{'fraction_non_alphanumeric': 0.017258883248730966, 'fraction_numerical': 0.0, 'mean_word_length': 5.280254777070064, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analysis as a Discrimination Method Applied to Wavelet Transformed Biomedical Spectra

It is often difficult to obtain diagnostic information from biomedical signals, such as are acquired from magnetic resonance spectrometers. This is due to their complex nature and the presence of noise. Many preprocessing methods exist that transform the original input data in order to diminish the effects of noise and/or reduce the dimensionality of the input space. To this end, we report on the efficacy of the multi-resolution wavelet transformation and two-sample profile analysis as a technique to deal with both of these issues. The combination of these methods, which compares slopes for repeated measures on two groups, discriminates well between spectral groups and appears promising as a general tool for nonlinear signal discrimination. Further, this technique appears to be statistically more powerful than Hotelling’s T-test, which compares the multivariate means of the two groups.