摘要
現代人因工作壓力及飲食習慣常常處於不良的生活型態，隨著年齡的老化，不斷累積血管中的脂肪與膽固醇，加上壓力造成內分泌失調，導致血糖上升，長年下來容易造成代謝症候群引發血管特性改變，進而增加現代人得到糖尿病、高血壓、中風等相關心血管疾病的風險，甚至誘發動脈粥狀硬化，也就是所謂的「動脈硬化」。目前能夠有效且檢測動脈硬化的儀器的成本通常都較為高昂且需要專業醫檢人員協助進行操作與量測，量測過程也較易受限於空間範圍大小，一般民眾通常都在健檢時都較傾向於簡易量測的方法，但這些方法除了需要一般民眾親自進入醫院接受侵入式檢測(如:抽血)、BMI等，同時需要等待一段時間才能獲取結果報告，因此，一套擁有便利性、即時性且具有高準確度的診斷工具，對於醫院及民眾是迫切且尚未滿足的醫療需求，希望能在各種環境即時測量，搭配AL將數據進行分析及應用，便能有效降低且預防上述疾病的發生機率。
目前已發現重要循環早期疾病(如:代謝症候群及糖尿病前期)皆可能影響動脈血管特性的變化。本研究將使用本實驗室自行開發的非侵入式穿戴式脈波量測系統應用於代謝症候群及糖尿病中，量測生理訊號包含:
1. 心電訊號描述放大器心電訊號描述放大器(Electrocardiography, ECG)
2. 動脈血壓波形感測器(Blood Pressure Waveform, BPW)
3. 血管光容積感測器(Photoplethysmography, PPG)
並與內湖三軍總醫院老人健檢中心合作，分別探討高齡化族群的代謝症候群(Metabolic Syndrome, MetS)與完全健康的民眾(Control)、糖尿病(Diabetes Mellitus, DM)與完全健康的民眾(Control)之間的血管特性差異，同時，也納入了代謝症候群前期(Pre-MetS)及糖尿病前期(Pre-DM)進行合併探討。
在收案過程中，接受雙盲實驗的民眾數據會進行頻域分析計算出生理參數，配合2種分析方式，分別為機器學習(ML)分析(以40個頻域諧波參數為特徵:能量比例(Cn)、變異係數(CVn)、相位角(Pn)及標準偏差(Pn_SD))以及脈波分佈區間分析法(PDA)來進行數據分析，並透過機器學習的8種演算法，建立出最合適的機器學習模型，將其應用在分類代謝症候群及糖尿病與前期的數據上。
本實驗結果發現，代謝症候群及糖尿病前期的主題皆具有顯著差異。基於這些分析，透過ML三倍交叉驗證(3-fold cross-validation)與PDA的表現，發現PDA與ML的效果皆有一定程度的分類能力且具有實際應用的潛力，後續再進行了其他干擾因子的分類分析。
1. 使用BPW及PPG量測，發現代謝症候群與糖尿病前期於生物統計上有顯著差異，因此有助於代謝症候群與糖尿病前期的早期預防。
2. 基於生物統計顯著差異，發現ML與PDA的分類效果有助於臨床研究早期進行AI分析的可行性評估。
可發現代謝症候群與糖尿病前期皆屬於早期循環疾病，並且在本穿戴式脈波量測裝置上產生顯著差異，代表直接疾病對血管特性帶來明顯的影響，可能是脈波分析在分類效果上可具有良好的成果原因，因此，本穿戴式脈波量測裝置除了擁有非侵入式、量測簡易快速且攜帶方便等優勢，配合ML及PDA的分類方式，在臨床層面上也有一定的應用價值，符合院方及民眾目前醫療市場需求主流，並且有助於加速未來醫療市場的發展及強化居家保健上的疾病預防措施。

Abstract
Modern people often have unhealthy lifestyles due to work pressure and dietary habits. As they age, fat and cholesterol accumulate in their blood vessels, and stress causes hormonal imbalances, leading to elevated blood sugar levels. Over time, this can result in metabolic syndrome, which causes changes in blood vessel characteristics and increases the risk of cardiovascular diseases such as diabetes, hypertension, and stroke. It can even lead to atherosclerosis, also known as "hardening of the arteries." Currently, the cost of effective instruments for detecting and measuring atherosclerosis is usually high, and they require assistance from professional medical personnel. The measurement process is also often limited by the size of the space available. Generally, people prefer simpler measurement methods during health check-ups. However, these methods require individuals to undergo invasive tests (such as blood tests) and measure their BMI, and they also need to wait for some time to receive the results. Therefore, there is an urgent and unmet medical need for a diagnostic tool that is convenient, real-time, and highly accurate. It is hoped that such a tool can be used to measure in various environments and analyze and apply the data with the help of AI, effectively reducing and preventing the occurrence of the aforementioned diseases.
Currently, early-stage diseases such as metabolic syndrome and prediabetes have been found to potentially affect changes in arterial vascular characteristics. In this study, our laboratory-developed non-invasive wearable pulse wave measurement system will be applied to metabolic syndrome and diabetes. The system measures physiological signals including:
1. Electrocardiography (ECG)
2. Blood Pressure Waveform (BPW)
3. Photoplethysmography (PPG)
We will collaborate with the Elderly Health Examination Center at Tri-Service General Hospital in Neihu to investigate the differences in vascular characteristics between the aging population with metabolic syndrome (MetS) and completely healthy individuals (Control), as well as between individuals with diabetes mellitus (DM) and completely healthy individuals (Control). Additionally, we will also include individuals with prediabetes (Pre-DM) and prediabetes metabolic syndrome (Pre-MetS) for combined analysis.
During the enrollment process, the data of participants undergoing double-blind experiments will undergo frequency domain analysis to calculate physiological parameters. This analysis is done using two methods: machine learning (ML) analysis, which uses 40 frequency domain harmonic parameters as features (energy ratio (Cn), coefficient of variation (CVn), phase angle (Pn), and standard deviation (Pn_SD)), and pulse distribution interval analysis (PDA). The data is then analyzed using eight machine learning algorithms to establish the most suitable machine learning model, which is then applied to classify data related to metabolic syndrome and prediabetes.
In the study, it was found that there were significant differences in the characteristics of metabolic syndrome and prediabetes. Based on the performance of ML and PDA through 3-fold cross-validation, it was discovered that both ML and PDA had a certain degree of classification ability and practical application potential. Subsequent classification analyses were conducted on other interfering factors.
Using BPW and PPG measurements, it was found that there were significant differences in the biostatistics of metabolic syndrome and prediabetes, which could contribute to early prevention of these conditions.
1. Based on the biological statistics, significant differences were found between metabolic syndrome and prediabetes, as measured by BPW and PPG. This finding can contribute to early prevention of metabolic syndrome and prediabetes.
2. Based on the significant differences in biological statistics, the classification effects of ML and PDA are helpful for the feasibility assessment of early AI analysis in clinical research.
It is found that both metabolic syndrome and prediabetes belong to early-stage circulatory diseases, and they show significant differences in the wearable pulse wave measurement device. This indicates that the diseases directly affect vascular characteristics. The good classification results of pulse wave analysis may be the reason for this. Therefore, the wearable pulse wave measurement device has advantages such as non-invasiveness, easy and fast measurement, and convenient portability. Combined with ML and PDA classification methods, it also has certain clinical application value. It meets the current mainstream demands of hospitals and the public in the medical market and helps accelerate the development of the future medical market and strengthen disease prevention measures in home healthcare.

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