光子波動能量分析儀是一種屬於能量醫學範疇之針對各種疾病的非侵入式檢測儀器。在本文中是透過光子波動能量分析儀對糖尿病症的量測，再配合利用相位雜訊與功率頻譜密度理論所建構的分析方法，對量測得到的訊號做能量頻譜上的分析研究。甚至以「Top100 能量頻譜」與「Top100累積頻寬頻譜」兩種分析方法來發現訊號狀態改變時其能量之微小差異。為了提高兩分析方法的準確度，本論文透過反覆量測同一狀態的訊號，讓兩分析方法分析之，以其誤差結果建立誤差修正資料庫。本論文對於訊號裡特定的諧波成分加以定義，分別為共振頻率、共同頻率、糖尿病頻率、正常人頻率，並以此為精確分辨光子波動能量儀的量測訊號之組成的基礎。最後，本論文以八名受試者進行光子波動能量儀的糖尿病量測，以其訊號的分析結果驗證對特定諧波之定義其準確度，並且獲得其定義對於糖尿病病患之訊號有其分辨能力之結果。此結果甚至提供了一個針對光子波動能量儀的自動化與高準確度的可能性。

Photon code subtle energy analyzer (PCSEA) belonging to Energy Medicine is a kind of non-invasive detecting instrument. It focuses on detecting many kinds of diseases. This work detects the diabetes through PCSEA. In addition, the work utilizes the analyses constructed by the theory of phase noise and power spectral density to analyze the signal in energy spectrum. Moreover, this work utilizes two analyses called as Top100 energy spectrum and Top100 cumulative bandwidth spectrum to find out the subtle difference between the energies of the signals in different state. For promote the degree of accuracy of two analyses, this work repeatedly measure the signal in the same state, analyzes the signals by two analyses to acquire the value of error, as well as further constructs a fixing database. This study even have defined the specific harmonic named as resonance frequency, common frequency, diabetes frequency and normal frequency to exactly recognize the composition of the detecting signal of the photon code subtle energy analyzer. At final, this work utilizes eight subjects to verify the accuracy of experimental results and receives the content identifying ability for diabetes. Furthermore, this work provides a probability for automatizing PCSEA with high precision and no artificial error.

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