Ramakrishnan Swaminathan

In this work, an attempt has been made to classify various emotional states in Electrodermal Activity (EDA) signals using modified Hjorth features and non-parametric classifiers. For this, the EDA signals are collected from a publicly available online database. The EDA is decomposed into SCL (Skin Conductance Level) and SCR (Skin Conductance Response). Five features, namely activity, mobility, complexity, chaos, and hazard, collectively known as modified Hjorth features, are extracted from SCR and SCL. Four non-parametric classifiers, namely, random forest, k-nearest neighbor, support vector machine, and rotation forest, are used for the classification. The results demonstrate that the proposed approach can classify the emotional states in EDA. Most of the features exhibit statistical significance in discriminating emotional states. It is found that the combination of modified Hjorth features and rotation forest is most accurate in classifying the emotional states. Thus, the result demonstrates that this method can recognize valence and arousal dimensions under various clinical conditions. © 2021 European Federation for Medical Informatics (EFMI) and IOS Press.

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.

In this study, an attempt has been made to differentiate Alzheimer's Disease (AD) stages in structural Magnetic Resonance (MR) images using single inception module network. For this, T1-weighted MR brain images of AD, mild cognitive impairment and Normal Controls (NC) are obtained from a public database. From the images, significant features are extracted and classified using an inception module network. The performance of the model is computed and analyzed for different input image sizes. Results show that the single inception module is able to classify AD stages using MR images. The end-to-end network differentiates AD from NC with 85% precision. The model is found to be effective for varied sizes of input images. Since the proposed approach is able to categorize AD stages, single inception module networks could be used for the automated AD diagnosis with minimum medical expertise. © 2021 European Federation for Medical Informatics (EFMI) and IOS Press.

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.

In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera‐based techniques? Following the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) statement, we conduct a systematic review of continuous camera‐based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. We consider articles that were published between January 2018 and April 2021 in the English language. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO2). In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO2, respectively, and 15 on multiple vital signs. HR and RR can be measured using red, green, and blue (RGB) and near‐infrared (NIR) as well as far‐infrared (FIR) cameras. So far, BP and SpO2 are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 °C for HR, RR, systolic BP, diastolic BP, and BST, respectively. The estimated error for SpO2 is less than 1%, but it increases with movements of the subject and the camera‐subject distance. Camera‐based remote monitoring mainly explores intensive care, post‐anaesthesia care, and sleep monitoring, but also explores special diseases such as heart failure. The monitored targets are newborn and pediatric patients, geriatric patients, athletes (e.g., exercising, cycling), and vehicle drivers. Camera‐based techniques monitor HR, RR, and BST in static conditions within acceptable ranges for certain applications. The research gaps are large and heterogeneous populations, real‐time scenarios, moving subjects, and accuracy of BP and SpO2 monitoring.

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.

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.

In this work, an attempt has been made to classify emotional states using Electrodermal Activity (EDA) signals and Convolutional Neural Network (CNN) learned features. The EDA signals are obtained from the publicly available DEAP database and are decomposed into tonic and phasic components. The phasic component is subjected to the short-time Fourier transform. Thirty-eight features of time, frequency, and time–frequency domain are extracted from the phasic signal. These extracted features are applied to CNN to learn robust and prominent features. Five machine learning algorithms, namely linear discriminant analysis, multilayer perceptron, support vector machine, decision tree, and extreme learning machine are used for the classification. The results show that the proposed approach is able to classify the emotional states using arousal-valence dimensions. Classification using CNN learned features are found to be better than the conventional features. The trained end-to-end CNN model is found to be accurate (F-measure = 79.30% and 71.41% for arousal and valence dimensions) in classifying various emotional states. The proposed method is found to be robust in handling the dynamic variation of EDA signals for different emotional states. The results show that the proposed approach outperformed most of the state-of-the-art methods. Thus, it appears that the proposed method could be beneficial in analyzing various emotional states in both normal and clinical conditions.

Emotions are essential for the intellectual ability of human beings defined by perception, concentration, and actions. Electroencephalogram (EEG) responses have been studied in different lobes of the brain for emotion recognition. An attempt has been made in this work to identify emotional states using time-domain features, and probabilistic random forest based decision fusion. The EEG signals are collected for this from an online public database. The prefrontal and frontal electrodes, namely Fp1, Fp2, F3, F4, and Fz are considered. Eleven features are extracted from each electrode, and subjected to a probabilistic random forest. The probabilities are employed to Dempster-Shafer's (D-S) based evidence theory for electrode selection using decision fusion. Results demonstrate that the method suggested is capable of classifying emotional states. The decision fusion based electrode selection appears to be most accurate (arousal F-measure = 77.9%) in classifying the emotional states. The combination of Fp2, F3, and F4 electrodes yields higher accuracy for characterizing arousal (65.1%) and valence (57.9%) dimension. Thus, the proposed method can be used to select the critical electrodes for the classification of emotions. © 2021 European Federation for Medical Informatics (EFMI) and IOS Press.

Recognition of dichotomous emotional states such as happy and sad play important roles in many aspects of human life. Existing literature has recorded diverse attempts in extracting physiological and non-physiological traits to record these emotional states. Selection of the right instrumental approach for measuring these traits plays a critical role in emotion recognition. Moreover, various stimuli have been used to induce emotions. Therefore, there is a current need to perform a comprehensive overview of instrumental approaches and their outcomes for the new generation of researchers. In this direction, this study surveys the instrumental approaches in discriminating happy and sad emotional states that are elicited using audio-visual stimuli. A comprehensive literature review is performed using PubMed, Scopus, and ACM digital library repositories. The reviewed articles are classified with respect to the i) stimulation modality, ii) acquisition protocol, iii) instrumentation approaches, iv) feature extraction, and v) classification methods. In total, 39 research articles were published on the selected topic of instrumental approaches in differentiating dichotomous emotional states using audio-visual stimuli between January 2011 and April 2021. The majority of the papers used physiological traits, namely electrocardiogram, electrodermal activity, heart rate variability, photoplethysmogram, and electroencephalogram based instrumental approaches for recognizing the emotional states. The results show that only a few articles have focused on audio-visual stimuli for the elicitation of happy and sad emotional states. This review is expected to seed research in the areas of standardization of protocols, enhancing the diagnostic relevance of these instruments, and extraction of more reliable biomarkers.