根據內政部資料顯示，目前我國65歲以上老年人口數占總人口數比率達14.05%，已進入高齡化社會。可預期的年長者的健康照護所佔的醫療資源比重將逐年快速成長，其中心電圖(Electrocardiography, ECG)的長期記錄與辨讀是重點項目之一，廣泛應用於心臟疾病的臨床診斷。為了方便遠端監控和長時間記錄，使用無線穿戴式裝置是最可行的方法之一。穿戴式裝置系統包含心電圖訊號感測、資料壓縮和無線傳輸等三部分，其中資料壓縮的目的在於減少資料傳輸量以延長裝置的生命週期，方便長時間的資料紀錄。由於心電圖具有醫療價值，確保重建資料品質的穩定性是心電圖資料壓縮的基本要求。為兼具即時資料壓縮功能和品質保證機制，於先期研究中，我們開發出一套基於簡化失真指標(SPRD2)的小波轉換資料壓縮系統。此系統由三個處裡單元以串流結構所組成;即可逆四捨五入非遞迴離散週期性小波轉換、具品質保證機制之非線性量化處理和無失真改良式階層樹集合分割編碼。惟其11階量化仍採用傳統的尺度量化(Scalar Quantization;SC)，需要除法運算硬體成本高，不利穿戴式裝置的應用。
為克服此困難，本論文提出一種基於位元平面的無除小波係數量化演算法。此演算法包含五個程序：失真度上下限計算、起始量化尺度THBP預估、失真度qe計算、THBP精煉和量化模式的Huffman 編碼。首先由原始信號〖 S〗_J預估THBP起始值並計算誤差上下限。將THBP投射到位元平面上，並統計THBP下的位元數即為實際誤差值qe。令誤差上下限分別為Errorup與Errorlow，將THBP精煉至qe符合條件Error_low≤qe≤Error_up。精煉演算法是當失真度大於Errorup時調降低頻的THBP，失真度小於Errorlow則調高高頻的THBP，逐漸將失真度收斂至誤差範圍。以MIT心律不整資料庫中的48筆心電圖為實驗對象，每筆15分鐘分割為316段。實驗結果顯示，在重建品質SPRD2=3%±0.05的情況下，精煉次數最高22次，最低0次，平均5.62次，平均壓縮率為10.78。

According to the Ministry of the Interior data, at present, the number of elderly people over the age of the population as a proportion of the total population 14.05%. Has entered the aging society. The proportion of medical resources that can be expected from the health care of the elderly will grow rapidly year by year, in which the long-term recording and differentiation of ECG (Electrocardiography, ECG) is one of the key projects, Widely used in the diagnosis of heart disease in the bed. In order to facilitate remote monitoring and long-time recording, the use of wireless wearable devices is one of the most feasible methods. The wearable device system includes three parts, such as ECG signal sensing, data compression and wireless transmission, in which the purpose of data compression is to reduce the amount of traffic data in order to prolong the life cycle of the device and to facilitate long time data recording. Because ECG has medical value, ensuring the stability of reconstruction data quality is the basic requirement of ECG data compression. In order to combine real-time data compression function and quality assurance mechanism, we developed a set of wavelet Transform data compression system based on simplified distortion Pointer (SPRD2)in advance research. The system consists of three units in a cascade structure; That is, reversible rounding non-recursive discrete periodic wavelet transform, nonlinear quantization processing with quality assurance mechanism and non-distortion improved stratum tree set segmentation coding. The traditional scale quantification is still used in the one-order quantification (Scalar quantization; SC), requires a high cost of division operation hardware, adverse application of wearable devices
In order to overcome this difficulty, this paper proposes a quantization algorithm of non-removal wavelet coefficients based on bit plane. This algorithm contains five programs : Calculation of upper and lower limits of distortion、Initial quantitative scale THBP estimation、Distortion qe calculation、THBP refining、Quantization mode Huffman coding. First, the THBP starting value is estimated by the original signal SJ and the upper and lower limits of the error are calculated. The THBP is projected on the place plane, and the number of digits under THBP is the actual error of qe. The upper and lower limits of the error are Errorup and Errorlow respectively, and the THBP is refined to the qe eligible Error_low≤qe≤Error_up. Refining algorithm is when the distortion degree is greater than Errorup tuned reduce the frequency of the THBP, the distortion is less than Errorlow to increase the high frequency of the THBP, gradually converge the distortion to the error range. The 48 electrocardiogram in MIT arrhythmia database was used as the experimental object, and each 15 minutes was divided into 316 segments. The experimental results show that the number of refinements is up to 22 times, the lowest 0 times, the average 5.62 times, and the average compression rate is 10.78 in the case of reconstruction of mass SPRD2=3%±0.05.

目錄
摘要 i
Abstract iii
致謝 v
目錄 vi
表目錄 viii
圖目錄 ix
第一章 緒論……………………………………………………………………… 2
1.1 研究動機 2
1.2 研究目標與方法 4
1.3 論文架構 5
第二章 可逆式四捨五入非遞迴離散週期性小波轉換 6
2.1 小波轉換理論 6
2.2 遞迴離散週期性小波轉換 7
2.3 非遞迴式離散週期性小波轉換 8
2.4 可逆式四捨五入線性轉換 14
2.5 有限位元分析 16
第三章 具品質保證之非線性量化演算法 20
3.1 非線性量化演算法 21
3.2 使用頻域數據中的失真量測 24
3.3 基於簡化失真度的重建品質控制 29
3.4 有限位元量化因子 32
第四章 基於位元平面之量化處理演算法 34
4.1 基於位元平面的量化演算法 36
4.2 簡化SPRD2上下限判斷 37
4.3 降低量化尺度運算時間 38
4.4 誤差調整演算法 41
4.5 跳躍問題解決方案 44
4.6 THBP Indexing 45
4.7 軟體實驗結果 52
4.8 問題與討論 57
第五章 結論………………………………………………………………… 58
參考文獻……………… 59

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