2019年以來新冠肺炎已對全球經貿、人民健康造成極大衝擊，而新冠肺炎疫苗是現有比較有效堵住疫情惡化的預防方法，不過疫苗也存在著一些副作用隱憂。根據研究，目前疫苗嚴重副作用大多與心血管系統有關，因此心血管副作用監測的需求是與日俱增且必要的，不過現今常見的心血管副作用臨床檢驗方式仍有諸多不便。
本研究將藉由實驗室自行開發之穿戴式脈波量測系統結合機器學習、脈波變異度分析，探討穿戴式裝置可否觀察出疫苗副作用對心血管系統造成的生理影響，並且是否可對此影響具有區別能力。
研究與雙和醫院心臟內科聯合收案，納入本研究總人數為194位。以臨床檢驗結果將BNT受試者分為健康組(N=28)、心血管副作用組(N=39)、血管副作用組(N=11)、心臟副作用組(N=34)。實驗會對受試者進行一分鐘的脈波量測取得BPW、PPG數據，再以機器學習、脈波變異度分析強化對脈波變動的解析能力，進而區別出疫苗心血管副作用的有無。
實驗結果顯示，此系統確實可區別出疫苗有無心血管副作用的差異，機器學習(LDA)與脈波變異度分析各自可對心血管副作用的判別達到準確率0.69(AUC=0.67)與準確率0.67(AUC=0.66)的表現。若排除模糊地帶則脈波變異度分析可對心血管組做到準確率0.72、AUC=0.75(適用範圍65.67%)的判別；對血管組做到準確率0.92、AUC=0.94(適用範圍64.1%)的判別。
研究結論可以看出，穿戴式脈波量測系統未來可望實際運用於臨床監測，也方便於社區居家環境中使用，至此本篇研究已為穿戴式裝置應用於疫苗心血管副作用的監測建立了一個新方向與基礎。

Since 2019, COVID-19 has significantly impacted the global economy, trade, and people's health. The COVID-19 vaccine is a more effective method to block the deterioration of the epidemic. However, there are some side effects of the vaccine. According to research, most of the severe side effects of vaccines are related to the cardiovascular system. Therefore, the demand for monitoring cardiovascular side effects is increasing and necessary. However, there are still many inconveniences in the typical clinical testing methods for cardiovascular side effects.
This study will use our laboratory's wearable pulse measurement system to combine machine learning and pulse variability analysis. Exploring whether the wearable device can observe the physiological effects of vaccine side effects on the cardiovascular system and whether it can this effect be discriminatory.
This study is cooperated with The Cardiology Department of Shuang-Ho Hospital, and the total number of people included was 194. BNT subjects were divided into the healthy group (N=28), cardiovascular side effect group (N=39), vascular side effect group (N=11) and cardiac side effect group (N=34) according to the clinical test results. In the experiment, subjects will be measured by pulse wave for one minute to obtain BPW and PPG data. Then machine learning and pulse variability analysis will be used to strengthen the ability to analyze pulse wave changes and then distinguish whether the subject have cardiovascular side effects.
The experimental results show that the system can distinguish whether people have cardiovascular side effects. Machine learning (LDA) and pulse variability analysis can determine cardiovascular side effects with an accuracy rate of 0.69 (AUC=0.67) and an accuracy rate of 0.67. (AUC=0.66) performance. If the fuzzy area is excluded, the pulse variability analysis can achieve an accuracy rate of 0.72 and AUC=0.75 for the cardiovascular group (65.67% of the applicable range); for the blood vessel group, the accuracy rate is 0.92 and AUC=0.94 (the useful range is 64.1%). ) judgment.
From the result we can see that the wearable pulse measurement system is expected to be used in clinical monitoring in the future, and it is also convenient for use in the community home environment. So far, this study has established a system for wearable devices to monitor the cardiovascular side effects of vaccines. A new direction and foundation.

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