隨著行動裝置使用人口及運算效能的與日俱增，與個人健康意識的抬頭，行動醫療的概念已成為一個趨勢。透過行動裝置收集並分析生理訊號，是行動醫療中十分重要的功能，除即時得知資訊外，雲端伺服器的負載也能大幅降低。為此，我們需要適用於行動裝置上之輕量卻有效的分析工具。

在這篇論文中，針對經驗模態分解對於非穩態與非線性訊號的良好分析特性，我們提出一個低延遲且低邊緣效應的線上分段經驗模態分解，以解決其在分析長時訊號時高延遲及高記憶體需求的特性。線上分段經驗模態分解能夠利用前段資料的斜率的座標，搭配事前的訊號特性分析，分段地對連續訊號進行分解。在分解出之各層本質模態函數之中，最差的標準化均方誤差不超過百分之九。以分解一段八小時長的心電圖而言，運算時間雖為傳統經驗模態分解的兩倍，記憶體需求卻能低至百分之一以下。與其他演算法相比，運算時間可減少百分之八十三，記憶體需求可減少百分之六十三。因此提出之線上分段經驗模態分解，特別適用於硬體規格有所限制的裝置中，例如智慧型手機、平板電腦或其他行動裝置等。

線上分段經驗模態分解在論文中，亦實際應用於提取心電呼吸訊號。相較於傳統睡眠多項生理檢查中的複雜呼吸量測方式，心電呼吸訊號僅需在胸口及體側黏貼兩片電極，取得心電訊號後即可求得，以相對舒適且行動性高的方式獲得呼吸資訊。實驗平台為安卓系統，實作上亦利用了該系統上新開發的應用程式介面，同時使用多顆處理器甚至圖形處理單元進行運算。在平板電腦上收取一段長度十秒鐘，包含一千兩百個取樣點的心電訊號，濾除基線飄移且提取心電呼吸訊號，所需時間僅在兩秒鐘以內。該應用程式可以安裝在特定版本以上的任一安卓系統中，為個人行動醫療提供最大的可能性。

As mHealth (Mobile Health) thrives, advances in collection and analysis of vital signals on portable devices have become more and more important. In this thesis, a low latency and low end effect on-line segmental empirical mode decomposition (SegEMD) is proposed, which aims at the adaptive characteristics for nonlinear and non-stationary signal of the conventional empirical mode decomposition (EMD) , as well as the notorious lengthy latency and highly demanded computing resources of it. The SegEMD is capable of processing continuous signals segment by segment with EMD, by reusing the previous slopes, the previous
data and the estimation of signal characteristics in advance. Worst normalized mean squared error (NMSE) compared to the results carried out by the conventional EMD is less than 9%. For an 8-hour overnight electrocardiogram (ECG) signal, the processing time is twice the conventional EMD, but the memory requirement is reduced to below 1%. Compared with SEMD, the processing time is 83% less and the memory used is 63% less. Thus the proposed SegEMD is especially suitable for limited hardware, such as smartphones, tablets, and other mobile devices.
SegEMD is also applied to bring out the ECG-derived respiratory (EDR) from ECG, which is able to reveals the status of respiration without the uncomfortable sensors in the traditional polysomnography (PSG). RenderScript, a novel application programming interface (API) of the Android OS is introduced to provide acceleration of the Android application (App). A 10-second, 1200 samples data sequence can be detrended and the EDR be extracted within 2 seconds in a Nexus 7 tablet. Such app can be installed on any Android devices with version 4.3 and later version, roviding possibilities to personal healthcare at home.

Abstract i
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Mobile Health 5
2.1 mHealth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.3 Computational Capabilities of Mobile Devices . . . . . . . . . . . . 6
2.2 ECG and the Respiratory Application . . . . . . . . . . . . . . . . . . . . . 7
2.2.1 Electrocardiography (ECG) . . . . . . . . . . . . . . . . . . . . . . 7
2.2.2 ECG-Derived Respiration (EDR) . . . . . . . . . . . . . . . . . . . 9
2.2.3 Sleep Apnea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.4 Apnea-ECG Database . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 EMD and Mobile Platform 13
3.1 Empirical Mode Decomposition (EMD) . . . . . . . . . . . . . . . . . . . . 13
3.1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1.2 Intrinsic Mode Function (IMF) . . . . . . . . . . . . . . . . . . . . . 13
3.1.3 Sifting Process and Stoppage Criterion . . . . . . . . . . . . . . . . 14
3.1.4 End Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2 Cubic Spline Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2.1 Formulation of Equations . . . . . . . . . . . . . . . . . . . . . . . 18
3.2.2 Tridiagonal Matrix Algorithm (TDMA) . . . . . . . . . . . . . . . . 22
3.2.3 Spline Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3 Derivational Forms of EMD . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3.1 Ensemble Empirical Mode Decomposition (EEMD) . . . . . . . . . 23
3.3.2 Sliding Empirical Mode Decomposition (SEMD) . . . . . . . . . . . 26
3.3.3 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.4 Android Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.4.3 RenderScript . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4.4 Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4 Proposed On-line Segmental EMD (SegEMD) Schedule for ECG Signals 33
4.1 Block Diagram of the Proposed SegEMD Schedule . . . . . . . . . . . . . . 33
4.2 Sifting Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2.1 Reliable and Unreliable Intervals . . . . . . . . . . . . . . . . . . . . 35
4.2.2 Defining the Reliable Boundary . . . . . . . . . . . . . . . . . . . . 36
4.2.3 Coordinate and Slope Re-use . . . . . . . . . . . . . . . . . . . . . . 37
4.2.4 Hybrid Cubic Spline Interpolation . . . . . . . . . . . . . . . . . . . 38
4.3 IMF Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.3.1 Adjustment of Reliable Boundary . . . . . . . . . . . . . . . . . . . 40
4.3.2 Continuous Generation of IMFs . . . . . . . . . . . . . . . . . . . . 41
4.3.3 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.4 Computational Complexity and Application to EDR . . . . . . . . . . . . . . 43
4.4.1 Cubic Spline Interpolation . . . . . . . . . . . . . . . . . . . . . . . 43
4.4.2 Derivation of EDR . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5 Experiment Result 53
5.1 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.1.1 Real-time ECG Capturing . . . . . . . . . . . . . . . . . . . . . . . 53
5.1.2 Real-time Respiration Capturing . . . . . . . . . . . . . . . . . . . . 54
5.1.3 Android Application (App) . . . . . . . . . . . . . . . . . . . . . . . 54
5.1.4 Experiment Result . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
6 Conclusion and Future Work 61
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

[1] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C.
Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for
nonlinear and non-stationary time series analysis,” Journal of Proceedings of the Royal
Society, vol. 454, no. 1, pp. 903–995, Mar. 1998.
[2] R. Faltermeier, A. Zeiler, I. Keck, A. Tome, A. Brawanski, and E. Lang, “Sliding empirical
mode decomposition,” in IEEE International Joint Conference on Neural Networks
(IJCNN), Barcelona, Spain, Jul. 2010, pp. 1–8.
[3] G. Chiarini, P. Ray, S. Akter, C. Masella, and A. Ganz, “mHealth technologies for
chronic diseases and elders: a systematic review,” IEEE Journal on Selected Areas in
Communications, vol. 31, no. 9, pp. 6–18, Sept. 2013.
[4] P. Germanakos, C. Mourlas, and G. Samaras, “A mobile agent approach for ubiquitous
and personalized ehealth information systems,” in Proceedings of the Workshop on
’Personalization for e-Health’ of the 10th International Conference on User Modeling
(UM’05), Edinburgh, Scotland, Jul. 2005, pp. 67–70.
[5] A. Cox. (2011, Nov.) Mobile healthcare and medical app downloads to reach
44 million next year, rising to 142 million in 2016. [Online]. Available:
http://www.juniperresearch.com/viewpressrelease.php?pr=275
[6] B. Dolan. (2011, Sept.) Report: 13k iphone consumer health
apps in 2012. [Online]. Available: http://mobihealthnews.com/13368/
report-13k-iphone-consumer-health-apps-in-2012/
[7] J. A. Blaya, H. S. F. Fraser, and B. Holt, “E-health technologies show promise in developing
countries,” vol. 29, no. 2, pp. 244–251, Sept. 2010.
[8] N. Ahmad, D. Hoang, and M. Phung, “Robust preprocessing for health care monitoring
framework,” in IEEE International Conference on e-Health Networking, Applications
and Services, Sydney, Australia, 2009, pp. 169–174.
[9] J. Loscalzo, Harrison’s Cardiovascular Medicine, 1st ed. Boston, MA, USA: McGraw-
Hill Professional, May 2010.
[10] G. B. Moody, R. G. Mark, A. Zoccola, and S. Mantero, “Derivation of respiratory signals
from multi-lead ECGs,” IEEE Computers in Cardiology, vol. 12, pp. 113–116, 1985.
[11] W. McNicholas and P. Levy, “Sleep-related breathing disorders: definitions and measurements,”
Journal of European Respiratory, vol. 15, pp. 988–989, Jun. 2000.
[12] J. Tern-Santos, A. Jimenez-Gomez, and J. Cordero-Guevara, “The association between
sleep apnea and the risk of traffic accidents,” New England Journal of Medicine, vol.
340, no. 11, pp. 847–851, 1999.
[13] C. Guilleminault, J. Motta, F. Mihm, and K. Melvin, “Obstructive sleep apnea and cardiac
index,” CHEST Journal, vol. 89, no. 3, pp. 331–334, Mar. 1986.
[14] A. J. Block, P. G. Boisen, J. W. Wynne, and L. A. Hunt, “Sleep apnea, hypopnea and
oxygen desaturation in normal subjects a strong male predominance,” The new England
Journal of Medicine, vol. 300, no. 10, pp. 513–517, Mar. 1979.
[15] G. Faiq and E. Colin, “Reversal of central sleep apnea using nasal cpap,” CHEST Journal,
vol. 90, no. 2, pp. 165–171, Aug. 1986.
[16] A. L. Chesson, R. A. Ferber, J. M. Fry, M. Grigg-Damberger, K. M. Hartse, T. D.
Hurwitz, S. Johnson, G. A. Kader, M. Littner, G. Rosen, R. B. Sangal, W. Schmidt-
Nowara, and A. Sher, “The indications for polysomnography and related procedures,”
Sleep, vol. 20, no. 6, pp. 423–487, 1997.
[17] T. Penzel, G. Moody, R. Mark, A. Goldberger, and J. Peter, “The apnea-ECG database,”
in IEEE International Conference on Computers in Cardiology (CinC), Cambridge, MA,
USA, Sept. 2000, pp. 255–258.
[18] Z. Wu and N. E. Huang, “A review on Hilbert-Huang transform: Method and its applications
to geophysical studies,” Reviews of Geophysics, vol. 46, no. 2, Jun. 2008.
[19] C. D. Boor and R. E. Lynch, “On splines and their minimum properties,” J. Math. Mech,
vol. 15, pp. 953–969, 1966.
[20] S. D. Conte and C. deBoor, Elementary numerical analysis: an algorithmic approach,
3rd ed. New York, USA: McGraw-Hill, 1980.
[21] Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted
data analysis method,” Journal of Advances in Adaptive Data Analysis, vol. 1, no. 1, pp.
1–41, Dec. 2009.
[22] L. W. Chang, M. T. Lo, N. Anssari, K. H. Hsu, N. Huang, and W. Hwu, “Parallel implementation
of multi-dimensional ensemble empirical mode decomposition,” in IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague,
Czech, May 2011, pp. 1621–1624.
[23] G. Inc. (2013) Renderscript. [Online]. Available: http://developer.android.com/guide/
topics/renderscript/compute.html
[24] A. Khokhar, V. Prasanna, M. Shaaban, and C. L. Wang, “Heterogeneous computing:
challenges and opportunities,” Journal of Computer, vol. 26, no. 6, pp. 18–27, Jun. 1993.
[25] P. Brady. (2008) Android anatomy and physiology. [Online]. Available: https:
//sites.google.com/site/io/anatomy--physiology-of-an-android/
[26] N. F. Chang, C. Y. Chiang, T. C. Chen, and L. G. Chen, “Cubic spline interpolation
with overlapped window and data reuse for on-line Hilbert Huang transform biomedical
microprocessor,” in IEEE International Conference on Engineering in Medicine and
Biology Society (EMBC), Boston, MA, USA, Aug. 2011, pp. 7091–7094.
[27] S. B. Park, Y. S. Noh, S. J. Park, and H. R. Yoon, “An improved algorithm for respiration
signal extraction from electrocardiogram measured by conductive textile electrodes
using instantaneous frequency estimation,” Journal of Medical and Biological Engineering,
vol. 46, no. 2, pp. 147–158, Feb. 2008.
[28] P. de Chazal, T. Penzel, and C. Heneghan, “Automated detection of obstructive sleep
apnoea at different time scales using the electrocardiogram,” Journal of Physiological
Measurement, vol. 25, no. 4, pp. 967–983, Aug. 2004.
[29] O. C and H. C., “A comparison of algorithms for estimation of a respiratory signal from
the surface electrocardiogram,” Computers in Biology and Medicine, vol. 27, no. 3, pp.
305–314, Mar. 2007.
[30] J. E. Mietus, C.-K. Peng, P. C. Ivanov, and A. Goldberger, “Detection of obstructive sleep
apnea from cardiac interbeat interval time series,” in IEEE International Conference on
Computers in Cardiology (CinC), Cambridge, MA, USA, Sept. 2000, pp. 753–756.
[31] M. Mendez, D. Ruini, O. Villantieri, M. Matteucci, T. Penzel, S. Cerutti, and A. Bianchi,
“Detection of sleep apnea from surface ECG based on features extracted by an autoregressive
model,” in IEEE International Conference on Engineering in Medicine and
Biology Society (EMBS), Lyon, France, Aug. 2007, pp. 6105–6108.
[32] L. Correa, E. Laciar, V. Mut, A. Torres, and R. Jane, “Sleep apnea detection based on
spectral analysis of three ECG - derived respiratory signals,” in IEEE International Conference
on Engineering in Medicine and Biology Society (EMBC), Minneapolis, MN,
Sept. 2009, pp. 4723–4726.
[33] G. D. Clifford, Advanced methods and tools for ECG data analysis, 1st ed. London,
UK: Artech House, May 2006.
[34] P. Sasikala and R. S. D. Wahidabanu, “Robust R peak and QRS detection in electrocardiogram
using wavelet transform,” Journal of Advanced Computer Science and Applications
(IJACSA), vol. 1, no. 6, pp. 48–53, Dec. 2010.
[35] A. Karagiannis, L. Loizou, and P. Constantinou, “Experimental respiratory signal analysis
based on empirical mode decomposition,” in Applied Sciences on Biomedical and
Communication Technologies, 2008, pp. 1–5.
[36] M. Campolo, D. Labate, F. La Foresta, F. Morabito, A. Lay-Ekuakille, and P. Vergallo,
“Ecg-derived respiratory signal using empirical mode decomposition,” in IEEE International
Workshop on Medical Measurements and Applications Proceedings (MeMeA),
2011, pp. 399–403.