行動式心電圖訊號分析與監測系統

Abstract

因應高齡化社會及城鄉差距的未來趨勢，遠距醫療與居家健康照護已成為先進國家重點發展的新興服務產業。本論文旨在探討以無線通訊網路為基礎的行動心臟照護系統，其關鍵在於心電圖訊號的壓縮與傳輸機制，以及系統使用者的身份辨識。心電圖是記錄心臟搏動的電位變化圖，常用於心血管疾病的監測、診斷及治療。基於無線感測器的低耗能設計需求，我們提出基於分散式訊源編碼架構的心電圖訊號壓縮機制，其特點是強調傳送端編碼演算法的低運算量，而較複雜的解碼運算則轉由遠端醫院的伺服器執行。分散式訊源編碼架構的具體實現源自於通道編碼理論的碼分級校驗子觀念，其關鍵則在於訊源相關模型及校驗子生成機制的建構。我們利用多重索引向量 化技術，建立心跳週期的訊源相關模型，同時並設計迴旋碼的索引層級軟性輸出解碼演算法，以有效整合量化索引的事前冗息於訊號的解碼還原過程。在系統實作上，我們利用無線感測器及嵌入式運算開發平台，並整合自行開發的心電圖訊號壓縮模組，具體實現了以手機傳輸的無線心電圖儀。為了進一步提升無線通訊環境下的強健性能，我們參考校驗子機率分佈設計一組由迴旋碼保護的可變長度碼，並依據渦旋碼原則推導其疊代訊源通道解碼演算法。傳統的疊代解碼演算法受限於位元層級通道解碼模式，既無法有效整合相鄰索引間的訊源事前訊息，與索引層級的訊源解碼演算法也存在著相容性的問題。有鑑於此，我們將可變長度碼視為一有限狀態機而展開取得索引層級籬柵圖，並結合通道編碼器之狀態轉移以建構由二維狀態組成的三維籬柵圖。透過三維籬柵圖的建立，疊代訊源通道解碼器的處理模式可以提升至索引層級，進而有效整合訊源殘餘冗息及通道訊息於其碼字後驗機率的估算。此外，我們也探討基於心電圖的身份辨識機制，以保障個人隱私及行動健康照護系統的安全性。主要是將一維心電圖訊號經過預處理程序轉換成二維心電圖影像，並經過JPEG2000影像編碼處理將生物特徵轉換成灰階影像的紋理內容，進而利用內涵式影像資訊檢索技術執行用戶的身份辨識。

Wireless patient monitoring has been of recent interest to academic and industrial circles with the goal of ubiquitous healthcare services. The purpose of this dissertation is to investigate three important aspects of a mobile ECG signal monitoring system: data compression, robust data transmission, and biometric identification. In this study, we present a novel means of exploiting the distributed source coding (DSC) in low-complexity encoding of ECG signals. We first convert the ECG data compression to an equivalent channel coding problem and exploit the convolutional code for practical DSC construction. Performance is further enhanced by the use of a correlation channel that more precisely characterizes the statistical dependencies of ECG signals. Also proposed is a modified BCJR algorithm which performs symbol decoding of binary convolutional codes to better exploit the source’s a priori information. A complete setup system for online ambulatory ECG signal monitoring via mobile cellular networks is also presented. As a further step toward increased robustness against transmission errors, we also investigate the noisy-channel DSC problem for ECG data compression in conjunction with variable-length codes (VLCs) and channel codes. Using the concept of extrinsic information transfer (EXIT) from Turbo codes, we present a symbol-level iterative source-channel decoding (ISCD) algorithm for reliable transmission of variable-length encoded ECG data. Firstly, an improved source a posteriori probability (APP) decoding approach is proposed for packetized variable-length codes. Also proposed is a recursive implementation based on a three-dimensional (3-D) joint trellis for symbol decoding of binary convolutional codes. APP channel decoding on this joint trellis is realized by modification of the BCJR algorithm and adaptation to the non-stationary VLC trellis. The proposed symbol-level ISCD algorithm allows the receiver to exploit the source residual redundancy as well as the channel code redundancy to the fullest extent as it avoids the conventional symbol-to-bit probability conversion problem between the two constituent decoders. Another important issue to address is the demand for improved security and privacy in wireless telecardiology applications. To this end, we propose a novel ECG biometric system which performs person identification using content-based image retrieval (CBIR) techniques. To proceed with this, 1-D ECG signals are converted to 2-D images and afterwards part of the JPEG2000 encoding process is applied. Features relating to ECG morphology are then computed directly from the DWT coefficients and applied for indexing person identity by texture content in an enrollment database.