應用特徵分群技術於非侵入式神經活性與行 為活動訊號之生物指標萃取;Application of feature clustering technology in non-invasive neural activity and human activity signal extraction of biological indicators

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题名:	應用特徵分群技術於非侵入式神經活性與行為活動訊號之生物指標萃取;Application of feature clustering technology in non-invasive neural activity and human activity signal extraction of biological indicators
作者:	莊淵程;chuang, yuan-cheng
贡献者:	系統生物與生物資訊研究所
关键词:	心房顫動;支援向量機;物聯網;人體活動辨識;皮膚交感神經;Atrial Fibrillation;support vector machine;Internet of Thing;Human Activity Recognition;Human Activity Recognition
日期:	2021-10-27
上传时间:	2021-12-07 11:54:15 (UTC+8)
出版者:	國立中央大學
摘要:	心臟疾病的發生多半與平日生活型態有關，根據先前的研究表明[1]，現代人因工作多不動而造成罹患心臟疾病的風險增加 1.18 倍，而為了能夠檢測心臟疾病與平時活動狀況，本文將建立一個能夠快速檢測的平台，分別辨識 AF 有無復發以及辨識人體活動型態，用於了解心房顫動與活動型態的關係。為了觀察心房顫動復發的問題，且透過電燒後能減少復發的風險[2]，並能利用非侵入式訊號觀察取得神經訊號，之後透過對交感神經訊號資料分析，能夠找出心 AF 動復發與沒有復發之間統計上差別，能既方便又安全的方式診斷心 AF 動的問題。心房顫動(atrial fibrillation; AF)是指心房快速且不規律的收縮，當心房無法有效地收縮，造成血液流動不佳，增加血管產生血栓的風險。一旦血栓順著血流流到腦部血管使腦部血管阻塞，則會造成腦中風。心 AF 動約會增加 5 倍腦中風的風險，而心房顫動導致的中風，預後很差且復發率高。[3] 自主神經系統在調節心臟離子通道與心肌收縮扮演很重要的角色，而以往周邊交感神經活動(SNA)在量測時，須透過侵入式的電極量測，在技術上有困難且容易產生動作雜訊，而透過標準 ECG 貼片電極，可檢測多種電生理信號。[4] 將提取的電生理訊號透過數位濾波與其他資料處理方法提取其訊號的特徵，並用於訓練機器學習模型上或其他資料分析。為了人體活動偵測分析，我們研發無線藍芽低功耗三軸加速度計穿戴裝置，透過「物聯網」以手機 APP 為介面，測量人體活動加速度，再提取三軸加速度訊號的特徵，透過支援向量機(support vector machine)的方式分析人體活動型態。三軸加速度計(G-sensor)為記錄加速度變化資訊的微機電零件，在工程上廣泛應用，再加上固態微機電系統(Micro Electro Mechanical Systems , MEMS)的發展，使零件尺寸能越來越小，製作成本隨著技術與發展也越來越低，已被廣泛應用在穿戴式裝置中收集人體活動資料，用以訓練人體活動偵測模型。本文將藉由多項分析了解電燒後心房顫動復發與沒有復發在 SKNA 訊號上是否有統 ii 計上的差別;也透過低功耗無線藍芽三軸加速度計的資料訓練人體活動偵測模型。;The occurrence of heart disease is mostly related to the daily life style. According to previous studies [1], modern people’s risk of heart disease increases by 1.18 times due to more inactive at work. In order to be able to detect heart disease and daily activities, this study will build a platform for rapid detection to understand the relationship between atrial fibrillation and activity patterns. To measure atrial fibrillation recurrence and reduce the risk of atrial fibrillation recurrence after ablation, we extracted nerve signal with noninvasive electrode, then analyzed sympathetic nerve activity signal to find out the difference between atrial fibrillation recurrence and no recurrence, it is convenience and security method to diagnose atrial fibrillation recurrence. Atrial fibrillation is an irregular and often rapid heart rate, When the atria cannot be effectively contracted, poor blood flow is caused, which increases the risk of blood clots. Once the thrombus flows along the bloodstream to the blood vessels in the brain and blocks the blood vessels in the brain, it will cause a stroke. Atrial fibrillation increases the risk of stroke by 5 times. Stroke caused by atrial fibrillation has a poor prognosis and a high recurrence rate. [3] The autonomic nervous system is important to modulate cardiac ion channel and myocardial contractility. Sympathetic nerve activity can be measured with invasive microneurography techniques, these are technically difficult and easy to produce motion artifact, but with standard ECG patch electrode, we can detect multiple electrophysiological signals. [4] iv The electrophysiological signal is extracted through digital filtering and other data processing methods to extract the characteristics of the signal, and used for training machine learning models or other data analysis. In order to recognize human activity, we designed a wireless Bluetooth Low Energy three-axis accelerometer wearable device, through the "Internet of Things" using the mobile phone APP as the interface to measure the acceleration of human activities, and then through the support vector machine to analyze the types of human activity. Three-axis accelerometer (G-sensor) is a micro-electromechanical part that records acceleration change information. It is widely used in engineering. With the development of Micro Electromechanical Systems (MEMS), the size of components can be smaller, and the production cost is getting lower and lower with technology and development. It has been widely used in wearable devices to collect human activity data to train human activity recognition models. In the study, we will find out the statistically difference between AF recurrence and AF no recurrence after ablation, and we will also train human activity recognition model with the data of Bluetooth low energy wireless three-axis accelerometer device.
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