基於卷積神經網路之多導程孕婦腹部胎兒心電訊號偵測

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姓名

羅方雯(Fang-Wen Lo) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於卷積神經網路之多導程孕婦腹部胎兒心電訊號偵測
(Design of Convolutional Neural Network for Detection of Fetal ECG from Multi-lead Maternal Abdominal ECG Recordings)

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摘要(中)

本文提出應用於孕婦腹部之胎兒心電訊號(electrocardiogram, ECG)識別與監測系統，使用深度學習(deep learning)之卷積神經網路(convolutional neural network)演算法來達到此目的。主要透過無侵入式(noninvasive)感測器電極量測出孕婦腹部的心電訊號，並透過無線傳輸再經由電腦分析出胎兒心電訊號，可以快速並準確的處理長時間的心電訊號數據流，達到監控胎兒健康狀況的目的。我們從孕婦腹部心電資料庫擷取心電訊號以250ms做樣本分割(segmentation)，接著設立前後共20ms的保護區間(protection interval)，確保樣本分割時心電波形的完整性，以方便正確標示(label R peak)心電訊號的類別。為了降低在時域上雜訊對心電圖所造成的影響，透過短時距傅立葉轉換(short-time Fourier transform)，將心電訊號由時域轉為頻域，使得心電訊號可以二維時頻的形式表現，再經由卷積神經網路訓練與測試數據。由於心電資料庫為使用四條導線量測紀錄孕婦腹部心電訊號，最後採用融合(fusion)的方法，對於不同電極之採用不同權重進行分類結果的加權，從而該心電訊號波型之類別。以編號r01、r08與r10的孕婦之個別偵測率分別為92.65%、88.24%以及81.86%，混合三者孕婦心電訊號訓練與測試得到平均偵測率為91.83%，相較於其他文獻使用的K近鄰演算法，卷積神經網路在偵測上獲得較良好的效果。為了達到穿戴式裝置低功耗的目標，進行了硬體的設計與實現。在硬體設計上，為了得到較佳的產出率與硬體複雜度之折衷方案，我們採用了管線化之設計，為了降低硬體面積大小，在兩層卷積層(convolutional layer)與全連接層中(fully connected layer)共使用235個乘法器，並透過排程與硬體架構設計來降低對外部記憶體資料的提取以及提升硬體使用效率來達到省時省面積之目的。

摘要(英)

In this thesis, we present a recognition and monitoring system for detecting fetal Electrocardiogram (ECG) from pregnant abdominal ECG recordings. The deep learning algorithm, 2-dimensional (2D) convolutional neural network, is used to realize the classification. With the non-invasive electrodes, our system can detect long-term fetal ECG signals accurately. Thus, the growth of health condition of the baby can be monitored with the wearable device. The abdominal ECG waveform is partitioned into 250ms segments with an extra overlapping 20ms interval at the beginning and at the end of each segment to reserve the signal integrity. In order to reduce the noise effect of the ECG signals in the time domain, short-time Fourier transform (STFT) is adopt which transforms one-dimensional ECG signals into two-dimensional time-frequency representation. Consequently, the 2D convolutional neural network can extract the feature on the time-frequency plane to enhance the classification. Because 4 leads are used to measure and record maternal abdominal ECG, fusion is required in the last phase. The probabilities of respective classes judged from different electrodes are then combined to generate the final results. The individual detection rates of pregnant r01, r08 and r10 are 92.65% 88.24% and 81.86%, respectively. The average detection rate achieves 91.83% if three pregnant ECG signals are mixed for training and inference. Compared with the conventional K-nearest neighbor algorithm, a higher detection accuracy is achieved. For hardware implementation, pipeline architecture is used to tradeoff throughput and complexity. To save silicon area, 235 multipliers are adopted for the two convolution layers and one fully-connected layer. The architecture is designed with proper scheduling to reduce the global memory access and to increase hardware utilization.

關鍵字(中)

★ 胎兒心電圖
★ 短時距傅立葉轉換
★ 深度學習
★ 機器學習
★ 卷積神經網路
★ K-近鄰演算法

關鍵字(英)

★ Fetal Electrocardiogram
★ Short-time Fourier transform
★ Deep learning
★ Machine learning
★ Convolutional neural network
★ K-nearest neighbor

論文目次

摘要 I
Abstract II
目錄 III
圖示目錄 VI
表格目錄 IX
第一章緒論 1
1.1 研究動機 1
1.2 研究方法 2
1.3 論文組織 3
第二章孕婦之腹部心電訊號偵測相關知識介紹 4
2.1 孕婦腹部之胎兒心電訊號偵測之無線穿戴式裝置與系統 4
2.2 孕婦與胎兒心電圖(Electrocardiogram, ECG)介紹 5
2.3 孕婦腹部心電訊號資料庫 7
第三章傳統胎兒心電訊號偵測演算法[2] 10
3.1 心電訊號預處理 11
3.1.1 正規化(Normalization) 11
3.1.2 樣本分割(Segmentation) 12
3.1.3 標記心電訊號(Label R peak) 14
3.1.4 哈爾小波轉換(Haar Wavelet Transform) 17
3.1.5 主成份分析(Principal Components Analysis) 27
3.2 K-近鄰演算法(k-Nearest Neighbor) 30
3.2.1 十折交叉驗證(Ten Fold Cross Validation) 30
3.2.2 歐基里德距離(Euclidean Distance) 31
3.2.3 加權投票法(Weighted Voting) 31
3.2.4 樣本不平衡性(Sampling without Replacement) 32
3.3 模擬參數設定與結果 33
第四章卷積神經網路之孕婦腹部胎兒心電訊號偵測演算法 35
4.1 心電訊號預處理 36
4.1.1 重疊(Overlapping) 36
4.1.2 短時距傅立葉轉換(Short-Time Fourier Transform) 37
4.1.3 樣本分組與標記類別(Grouping and Labeling) 45
4.2 卷積神經網路(Convolutional Neural Network) 45
4.2.1 卷積神經網路簡介 48
4.2.2 卷積神經網路架構(Architecture) 50
4.2.3 參數設置(Initial Parameter)與權值初始化(Weight Initial) 51
4.2.4 前向傳播(Feedforward) 54
4.2.5 活化函數(Activation Function) 58
4.2.6 損失函數(Loss Function) 59
4.2.7 反向傳播(Backpropagation) 61
4.2.8 更新權重值(Weight)與偏移量(Bias) 62
4.3 融合(Fusion) 64
4.4 演算法模擬結果 65
4.5 與傳統胎兒心電訊號偵測演算法[2]之比較結果 71
第五章硬體設計與實現 72
5.1 硬體設計流程 72
5.1.1 硬體複雜度評估 73
5.1.2 量化分析 74
5.2 硬體架構設計 76
5.2.1 記憶體設計 78
5.2.2 硬體方塊圖 79
5.3 資料流 82
5.4 硬體實現與結果 84
第六章結論 85
參考文獻 86

參考文獻

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指導教授

蔡佩芸(Pei-Yun Tsai)

審核日期

2018-8-21

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