單導程心電圖時序波型訊號蘊含豐富心臟運作訊息，包括心搏整體時間的心率變異與時頻分析的現行臨床標準。本項研究運用中研院黃鍔院士建立非線性(non-linear)解析演算法的經驗模態解析(empirical mode decomposition, EMD)，實作心搏細節時序動作相關非侵襲性心電圖時序波型訊號的非線性解析。初步解析獲得神經傳導與心肌收縮相關心搏細節動作依序時間；分析心電圖解析連續R點三合體R0-R1-R2成為雙併式雙重心搏時距RRi2 (R-R interval duo)，採取三合體R1真實時間(mSec)作為個別心搏真實時距(RRi2)校正歸零(0 mSec)的心搏原點，分析連續心搏R點基準相關內部前導型依序放電的四個神經傳導動作AV-His-LBBseptal-RBB，歸零化真實時距數值作圖發現個別動作的時序波型圖呈現的初步規律性；同時地，分析三合體R0-R1-R2真實時距(RRi2)作為連續心搏真實時距常規百分率(RRi2%)校正歸貳(200%)的雙重心搏尺度，採取三合體R1真實時距常規百分率(RRi2%)作為個別心搏校正歸壹(100%)的心搏滿點，比較解析連續心搏所得內部個別動作的真實時距百分率(RRi2%)連續心搏時序波型圖，顯現R點前導四個神經傳導動作時序波型的增進規律性，其中His與LBBseptal波型圖呈現極小變異範圍；連續地，分析實測組個別心搏解析二十動作統計演算獲得個別動作機率最高值的真實時距常規百分率(RRi2%)，比較模板組典籍測定僅有十二動作在於參考心搏時間749mSec的真實時距常規百分率(RRi2%)，整體十二動作呈現5.81%最小差異程度，其中神經傳導AV-His-xxx-RBB三項動作僅見0.75%差異。
關鍵字：心電圖，心搏程序解析，經驗模態解析。

The temporal composite waveform signal of single-lead electrocardiogram (ECG) comprising QRS waveform complex can be may be seen as enriched cardiac operation information including overall heartbeat true-time related heart-rate variability and temporal analysis towards standards of routine practice. This study applied empirical mode decomposition (EMD) of non-linear decomposition algorithm established by Dr. Norden Huang, Academician of the Academia Sinica, in order for implementing non-linear decomposition on temporal waveform data of non-invasive ECG towards detailed temporal cardiac actions within individual heartbeats. Preliminary ECG decomposition retrieved temporal cardiac actions within individual heartbeats pertaining to the neurocardial conductance (Ncc) and/or myocardial contraction (Mcc). The ECG decomposition adopted continuous R-trio R0-R1-R2 as of jointed R-R interval duo (RRi2) and as well R1 true-time (mSec) value as of heartbeat origin point for RRi2 zero calibration of true-time action intervals in order for plotting waveform data from continuous heartbeats. Respective RRi2 waveforms of the four Ncc actions as of AV-His-LBBseptal-RBB revealed preliminary regularity. Consequently, ECG decomposition adopted continuous R0-R1-R2 true-time interval RRi2 as of 200% normalized true-time intervals (RRi2%) and as well R1 normalized (RRi2%) value as of heartbeat unit scale for RRi2% 100% calibration of true-time action intervals in order for plotting waveform data from continuous heartbeats. Respective RRi2% waveforms of the four Ncc actions AV-His-LBBseptal-RBB revealed increased regularity in which His and LBBseptal showed rather narrow variation range. Continuously, ECG decomposition retrieved the normalized and aligned RRi2% values of 20 heartbeat actions based on highest statistical possibility in order for comparing the reference template RRi2% values of 12 heartbeat actions. The yield of 5.81% minimal difference among all 12 heartbeat actions was at 749x2 mSec of referral RRi2 value in which AV-His-xxx-RBB showed only 0.75% difference.

1. White, P. C. and Slutsker, L., “Haplotype Analysis of CYP11B2,” Endocrine Research, Vol. 21, No. 1-2, pp. 437-442 (1995).
2. Hyndman, B. W. and Gregory, J. R., “Spectral Analysis of Sinus Arrhythmia During Mental Loading,” Ergonomics, Vol. 18, No. 3, pp. 255-270 (1975).
3. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C., and Liu, H. H.“The Empirical Mode Decomposition and The Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis,” Proceedings of the Royal Society A: Mathematical,Physical and Engineering Sciences, Vol. 454, pp. 903-995 (1998).
4. Pagani, M., Lombardi, F., Guzzetti, S., Rimoldi, O., Furlan,R., Pizzinelli, P., Sandrone, G., Malfatto, G., Dell'Orto, S.,Piccaluga, E., Turiel, M., Baselli, G., Gerutti, S., and Malliani, A., “Power Spectral Analysis of Heart Rate and Arterial Pressure Variabilities as A Marker of Sympatho-Vagal Interaction in Man and Conscious Dog,” Circulation Research, Vol. 59, No. 2, pp. 178-193 (1986).
5. Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology, ”Heart Rate Variability: Standard of Measurement, Physiological Interpretation, and Clinical Use,” Circulation, Vol. 93, No. 5, pp. 1043-1065 (1996).
6. Pan, J. and Tompkins, W. J., “A Real-Time QRS Detection Algorithm,” IEEE Transactions on Biomedical Engineering,Vol. 32, No. 3, pp. 230-236 (1985).
7. Blanco-Velasco, M., Weng, B., and Barner, K. E., “ECG Signal Denoising and Baseline Wander Correction Based on the Empirical Mode Decomposition,” Computers in
Biology and Medicine, Vol. 38, No. 1, pp. 1-13 (2008).
8. Niskanen, J. P., Tarvainen, M. P., Ranta-Aho, P. O., and Karjalainen, P. A.,“Software for Advanced HRV Analysis,” Computer Methods and Programs in Biomedicine, Vol. 76,
9. Institue of Heartmath, “Science of The Heart: Exploring the Role of the Heart in uman Performance,” http://www.heartmath.org/research/science-of-the-heart/introduction.html
10. Jaakko Malmivuo and Robert Plonsey, 1995: Bioelectromagnetism -- Principles and applications of bioelectric and biomagnetic fields, Oxford University Press, New York.
11. The standard lead-II ECG aligns the P wave (LA depolarization), the QRS complex (LV depolarization), and the T wave (LV repolarization), respectively (Mitchell and Wang, 2014).
12.The P wave, QRS wave, T wave, U waves, between the respective wave type can be distinguished among flatten segments and bumpy intervals (Al-Ziarjawey and Çankaya, 2015; Reisner et al., 2006)