Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification.

The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.

Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment.

Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.

Investigation on the performance of hybrid anaerobic membrane bioreactors for fouling control and biogas production in palm oil mill effluent treatment.

Three different sizes of powdered activated carbon (PAC) were added in hybrid anaerobic membrane bioreactors (AnMBRs) and their performance was compared with a conventional AnMBR without PAC in treating palm oil mill effluent. Their working volume was 1 L each. From the result, AnMBRs with PAC performed better than the AnMBR without PAC. It was also found that adding a relatively smaller size of PAC (approximately 100 µm) enhanced the chemical oxygen demand removal efficiency to 78.53 ± 0.66%, while the concentration of mixed liquor suspended solid and mixed liquor volatile suspended solid were 8,050 and 6,850 mg/L, respectively. The smaller size of PAC could also enhance the biofloc formation and biogas production. In addition, the smaller particle sizes of PAC incorporated into polyethersulfone membrane resulted in higher performance of membrane fouling control and produced better quality of effluent as compared to the membrane without the addition of PAC.

Tracking of EEG activity using motion estimation to understand brain wiring.

The fundamental step in brain research deals with recording electroencephalogram (EEG) signals and then investigating the recorded signals quantitatively. Topographic EEG (visual spatial representation of EEG signal) is commonly referred to as brain topomaps or brain EEG maps. In this chapter, full search full search block motion estimation algorithm has been employed to track the brain activity in brain topomaps to understand the mechanism of brain wiring. The behavior of EEG topomaps is examined throughout a particular brain activation with respect to time. Motion vectors are used to track the brain activation over the scalp during the activation period. Using motion estimation it is possible to track the path from the starting point of activation to the final point of activation. Thus it is possible to track the path of a signal across various lobes.

Digital image processing and analysis for activated sludge wastewater treatment.

Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.

Exploring the Effects of EEG-Based Alpha Neurofeedback on Working Memory Capacity in Healthy Participants.

Neurofeedback, an operant conditioning neuromodulation technique, uses information from brain activities in real-time via brain-computer interface (BCI) technology. This technique has been utilized to enhance the cognitive abilities, including working memory performance, of human beings. The aims of this study are to investigate how alpha neurofeedback can improve working memory performance in healthy participants and to explore the underlying neural mechanisms in a working memory task before and after neurofeedback. Thirty-six participants divided into the NFT group and the control group participated in this study. This study was not blinded, and both the participants and the researcher were aware of their group assignments. Increasing power in the alpha EEG band was used as a neurofeedback in the eyes-open condition only in the NFT group. The data were collected before and after neurofeedback while they were performing the N-back memory task (N = 1 and N = 2). Both groups showed improvement in their working memory performance. There was an enhancement in the power of their frontal alpha and beta activities with increased working memory load (i.e., 2-back). The experimental group showed improvements in their functional connections between different brain regions at the theta level. This effect was absent in the control group. Furthermore, brain hemispheric lateralization was found during the N-back task, and there were more intra-hemisphere connections than inter-hemisphere connections of the brain. These results suggest that healthy participants can benefit from neurofeedback and from having their brain networks changed after the training.

Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images.

Debates persist regarding the impact of Stain Normalization (SN) on recent breast cancer histopathological studies. While some studies propose no influence on classification outcomes, others argue for improvement. This study aims to assess the efficacy of SN in breast cancer histopathological classification, specifically focusing on Invasive Ductal Carcinoma (IDC) grading using Convolutional Neural Networks (CNNs). The null hypothesis asserts that SN has no effect on the accuracy of CNN-based IDC grading, while the alternative hypothesis suggests the contrary. We evaluated six SN techniques, with five templates selected as target images for the conventional SN techniques. We also utilized seven ImageNet pre-trained CNNs for IDC grading. The performance of models trained with and without SN was compared to discern the influence of SN on classification outcomes. The analysis unveiled a p-value of 0.11, indicating no statistically significant difference in Balanced Accuracy Scores between models trained with StainGAN-normalized images, achieving a score of 0.9196 (the best-performing SN technique), and models trained with non-normalized images, which scored 0.9308. As a result, we did not reject the null hypothesis, indicating that we found no evidence to support a significant discrepancy in effectiveness between stain-normalized and non-normalized datasets for IDC grading tasks. This study demonstrates that SN has a limited impact on IDC grading, challenging the assumption of performance enhancement through SN.

Mitotic nuclei analysis in breast cancer histopathology images using deep ensemble classifier.

Mitotic nuclei estimation in breast tumour samples has a prognostic significance in analysing tumour aggressiveness and grading system. The automated assessment of mitotic nuclei is challenging because of their high similarity with non-mitotic nuclei and heteromorphic appearance. In this work, we have proposed a new Deep Convolutional Neural Network (CNN) based Heterogeneous Ensemble technique "DHE-Mit-Classifier" for analysis of mitotic nuclei in breast histopathology images. The proposed technique in the first step detects candidate mitotic patches from the histopathological biopsy regions, whereas, in the second step, these patches are classified into mitotic and non-mitotic nuclei using the proposed DHE-Mit-Classifier. For the development of a heterogeneous ensemble, five different deep CNNs are designed and used as base-classifiers. These deep CNNs have varying architectural designs to capture the structural, textural, and morphological properties of the mitotic nuclei. The developed base-classifiers exploit different ideas, including (i) region homogeneity and feature invariance, (ii) asymmetric split-transform-merge, (iii) dilated convolution based multi-scale transformation, (iv) spatial and channel attention, and (v) residual learning. Multi-layer-perceptron is used as a meta-classifier to develop a robust and accurate classifier for providing the final decision. The performance of the proposed ensemble "DHE-Mit-Classifier" is evaluated against state-of-the-art CNNs. The performance evaluation on the test set suggests the superiority of the proposed ensemble with an F-score (0.77), recall (0.71), precision (0.83), and area under the precision-recall curve (0.80). The good generalisation of the proposed ensemble with a considerably high F-score and precision suggests its potential use in the development of an assistance tool for pathologists.

Investigating Feature Ranking Methods for Sub-Band and Relative Power Features in Motor Imagery Task Classification.

Interpreting the brain commands is now easier using brain-computer interface (BCI) technologies. Motor imagery (MI) signal detection is one of the BCI applications, where the movements of the hand and feet can be recognized via brain commands that can be further used to handle emergency situations. Design of BCI techniques encountered challenges of BCI illiteracy, poor signal to noise ratio, intersubject variability, complexity, and performance. The automated models designed for emergency should have lesser complexity and higher performance. To deal with the challenges related to the complexity performance tradeoff, the frequency features of brain signal are utilized in this study. Feature matrix is created from the power of brain frequencies, and newly proposed relative power features are used. Analysis of the relative power of alpha sub-band to beta, gamma, and theta sub-band has been done. These proposed relative features are evaluated with the help of different classifiers. For motor imagery classification, the proposed approach resulted in a maximum accuracy of 93.51% compared to other existing approaches. To check the significance of newly added features, feature ranking approaches, namely, mutual information, chi-square, and correlation, are used. The ranking of features shows that the relative power features are significant for MI task classification. The chi-square provides the best tradeoff between accuracy and feature space. We found that the addition of relative power features improves the overall performance. The proposed models could also provide quick response having reduced complexity.

Optimizing Residual Networks and VGG for Classification of EEG Signals: Identifying Ideal Channels for Emotion Recognition.

Emotion is a crucial aspect of human health, and emotion recognition systems serve important roles in the development of neurofeedback applications. Most of the emotion recognition methods proposed in previous research take predefined EEG features as input to the classification algorithms. This paper investigates the less studied method of using plain EEG signals as the classifier input, with the residual networks (ResNet) as the classifier of interest. ResNet having excelled in the automated hierarchical feature extraction in raw data domains with vast number of samples (e.g., image processing) is potentially promising in the future as the amount of publicly available EEG databases has been increasing. Architecture of the original ResNet designed for image processing is restructured for optimal performance on EEG signals. The arrangement of convolutional kernel dimension is demonstrated to largely affect the model's performance on EEG signal processing. The study is conducted on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED), with our proposed ResNet18 architecture achieving 93.42% accuracy on the 3-class emotion classification, compared to the original ResNet18 at 87.06% accuracy. Our proposed ResNet18 architecture has also achieved a model parameter reduction of 52.22% from the original ResNet18. We have also compared the importance of different subsets of EEG channels from a total of 62 channels for emotion recognition. The channels placed near the anterior pole of the temporal lobes appeared to be most emotionally relevant. This agrees with the location of emotion-processing brain structures like the insular cortex and amygdala.

Image segmentation of activated sludge phase contrast images using phase stretch transform.

Activated sludge (AS) is a biological treatment process that is employed in wastewater treatment plants. Filamentous bacteria in AS plays an important role in the settling ability of the sludge. Proper settling of the sludge is essential for normal functionality of the wastewater plants, where filamentous bulking is always a persistent problem preventing sludge from settling. The performance of AS plants is conventionally monitored by physico-chemical procedures. An alternative way of monitoring the AS in wastewater treatment process is to use image processing and analysis. Good performance of the image segmentation algorithms is important to quantify flocs and filaments in AS. In this article, an algorithm is proposed to perform segmentation of filaments in the phase contrast images using phase stretch transform. Different values of strength (S) and warp (W) are tested to obtain optimum segmentation results and decrease the halo and shade-off artefacts encountered in phase contrast microscopy. The performance of the algorithm is assessed using DICE coefficient, accuracy, false positive rate (FPR), false negative rate (FNR) and Rand index (RI). Sixty-one gold approximations of ground truth images were manually prepared to assess the segmentation results. Thirty-two of them were acquired at 10× magnification and 29 of them were acquired at 20× magnification. The proposed algorithm exhibits better segmentation performance with an average DICE coefficient equal to 52.25%, accuracy 99.74%, FNR 41.8% and FPR 0.14% and RI 99.49%, based on 61 images.

Generalized classification modeling of activated sludge process based on microscopic image analysis.

The state of activated sludge wastewater treatment process (AS WWTP) is conventionally identified by physico-chemical measurements which are costly, time-consuming and have associated environmental hazards. Image processing and analysis-based linear regression modeling has been used to monitor the AS WWTP. But it is plant- and state-specific in the sense that it cannot be generalized to multiple plants and states. Generalized classification modeling for state identification is the main objective of this work. By generalized classification, we mean that the identification model does not require any prior information about the state of the plant, and the resultant identification is valid for any plant in any state. In this paper, the generalized classification model for the AS process is proposed based on features extracted using morphological parameters of flocs. The images of the AS samples, collected from aeration tanks of nine plants, are acquired through bright-field microscopy. Feature-selection is performed in context of classification using sequential feature selection and least absolute shrinkage and selection operator. A support vector machine (SVM)-based state identification strategy was proposed with a new agreement solver module for imbalanced data of the states of AS plants. The classification results were compared with state-of-the-art multiclass SVMs (one-vs.-one and one-vs.-all), and ensemble classifiers using the performance metrics: accuracy, recall, specificity, precision, F measure and kappa coefficient (κ). The proposed strategy exhibits better results by identification of different states of different plants with accuracy 0.9423, and κ 0.6681 for the minority class data of bulking.

EEG-based alpha neurofeedback training for mood enhancement.

The aim of this paper is to develop a preliminary neurofeedback system to improve the mood of the subjects using audio signals by enhancing their alpha brainwaves. Assessment of the effect of music on the human subjects is performed using three methods; subjective assessment of mood with the help of a questionnaire, the effect on brain by analysing EEG signals, and the effect on body by physiological assessment. In this study, two experiments have been designed. The first experiment was to determine the short-term effect of music on soothing human subjects, whereas the second experiment was to determine its long-term effect. Two types of music were used in the first experiment, the favourite music selected by the participants and a relaxing music with alpha wave binaural beats. The research findings showed that the relaxing music has a better soothing effect on the participants psychologically and physiologically. However, the one-way analysis of variance (ANOVA) results showed that the short-term soothing effect of both favourite music and relaxing music was not significant in changing the mean alpha absolute power and mean physiological measures (blood pressure and heart rate) at the significance level of 0.05. The second experiment was somewhat similar to an alpha neurofeedback training whereby the participants trained their brains to produce more alpha brainwaves by listening to the relaxing music with alpha wave binaural beats for a duration of 30 min daily. The results showed that the relaxing music has a long-term psychological and physiological effect on soothing the participants, as can be observed from the increase in alpha power and decrease in physiological measures after each session of training. The training was found to be effective in increasing the alpha power significantly [F(2,12) = 11.5458 and p = 0.0016], but no significant reduction in physiological measures was observed at the significance level of 0.05.

Optimization of membrane bioreactors by the addition of powdered activated carbon.

It was found that with replenishment, powdered activated carbon (PAC) in the membrane bioreactor (MBR) would develop biologically activated carbon (BAC) which could enhance filtration performance of a conventional MBR. This paper addresses two issues (i) effect of PAC size on MBR (BAC) performance; and (ii) effect of sludge retention time (SRT) on the MBR performance with and without PAC. To interpret the trends, particle/floc size, concentration of mixed liquor suspended solid (MLSS), total organic carbon (TOC), short-term filtration properties and transmembrane pressure (TMP) versus time are measured. The results showed improved fouling control with fine, rather than coarse, PAC provided the flux did not exceed the deposition flux for the fine PAC. Without PAC, the longer SRT operation gave lower fouling at modest fluxes. With PAC addition, the shorter SRT gave better fouling control, possibly due to greater replenishment of the fresh PAC.