面对全球死亡率最高的心血管疾病，治防同步，以防为主已成共识，穿戴式实时心电监控无疑是最有效的手段之一。动态长时间监测环境下，信号复杂多变,导致特征提取遇到本质困难。隐马尔可夫模型（HMM）因融入信号统计特性而具有更好的鲁棒性，但面对复杂多变的穿戴式心电，缺少状态持续时间分布控制的HMM稳健性难以保证。鉴于此，本项目通过松弛马尔可夫假设增加模型底层灵活性使其能够纳入任意形式的持续时间分布，修正和扩展Viterbi算法，联合Logistic回归和贝叶斯理论发展新的半隐马尔可夫模型（HSMM）。为提高模型的泛化能力，本项目基于质量评估和主动学习算法构建穿戴式动态心电信号优选机制，强化状态持续分布密度函数和状态幅值分布函数的泛化能力，发展增强型HSMM算法，提高穿戴式动态心电信号精准分割稳健性，进而为心血管病早期筛查和长期监测等医学临床应用提供理论和技术支持。

For the world's highest mortality cardiovascular disease, prevention has become a consensus. Wearable real-time ECG monitoring is undoubtedly one of the most effective means. In the dynamic long-term monitoring environment, the signal noise is serious and unpredictable, which leads to the essential difficulty for the feature extraction. Hidden Markov Model (HMM) has better robustness due to the integration of signal statistics. However, in the face of complex and variable wearable ECG signal, the lack of state duration distribution, the robustness of HMM is difficult to guarantee. In view of this, the project increases the underlying flexibility of the model by relaxing the Markov assumption to enable it to incorporate any form of duration distribution. The modified and extended Viterbi algorithm is added to overcome the implicit hypothesis. The joint logistic regression and naive Bayesian theory use the probability statistics information to determine the observed probability matrix of the model. Based on these two aspects, a new semi-hidden Markov model (HSMM) is developed. Considering the wearable Holter that is difficult to control with complex signal quality, waveform and rhythm, it is necessary to establish high cost of expert mark to establish a stable new HSMM model. This project builds a wearable dynamic ECG signal optimization mechanism based on integrating quality assessment system and signal active selection mechanism. And an enhanced HSMM algorithm is developed, which can improve the robustness of accurate segmentation for wearable dynamic ECG signals. This work could provide theoretical and technical support for medical clinical applications such as early screening and long-term monitoring of cardiovascular disease.