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研究生(外文):Fu, Chih-Chung
論文名稱(外文):A Power-Efficient VLSI Design of a 4-Channel Independent Component Analysis Processor for Portable Brain-Heart Monitoring Systems
指導教授(外文):Fang, Wai-Chi
外文關鍵詞:InfomaxIndependent Component AnalysisEEG processingHeart Rate VariabilityDiffuse Optical TomographyIntegrated Health-Care SystemBluetooth Data TransmissionPortable SystemDigital Signal Processing
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  由於生理電信號中最微弱的腦電信號通常與肌電信號(EMG)中的眼動信號與眨眼信號一起混合並量測,以獨立成份分析為方法的人工雜訊濾除技術已發展許久。但由於獨立成份分析的運算複雜度過高,腦波的應用通常受到離線運算的嚴重限制。本論文針對所提出整合型系統中的腦電信號處理所使用的四通道即時獨立成份分析器之設計與實作以一完整章節加以詳述。由於可攜式儀器的基本需求即為低功率與低成本,多種設計技巧與最佳化規格分析如三重循環記憶體的配置、鏡像非線性查表單元的設計與ICA訓練、成份萃取間的管線排程皆被用來降低功率消耗與硬體成本。此ICA硬體設計已由聯電90奈米製程下線並測試。晶片的核心面積為0.58平方毫米。量測數據顯示若使用80 Hz的取樣頻率,並使用0.5 MHz的工作頻率與0.6 V的核心電壓時,可達到最低0.312毫瓦的功率消耗。
    Since the twenty-first century, the fast increment of an aged population has become a worldwide problem. Therefore, integrated health-care systems have become an important topic for electrical engineers. In this thesis, focusing on three application scenarios including emergency medical care (e.g. EEG, EKG measurements on ambulance or DOT for fast cerebral hemorrhage check), long-term observation and monitoring (for patients suffer from chronic ailment) and researches on brain and cognitive science, we propose a preliminary design of an integrated health-care system comprising electroencephalogram (EEG) and electrocardiogram (EKG) signal analysis and processing together with diffuse optical tomography for brain imaging. The significance of this system is to enable the practical development of such portable health-care devices for brain heart monitoring.
Since the EEG is the feeblest one of all physiological electrical signals usually contaminated by ocular artifacts (e.g. eye-blink artifact and eye-movement artifact), the artifact removal techniques using independent component analysis (ICA) has been developed for a long time. Because of the compelling computation complexity of ICA algorithm directly inherits from the possible dependency in each channel, applications that analyze EEG signals are usually heavily restricted by the off-line ICA computation. One complete chapter is used to describe the design and implementation of the 4-channel ICA processor employed in the proposed integrated system as the EEG processing element. Since the two basic requirements for portable instruments are low-power and low-cost, various design techniques and optimized specification analyses like three-bank circular memory allocation, an mirrored non-linear lookup unit and the operation pipelining between the ICA training and component extraction are all adopted to reduce the power consumption and hardware cost. The designed ICA processor is fabricated using UMC 90 nm CMOS technology, and the core area of the chip is 0.58 mm2. Performance measurements done by Agilent 93000 SoC Tester have shown that when using 80 Hz sample rate, 0.5 MHz operation frequency and 0.6 V core power, the lowest power consumption of 0.312 mW is achieved under the worst cast of 512 training iterations.
Together with an HRV and fNIR-DOT processor, the designed ICA processor is integrated in an experimental brain heart monitoring system. EEG, EKG and near-infrared signals acquired from the analog front-end IC are processed in real-time or bypassed according to user configurations. Processed data and raw data are compressed by a lossless biomedical data compressor and sent to a remote science station by a commercial Bluetooth module for further analysis, 3-D visualization and remote diagnosis. The biomedical signal compressor achieves an average compression ratio (CR) of 2.5 which is translated into power saving during wireless transmission. The data flow in the system is mainly controlled by a prioritized data selector and a three-stage backward handshaking mechanism, and the design can increase the utilization of the output buffers inside each processor so that the data transmission buffer can be reduced. The ICA and HRV processor are verified by real EEG and EKG signals while the DOT processor is verified by an experimental model.
中文摘要 i
Abstract iii
誌謝 vi
Contents vii
List of Figures ix
List of Tables xii
Chapter 1 Introduction 1
1.1 Three Common Human Health Indicators 1
1.1.1 Electroencephalogram 1
1.1.2 Near-Infrared Spectrogram on Human Tissue 4
1.1.3 Electrocardiogram 5
1.2 The Need for an Integrated Health-Care Solution 8
1.3 Application Scenarios 9
1.3.1 Emergency Use 10
1.3.2 Long-term Observation and Monitoring 10
1.3.3 Research on Brain and Cognitive Science 12
1.4 Importance of This Work 12
1.5 Organization of the Thesis 13
Chapter 2 4-Channel Independent Component Analysis Processor 15
2.1 Independent Component Analysis 15
2.1.1 Blind Source Separation 15
2.1.2 Entropy and Mutual Information 17
2.1.3 Infomax ICA 19
2.2 Design of the 4-Channel ICA Processor 25
2.2.1 Overall Architecture of the ICA Processor 26
2.2.2 Stage1 Unit and the Data Windowing Technique 28
2.2.3 Whitening Unit 34
2.2.4 Infomax ICA Training Unit 35
2.2.5 ICA Computation Unit 41
2.3 Performance Analysis of the 4-Channel ICA Processor 42
2.3.1 Performance Analysis Using Super-Gaussian Pattern 43
2.3.2 Performance Analysis Using Real EEG Patterns 44 Pattern 1 – Stable EEG without Artifact 45 Pattern 2 – Stable EEG without Artifact 47 Pattern 3 – EEG Contaminated by Eye-Blink Artifact 49 Pattern 4 – EEG Contaminated by Eye-Blink Artifact 51
2.3.3 Comparisons with Other Works 53
2.4 UMC 90nm 4-Channel ICA Processor Tape-out 54
2.4.1 Functional Verification 55
2.4.2 Power Consumption Analysis 56
2.4.3 A FPGA Based-Testbed 59
Chapter 3 Experimental Brain-Heart Monitoring System 63
3.1 Overall System Architecture 63
3.2 The Interface of the Analog Front-End Circuitry 64
3.3 Three Bio-Signal Processors 66
3.3.1 4-Channel ICA Processor 66
3.3.2 Heart-Rate Variability Analysis Processor 68
3.3.3 Near-Infrared Diffuse Optical Tomography Processor 71
3.4 System Control Unit 73
3.5 Front-End Interface Control Unit 74
3.6 Three-Stage Backward Handshaking Mechanism 78
Chapter 4 Conclusion and Future Works 79
4.1 Conclusions 79
4.2 Future Works 81
Reference 82

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