本論文將基於心率變異性與呼吸竇性心律不整兩項議題，心率變異性可表明出人的健康狀況和精神壓力，呼吸竇性心律不整則是影響心率的常被討論的一項因素。而對於量測心率變異性，需要在短時間窗口的情況下判斷心率，這在使用都卜勒雷達系統進行非接觸式生命體徵檢測中是一項挑戰，以往研究大多使用長週期時間窗口來保證在頻譜上使用峰值搜索方法進行心率測量時有足夠的頻譜分辨率，當使用小於5秒的時間窗口時，會有頻譜分辨率不足的問題，使準確度顯著下降。此外，還須考慮到通過雷達訊號檢測心跳的性能是很容易因呼吸、雜訊與身體運動影響而降低。所以，要如何提取出乾淨且未失真的心跳訊號也是一項挑戰。
針對這些挑戰，本論文提出一套訊號處理流程，首先，對雷達輸出的正交訊號進行反正切解調後，再透過離散小波轉換與變分模態分解兩種方式來分解重建生理訊號，得到較為乾淨的心跳與呼吸訊號，接著，使用了餘弦轉換結合可變窗口長度技術的演算法來改善頻譜分辨率的問題，並可減輕非線性的干擾。在模擬與實際的生理監測實驗中，可實現1至2秒短時間窗口分析，呼吸與心跳量測與分析的平均誤差分別僅為每分鐘0.41493下與每分鐘2.4735下。

This thesis will be based on the two topics of heart rate variability and respiratory sinus arrhythmia. Heart rate variability can indicate a person's health and mental stress. Respiratory sinus arrhythmia is a frequently discussed factor affecting heart rate. To measure heart rate variability, the heart rate needs to be judged in a short-time window, which is a challenge for non-contact vital sign detection using Doppler radar systems. Most previous studies have used long period time windows to ensure sufficient spectral resolution when using peak search method for heart rate measurements on the spectrum. When using a time window of less than 5 seconds, insufficient spectral resolution occurs, resulting in a significant decrease in accuracy. In addition, it must be taken into account that the performance of radar signals in detecting heartbeats is susceptible to reduced accuracy due to the influence of breathing, noise, and body movements. Therefore, how to extract a clean and undistorted heartbeat signal is also a challenge.
In response to these challenges, this thesis proposes a signal processing process. First, the quadrature output signals of the radar are demodulated by arctangent, and then the vital sign signal is decomposed and reconstructed by discrete wavelet transform and variational mode decomposition. The relatively clean heartbeat and breath signal can be obtained. After that, cosine transform with varying window length is used to improve spectral resolution and mitigate nonlinear interference. In simulation and vital sign monitoring experiments, short-time window analysis of 1 to 2 seconds can be achieved, and the average errors of respiration and heartbeat measurement and analysis are only 0.41493 beats per minute and 2.4735 beats per minute, respectively.

論文審定書i
誌謝ii
摘要iii
Abstractiv
目錄v
圖次vii
表次ix
第一章序論1
1.1 研究背景與動機1
1.2 雷達2
1.3 演算法文獻回顧4
1.3.1 訊號解調法4
1.3.2 小波轉換6
1.3.3 模態分解法7
1.4 章節規劃9
第二章 系統架構與演算法理論10
2.1 正交相位與自我注入鎖定雷達10
2.2 離散小波轉換12
2.2.1 離散小波轉換12
2.2.2 最大重疊離散小波轉換14
2.3 變分模態分解16
2.4餘弦轉換結合可變窗口長度技術20
第三章 模擬訊號及演算法結果24
3.1 模擬呼吸竇性心律不整24
3.1.1 使用頻率調變訊號模擬呼吸竇性心律不整24
3.1.2 窗口長度分析27
3.2 訊號分解演算法之效果30
3.2.1 離散小波轉換 vs. 最大重疊離散小波轉換30
3.2.2 變分模態分解34
3.2.3 最大重疊離散小波轉換+變分模態分解36
3.3 模擬結果評估比較37
第四章 生理感測實驗42
4.1 雷達實驗設置42
4.2 生理感測實驗結果46
4.3 短時訊號分解結果57
第五章 結論59
參考文獻60

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