Ramakrishnan Swaminathan

In this study, an attempt has been made to differentiate Novel Coronavirus-2019 (COVID-19) conditions from healthy subjects in Chest radiographs using a simplified end-to-end Convolutional Neural Network (CNN) model and occlusion sensitivity maps. Early detection and faster automated screening of the COVID-19 patients is essential. For this, the images are considered from publicly available datasets. Significant biomarkers representing critical image features are extracted from CNN by experimentally investigating on cross-validation methods and hyperparameter settings. The performance of the network is evaluated using standard metrics. Perturbation based occlusion sensitivity maps are employed on the features obtained from the classification model to visualise the localization of abnormal areas. Results demonstrate that the simplified CNN model with optimised parameters is able to extract significant features with a sensitivity of 97.35% and F-measure of 96.71% to detect COVID-19 images. The algorithm achieves an Area Under the Curve-Receiver Operating Characteristic score of 99.4% with Matthews correlation coefficient of 0.93. High value of Diagnostic odds ratio is also obtained. Occlusion sensitivity maps provide precise localization of abnormal regions by identifying COVID-19 conditions. As early detection through chest radiographic images are useful for automated screening of the disease, this method appears to be clinically relevant in providing a visual diagnostic solution using a simplified and efficient model.

Background and Objective: Computer aided diagnostics of Pulmonary Tuberculosis in chest radiographs relies on the differentiation of subtle and non-specific alterations in the images. In this study, an attempt has been made to identify and classify Tuberculosis conditions from healthy subjects in chest radiographs using integrated local feature descriptors and variants of extreme learning machine. Methods: Lung fields in the chest images are segmented using Reaction Diffusion Level Set method. Local feature descriptors such as Median Robust Extended Local Binary Patterns and Gradient Local Ternary Patterns are extracted. Extreme Learning Machine (ELM) and Online Sequential ELM (OSELM) classifiers are employed to identify Tuberculosis conditions and, their performances are analysed using standard metrics. Results: Results show that the adopted segmentation method is able to delineate lung fields in both healthy and Tuberculosis images. Extracted features are statistically significant even in images with inter and intra subject variability. Sigmoid activation function yields accuracy and sensitivity values greater than 98% for both the classifiers. Highest sensitivity is observed with OSELM for minimal significant features in detecting Tuberculosis images. Conclusion: As ELM based method is able to differentiate the subtle changes in inter and intra subject variations of chest X-ray images, the proposed methodology seems to be useful for computer-based detection of Pulmonary Tuberculosis.

Viscoelastic properties of skeletal muscle tissue are known to be impacted by fatiguing contractions. In this study, an attempt has been made to utilize myotonometry for analyzing the relationship between muscle viscoelasticity and contractile behaviors in a fatiguing task. For this purpose, thirteen young healthy volunteers are recruited to perform the fatiguing isometric task and the time to task failure (TTF) is recorded. Myotonometric parameters and simultaneous surface electromyographic (sEMG) signals are recorded from the Biceps Brachii muscle of the flexed arm. The correlation between myotonometric parameters and TTF is further analyzed. Cross-validation with sEMG features is also performed. Stiffness of muscle has a positive correlation with TTF in the left hand (p<0.05). Damping property of the nonfatigued muscle is positively associated with the fatigue-induced changes in amplitude features of sEMG signal in the right hand (p<0.05). The normalized rate of change of mean frequency of sEMG signal has a positive correlation with stiffness values in both of the hands (p<0.05). Muscle viscoelasticity is demonstrated to influence the progression of fatigue, although the difference in motor control due to handedness is also found to be an important factor. The results are promising to improve the understanding of the effect of muscle mechanics in fatigue-induced task failure.

Analysis of uterine contractions using electromyography signals is gaining importance due to its capability to measure the dynamics of uterus. Uterine electromyography (uEMG) provides information on the nature of uterine contractions non-invasively. In this study, the fluctuations in uEMG signals associated with Term pregnancies are analyzed. For this, Term uEMG signals corresponding to second (T1) and third (T2) trimesters are considered. The signals are subjected to Adaptive Fractal Analysis (AFA), wherein a global trend is obtained by using overlapping windows of three orders namely, 25%, 50% and 75%. The signals are detrended and the fluctuation function is estimated. Two Hurst exponent features computed at short range (Hs) and long range (Hl) are extracted and statistically analyzed. Results show that AFA is able to characterize variations in the fluctuations of Term delivery signals. The feature values are observed to vary significantly during different weeks of gestation. It is found that features of T2 signals are higher than that of T1 signals for all the considered overlaps, indicating that T2 signals possess smoother characteristics than T1 signals. Further, coefficient of variation is observed to be low, indicating that these features are able to handle the inter-subject variations in Term signals. Therefore, it appears that the proposed approach could aid in investigation of progressive changes in uterine contractions during Term pregnancies.

In this work, an attempt has been made to utilise the time-frequency spectrum obtained using generalised Warblet transform (GWT) for fatigue analysis. Signals are acquired from the biceps brachii muscles of 20 healthy volunteers during isometric contractions. The first and last 500 ms lengths of a signal are assumed as non-fatigue and fatigue zones respectively. Further, the signals from these zones are subjected to GWT for the computation of time-frequency spectrum. Features such as instantaneous mean frequency (IMNF), instantaneous median frequency (IMDF), instantaneous spectral entropy (ISPEn), and instantaneous spectral skewness (ISSkw) are estimated. The results show that the IMNF, IMDF and ISPEn increased by 24%, 34% and 36% respectively in non-fatigue condition. In contrast, 22% higher ISSkw is observed for fatigue condition. The statistical analysis indicates that the features are significant with p < 0.001. It appears that the current method is useful in analysing muscle fatigue disorders using sEMG signals.

Diabetes mellitus is a group of metabolic disease, which has become globally prevalent, and affects a large population in socio-economically backward countries in Asian continent. Chronic diabetes can lead to ulceration in the plantar region and may result in amputation. Assessment of mechanical properties of plantar tissues can aid in early diagnosis of ulceration. Myotonometry, a technique to measure dynamic stiffness, is preferred due to its noninvasiveness, easy employability, and rapid investigation. In this study, an attempt has been made to analyze the changes in biomechanical properties of plantar soft tissue in diabetes. MyotonPro, a handheld device, is used for this purpose. 43 diabetic subjects with varied duration of diabetes are recruited. Site-specific mechanical properties of the plantar region for both the feet are acquired and statistical analysis is performed. Results show that the MyotonPro is able to differentiate the stages of diabetes. It is seen that there is a spatial variability in the mechanical properties of the plantar. Additionally, it is observed that there is a significant increment in the plantar stiffness value in the group with higher diabetic age (p < 0.05). Further, significant changes in dynamic mechanical properties are also observed in submetatarsal region. Additionally, a right-left asymmetry has been observed in frequency and stiffness values for later stages of diabetes. This study demonstrated the feasibility of MyotonPro in discriminating the stages of diabetic period. Thus, the proposed approach could be useful in early diagnosis of foot ulceration for various clinical conditions.

Alzheimer's Disease (AD) is a progressive fatal neurodegenerative disorder that causes cognitive decline in affected people. Image processing of brain MR images can aid in identifying significant imaging biomarkers for detection of AD and its prodromal stage Mild Cognitive Impairment (MCI). Bidimensional multiscale entropy-based texture analysis is a new approach to quantify the textural variations in images at multiple scales. This work is based on the application of bidimensional multiscale entropy for analyzing AD induced textural alterations in lateral ventricles of the brain MR images. For this T1 weighted MR brain images of normal, MCI and AD subjects are obtained from public database. Lateral ventricles (LV) are delineated using reaction–diffusion level set technique from transaxial image slice with high accuracy. Bidimensional multiscale entropy is then applied on segmented LV to extract entropy features at multiple image scales and complexity indices are evaluated for each scale to study textural variations. The parameters such as tolerance factor, window lengths and scales for computation of multiscale entropy for significant differentiation amongst the healthy and diseased subjects are experimentally evaluated. The obtained entropy values from healthy subjects are observed to be significantly lower from the pathological subjects across scales. Classification with extracted features using a linear discriminant classifier achieves an accuracy of 80.1% and 87.6% for Normal vs MCI and Normal vs AD classes, respectively. The proposed multiscale entropy-based approach captures the textural alterations in lateral ventricles of brain MR images and furthermore, can be used as automated tool for early diagnosis of AD.

The measurement and analysis of the electrical activity of muscle provide information that aids in the control of assistive devices. The investigation of these signals under varied physiological conditions, such as fatigue, enables reliable control. Muscle fatigue is a muscular condition associated with loss of muscle function. The early detection of muscle fatigue using surface Electromyography (sEMG)-based electrical measurements is challenging due to the nonlinear variations of the signal. In this work, an attempt has been made to understand the effect of dynamic nonlinear variations in the characteristics of the signal to develop a reliable fatigue index. The methodology involves the acquisition of myoelectric signals from the biceps brachii muscle of 52 healthy participants during dynamic contractions. The acquired signals are preprocessed and are analyzed with symbolic transition networks. Features such as symbolic entropy, network entropy, uniformity, and, minimum and maximum effective degrees (EDs) are extracted for further analysis. Appropriate decision boundaries are established for each feature using receiver operator characteristics (ROCs) and machine learning algorithms. The results indicate a decrease in signal complexity with fatigue. All the extracted features show a statistically significant difference (p < 0.05) between both conditions. Symbolic entropy achieves an accuracy of 89%, and the maximum ED yields an accuracy of 90% based on thresholds estimated with ROC. Furthermore, only a marginal improvement is observed with the combination of these features and the Naive Bayes classifier. It appears that the proposed maximum ED could be used as a reliable fatigue index in real-time applications for the improvement of rehabilitation efficacy.

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.

Preterm birth (gestational age < 37 weeks) is a serious pregnancy related complication that could lead to fetal morbidity and mortality. Monitoring the activity of uterus is considered to be crucial for the early diagnosis of preterm birth. Uterine Electromyography (uEMG) is a non-invasive technique that provides a quantitative measure of uterine activity from the abdominal surface. In this work, an attempt has been made to characterize preterm uEMG signals using Empirical Mode Decomposition based Detrended Fluctuation Analysis (EMD-DFA). Preterm signals with varied gestational ages are considered from an online database. EMD-DFA is applied on these signals to compute the fluctuation function. The double-logarithmic plot of fluctuation function versus scale is evaluated and Chi-square analysis is performed for identifying linear scaling regions. Five features namely shortterm exponent (Hs), long-term exponent (Hl), inflection point, short-term fractal angle (αHs) and long-term fractal angle (αHl) are extracted and analyzed. Further, Coefficient of Variation (CV) is computed to examine the variations of these features among different subjects. Results show that EMD-DFA is able to characterize the fluctuations of preterm signals. From the double-logarithmic plot, a slow variation of fluctuation function is observed with respect to scale when the time to delivery is more. This indicates the presence of rapid signal fluctuations in the early stages of pregnancy. Based on the feature values, it is observed that the signal fluctuations are more correlated and smoother as the time to delivery approaches. Among the extracted features, CV values of Hs, Hl, αHs and αHl are observed to be low indicating that these features have least inter-subject variations in preterm signals. The EMD-DFA based fractal features show the ability to detect the subtle variations in uEMG signals. As early diagnosis of preterm delivery is imperative for timely medical intervention and treatment, it appears that the proposed approach could aid in determining the changes in uterine contractions in preterm condition.