In this paper, a method for apnea bradycardia detection in preterm infants is presented based on coupled hidden semi Markov model (CHSMM). CHSMM is a generalization of hidden Markov models (HMM) used for modeling mutual interactions among different observations of a stochastic process through using finite number of hidden states with corresponding resting time. We introduce a new set of equations for CHSMM to be integrated in a detection algorithm. The detection algorithm was evaluated on a simulated data to detect a specific dynamic and on a clinical dataset of electrocardiogram signals collected from preterm infants for early detection of apnea bradycardia episodes. For simulated data, the proposed algorithm was able to detect the desired dynamic with sensitivity of 96.67% and specificity of 98.98%. Furthermore, the method detected the apnea bradycardia episodes with 94.87% sensitivity and 96.52% specificity with mean time delay of 0.73 s. The results show that the algorithm based on CHSMM is a robust tool for monitoring of preterm infants in detecting apnea bradycardia episodes.

在本文中，提出了一种基于耦合隐半马尔可夫模型（CHSMM）的早产儿呼吸暂停心动过缓检测方法。CHSMM 是隐马尔可夫模型 (HMM) 的推广，用于通过使用具有相应静止时间的有限数量的隐状态来对随机过程的不同观测值之间的相互交互进行建模。我们为 CHSMM 引入了一组新的方程，以便将其集成到检测算法中。检测算法在模拟数据上进行评估，以检测特定的动态和从早产儿收集的心电图信号的临床数据集，用于早期检测呼吸暂停心动过缓发作。对于模拟数据，所提出的算法能够以 96.67 % 的灵敏度和 98.98 的特异性检测所需的动态% . 此外，该方法以 94.87 % 的灵敏度和 96.52 % 的特异性检测呼吸暂停心动过缓发作，平均时间延迟为 0.73 秒。结果表明，基于 CHSMM 的算法是用于监测早产儿以检测呼吸暂停心动过缓发作的强大工具。