An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals.

Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods.

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications.

Traditional advertising techniques seek to govern the consumer's opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers' actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

A Parallel Architecture for the Partitioning Around Medoids (PAM) Algorithm for Scalable Multi-Core Processor Implementation with Applications in Healthcare.

Clustering is the most common method for organizing unlabeled data into its natural groups (called clusters), based on similarity (in some sense or another) among data objects. The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization, image analysis, bioinformatics and data compression, but due to its high time complexity, the PAM algorithm cannot be used with large datasets or in any embedded or real-time application. In this work, we propose a simple and scalable parallel architecture for the PAM algorithm to reduce its running time. This architecture can easily be implemented either on a multi-core processor system to deal with big data or on a reconfigurable hardware platform, such as FPGA and MPSoCs, which makes it suitable for real-time clustering applications. Our proposed model partitions data equally among multiple processing cores. Each core executes the same sequence of tasks simultaneously on its respective data subset and shares intermediate results with other cores to produce results. Experiments show that the computational complexity of the PAM algorithm is reduced exponentially as we increase the number of cores working in parallel. It is also observed that the speedup graph of our proposed model becomes more linear with the increase in number of data points and as the clusters become more uniform. The results also demonstrate that the proposed architecture produces the same results as the actual PAM algorithm, but with reduced computational complexity.

The clinical anatomy of fibromyalgia.

Fibromyalgia is a disorder characterized by pain and a spectrum of psychological comorbidities, rendering treatment difficult, and often a financial burden. Findings regarding diagnosis, prevalence, comorbidities, and potential pathophysiological links are discussed herein. Fibromyalgia is a complex disorder and there are specific criteria that patients must meet for diagnosis, including scores on fibromyalgia questionnaires, commonalities of age, gender, menopause status, sleep disturbances, and mood symptoms. The close relationship between fibromyalgia and other chronic disorders should alert the physician to explore for comorbid illnesses. In this review of the clinical anatomy of fibromyalgia, we review new studies that could be significant for the current use of clinical interventions for patients with symptoms. Using standard search engines, the clinical anatomy of fibromyalgia is investigated and many related studies are mentioned herein. Fibromyalgia is considered a prototypical central chronic pain syndrome and is associated with widespread pain that fluctuates spontaneously. There is also substantial lifetime psychiatric comorbidity in individuals with fibromyalgia, resulting in a low health-related quality of life. These results have important clinical and theoretical implications, including the possibility that fibromyalgia could share underlying pathophysiological links with some psychiatric disorders. This reveals that patients with fibromyalgia have findings compatible with tissue injury pain, the pain mechanisms involving both the primary afferent neuron and the nociceptive systems in the central nervous system. (1) There is a relationship between fibromyalgia and chronic disorders. This should alert the physician to explore for comorbid illnesses. (2) There is substantial lifetime psychiatric comorbidity resulting in a low health-related quality of life. (3) Patients with fibromyalgia have findings compatible with tissue injury pain Clin. Anat. 31:387-391, 2018. © 2018 Wiley Periodicals, Inc.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

Energy efficient strategy for throughput improvement in wireless sensor networks.

Network lifetime and throughput are one of the prime concerns while designing routing protocols for wireless sensor networks (WSNs). However, most of the existing schemes are either geared towards prolonging network lifetime or improving throughput. This paper presents an energy efficient routing scheme for throughput improvement in WSN. The proposed scheme exploits multilayer cluster design for energy efficient forwarding node selection, cluster heads rotation and both inter- and intra-cluster routing. To improve throughput, we rotate the role of cluster head among various nodes based on two threshold levels which reduces the number of dropped packets. We conducted simulations in the NS2 simulator to validate the performance of the proposed scheme. Simulation results demonstrate the performance efficiency of the proposed scheme in terms of various metrics compared to similar approaches published in the literature.

Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels.

Retinal blood vessels are the source to provide oxygen and nutrition to retina and any change in the normal structure may lead to different retinal abnormalities. Automated detection of vascular structure is very important while designing a computer aided diagnostic system for retinal diseases. Most popular methods for vessel segmentation are based on matched filters and Gabor wavelets which give good response against blood vessels. One major drawback in these techniques is that they also give strong response for lesion (exudates, hemorrhages) boundaries which give rise to false vessels. These false vessels may lead to incorrect detection of vascular changes. In this paper, we propose a new hybrid feature set along with new classification technique for accurate detection of blood vessels. The main motivation is to lower the false positives especially from retinal images with severe disease level. A novel region based hybrid feature set is presented for proper discrimination between true and false vessels. A new modified m-mediods based classification is also presented which uses most discriminating features to categorize vessel regions into true and false vessels. The evaluation of proposed system is done thoroughly on publicly available databases along with a locally gathered database with images of advanced level of retinal diseases. The results demonstrate the validity of the proposed system as compared to existing state of the art techniques.

Robust framework to combine diverse classifiers assigning distributed confidence to individual classifiers at class level.

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

Hierarchical lifelong topic modeling using rules extracted from network communities.

Topic models extract latent concepts from texts in the form of topics. Lifelong topic models extend topic models by learning topics continuously based on accumulated knowledge from the past which is updated continuously as new information becomes available. Hierarchical topic modeling extends topic modeling by extracting topics and organizing them into a hierarchical structure. In this study, we combine the two and introduce hierarchical lifelong topic models. Hierarchical lifelong topic models not only allow to examine the topics at different levels of granularity but also allows to continuously adjust the granularity of the topics as more information becomes available. A fundamental issue in hierarchical lifelong topic modeling is the extraction of rules that are used to preserve the hierarchical structural information among the rules and will continuously update based on new information. To address this issue, we introduce a network communities based rule mining approach for hierarchical lifelong topic models (NHLTM). The proposed approach extracts hierarchical structural information among the rules by representing textual documents as graphs and analyzing the underlying communities in the graph. Experimental results indicate improvement of the hierarchical topic structures in terms of topic coherence that increases from general to specific topics.

A deep learning based ensemble learning method for epileptic seizure prediction.

In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is extremely important so that they can be controlled with medication before they actually occur. Researchers have proposed multiple machine/deep learning based methods to predict epileptic seizures; however, accurate prediction of epileptic seizures with low false positive rate is still a challenge. In this research, we propose a deep learning based ensemble learning method to predict epileptic seizures. In the proposed method, EEG signals are preprocessed using empirical mode decomposition followed by bandpass filtering for noise removal. The class imbalance problem has been mitigated with synthetic preictal segments generated using generative adversarial networks. A three-layer customized convolutional neural network has been proposed to extract automated features from preprocessed EEG signals and combined them with handcrafted features to get a comprehensive feature set. The feature set is then used to train an ensemble classifier that combines the output of SVM, CNN and LSTM using Model agnostic meta learning. An average sensitivity of 96.28% and specificity of 95.65% with an average anticipation time of 33 min on all subjects of CHBMIT has been achieved by the proposed method, whereas, on American epilepsy society-Kaggle seizure prediction dataset, an average sensitivity of 94.2% and specificity of 95.8% has been achieved on all subjects.

Detection of preictal state in epileptic seizures using ensemble classifier.

OBJECTIVE: Epilepsy affected patient experiences more than one frequency seizures which can not be treated with medication or surgical procedures in 30% of the cases. Therefore, an early prediction of these seizures is inevitable for these cases to control them with therapeutic interventions. METHODS: In recent years, researchers have proposed multiple deep learning based methods for detection of preictal state in electroencephalogram (EEG) signals, however, accurate detection of start of preictal state remains a challenge. We propose a novel ensemble classifier based method that gets the comprehensive feature set as input and combines three different classifiers to detect the preictal state. RESULTS: We have applied the proposed method on the publicly available scalp EEG dataset CHBMIT of 22 subjects. An average accuracy of 94.31% with sensitivity and specificity of 94.73% and 93.72% respectively has been achieved with the method proposed in this study. CONCLUSIONS: Proposed study utilizes the preprocessing techniques for noise removal, combines deep learning based and handcrafted features and an ensemble classifier for detection of start of preictal state. Proposed method gives better results in terms of accuracy, sensitivity, and specificity.

Next-generation UWB antennas gadgets for human health care using SAR.

The body area network is now the most challenging and most popular network for study and research. Communication about the body has undoubtedly taken its place due to a wide variety of applications in industry, health care, and everyday life in wireless network technologies. The body area network requires such smart antennas that can provide the best benefits and reduce interference with the same channel. The discovery of this type of antenna design is at the initiative of this research. In this work, to get a good variety, the emphasis is on examining different techniques. The ultra-wide band is designed, simulated, and manufactured because the ultra-wide band offers better performance compared to narrowband antennas. To analyze the specific absorption rate, we designed a multilayer model of human head and hand in the high-frequency structure simulator. In the final stage, we simulated our antennas designed with the head and hand model to calculate the results of the specific absorption rate. The analysis of the specific absorption rate for the head and hand was calculated by placing the antennas on the designed model.

Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies.

Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a seizure before it occurs may prove useful as patients might be alerted to make themselves safe or seizures could be prevented with therapeutic interventions just before they occur. Abnormal neuronal activity, the preictal state, starts a few minutes before the onset of a seizure. In recent years, different methods have been proposed to predict the start of the preictal state. These studies follow some common steps, including recording of EEG signals, preprocessing, feature extraction, classification, and postprocessing. However, online prediction of epileptic seizures remains a challenge as all these steps need further refinement to achieve high sensitivity and low false positive rate. In this paper, we present a comparison of state-of-the-art methods used to predict seizures using both scalp and intracranial EEG signals and suggest improvements to existing methods.

Neuroanatomy of Anxiety: A Brief Review.

Anxiety disorders are among the most prevalent psychological issues worldwide, displaying the youngest age of onset and greatest chronicity of any mood or substance abuse disorder. Given the high social and economic cost imposed by these disorders, developing effective treatments is of the utmost importance. Anxiety disorders manifest in a variety of symptomatic phenotypes and are highly comorbid with other psychological diseases such as depression. These facts have made unraveling the complex underlying neural circuity an ever-present challenge for researchers. We offer a brief review on the neuroanatomy of anxiety disorders and discuss several currently available therapeutic options.

Neuroanatomy and Neuropsychology of Pain.

We have reviewed here the neuroanatomical and neuropsychological literature of the human brain and have proposed the various pain mechanisms that we currently know of. Essentially when tissue is damaged, peripheral nociceptors are activated continuously and prostanoids are hence produced. Nonsteroidal anti-inflammatory drugs (NSAIDs) and medications aim to target these prostanoids to treat the inflammatory component of pain. Normal pain tends to have a protective response. It is important for the nervous system to learn and recognize this painful stimulus earlier and quicker with repeated exposure to avoid tissue damage. This neuronal plasticity and gain in sensitivity result in sensitization of the nervous system, both centrally and peripherally and help in earlier detection of the pain sensation. However, persistent pain can become pathologic and will eventually result in the loss of protection pain offers to the body. Pain-related fear has been implicated in the transition from acute to chronic low back pain and the persistence of disabling low back pain, making it a key target for physiotherapy intervention. The current understanding of pain-related fear is that it is a psychopathological problem where people who catastrophise about the meaning of pain become trapped in a vicious cycle of avoidance behaviour, pain and disability, as recognised in the fear-avoidance model. We looked at how pain is perceived, especially in low-back pain patients. It has been hypothesized that individuals with low-back pain (LBP) can change their motor behavior, which is fundamentally an adaptation mechanism aimed at minimizing the real or perceived risk of further pain.

Bilateral Absence of the Zygomatic Nerve and Zygomaticofacial Nerve and Foramina.

The zygomaticofacial branch (ZFb) of the zygomatic nerve travels along the inferolateral angle of the orbit, traverses the zygomaticofacial foramen (ZFF) in the zygomatic bone, and then perforates the orbicularis oculi muscle to finally reach the skin of the malar area, which it innervates. The bilateral absence of the ZFb and the ZFF was found in an 80-year-old Caucasian cadaver. In addition, both zygomatic nerves were absent. A thin nerve arising from the lacrimal nerve passed below it and gave rise to the lacrimal branch and a communicating branch to the lacrimal nerve. This then entered the small bony canal, which opened at the medial aspect of the lateral wall of the orbit on the right and left sides. The bilateral absence of the ZFb of the zygomatic nerve and its foramen appears to be uncommon but should be realized during surgery or invasive procedures over the cheek or infraorbital region. The additional absence of both zygomatic nerves is exceptional.

Split Femoral Nerve Due to Psoas Tertius Muscle: A Review with Other Cases of Variant Muscles Traversing the Femoral Nerve.

Leg pain from lumbar disc herniation is a common presentation. However, certain muscular and peripheral nerve variants may present similarly and represent an unrecognized etiology of femoral nerve dysfunction. Such cases might affect the outcome of specific treatment regimes. Therefore, recognition of these variations in anatomy may be useful to the clinician when treating the patient with medically refractory lower limb pain. Some reports have reported variant slips of the psoas and iliacus muscles, which may split the femoral nerve causing a potential risk for nerve entrapment. Herein, we report a very unusual variant of the psoas muscles, the psoas tertius, which pierced the femoral nerve into two parts. Additionally, the literature of other similar muscle variants is reviewed. Clinicians should be aware of anatomical muscular variants of the posterior abdominal wall and the propensity of such anomalies to result in distortion of regional neural structures. In this regard, the anatomy of the psoas tertius should be known.

Neuroleukemiosis: Two Case Reports.

Extramedullary tumors composed of myeloblasts or monoblasts can present in various locations. Patients with a history of acute myeloid leukemia (AML) can present with neuropathic pain and no evidence of relapse of their leukemia. Neuroleukemiosis is a form of extramedullary tumor present in the peripheral nervous systems (PNS) of leukemia patients. We report two AML patients who were in remission and later presented with neurological symptoms due to neuroleukemiosis with negative bone marrow biopsies.

Online Knowledge-Based Model for Big Data Topic Extraction.

Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.

Computer Based Melanocytic and Nevus Image Enhancement and Segmentation.

Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the most critical yet tricky part in computerized lesion detection and classification. The effectiveness and accuracy of lesion classification are critically dependent on the quality of lesion segmentation. In this paper, we have proposed a novel approach that can automatically preprocess the image and then segment the lesion. The system filters unwanted artifacts including hairs, gel, bubbles, and specular reflection. A novel approach is presented using the concept of wavelets for detection and inpainting the hairs present in the cancer images. The contrast of lesion with the skin is enhanced using adaptive sigmoidal function that takes care of the localized intensity distribution within a given lesion's images. We then present a segmentation approach to precisely segment the lesion from the background. The proposed approach is tested on the European database of dermoscopic images. Results are compared with the competitors to demonstrate the superiority of the suggested approach.