Geva A B
Electrical & Computer Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Med Biol Eng Comput. 1998 Sep;36(5):608-14. doi: 10.1007/BF02524432.
Many problems in the field of biomedical signal processing can be reduced to a task of state recognition and event prediction. Examples can be found in tachycardia detection from ECG signals, epileptic seizure or psychotic attack prediction from an EEG signal, and prediction of vehicle drivers falling asleep from both signals. The problem generally treats a set of ordered measurements and asks for the recognition of some patterns of observed elements that will forecast an event or a transition between two different states of the biological system. It is proposed to apply clustering methods to grouping discontinuous related temporal patterns of a continuously sampled measurement. The vague switches from one stationary state to another are naturally treated by means of fuzzy clustering. In such cases, an adaptive selection of the number of clusters (the number of underlying semi-stationary processes) can overcome the general non-stationary nature of biomedical signals and enable the formation of a warning cluster. The algorithm suggested for the clustering is a new recursive algorithm for hierarchical fuzzy partitioning. Each pattern can have a non-zero membership in more than one data subset in the hierarchy. A 'natural' and feasible solution to the cluster validity problem is suggested by combining hierarchical and fuzzy concepts. The algorithm is shown to be effective for a variety of data sets with a wide dynamic range of both covariance matrices and number of members in each class. The new method is applied to state recognition during recovery from exercise using the heart rate signal and to the forecasting of generalised epileptic seizures from the EEG signal.
生物医学信号处理领域中的许多问题都可以归结为状态识别和事件预测任务。例如，从心电图（ECG）信号中检测心动过速、从脑电图（EEG）信号中预测癫痫发作或精神性发作，以及从这两种信号中预测车辆驾驶员是否入睡。该问题通常处理一组有序测量值，并要求识别观测元素的某些模式，这些模式将预测生物系统的一个事件或两种不同状态之间的转变。有人建议应用聚类方法对连续采样测量的不连续相关时间模式进行分组。从一个稳态到另一个稳态的模糊转换自然可以通过模糊聚类来处理。在这种情况下，自适应选择聚类数量（潜在半稳态过程的数量）可以克服生物医学信号的一般非平稳特性，并能够形成一个警告聚类。所建议的聚类算法是一种用于层次模糊划分的新递归算法。在层次结构中，每个模式在多个数据子集中可以具有非零隶属度。通过结合层次和模糊概念，提出了一种针对聚类有效性问题的“自然”且可行的解决方案。该算法对于各种数据集都有效，这些数据集的协方差矩阵和每个类中的成员数量具有广泛的动态范围。该新方法应用于利用心率信号对运动恢复过程中的状态进行识别，以及从脑电图信号中预测全身性癫痫发作。
