ZnO oxide nanoparticles-Copper metal-organic framework composite on 3D porous nickel foam: a novel electrochemical sensing platform to detect serotonin in blood

Herein, we report a simple non-enzymatic electrochemical sensor for the detection of serotonin (5-HT) in blood serum using ZnO oxide nanoparticles-Copper metal-organic framework (MOF) composite on 3D porous nickel foam, namely, ZnO-Cu MOF/NF. The XRD analysis reveals the crystalline nature of synthesized Cu MOF and Wurtzite structure of ZnO nanoparticles, whereas SEM characterization confirms the high surface area of the composite nanostructures. Differential pulse voltammetry (DPV) analysis under optimal conditions yields a wide linear detection range of 1 ng/ml to 1 mg/ml to 5-HT concentrations and a LOD (signal to noise ratio = 3.3) of 0.49 ng/ml, which is well below the lowest physiological concentration of 5-HT. The sensitivity of the fabricated sensor is found to be 0.0606 mA/ng mL^-1.cm², and it exhibited remarkable selectivity towards serotonin in the presence of various interferants, including dopamine and AA, which coexists in the real biological matrix. Further, successful determination of 5-HT is achieved in the simulated blood serum sample with a good recovery percentage from ~102.5 to ~99.25%. The synergistic combination of the excellent electrocatalytic properties and surface area of the constituent nanomaterials proves the overall efficacy of this novel platform shows immense potential to be used in developing versatile electrochemical sensors.&#xD.

Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel Learning

In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and respiration rate (RESP) from N = 10 healthy drivers in experiments of 25min duration with different stress states (5min resting, 10min driving, 10min driving + answering cognitive questions). We manually remove unusable segments and approximately 4h of data remain. Multimodal time and frequency features are extracted and employed to regularized deep kernel machine learning based on a fusion framework. Task-specific representations of different physiological signals are combined using intermediate fusion. Subsequently, the fused multimodal features are fed a support vector machine (SVM) and a random forest (RF) for stress classification. The experimental results show that the proposed approach can discriminate between stress states. The combination of PPG and ECG using RF as classifier yields the highest F1-score of 0.97 in the test set. PPG only and RF yield a maximum F1-score of 0.90. Furthermore, subject-specific cross-validation improves performance. ECG and PPG signals are reliable in classifying the stress state of a car driver. In summary, the proposed framework could be extended to real-time stress state assessment in driving conditions.

Automated Emotion Recognition System Using Blood Volume Pulse and XGBoost Learning

In this study, a new method for detecting emotions using Blood Volume Pulse (BVP) signals and machine learning was presented. The BVP of 30 subjects from the publicly available CASE dataset was pre-processed, and 39 features were extracted from various emotional states, such as amusing, boring, relaxing, and scary. The features were categorized into time, frequency, and time-frequency domains and used to build an emotion detection model with XGBoost. The model achieved the highest classification accuracy of 71.88% using the top 10 features. The most significant features of the model were computed from time (5 features), time-frequency (4 features), and frequency (1 feature) domains. The skewness calculated from the time-frequency representation of the BVP was ranked highest and played a crucial role in the classification. Our study suggests the potential of using BVP recorded from wearable devices to detect emotions in healthcare applications.

Identifying the Optimal Location of Facial EMG for Emotion Detection Using Logistic Regression

In this study, we analyzed the utility of electromyogram (EMG) signals recorded from the zygomaticus major (zEMG), the trapezius (tEMG), and the corrugator supercilii (cEMG) for emotion detection. We computed eleven-time domain features from the EMG signals to classify the emotions such as amusing, boring, relaxing, and scary. The features were fed to the logistic regression, support vector machine, and multilayer perceptron classifiers, and model performance was evaluated. We achieved an average 10-fold cross-validation classification accuracy of 67.29%. 67.92% and 64.58% by LR using the features extracted from the EMG signals recorded from the zEMG, tEMG, and cEMG, respectively. The classification accuracy improved to 70.6% while combining features from the zEMG and cEMG for the LR model. However, the performance dropped while including the features of EMG from all three locations. Our study shows the importance of utilizing the zEMG and cEMG combination for emotion recognition.

Deep Learning Framework for Categorical Emotional States Assessment Using Electrodermal Activity Signals

In this study, we attempted to classify categorical emotional states using Electrodermal Activity (EDA) signals and a configurable Convolutional Neural Network (cCNN). The EDA signals from the publicly available, Continuously Annotated Signals of Emotion dataset were down-sampled and decomposed into phasic components using the cvxEDA algorithm. The phasic component of EDA was subjected to Short-Time Fourier Transform-based time-frequency representation to obtain spectrograms. These spectrograms were input to the proposed cCNN to automatically learn the prominent features and discriminate varied emotions such as amusing, boring, relaxing, and scary. Nested k-Fold cross-validation was used to evaluate the robustness of the model. The results indicated that the proposed pipeline could discriminate the considered emotional states with a high average classification accuracy, recall, specificity, precision, and F-measure scores of 80.20%, 60.41%, 86.8%, 60.05%, and 58.61%, respectively. Thus, the proposed pipeline could be valuable in examining diverse emotional states in normal and clinical conditions.

Comparative Analysis of Electrodermal Activity Decomposition Methods in Emotion Detection Using Machine Learning

Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance. Decomposition analysis is used to deconvolve the EDA into slow and fast varying tonic and phasic activity, respectively. In this study, we used machine learning models to compare the performance of two EDA decomposition algorithms to detect emotions such as amusing, boring, relaxing, and scary. The EDA data considered in this study were obtained from the publicly available Continuously Annotated Signals of Emotion (CASE) dataset. Initially, we pre-processed and deconvolved the EDA data into tonic and phasic components using decomposition methods such as cvxEDA and BayesianEDA. Further, 12 time-domain features were extracted from the phasic component of EDA data. Finally, we applied machine learning algorithms such as logistic regression (LR) and support vector machine (SVM), to evaluate the performance of the decomposition method. Our results imply that the BayesianEDA decomposition method outperforms the cvxEDA. The mean of the first derivative feature discriminated all the considered emotional pairs with high statistical significance (p<0.05). SVM was able to detect emotions better than the LR classifier. We achieved a 10-fold average classification accuracy, sensitivity, specificity, precision, and f1-score of 88.2%, 76.25%, 92.08%, 76.16%, and 76.15% respectively, using BayesianEDA and SVM classifiers. The proposed framework can be utilized to detect emotional states for the early diagnosis of psychological conditions.

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms

BACKGROUND

This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD).

METHODOLOGY

This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers.

DISCUSSION

This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences.

TRIAL REGISTRATION

Systematic review registration: PROSPERO CRD42022334988.

Assessment of emotional states in EEG signals using multi-frequency power spectrum and functional connectivity patterns

In this work, an attempt has been made to characterize arousal and valence emotional states using Electroencephalogram (EEG) signals and Phase lag index (PLI) based functional connectivity features. For this, EEG signals are considered from a publicly available DEAP database. Signals are decomposed into four frequency bands, namely theta (θ, 4-7 Hz), alpha (a, 8-12 Hz), beta (ß, 13-30 Hz), and gamma (γ, 30-45 Hz). Two features, namely relative PSD and PLI, are calculated from each band of signals with Welch's periodogram. Four classifiers, namely Random Forest (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and K-Nearest Neighbor (KNN), are employed to discriminate the emotional states. Results show that the proposed approach can differentiate emotional states using EEG signals. It is observed that there is strong functional connectivity in Fp1-02 and Fp2-Pz in all emotional states for different frequency bands. SVM classifier yields the highest classification performance for arousal, and RF yields the highest performance for valence in the y band. The combination of all features performs the best for the valence dimension. Thus, the proposed approach could be extended for classifying various emotional states in clinical settings. Clinical Relevance- This establishes PLI based approach for improved classification (fl = 74.77% for Arousal fl = 74.94 for valence) of emotional states.

Human activity recognition from textile electrocardiograms

Textile sensors for physiological signals bear the potential of unobtrusive and continuous application in daily life. Recently, textile electrocardiography (ECG) sensors became available which are of particular interest for physical activity monitoring due to the high effect of exercise on the heart rate. In this work, we evaluate the effectiveness of a single-lead ECG signal acquired using a non-medical-grade ECG shirt for human activity recognition (HAR). Healthy volunteers (N=10) wore the shirt during four different activities (sleeping, sitting, walking, running) in an uncontrolled environment and ECG data (256 Hz, 12 Bit) was stored, manually checked, and unusable segments (e.g. no sensor contact) were removed, resulting in a total of 228 hours of recording. Signals were split in short segments of different duration (10, 30, 60s), transformed using the Short-time Fourier Transform (STFT) to a spectrogram image and fed into a state-of-the-art convolutional neural network (CNN). The best configuration results in an F'l-Score of 73% and an accuracy of 77% on the test set. Results with leave-one-subject-out cross-validation show F'l-Scores ranging from 41 % to 80%. Thus, a single-lead, wearable-generated ECG has an informative value for HAR to a certain extent. In future work, we aim at using more sensors of the smart shirt and sensor fusion.

Phenotypes based Classification of Blood-Brain-Barrier Drugs using Feature Selection Methods and Extreme Gradient Boosting

In this work, an attempt has been made to discriminate drug with blood brain barrier (BBB) permeability using clinical phenotypes and extreme gradient boosting (XGBoost) methods. For this, the drug name and their clinical phenotypes namely side effects and indications are obtained from public available database. Prominent clinical phenotypes are selected using genetic algorithm (GA) and binary particle swarm optimization (BPSO). Four machine algorithms namely k-Nearest Neighbours, support vector machines, rotation forest and XGBoost are used for classification of BBB drugs. The result show that the proposed clinical phenotypes based features are able to distinguish drugs with BBB permeability. The maximum number of clinical phenotypes (69%) is reduced by BPSO and GA for classification. The XGBoost method is found to be most accurate [Formula: see text] is discriminating drugs with BBB permeability. The proposed approach are found to be capable of handling multi-parametric characteristics of the drugs. Particularly, the combination of XGBoost with combination of side effects and indications could be used for precision medicine applications. Clinical relevance- This establishes XGBoost approach for improved BBB permeability based drug classification with F1 =98.7% using exclusively clinical phenotypes.

Automated Segmentation of Lateral Ventricles in Alzheimer's Conditions Using UNET++ Model

Accurate diagnosis of Alzheimer's disease (AD) in early stage can control the disease progression. Enlargement of Lateral Ventricles (LV) is one of the significant imaging biomarkers for the differentiation of Alzheimer's conditions. However, segmentation of accurate LV for analysis is still challenging. In this work, an attempt is made to segment LV regions from brain MR images using the UNet++ model. For this, axial scans of the MR images are taken from the publicly available Open Access Series of Imaging Studies (OASIS) Brain dataset. LV-based region of interest is segmented using the UNet++ network. Results show that the proposed approach is able to segment brain regions in Alzheimer's conditions. The UNet++ network model yields the highest dice score of 99.4% and sensitivity of 99.3% in segmenting the LV brain region. Thus, the proposed method could be useful for characterizing Alzheimer's condition.

FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.

Classification of Dichotomous Emotional States Using Electrodermal Activity Signals and Multispectral Analysis

In this work, an analysis based on complex demodulation is proposed to classify dichotomous emotional states using Electrodermal activity (EDA) signals. For this, annotated happy and sad EDA is obtained from an online public database. The sympathetic activity indices, namely Time-varying (TVSymp) and Modified TVSymp, are computed from the reconstructed EDA signal. Further, the derivative of phasic EDA is calculated from the phasic component obtained using the convex optimization (cvxEDA) based EDA decomposition method. Five statistical features are computed from each index and used for the classification. The results of the classification indicate that these features are capable of differentiating happy and sad emotional states with 75% accuracy. This technique could be effective in the identification of clinical disorders associated with happy and sad emotional states.

Extreme Gradient Boosting Based Improved Classification of Blood-Brain-Barrier Drugs

In this study, the analysis based on boosting approach namely linear and tree method are explored in extreme gradient boosting (XGBoost) to classify blood brain barrier drugs using clinical phenotype. The clinical phenotype features of BBB drugs are Public available SIDER dataset. The clinical features namely drug's side effect, drug's indication and the combination is fed to XGBoost. Results shows that the proposed approach is able to discriminate BBB drugs. The combination of XGBoost with tree boosting is found to be most accurate (F1=78.5%) in classifying BBB drugs. This method of tree boosting in XGBoost may be extended to access the drugs for precision medicine.

Classification of Emotional States Using EEG Signals and Wavelet Packet Transform Features

In this work, an attempt has been made to classify arousal and valence states of emotion using time-domain features extracted from the Wavelet Packet Transform. For this, Electroencephalogram (EEG) signals from the publicly available DEAP database are considered. EEG signals are first decomposed using wavelet packet decomposition into θ, α, β, and γ bands. Then featural, namely band energy, sub-band energy ratio, root mean of energy, and information entropy of band energy is estimated. These features are fed into various machine learning classifiers such as support vector machines, linear discriminant analysis, K-nearest neighbor, and random forest. Results indicate that features extracted from wavelet packet transform can predict the arousal and valence emotional states. It is also seen that Support Vector Machines perform the best for both arousal (f-m = 75.68%) and valence(f-m=57.53%). This method can be used for the recognition of emotional states for various clinical purposes in emotion-related psychological disorders like major depressive disorder.

A data driven learning approach for the assessment of data quality

Background

Data quality assessment is important but complex and task dependent. Identifying suitable measurement methods and reference ranges for assessing their results is challenging. Manually inspecting the measurement results and current data driven approaches for learning which results indicate data quality issues have considerable limitations, e.g. to identify task dependent thresholds for measurement results that indicate data quality issues.

Objectives

To explore the applicability and potential benefits of a data driven approach to learn task dependent knowledge about suitable measurement methods and assessment of their results. Such knowledge could be useful for others to determine whether a local data stock is suitable for a given task.

Methods

We started by creating artificial data with previously defined data quality issues and applied a set of generic measurement methods on this data (e.g. a method to count the number of values in a certain variable or the mean value of the values). We trained decision trees on exported measurement methods’ results and corresponding outcome data (data that indicated the data’s suitability for a use case). For evaluation, we derived rules for potential measurement methods and reference values from the decision trees and compared these regarding their coverage of the true data quality issues artificially created in the dataset. Three researchers independently derived these rules. One with knowledge about present data quality issues and two without.

Results

Our self-trained decision trees were able to indicate rules for 12 of 19 previously defined data quality issues. Learned knowledge about measurement methods and their assessment was complementary to manual interpretation of measurement methods’ results.

Conclusions

Our data driven approach derives sensible knowledge for task dependent data quality assessment and complements other current approaches. Based on labeled measurement methods’ results as training data, our approach successfully suggested applicable rules for checking data quality characteristics that determine whether a dataset is suitable for a given task.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01656-x.

Classification of Alzheimer Condition using MR Brain Images and Inception-Residual Network Model

Alzheimer’s Disease (AD) is an irreversible progressive neurodegenerative disorder. Magnetic Resonance (MR) imaging based deep learning models with visualization capabilities are essential for the precise diagnosis of AD. In this study, an attempt has been made to categorize AD and Healthy Controls (HC) using structural MR images and an Inception-Residual Network (ResNet) model. For this, T1- weighted MR brain images are acquired from a public database. These images are pre-processed and are applied to a two-layer Inception-ResNet-A model. Additionally, Gradient weighted Class Activation Mapping (Grad-CAM) is employed to visualize the significant regions in MR images identified by the model for AD classification. The network performance is validated using standard evaluation metrics. Results demonstrate that the proposed Inception-ResNet model differentiates AD from HC using MR brain images. The model achieves an average recall and precision of 69%. The Grad- CAM visualization identified lateral ventricles in the mid-axial slice as the most discriminative brain regions for AD classification. Thus, the computer aided diagnosis study could be useful in the visualization and automated analysis of AD diagnosis with minimal medical expertise.

EEG based emotion recognition using entropy features and Bayesian optimized random forest

Electroencephalography (EEG) based emotion recognition is a widely preferred technique due to its noninvasiveness. Also, frontal region-specific EEG signals have been associated with emotional processing. Feature reductionbased optimized machine learning methods can improve the automated analysis of frontal EEG signals. In this work, an attempt is made to classify emotional states using entropybased features and Bayesian optimized random forest. For this, the EEG signals of prefrontal and frontal regions (Fp1, Fp2, Fz, F3, and F4) are obtained from an online public database. The signals are decomposed into five frequency bands, namely delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (14-30 Hz), and gamma (30-45 Hz). Three entropy features, namely Dispersion Entropy (DE), Sample Entropy (SE), and Permutation Entropy (PE), are extracted and are dimensionally reduced using Principal Component Analysis (PCA). Further, the reduced features are applied to the Bayesian optimized random forest for the classification. The results show that the DE in the gamma band and SE in the alpha band exhibit a statistically significant (p < 0.05) difference for classifying arousal and valence emotional states. The selected features from PCA yield an F-measure of 73.24% for arousal and 46.98% for valence emotional states. Further, the combination of all features yields a higher F-measure of 48.13% for valence emotional states. The proposed method is capable of handling multicomponent variations of frontal region-specific EEG signals. Particularly the combination of selected features could be useful to characterize arousal and valence emotional states.

Electrodermal Activity Based Emotion Recognition using Time-Frequency Methods and Machine Learning Algorithms

In this work, the feasibility of time-frequency methods, namely short-time Fourier transform, Choi Williams distribution, and smoothed pseudo-Wigner-Ville distribution in the classification of happy and sad emotional states using Electrodermal activity signals have been explored. For this, the annotated happy and sad signals are obtained from an online public database and decomposed into phasic components. The time-frequency analysis has been performed on the phasic components using three different methods. Four statistical features, namely mean, variance, kurtosis, and skewness are extracted from each method. Four classifiers, namely logistic regression, Naive Bayes, random forest, and support vector machine, have been used for the classification. The combination of the smoothed pseudo-Wigner-Ville distribution and random forest yields the highest F-measure of 68.74% for classifying happy and sad emotional states. Thus, it appears that the suggested technique could be helpful in the diagnosis of clinical conditions linked to happy and sad emotional states.

Registered report protocol: Developing an artifact index for capacitive electrocardiography signals acquired with an armchair

Continuous monitoring of an electrocardiogram (ECG) in private diagnostic spaces such as vehicles or apartments allows early detection of cardiovascular diseases. We will use an armchair with integrated capacitive electrodes to record the capacitive electrocardiogram (cECG) during everyday activities. However, movements and other artifacts affect the signal quality. Therefore, an artifact index is needed to detect artifacts and classify the cECG. The unavailability of cECG data and reliable ground truth information requires new recordings to develop an artifact index. This study is designed to test the hypothesis: an artifact index can be devised, which intends to estimate the signal quality of segments and classify signals. In a single-arm study with 44 subjects, we will record two activities of 11-minute duration: reading and watching television. During recording, we will capture cECG, ECG, and oxygen saturation (SpO2) with time synchronization as well as keypoint-based movement indicators obtained from a video camera. SpO2 provides additional information on the subject's health status. The keypoint-based movements indicate artifacts in the cECG. We will combine all ground truth data to evaluate the index. In the future, we aim at using the artifact index to exclude cECG segments with artifacts from further analysis. This will improve cECG technology for the measurement of cardiovascular parameters.

Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network

In this work, an attempt has been made to classify emotional states using electrodermal activity (EDA) signals and multiscale convolutional neural networks. For this, EDA signals are considered from a publicly available "A Dataset for Emotion Analysis using Physiological Signals" (DEAP) database. These signals are decomposed into multiple-scales using the coarse-grained method. The multiscale signals are applied to the Multiscale Convolutional Neural Network (MSCNN) to automatically learn robust features directly from the raw signals. Experiments are performed with the MSCNN approach to evaluate the hypothesis (i) improved classification with electrodermal activity signals, and (ii) multiscale learning captures robust complementary features at a different scale. Results show that the proposed approach is able to differentiate various emotional states. The proposed approach yields a classification accuracy of 69.33% and 71.43% for valence and arousal states, respectively. It is observed that the number of layers and the signal length are the determinants for the classifier performance. The performance of the proposed approach outperforms the single-layer convolutional neural network. The MSCNN approach provides end-to-end learning and classification of emotional states without additional signal processing. Thus, it appears that the proposed method could be a useful tool to assess the difference in emotional states for automated decision making.

Adaptive learning and cross training improves R-wave detection in ECG

BACKGROUND AND OBJECTIVE

Automated R-wave detection plays a vital role in electrocardiography (ECG) and ECG-based computer-aided diagnosis. Recently, a multi-level one-dimensional (1D) deep learning approach was presented that shows good performance as compared to traditional methods.

METHODS

In this paper, we present several improvements of the multi-level 1D convolutional neural network (CNN)-based deep learning approach using: (i) adaptive deep learning, (ii) cross-database training, and (iii) cross-lead training. For this, we consider ECG signals from four publicly available databases: MIT-BIH, INCART, TELE, and SDDB, having 109,404, 175,660, 6,708, and 1,684,447 annotated beats, respectively. Except for TELE, all databases provide at least two-lead recordings. To evaluate the improvements, experiments are performed with adaptive k-times cross-trained databases validation scheme (k = 5). The hypothesis tested are: (i) the improvements outperform the state-of-the-art, (ii) cross-database training and adaptive deep learning contribute, and (iii) additional databases or cross-lead training further improves the results.

RESULTS

Our proposed approach outperforms the state-of-the-art. In terms of F-measure, F = 99.75% and F = 95.25% is obtained for the MIT-BIH and TELE databases, respectively. Further, cross-database training (F = 98.02%) is found to be more effective than training on individual databases (F = 97.33%). The performance of our approach further improves when additional databases and different leads are used for training.

CONCLUSION

Existing state-of-the-art methods perform low on noisy and pathological signals. Adaptive cross-data training identifies the optimal model. Using multiple datasets and leads allows analyzing noisy, pathological and mobile-recorded long-term ECG signals without ground truths. These conclusions are based on the comprehensive evaluation of four different databases, and in total, about 4.5 million annotated beats.

Emotion Analysis Using Electrodermal Signals and Spiking Deep Belief Network

In this work, an attempt has been made to discriminate arousal and valence dimensions in Electrodermal Activity (EDA) signals using Spiking Deep Belief Network (SDBN). EDA signals having different arousal and valence dimensions are obtained from public online database. These signals are divided into equal parts and normalized using channel normalization. Later, signals are subjected to SDBN for event-related features and classification. A leave-one-out cross validation is used to investigate the classification performance. The result shows that the SDBN classifiers are able to discriminate the emotional states. The network yields better classification performance for emotional dimensions of arousal and valence. It appears that the proposed approach can be used to differentiate autonomic and pathological conditions.

Binary Grey Wolf Optimizer based Feature Selection for Nucleolar and Centromere Staining Pattern Classification in Indirect Immunofluorescence Images

In this work, an attempt is made to distinguish nucleolar and centromere staining patterns using Bag-of-Keypoint Features (BoKF) model and Binary Grey Wolf Optimization (BGWO) based feature selection. Fluorescent staining patterns are produced by Indirect Immunofluorescence (IIF) Imaging and the patterns considered for this study are taken from a publicly available online database. The IIF images are pre-processed using edge-aware local contrast enhancement method. The contrast enhanced images are subjected to BoKF framework and Speeded up Robust Feature keypoints are extracted. Further, the most significant features are identified using BGWO and are fed to k-Nearest Neighbor (kNN) for classification. The results show that the BGWO features are able to classify the nucleolar and centromere patterns with an average accuracy of 91.6%. Results also indicate that the prominent features obtained using BGWO can improve the discrimination performance of IIF staining patterns. Hence it appears that the BGWO based feature selection could enable the computer aided diagnosis of autoimmune diseases.

Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolution Neural Network

Automated emotion recognition plays a vital role in problem solving, decision making and social activities of human life. An emotion is a set of reactions and experience to a given conditions, which are modeled as a linear combination of arousal and valence dimensions. Emotional pattern recognition based on physiological signals is a relatively new and fast growing area of research. Several physiological signals such as ECG, EEG, and EMG have been used for emotion recognition. Analysis of Electrodermal Activity (EDA) signals is one of the popular technique for emotion state analysis. In this work, an attempt is made to discriminate arousal-valence dimensions using EDA signals and multiscale one dimensional convolution neural network (MCNN). For this, EDA signals are obtained from publically available online DEAP database. These signals are normalized using channel normalization and subjected to MCNN for event-related robust features and classification. K-fold cross validation is used to investigate the performance of classifier. The result shows that the MCNN are able to discriminate the emotional states in arousal/valence dimensions. The proposed approach obtained an overall classification accuracy of 83.75% and 81.25% for arousal and valence scale, respectively. The network yields better classification performance for arousal scale then valence diemension. This might be due to the fact that arousal represent the intensity of emotions. The result also show that the proposed approach is better than the conventional hand-crafted feature based approach. Thus, it appears that the proposed approach can be used to differentiate autonomic and clinical conditions.

Deep Learning on 1-D Biosignals: a Taxonomy-based Survey

OBJECTIVES

 Deep learning models such as convolutional neural networks (CNNs) have been applied successfully to medical imaging, but biomedical signal analysis has yet to fully benefit from this novel approach. Our survey aims at (i) reviewing deep learning techniques for biosignal analysis in computer- aided diagnosis; and (ii) deriving a taxonomy for organizing the growing number of applications in the field.

METHODS

 A comprehensive literature research was performed using PubMed, Scopus, and ACM. Deep learning models were classified with respect to the (i) origin, (ii) dimension, and (iii) type of the biosignal as input to the deep learning model; (iv) the goal of the application; (v) the size and (vi) type of ground truth data; (vii) the type and (viii) schedule of learning the network; and (ix) the topology of the model.

RESULTS

 Between January 2010 and December 2017, a total 71 papers were published on the topic. The majority (n = 36) of papers are on electrocariography (ECG) signals. Most applications (n = 25) aim at detection of patterns, while only a few (n = 6) at predection of events. Out of 36 ECG-based works, many (n = 17) relate to multi-lead ECG. Other biosignals that have been identified in the survey are electromyography, phonocardiography, photoplethysmography, electrooculography, continuous glucose monitoring, acoustic respiratory signal, blood pressure, and electrodermal activity signal, while ballistocardiography or seismocardiography have yet to be analyzed using deep learning techniques. In supervised and unsupervised applications, CNNs and restricted Boltzmann machines are the most and least frequently used, (n = 34) and (n = 15), respectively.

CONCLUSION

 Our key-code classification of relevant papers was used to cluster the approaches that have been published to date and demonstrated a large variability of research with respect to data, application, and network topology. Future research is expected to focus on the standardization of deep learning architectures and on the optimization of the network parameters to increase performance and robustness. Furthermore, application-driven approaches and updated training data from mobile recordings are needed.

Clinical evaluation of nonsyndromic dental anomalies in Dravidian population: A cluster sample analysis

AIM

To record the prevalence rate of dental anomalies in Dravidian population and analyze the percentage of individual anomalies in the population.

METHODOLOGY

A cluster sample analysis was done, where 244 subjects studying in a dental institution were all included and analyzed for occurrence of dental anomalies by clinical examination, excluding third molars from analysis.

RESULTS

31.55% of the study subjects had dental anomalies and shape anomalies were more prevalent (22.1%), followed by size (8.6%), number (3.2%) and position anomalies (0.4%). Retained deciduous was seen in 1.63%. Among the individual anomalies, Talon's cusp (TC) was seen predominantly (14.34%), followed by microdontia (6.6%) and supernumerary cusps (5.73%).

CONCLUSION

Prevalence rate of dental anomalies in the Dravidian population is 31.55% in the present study, exclusive of third molars. Shape anomalies are more common, and TC is the most commonly noted anomaly. Varying prevalence rate is reported in different geographical regions of the world.

Granularity in granular cell ameloblastoma

Granular cell ameloblastoma (GCA) is one of the rare histological variants of ameloblastoma (1.5-3.5%), identified by Krompechner in 1918 and is diagnosed by the characteristic presence of granular cells. These granular cells are seen in several physiological and pathological conditions and the granularity in GCA is due to lysosomal aggregates. This review is about the clinical features, histopathological features and differential diagnosis of GCA and also adds the theories for occurrence of granularity, electron microscopic findings, cell signaling pathways and immunohistochemistry findings related to these granular cells in GCA.

Extracellular currents and potentials of the active myelinated nerve fiber

This paper is concerned with the accurate and rapid calculation of extracellular potentials and currents from an active myelinated nerve fiber in a volume conductor, under conditions of normal and abnormal conduction. The neuroelectric source for the problem is characterized mathematically by using a modified version of the distributed parameter model of L. Goldman and J. S. Albus (1968, Biophys. J., 8:596-607) for the myelinated nerve fiber. Solution of the partial differential equation associated with the model provides a waveform for the spatial distribution of the transmembrane potential V(z). This model-generated waveform is then used as input to a second model that is based on the principles of electromagnetic field theory, and allows one to calculate easily the spatial distribution for the potential everywhere in the surrounding volume conductor for the nerve fiber. In addition, the field theoretic model may be used to calculate the total longitudinal current in the extracellular medium (I0L(z)) and the transmembrane current per unit length (im(z)); both of these quantities are defined in connection with the well-known core conductor model and associated cable equations in electrophysiology. These potential and current quantities may also be calculated as functions of time and as such, are useful in interpreting measured I0L(t) and im(t) data waveforms. An analysis of the accuracy of conventionally used measurement techniques to determine I0L(t) and im(t) is performed, particularly with regard to the effect of electrode separation distance and size of the volume conductor on these measurements. Also, a simulation of paranodal demyelination at a single node of Ranvier is made and its effects on potential and current waveforms as well as on the conduction process are determined. In particular, our field theoretic model is used to predict the temporal waveshape of the field potentials from the active, non-uniformly conducting nerve fiber in a finite volume conductor.