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Design of a Low Complexity Real-Time Arrhythmia Detection System
Cardiovascular disease (CVD)
Premature Ventricular Contraction (PVC)
Premature Atrial Contraction (PAC)
Right Bundle Branch Block (RBBB)
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|摘要:||Cardiovascular disease (CVD) has become the main cause of human's death. Hence, if it can be discovered and cured earlier, the possibility of surviving will be increased. That's why CVD examination has been very important for years. Moreover, with the raise of health-conscious and the advancement of science and technology, people have paid more attention to their physical condition. In recent years, wearable technology has developed rapidly. Therefore, more welfare can be provided to human if CVD examination can be combined with wearable technology.
A low complexity real-time arrhythmia detection system is presented in this dissertation, including QRS complex and arrhythmia detection. For QRS complex detection, the advanced So and Chan method is proposed to detect the R-peak and the baseline of QRS complex. For arrhythmia detection, it is implemented with the advanced sum of trough and various features of disease symptoms.
The ECG data in MIT-BIH Arrhythmia Database (mitdb) was used for simulation and verification. The advanced So an Chan method can reduce the error rate of R-peak detection efficiently. Compared to the original So and Chan method, the accuracy increases from 94.61% to 99.29%. For arrhythmia detection, it can identify tachycardia, bradycardia, premature contraction and two kinds of CVDs; its detection accuracy can reach 98.05%. If the morbid state happens, a warning message will be sent to the user. Because of its low complexity, the detection system can be integrated in wearable electronics and arrhythmia can be detected immediately.|
心血管疾病(Cardiovascular disease, CVD)已成為全世界人類的第一大死因，如能即早發現、及時處理，就能夠減少重症與致命危機，因此診斷心血管疾病顯得格外重要。而隨著科技的進步與健康意識的抬頭，人類對自己的身體狀況也更加重視。近年來穿戴式科技(Wearable Technology)市場急速擴張，如能將穿戴式電子運用在心血管疾病偵測上，將能為人類帶來更多的福祉。 本論文提出一個低複雜度即時心律不整(Arrhythmia)的偵測系統，包含了QRS複合波與心律不整偵測兩部分。QRS複合波偵測採用Advanced So and Chan方法來偵測R波波峰，並利用其特點可直接找到QRS複合波的基線，之後再利用Advanced sum of trough和各疾病的特徵條件做判斷，即能完成心律不整的偵測。 模擬與驗證採用MIT-BIH Arrhythmia Database (mitdb)所提供的ECG資料。 Advanced So and Chan方法可有效降低R波的偵測錯誤，將原來So and Chan方法的R波偵測正確率從94.61%提升到99.29%。而心律不整偵測方法可以辨識出心搏過速、心搏過緩和早發性收縮的症狀以及束支傳導阻斷和心室撲動的心臟疾病，偵測正確率可達98.05%，並在發生病態時發出訊息給使用者。由於演算法複雜度低，可達到即時心律不整偵測，適合整合於穿戴式電子產品上。
|Appears in Collections:||電機工程學系所|
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