**3.9.1 The effect of window length on the bispectral contour variance**

The variance of the bispectrum for the optimum window length of 250 msec with FHB occurrence ranges from 0.47 to 3.3 with an average value of 1.716. The variance of the bispectrum is smaller than that of the third-order cumulants. A further 15% increase in the variance of the bispectrum is due to an increase in the maternal heartbeat from 60 bpm to 100 bpm. The latter has resulted in an 30% decrease in segment size.

Detection and Classification of Adult and

Fetal ECG Using Recurrent Neural Networks, Embedded Volterra and Higher-Order Statistics 243

Fig. 4.1. Typical electrocardiogram biphase signature of an Acute myocardial infarction (AMI) presented with a normal looking 12-lead ECG without S-T segment and T-wave changes and normal sinus rhythm. (a) and (b) are ECGs of normal and AMI subjects, respectively. (c) and (d) are the corresponding bispectral phases using the direct method with frequency domain smoothing and Kaiser window. The bispectrum is the average of 10 ST bispectra, the S-T segment is 150 samples starting from the R peak (**Rizk et al., 1999b**).

This section describes a simple and accurate multi-layer recurrent neural network classifier specifically designed to successfully distinguish between normal and abnormal higher-order statistics features of electrocardiogram (ECG) signals. The concerned abnormality in ECG is associated with ventricular late potentials (LPs) indicative of life threatening heart diseases. LPs are defined as signals from areas of delayed conduction which outlast the normal QRS period (80-100 msec). The QRS along with the P and T waves constitute the heartbeat cycle. This classifier incorporates both pre-processing and adaptive weight adjustments across the input layer during the training phase of the network to enhance extraction of features pertinent to LPs found in 1-d cumulants. The latter is deemed necessary to offset the low SNR ratio in the cumulant domains concomitant to performing short data segmentation in order to capture the LPs transient appearance. The procedures of feature selection for neural network training, modification to the back propagation algorithm to speed its rate of

conversion, and the pilot trial results of the neural ECG classifier are summarised.

The relationship between myocardial infarction (MI) and short-duration high-frequency components occurring around the terminal end of the QRS complex in the cardiac cycle of

**5. ECG abnormality classification using cumulants** 

**5.1 Background** 

#### **3.9.2 Parameters of the single-hidden layer perceptron**

The network has been optimised in terms of its learning rate, momentum constant, and hidden layer size to achieve the minimum mean-squared error. The optimum learning rate is found to be 0.2. The optimum momentum constant is found to be 0.2. The single-hidden-layer has an optimum dimension of 6 x 6. The input to the first layer is the bispectral contours of the four transabdominally-measured ECG segments. The network is trained using the BIC templates. During the training phase, the input to the network is four template patterns. These are the BIC of four segments from one transabdominal cardiac cycle. For example the first is the maternal QRS-complex BIC, the second is the first fetal heartbeat BIC, the third is the QRS-free ECG BIC, and the fourth is the second fetal heartbeat BIC. The network is trained over ten templates of each of the four segments. The training terminates when the worst error in all patterns in one pass is less than 0.1. Typically the average error will be in the range of 0.001.
