進階搜尋


下載電子全文  
系統識別號 U0026-3008201815561300
論文名稱(中文) 居家無線網路的睡眠分期訊號蒐集上使用壓縮感知
論文名稱(英文) Using compressed sensing for wireless in-home sleep staging
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 106
學期 2
出版年 107
研究生(中文) 施冠竹
研究生(英文) Kuan-Chu Shih
學號 P76031496
學位類別 碩士
語文別 英文
論文頁數 30頁
口試委員 指導教授-藍崑展
口試委員-蕭富仁
口試委員-張大緯
口試委員-郭至恩
中文關鍵字 腦波  壓縮感知  睡眠分期 
英文關鍵字 electroencephalography (EEG)  compressed sensing (CS)  sleep staging 
學科別分類
中文摘要 近年來越來越多人感興趣開發居家睡眠信號蒐集和判讀系統,但是如何處理在互聯網上傳送的大量PSG訊號是一個重大的議題,前人提出在居家蒐集PSG訊號的系統架構。架構中存在個缺陷,為解決PSG資料量太大的問題系統上使用到影像壓縮著名演算法SPIHT(set partitioning in hierarchical tree),因在微處理器上有著較嚴格的資源限制,為解決這些問題我們在壓縮演算法上改採用近年來在sensor network上所使用的縮演算法CS(compressed sensing),在相同的品質要求下我們所使用的壓縮演算法CS可以使用比SPIHT更好的省電效果。
英文摘要 There is increasing interest in the development of wireless in-home sleep staging systems that allow the patient to be monitored remotely while remaining in the comfort of their home. However, the problem of transmitting large amounts of polysomnography data over the Internet must be solved. A previously proposed system for in-home sleep staging deals with the high data rate by using the set partitioning in hierarchical trees (SPIHT) algorithm, a compression algorithm for image processing. However, a microcontroller cannot meet the requirements for SPIHT. The present study proposes a system architecture that uses the compressed sensing (CS) algorithm which has lower complex than the traditional algorithm in microcontroller. The results show that the CS algorithm has a higher compression ratio and lower resource requirements compared to SPIHT.
論文目次 List of Figure.................................................................. III
List of Table................................................................... IV
1. Introduction.............................................................. 1
1.1 Motivation.............................................................. 2
1.2 Contribution........................................................... 3
1.3 Other chapter......................................................... 3
2. Related work............................................................ 4
2.1 Compressed sensing for biomedical signal……………4
2.2 Home sleep staging.................................................5
3. Methodology............................................................. 6
3.1 Compressed sensing for transmission side……………6
3.2 Compressed sensing for receiving end.................... 7
3.3 Compression matrix................................................. 7
3.4 Complexity compare................................................ 8
4. Experimental.............................................................. 9
4.1 Accuracy measurement............................................ 9
4.2 Filter.......................................................................... 9
4.3 Auto label algorithm................................................. 10
5. Results......................................................................... 14
5.1 DBBD......................................................................... 14
5.2 Recovered signals...................................................... 15
5.3 The m-value of EMG.................................................. 18
5.4 Accuracy and compressed data results……………………18
5.5 Statistical analysis...................................................... 20
5.6 Power and memory estimate.................................... 21
5.7 Discussion.................................................................. 23
6. Conclusion................................................................... 25
7. Appendix..................................................................... 26
8. Reference.................................................................... 27

參考文獻 [1]Lan, K. C., Chang, D. W., Kuo, C. E., Wei, M. Z., Li, Y. H., Shaw, F. Z., & Liang, S. F. (2015). Using off-the-shelf lossy compression for wireless home sleep staging. Journal of neuroscience methods, 246, 142-152.
[2] Fauvel, S., Agarwal, A., & Ward, R. (2013, May). Compressed sensing and energy-aware independent component analysis for compression of eeg signals. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on(pp. 973-977). IEEE.
[3] Fauvel, S., & Ward, R. K. (2014). An energy efficient compressed sensing framework for the compression of electroencephalogram signals. Sensors, 14(1), 1474-1496.
[4] Aviyente, S. (2007, August). Compressed sensing framework for EEG compression. In Statistical Signal Processing, 2007. SSP'07. IEEE/SP 14th Workshop on (pp. 181-184). IEEE.
[5] Shoaran, M., Pollo, C., Schindler, K., & Schmid, A. (2015). A Fully Integrated IC With 0.85-μW/Channel Consumption for Epileptic iEEG Detection. IEEE Transactions on Circuits and Systems II: Express Briefs, 62(2), 114-118.
[6] Shoaran, M., Afshari, H., & Schmid, A. (2014, October). A novel compressive sensing architecture for high-density biological signal recording. In Biomedical Circuits and Systems Conference (BioCAS), 2014 IEEE (pp. 13-16). IEEE.
[7] Haboba, J., Mangia, M., Rovatti, R., & Setti, G. (2011, November). An architecture for 1-bit localized compressive sensing with applications to EEG. In Biomedical Circuits and Systems Conference (BioCAS), 2011 IEEE (pp. 137-140). IEEE.
[8] Shoaran, M., Kamal, M. H., Pollo, C., Vandergheynst, P., & Schmid, A. (2014). Compact low-power cortical recording architecture for compressive multichannel data acquisition. IEEE transactions on biomedical circuits and systems, 8(6), 857-870.
[9] Zhang, Z., Jung, T. P., Makeig, S., & Rao, B. D. (2013). Compressed sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware. IEEE Transactions on Biomedical Engineering, 60(1), 221-224.
[10]Tamaki, M., Bang, J. W., Watanabe, T., & Sasaki, Y. (2014). The first-night effect suppresses the strength of slow-wave activity originating in the visual areas during sleep. Vision research, 99, 154-161.
[11] Wang, A., Jin, Z., Song, C., & Xu, W. (2015, June). Adaptive compressed sensing architecture in wireless brain-computer interface. In Proceedings of the 52nd Annual Design Automation Conference (p. 173). ACM.
[12] Baldassarre, L., Aprile, C., Shoaran, M., Leblebici, Y., & Cevher, V. (2015, December). Structured sampling and recovery of ieeg signals. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015 IEEE 6th International Workshop on (pp. 269-272). IEEE.
[13] Chen, F., Chandrakasan, A. P., & Stojanović, V. (2010, September). A signal-agnostic compressed sensing acquisition system for wireless and implantable sensors. In Custom Integrated Circuits Conference (CICC), 2010 IEEE (pp. 1-4). IEEE.
[14] Salman, A., Allstot, E. G., Chen, A. Y., Dixon, A. M., Gangopadhyay, D., & Allstot, D. J. (2011, May). Compressive sampling of EMG bio-signals. In Circuits and systems (ISCAS), 2011 IEEE international symposium on (pp. 2095-2098). IEEE.
[15] Gangopadhyay, D., Allstot, E. G., Dixon, A. M., & Allstot, D. J. (2011, November). System considerations for the compressive sampling of EEG and ECoG bio-signals. In Biomedical Circuits and Systems Conference (BioCAS), 2011 IEEE (pp. 129-132). IEEE.
[16] Dixon, A. M., Allstot, E. G., Gangopadhyay, D., & Allstot, D. J. (2012). Compressed sensing system considerations for ECG and EMG wireless biosensors. IEEE Transactions on Biomedical Circuits and Systems, 6(2), 156-166.
[17] Casson, A. J., & Rodriguez-Villegas, E. (2012, August). Signal agnostic compressive sensing for body area networks: Comparison of signal reconstructions. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE (pp. 4497-4500). IEEE.
[18] Ravelomanantsoa, A., Rabah, H., & Rouane, A. (2013, December). SystemC-AMS based virtual prototyping of wireless body sensor network using compressed sensing. In Microelectronics (ICM), 2013 25th International Conference on(pp. 1-4). IEEE.
[19] Chen, Y. S., Lin, H. Y., Chiu, H. C., & Ma, H. P. (2014, June). A compressive sensing framework for electromyogram and electroencephalogram. In Medical Measurements and Applications (MeMeA), 2014 IEEE International Symposium on(pp. 1-6). IEEE.
[20] Ravelomanantsoa, A., Rabah, H., & Rouane, A. (2014, December). Simple deterministic measurement matrix: application to EMG signals. In Microelectronics (ICM), 2014 26th International Conference on (pp. 76-79). IEEE.
[21] Ravelomanantsoa, A., Rabah, H., & Rouane, A. (2015). Compressed sensing: a simple deterministic measurement matrix and a fast recovery algorithm. IEEE Transactions on Instrumentation and Measurement, 64(12), 3405-3413
[22]Kourtis, A. K. (2010). Data compression techniques for performance improvement of memory-intensive applications on shared memory architectures (Doctoral dissertation, Ph. D. Thesis, Athens, pp: 1-109. Retrieved from: http://www. cslab. ntua. gr/~ kkourt/phd/phd-en. pdf).
[23] Linear Algebra (Fourth Edition) P.413 By Stephen H Friedberg, Arnold J Insel, Lawrence E Spence
[24] HSU, C. Y., AHUJA, A., YUE, S., HRISTOV, R., & KABELAC, Z. (2017). Zero-E ort In-Home Sleep and Insomnia Monitoring using Radio Signals.
[25] Home Sleep Tests for Obstructive Sleep Apnea (OSA) Mukesh Kapoor, MD and
Glen Greenough, MD
[26]Kelly, J. M., Strecker, R. E., & Bianchi, M. T. (2012). Recent developments in home sleep-monitoring devices. ISRN neurology, 2012.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2018-09-06起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2018-09-06起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw