本篇提出在多模態記錄帶下，利用最大似然偵測法(maximum likelihood estimation, ML estimation)實現心跳偵測的演算法。此演算法使用到心電圖訊號(electrocardiogram, ECG)以及動脈血壓訊號(arterial blood pressure, ABP)來做運算。首先，將兩個訊號同時使用最大似然偵測法找出心跳的位置，再將位置的資訊結合，藉此得到更精確的偵測結果，而在ECG訊號使用ML演算法的過程中，利用其運算出的參數可標記出QRS複合波之起始點與終點的位置，使醫療單位能做更進一步的診斷與分析。
使用MGH/MF資料庫所提供的ECG與ABP訊號及參考標籤進行模擬及驗證，在單使用ECG訊號偵測下，偵測靈敏度(Sensitivity, Se)是98.94%，預測力(Positive predictivity, P+)是98.62%，而同時使用ECG訊號以及ABP訊號偵測後，偵測靈敏度提升為98.98%，預測力提升為98.71%。使用QT資料庫所提供的基準點(fiducial point)來驗證，QRS複合波起始點及終點的平均值皆在1個sample以內，而標準差大約為3個samples。

We present a new algorithm for the heartbeat detection based on the maximum-likelihood (ML) estimation in multimodal records. The detector uses electrocardiogram (ECG) signals and arterial blood pressure (ABP) signals. In first step, heartbeats are detected separately in both of the ECG and ABP signals. Then, the detected heartbeat positions from ECG and ABP signals are combined into a stream of detected heartbeat positions. Meanwhile, fiducial points are extracted in the process of heartbeat detection only using ECG signal. The algorithm was evaluated on several database, such as MIT-BIH Arrhythmia, MGH/MF and QT database. For the MGH/MF database, the multimodal heartbeat detector obtained a sensitivity of =98.98% and a positive predictivity of = 98.71%. As for the fiducial points of the QRS waves, the mean and standard deviation of the differences between the automatic and manual annotations were assessed. The mean error did not exceed one sample, while the standard deviations were about three samples.

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