A Slantlet transform based intelligent system for magnetic resonance brain image classification

The present paper proposes the development of a new approach for automated diagnosis, based on classification of magnetic resonance (MR) human brain images. Wavelet transform based methods are a well-known tool for extracting frequency space information from non-stationary signals. In this paper, the proposed method employs an improved version of orthogonal discrete wavelet transform (DWT) for feature extraction, called Slantlet transform, which can especially be useful to provide improved time localization with simultaneous achievement of shorter supports for the filters. For each two-dimensional MR image, we have computed its intensity histogram and Slantlet transform has been applied on this histogram signal. Then a feature vector, for each image, is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions, chosen according to a specific logic. The features hence derived are used to train a neural network based binary classifier, which can automatically infer whether the image is that of a normal brain or a pathological brain, suffering from Alzheimer's disease. An excellent classification ratio of 100% could be achieved for a set of benchmark MR brain images, which was significantly better than the results reported in a very recent research work employing wavelet transform, neural networks and support vector machines.     

Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper features and remove the redundancy from the primary feature vector. Finally, the extracted features are applied to the K-nearest neighbor (KNN) and support vector machine (SVM) classifiers separately to determine the normal image or disease type. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs less number of features for classification.     

Voice pitch predicts electability,but does not signal leadership ability

Voice pitch, the perceived “highness” or “lowness” of a voice, influences how humans perceive and treat each other in various ways. One example is the selection of leaders. A growing number of studies, both experimental and observational, show that individuals with lower-pitched voices are more likely to win elected office. This leads to the yet untested question of whether individuals with lower voices are actually better leaders. That is, is voice pitch a reliable signal of leadership ability? Here we address this question with an observational study of the vocal pitch and leadership ability of elected officials, and an experiment where subjects were asked to respond to persuasive political policy statements made by speakers with different pitched voices. Both studies lead to the same conclusion: voice pitch does not correlate with leadership ability.     

Age- and sex-related variations in vocal-tract morphology and voice acoustics during adolescence

Distinct differences in the human voice emerge during adolescence, with males producing deeper and more resonant voices than females by the end of sexual maturation. Using magnetic resonance images of heads and voice recordings obtained in 532 typically developing adolescents, we investigate what might be the drivers of this change in voice, and the subjective judgment of the voice “maleness” and “femaleness”.We show clear sex differences in the morphology of voice-related structures during adolescence, with males displaying strong associations between age (and puberty) and both vocal-fold and vocal-tract length; this was not the case in female adolescents. At the same time, males (compared with females) display stronger associations between age (and puberty) with both fundamental frequency and formant position. In males, vocal morphology was a mediator in the relationship between bioavailable testosterone and acoustic indices.Subjective judgment of the voice sex could be predicted by the morphological and acoustic parameters in males only: the length of vocal folds and its acoustic counterpart, fundamental frequency, is a larger predictor of subjective “maleness” of a voice than vocal-tract length and formant position.     

Di-codon usage for classification of genes

Genes are often classified into biologically related groups so that inferences on their functions can be made. This paper demonstrates that the di-codon usage is a useful feature for gene classification and gives better classification accuracy than the codon usage. Our experiments with different classifiers show that support vector machines performs better than other classifiers in classifying genes by using di-codon usage as features. The method is illustrated on 1841 HLA sequences which are classified into two major classes, HLA-I and HLA-II, and further classified into the subclasses of major classes. By using both codon and di-codon features, we show near perfect accuracies in the classification of HLA molecules into major classes and their sub-classes.     

An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features,linear discriminant analysis and support vector machine

Voice impairments, attention to increased unhealthy social behavior and voice abuse, have been increasing dramatically. Therefore, diagnosis of voice diseases has an important role in the opportune treatment of pathologic voices. This paper presents an extensive study in identification of different voice disorders which their origin is in the vocal folds. Firstly, a qualitative study is applied based on short-time Fourier transform (STFT) and continuous wavelet transform (CWT) in order to investigate their aptitude in the presentation of discriminative features to identify disordered voices from normal ones. Therefore, wavelet packet transform (WPT) for their ability to analyze scrutinizingly a signal at several levels of resolution is chosen as strong speech signal parameterization method. The ability of energy and entropy features, obtained from the coefficients in the output nodes of the optimum wavelet packet tree, is investigated. Linear discriminant analysis (LDA) and principal component analysis (PCA) are evaluated as feature dimension reduction methods in order to optimize recognition algorithm. The performance of each structure is evaluated in terms of the accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC). Eventually, entropy features in the sixth level of WPT decomposition along with feature dimension reduction by LDA and a support vector machine-based classification method is the most optimum algorithm that leads to the recognition rate of 100% and AUC of 100%. Proposed system clearly outperforms previous works in both respect of accuracy and reduction of residues; which may lead in full accuracy and high speed diagnosis procedure.     

Detection of dairy fouling: Combining ultrasonic measurements and classification methods

Fouling and cleaning in heat exchangers are severe and costly (up to 0.3% of gross national product) issues in dairy and food processing. Therefore, reducing cleaning time and cost is urgently needed. In this study, two classification methods [artificial neural network (ANN) and support vector machine (SVM)] for detecting protein and mineral fouling presence and absence based on ultrasonic measurements were presented and compared. ANN is based on a multilayer perceptron feed forward neural network, whereas SVM is based on clustering between fouling and no fouling using a hyperplane. When both fouling types (1239 datasets) were combined, ANN showed an accuracy of 71.9% while SVM displayed an accuracy of 97.6%. Separate fouling detection of mineral/protein fouling by ANN/SVM was comparable: dependent on fouling type detection accuracies of 100% (protein fouling, ANN and SVM), and 98.2% (SVM), and 93.5% (ANN) for mineral fouling was reached. It was shown that it was possible to detect fouling presence and absence offline in a static setup using ultrasonic measurements in combination with a classification method. This study proved the applicability of combining classification methods and fouling measurements to take a step toward reducing cleaning costs and time.     

Perceptual auditory aftereffects on voice identity using brief vowel stimuli

Humans can identify individuals from their voice, suggesting the existence of a perceptual representation of voice identity. We used perceptual aftereffects - shifts in perceived stimulus quality after brief exposure to a repeated adaptor stimulus - to further investigate the representation of voice identity in two experiments. Healthy adult listeners were familiarized with several voices until they reached a recognition criterion. They were then tested on identification tasks that used vowel stimuli generated by morphing between the different identities, presented either in isolation (baseline) or following short exposure to different types of voice adaptors (adaptation). Experiment 1 showed that adaptation to a given voice induced categorization shifts away from that adaptor's identity even when the adaptors consisted of vowels different from the probe stimuli. Moreover, original voices and caricatures resulted in comparable aftereffects, ruling out an explanation of identity aftereffects in terms of adaptation to low-level features. In Experiment 2, we show that adaptors with a disrupted configuration, i.e., altered fundamental frequency or formant frequencies, failed to produce perceptual aftereffects showing the importance of the preserved configuration of these acoustical cues in the representation of voices. These two experiments indicate a high-level, dynamic representation of voice identity based on the combination of several lower-level acoustical features into a specific voice configuration.     

Beyond apical ballooning: computational modelling reveals morphological features of Takotsubo cardiomyopathy

Abstract

Takotsubo cardiomyopathy (TCM) is characterized by transient myocardial dysfunction, typically at the left ventricular (LV) apex. Its pathophysiology and recovery mechanisms remain unknown. We investigated LV morphology and deformation in n?=?28 TCM patients. Patients with MRI within 5?days from admission (“early TCM”) showed reduced LVEF and higher ventricular volumes, but no differences in ECG, global strains or myocardial oedema. Statistical shape modelling described LV size (Mode 1), apical sphericity (Mode 2) and height (Mode 3). Significant differences in Mode 1 suggest that “early TCM” LV remodeling is mainly influenced by a change in ventricular size rather than apical sphericity.     

Benefits for Voice Learning Caused by Concurrent Faces Develop over Time

Recognition of personally familiar voices benefits from the concurrent presentation of the corresponding speakers’ faces. This effect of audiovisual integration is most pronounced for voices combined with dynamic articulating faces. However, it is unclear if learning unfamiliar voices also benefits from audiovisual face-voice integration or, alternatively, is hampered by attentional capture of faces, i.e., “face-overshadowing”. In six study-test cycles we compared the recognition of newly-learned voices following unimodal voice learning vs. bimodal face-voice learning with either static (Exp. 1) or dynamic articulating faces (Exp. 2). Voice recognition accuracies significantly increased for bimodal learning across study-test cycles while remaining stable for unimodal learning, as reflected in numerical costs of bimodal relative to unimodal voice learning in the first two study-test cycles and benefits in the last two cycles. This was independent of whether faces were static images (Exp. 1) or dynamic videos (Exp. 2). In both experiments, slower reaction times to voices previously studied with faces compared to voices only may result from visual search for faces during memory retrieval. A general decrease of reaction times across study-test cycles suggests facilitated recognition with more speaker repetitions. Overall, our data suggest two simultaneous and opposing mechanisms during bimodal face-voice learning: while attentional capture of faces may initially impede voice learning, audiovisual integration may facilitate it thereafter.     

Analysis of Wrist Pulse Signal: Emotions and Physical Pain

BackgroundPulse diagnosis (wrist pulse signal) is a well-known traditional technique used for a health examination. It has the potential to detect cardiac and non-cardiac diseases.ObjectiveA study was conducted to investigate human emotions using wrist pulse signal assessment. The aim was to categorize anxiety, boredom, physical pain, and reference state by processing and analysis of acquired signals.MethodA protocol was designed to induce emotions. Data were acquired from 24 healthy volunteers. Signals were processed and further analyzed using paired t-test and Analysis of Variance (ANOVA). Machine learning algorithms, Linear Discriminant Function (LDF), Quadratic Discriminant Function (QDF), and Support Vector Machine using kernel Gaussian radial basis function (RBF-SVM) were used to evaluate significant features and classify the emotions.ResultsComputing significant plus ranked features performed better over randomly selected features for pairwise emotion classification. Here, the QDF classifier outperforms LDF. Additionally, ANOVA validated the effectiveness of statistically prominent features to classify emotional states. Ratio_Pulse_Strength, total_power, Spectral Entropy, and meancd5 came out as the four most significant features to classify the emotion “Anxiety”, “Boredom”, “Pain”, and “Reference” with positive prediction rate of 100%, 73%, 100%, and 86% respectively using RBF-SVM in the user-independent model.ConclusionPreviously, WPS has been used mainly to detect physical abnormality in the human body. The results endorse the potential of user-independent human emotion detection using a wrist pulse signal. The present work was focusing on a few emotional states. Results are encouraging and may be well applied to many more states.     

Shame,reputation and Egypt's lovers: A controversy over the nation's image

This paper addresses aspects of contemporary Egyptian nationalism by focusing on reactions within Egypt to a BBC‐produced documentary dealing with the life of a lower class Cairene woman. In particular, it explores how the notions of honor, shame and reputation are central to the construction and expression of this nationalistic voice, and how the concern over “Egypt's image” involves a contest over who is to represent the nation. This article argues that the contemporary nationalistic voice, championed by the urban middle classes, expresses an ambivalent attitude to both “the West” and “the Egyptian people”, and that it necessarily involves the hiding and undermining of subordinate social groups.     

Time Domain Analysis of Heart Rate Variability Signals in Valence Recognition for Children with Autism Spectrum Disorder (ASD)

BackgroundAutism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by various social impairments. Children with ASD have major difficulties in expressing themselves, resulting in stress and meltdowns. Understanding their hidden feelings and needs may help in tackling and avoiding such strenuous behaviors.ObjectiveThis research aims to aid the parents and caretakers of children with ASD to understand the hidden and unexpressed emotional state by using physiological signals obtained from wearable devices.MethodsHere, electrocardiogram (ECG) signals pertaining to two valence states (‘like’ and ‘dislike’) were recorded from twenty children (10 Control and 10 children with ASD). The heart rate variability (HRV) signals were then obtained from the ECG signals using the Pan-Tompkins's algorithm. The statistical, higher order statistics (HOS) and geometrical features which were statistically significant were trained using the K Nearest Neighbor (KNN) and Ensemble Classifier algorithms.ResultsThe findings of our analysis indicate that the integration of major statistical features resulted in an overall average accuracy of 84.8% and 75.3% using HRV data for the control and test population, respectively. Similarly, geometrical features resulted in a maximum average accuracy of 84.8% and 74.2% for control and test population respectively. The decreased HRV in the test population indicates the presence of autonomic dysregulation in children with ASD when compared to their control peers.     

The evolved psychology of voice: evaluating interrelationships in listeners' assessments of the size,masculinity, and attractiveness of unseen speakers

A growing body of research has examined how voice characteristics advertise personal dimensions relevant in mate competition and mate choice. This work has centered on two key voice features, namely, fundamental frequency (F₀) and formants (Fn), and has consistently found that speakers with low F₀, low Fn, or both are rated as being larger, more masculine, and more attractive if men but less attractive if women. However, this consistency in listeners' perceptions is not matched by an equivalent consensus in how these mate-relevant dimensions are causally related or signaled by voice characteristics. Consequently, it is critical to test whether the strong correlations in listeners' perceptions reflect reliable causal relationships between these dimensions or, alternatively, whether they reflect some perceptual or cognitive nonindependence, for example, “what is large is masculine” and “what is small is feminine.” To test this latter possibility, we report detailed analyses of interdependence in listeners' ratings of perceived size, masculinity or femininity, and attractiveness of natural and manipulated voices of the opposite sex. We found strong correlations in listeners' ratings of all three dimensions, confirming past research. Principal component analysis corroborated these interrelationships but also revealed some independence in women's ratings of men's attractiveness and additional (but weaker) independence in men's ratings of women's size. We discuss possible implications for future research on the evolved psychology of voice and whether and how it reflects adaptive functional heuristics for discriminating mates.     

Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals

ObjectivesElectromyography (EMG) is recording of the electrical activity produced by skeletal muscles. The classification of the EMG signals for different physical actions can be useful in restoring some or all of the lost motor functionalities in these individuals. Accuracy in classifying the EMG signal indicates efficient control of prosthesis.Material and methodsThe flexible analytic wavelet transform (FAWT) is used for classification of surface electromyography (sEMG) signals for identification of physical actions. FAWT is an efficient method for decomposition of sEMG signal into eight sub-bands, features namely neg-entropy, mean absolute value (MAV), variance (VAR), modified mean absolute value type 1 (MAV1), waveform length (WL), simple square integral (SSI), Tsallis entropy, integrated EMG (IEMG) are extracted from the sub-bands. Extracted features are fed into an extreme learning machine (ELM) classifier with sigmoid activation function.ResultsComprehensive experiments are conducted on the input sEMG signals and the accuracy, sensitivity and specificity scores are used for performance measurement. Experiments showed that among all sub-bands, the seventh sub-band provided the best performance where the recorded accuracy, sensitivity and specificity values were 99.36%, 99.36% and 99.93%, respectively. The comparison results showed best efficiency of proposed method as compared to other methods on the same dataset.ConclusionThis paper investigates the usage of the FAWT and ELM on sEMG signal classification. The results show that the proposed method is quite efficient in classification of the sEMG signals. It is also observed that the seventh sub-band of the FAWT provides the best discrimination property. In the future works, recent wavelet transform methods will be used for improving the classification performance.     

Social dialect and men's voice pitch influence women's mate preferences

Low male voice pitch may communicate potential benefits for offspring in the form of heritable health and/or dominance, whereas access to resources may be indicated by correlates of socioeconomic status, such as sociolinguistic features. Here, we examine if voice pitch and social dialect influence women's perceptions of men's socioeconomic status and attractiveness. In Study 1, women perceived lower pitched male voices as higher in socioeconomic status than higher pitched male voices. In Study 2, women independently perceived lower pitched voices and higher status sociolinguistic dialects as higher in socioeconomic status and attractiveness. We also found a significant interaction wherein women preferred lower pitched men's voices more often when dialects were lower in sociolinguistic status than when they were higher in sociolinguistic status. Women also perceived lower pitched voices as higher in socioeconomic status more often when dialects were higher in sociolinguistic status than when lower in sociolinguistic status. Finally, women's own self-rated socioeconomic status was positively related to their preferences for voices with higher status sociolinguistic dialects, but not to their preferences for voice pitch. Hence, women's preferences for traits associated with potentially biologically heritable benefits, such as low voice pitch, are moderated by the presence of traits associated with resource accrual, such as social dialect markers. However, women's preferences for language markers of resource accrual may be functionally independent from preferences for potential biological indicators of heritable benefits, such as voice pitch.     

Feature Extraction and Classification of Heart Murmurs Based on Acoustic Qualities

Background and objectiveHeart murmur characterization is a crucial part of cardiac auscultation for determining the potential etiology and severity of heart diseases. One such helpful murmur characterization is the sonic qualities, which reflect both structural and hemodynamical states of the heart. Therefore, the objective is to develop a machine learning based solution for classifying murmur qualities.MethodsFour medically defined murmur qualities, namely the musical quality, blowing-like quality, coarse quality, and soft quality were examined. Feature was extracted from heart murmurs signals in their time domain, frequency domain, time-frequency domain, and phase space domain. Sequential forward floating selection (SFFS) was implemented along with three classifiers, including k-nearest neighbor (KNN), Naïve-Bayes (NB), and linear support vector machine (SVM).ResultsIt was found that multi-domain features are suited for better classification results and linear SVM was able to achieve a better balance between performance and the size of feature subsets among tested classifiers. Using the derived features, classification accuracies of 86%, 91%, 90%, and 84% were achieved for musical quality, blowing-like quality, coarse quality, and soft quality classifications respectively.ConclusionsThe study demonstrated that it is possible to effectively characterize heart murmur through its diagnostic characteristics instead of drawing direct conclusions, which is helpful for retaining versatility and generality found in the conventional cardiac auscultation.     

基于序列特征的人类PolⅡ启动子理论预测

基于已知的人类PolII启动子序列数据,综合选取启动子序列内容和序列信号特征,构建启动子的支持向量机分类器．分别以启动子序列的6-mer频数作为离散源参数构建序列内容特征。同时选取24个位点的3-mer频数作为序列信号特征构建PWM,将所得到的两类参数输入支持向量机对人类启动子进行预测．用10折叠交叉检验和独立数据集来衡量算法的预测能力,相关系数指标达到95％以上,结果显示结合了支持向量机的离散增量算法能够有效的提高预测成功率,是进行真核生物启动子预测的一种很有效的方法．     

Epileptic Seizure Detection Based on New Hybrid Models with Electroencephalogram Signals

Objectives: Epileptic seizures are one of the most common diseases in society and difficult to detect. In this study, a new method was proposed to automatically detect and classify epileptic seizures from EEG (Electroencephalography) signals.Methods: In the proposed method, EEG signals classification five-classes including the cases of eyes open, eyes closed, healthy, from the tumor region, an epileptic seizure, has been carried out by using the support vector machine (SVM) and the normalization methods comprising the z-score, minimum-maximum, and MAD normalizations. To classify the EEG signals, the support vector machine classifiers having different kernel functions, including Linear, Cubic, and Medium Gaussian, have been used. In order to evaluate the performance of the proposed hybrid models, the confusion matrix, ROC curves, and classification accuracy have been used. The used SVM models are Linear SVM, Cubic SVM, and Medium Gaussian SVM.Results: Without the normalizations, the obtained classification accuracies are 76.90%, 82.40%, and 81.70% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively. After applying the z-score normalization to the multi-class EEG signals dataset, the obtained classification accuracies are 77.10%, 82.30%, and 81.70% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively. With the minimum-maximum normalization, the obtained classification accuracies are 77.20%, 82.40%, and 81.50% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively. Moreover, finally, after applying the MAD normalization to the multi-class EEG signals dataset, the obtained classification accuracies are 76.70%, 82.50%, and 81.40% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively.Conclusion: The obtained results have shown that the best hybrid model is the combination of cubic SVM and MAD normalization in the classification of EEG signals classification five-classes.     

Linking Industrial Ecology with Business Strategy: Creating Value for Green Product Design

As organizations practice environmental design, some discover green design positively impacts business performance. This article demonstrates how an organization can employ existing design methods and tools with the Kano technique to craft an environmental product design strategy that enhances its business strategy. These tools expand the toolbox of the industrial ecologist and enable the link between green design and business improvement. The Kano technique was developed in the 1980s to facilitate design of innovative products. We also introduce terminology and concepts such as “voices of the environment,”“environmental knowledge management,”“environmental profile,” and “environmental product attribute” in order to bridge the gap between industrial ecology and business concerns. To demonstrate how an organization can find the synergy between business value and environmental value, this article describes three activities and their corresponding tools and exhibits their use with industry examples. First, we present techniques by which designers can identify and prioritize customers and stakeholders who voice both environmental and business concerns. Second, we describe how voice‐of‐the‐customer translation techniques can be used to efficiently collect and translate data from these customers and stakeholders into critical environmental product and service attributes. Third, we discuss how the Kano technique can be used to connect green design to business strategy by making visible the variety of stakeholder and customer perceptions of these critical environmental attributes. Examples then demonstrate how those perceptions suggest appropriate approaches for integrating the critical environmental attributes into product and business strategy. Finally, we provide examples based on work done with General Electric Medical Systems (GEMS) to illustrate the design of products that improve environmental performance while adding greater perceived value for numerous customers along material‐flow value chains.