continuous wavelet transform

Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool, which makes the processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate.

In this paper, a hybrid approach based on deep features extracted from wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) was proposed to improve the recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to Time-Frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19, and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, the subject-independent leave-one-subject-out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases.

Results showed that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increased the average accuracy, precision, and recall by about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal, and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively.

Combining CNN and MSVM increased the recognition of emotion from EEG signals and the results were comparable to state-of-the art studies.

Emotions are integral brain states that can influence our behavior, decision-making, and functions. Electroencephalogram (EEG) is an appropriate modality for emotion recognition since it has high temporal resolution and is a noninvasive and cheap technique.

A novel approach based on Ensemble pre-trained Convolutional Neural Networks (ECNNs) is proposed to recognize four emotional classes from EEG channels of individuals watching music video clips. First, scalograms are built from one-dimensional EEG signals by applying the Continuous Wavelet Transform (CWT) method. Then, these images are used to re-train five CNNs: AlexNet, VGG-19, Inception-v1, ResNet-18, and Inception-v3. Then, the majority voting method is applied to make the final decision about emotional classes. The 10-fold cross-validation method is used to evaluate the performance of the proposed method on EEG signals of 32 subjects from the DEAP database.

The experiments showed that applying the proposed ensemble approach in combinations of scalograms of frontal and parietal regions improved results. The best accuracy, sensitivity, precision, and F-score to recognize four emotional states achieved 96.90%±0.52, 97.30±0.55, 96.97±0.62, and 96.74±0.56, respectively.

So, the newly proposed model from EEG signals improves recognition of the four emotional states in the DEAP database.

A rapid, simple, precise, accurate, and environmentally friendly spectrophotometric method was developed and validated for simultaneous determination of Sacubitril and Valsartan in their combined dosage form, using continuous wavelet transform (CWT) and zero-crossing techniques without using organic solvents and the timeconsuming extraction step. Initially, UV spectra of two pure components in water were processed via various mother wavelet families. Then, applying zero-crossing technique, the optimum points were found to obtain appropriate calibration curves for each point. The calibration curves were linear for both Sacubitril and Valsartan. The validation of these methods was investigated by analyzing several synthetic mixtures with known concentrations. Applying oneway analysis of variance (ANOVA) test and Fisher pairwise comparisons, the following were found to yield the best results Discrete Meyer (dmey) wavelet functions with scaling factor of 61 at 232 nm and Symlet5 (sym5) with 48 at 232 nm for Sacubitril and Meyer (meyr) with 50 at 272 nm, meyr with 59 at 247 nm, Daubechies (db5) with 53 at 237 nm, and sym5 with 59 at 226 nm for Valsartan. The mean recovery values in synthetic mixtures were between 99.09% and 101.16% using the proposed methods, where relative standard deviation (RSD) not more than 1.23% proved to have good precision. The obtained results from the commercial tablets, applying the developed methods, were compared to those yielded by HPLC method by one-way ANOVA test. According to the results, they were in good agreement and showed no significant differences, thereby suggesting successful determination in accordance with green chemistry.