Khadra L, al-Fahoum A S, al-Nashash H
Department of Electrical Engineering, Jordan University of Science & Technology, Irbid, Jordan.
Med Biol Eng Comput. 1997 Nov;35(6):626-32. doi: 10.1007/BF02510970.
Time-frequency wavelet theory is used for the detection of life threatening electrocardiography (ECG) arrhythmias. This is achieved through the use of the raised cosine wavelet transform (RCWT). The RCWT is found to be useful in differentiating between ventricular fibrillation, ventricular tachycardia and atrial fibrillation. Ventricular fibrillation is characterised by continuous bands in the range of 2-10 Hz; ventricular tachycardia is characterised by two distinct bands: the first band in the range of 2-5 Hz and the second in the range of 6-8 Hz; and atrial fibrillation is determined by a low frequency band in the range of 0-5 Hz. A classification algorithm is developed to classify ECG records on the basis of the computation of three parameters defined in the time-frequency plane of the wavelet transform. Furthermore, the advantage of localising and separating ECG signals from high as well as intermediate frequencies is demonstrated. The above capabilities of the wavelet technique are supported by results obtained from ECG signals obtained from normal and abnormal subjects.
时频小波理论用于检测危及生命的心电图（ECG）心律失常。这是通过使用升余弦小波变换（RCWT）来实现的。发现RCWT在区分心室颤动、室性心动过速和心房颤动方面很有用。心室颤动的特征是在2 - 10Hz范围内有连续频段；室性心动过速的特征是有两个不同的频段：第一个频段在2 - 5Hz范围内，第二个频段在6 - 8Hz范围内；心房颤动由0 - 5Hz范围内的低频段确定。开发了一种分类算法，根据小波变换时频平面中定义的三个参数的计算对心电图记录进行分类。此外，还展示了从小波变换的高频和中频中定位和分离心电图信号的优势。小波技术的上述能力得到了从正常和异常受试者获得的心电图信号结果的支持。
