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Advanced Methods and Tools for ECG Data Analysis - Part 9

Bước đầu tiên trong việc áp dụng mô hình Markov ẩn nhiệm vụ của phân khúc điện tâm đồ là kết hợp mỗi nhà nước trong mô hình với một khu vực cụ thể của điện tâm đồ. Như đã thảo luận trước đó trong mục 11.6.5, điều này có thể đạt được một cách giám sát | 11.7 Hidden Markov Models for ECG Segmentation 305 11.7.1 Overview The first step in applying hidden Markov models to the task of ECG segmentation is to associate each state in the model with a particular region of the ECG. As discussed previously in Section 11.6.5 this can either be achieved in a supervised manner i.e. using expert measurements or an unsupervised manner i.e. using the EM algorithm . Although the former approach requires each ECG waveform in the training data set to be associated with expert measurements of the waveform feature boundaries i.e. the Pon Q Toff points and so forth the resulting models generally produce more accurate segmentation results compared with their unsupervised counterparts. Figure 11.5 shows a variety of different HMM architectures for ECG interval analysis. A simple way of associating each HMM state with a region of the ECG is to use individual hidden states to represent the P wave QRS complex JT interval and baseline regions of the ECG as shown in Figure 11.5 a . In practice it is advantageous to partition the single baseline state into multiple baseline states 9 one of which is used to model the baseline region between the end of the P wave and the start of the QRS complex termed baseline 1 and another which is used to model the baseline region following the end of the T wave termed baseline 2 . This model architecture which is shown in Figure 11.5 b will be used throughout the rest of this chapter.5 Following the choice of model architecture the next step in training an HMM is to decide upon the specific type of observation model which will be used to capture the statistical characteristics of the signal samples from each hidden state. Common choices for the observation models in an HMM are the Gaussian density the Gaussian mixture model GMM and the autoregressive AR model. Section 11.7.4 discusses the different types of observation models in the context of ECG segmentation. Before training a hidden Markov model for ECG .