
[21] I. Hatai, R. Biswas and S. Banerjee, "ASIC implementation of a 512-point FFT/IFFT processor for 2D CT image reconstruction algorithm," Students' Technology Symposium (TechSym), 2011 IEEE, Kharagpur, 2011, pp. 220-225.
[22] Frequency Domain HRV [Online.]
Available:https://hrvlab.wustl.edu/research/methods/frequency-domain-hrv/