**5.2 Higher-order statistics feature selection and enhancement**

The experimental setup consists of an ECG monitor, interface card and a workstation. Raw ECG data are measured using three orthogonal surface electrodes, sampled at 500 Hz and fed to the computer which performs the following operations. Accurate on-line QRS detection. This involves Volterra whitening filters in the time domain or / and the highresolution spectral MUSIC in the frequency domain. Positions of ECG peaks are pinpointed in the time domain. The MUSIC algorithm incorporates two sliding sets of three overlapping Kaiser windows and adaptive thresholding operations which not only pinpoint the high-level low-frequency QRS spectral peaks (LFQRSSPs) per cycle, but also performs the preliminary spotting of the low-level high-frequency late potential components over a range of frequencies from 100-250 Hz. Detection of late potential highfrequency spectral peaks is carried out off-line every 5 LFQRSSPs to allow appropriate segmentation between the R-R marking in the time domain processing which runs almost synchronisingly with the MUSIC routine. A detailed procedure for segmentation involves calculating the bicoherence squared and mapping a particular region for each individual segment to confirm existence of quadratic non-linearity before moving on to interrogate another segment or skip a few segments up to the next R peak. This controlled skipping helps to avoid the highly non-linear T wave of the present cycle and the P wave of the adjacent one.

The Volterra filtering can be used to partially suppresses motion artifact only in those cases of missing LFQRSSPs and the MUSIC routine is repeated over the same cardiac cycle for confirmation of the presence or absence of QRSs. This has been found to be necessary in extreme cases and in the absence of QRS waves (ventricular fibrillation). Offline calculations of the cumulant diagonal and wall 1-d slices are performed on those segments suspected of having LPs as depicted in Fig. 5.1. It is clearly seen that abnormality is manifested in the eminent petal pattern (a horizontal slice has a petal shape) in the cumulant domains. Five thousand cardiac cycles of normal and abnormal ECGs were put to the test. An arbitrarily chosen non-linear function modifies the envelope of the so-called 'petal pattern' to enhance its peculiarity against background artifact. The non-linear function is then sampled across the input layer of the neural network.
