在現今嚴重高齡化的社會環境中，老人看護及居家看護的議題被廣泛的討論，然而在臨床上，大部份的心臟疾病是偶發性的，若能即時監測、記錄病人心跳的發生，隨即可讓醫療人員進行分析、評估甚至是治療、搶救。然而由於很大的異常心跳之變異會導致偵測的困難度提升，因此，即時(real-time)的偵測出病人心跳的發生是十分重要且具挑戰性的議題。
與傳統的演算法相比，本篇側重於開發一個基於貝氏架構(Bayesian framework)下的新方法。具體來說，本篇提出在心律不整的心電圖中(electrocardiogram, ECG)，利用基於變異數之QRS偵測器(variance-based QRS detector, VBD)結合最大似然估測法(maximum likelihood estimation, MLE)實現即時心跳偵測的演算法。首先，ECG訊號經由訊號前處理移除雜訊干擾，而後利用VBD及MLE由ECG訊號起始時間開始連續地遞迴偵測出往後QRS複合波的位置、寬度及週期。
使用MIT-BIH心律不整資料庫所提供的48條ECG訊號記錄帶及其參考標籤進行模擬及驗證，結果得到的偵測靈敏度(sensitivity, Se)為99.85%，預測力(positive predictivity, P+)為99.85%及0.29%的偵測失敗率(failed detection, Fd)。本篇也使用了QT資料庫所提供的105條ECG訊號記錄帶及其參考標籤進行模擬及驗證，偵測結果得到的偵測靈敏度(sensitivity, Se)為99.96%，預測力(positive predictivity, P+)為99.94%及0.1%的偵測失敗率(failed detection, Fd)。

The real-time heartbeat detection in electrocardiogram (ECG) signal is still an important and challenging issue due to the advancement of medical equipment and healthcare. In contrast to conventional works, this work focuses on developing a new methodology based on the Bayesian framework. More specifically, we propose two new algorithms for the real-time heartbeat detection, i.e., variance-based QRS detector (VBD) and maximum-likelihood estimation (MLE). Firstly, we reduce the physiological interference in ECG signal through signal preprocessing using a band-pass filter. Then, continuously detect the onset, duration, and period of a QRS complex with the proposed VBD and MLE. The algorithms are evaluated and verified by two database, such as MIT-BIH arrhythmia and QT database. For the 48 records of MIT-BIH arrhythmia database, the obtained detection result shows a sensitivity, Se, of 99.85% and a positive predictivity, P+, of 99.85%. For the 105 records of QT database, the obtained detection result shows a sensitivity, Se , of 99.96% and a positive predictivity, P+, of 99.94%, is accurate as well.

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