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Sleep is a natural state of rest for the body and mind. It is essential for a human's physical and mental health because it helps the body restore itself. Insomnia is a sleep disorder that causes difficulty falling asleep or staying asleep and can lead to several health problems. Conventional sleep monitoring and insomnia detection systems are expensive, laborious, and time-consuming. This is the first study that integrates an electrocardiogram (ECG) scalogram with a convolutional neural network (CNN) to develop a model for the accurate measurement of the quality of sleep in identifying insomnia. Continuous wavelet transform has been employed to convert 1-D time-domain ECG signals into 2-D scalograms. Obtained scalograms are fed to AlexNet, MobileNetV2, VGG16, and newly developed CNN for automated detection of insomnia. The proposed INSOMNet system is validated on the cyclic alternating pattern (CAP) and sleep disorder research center (SDRC) datasets. Six performance measures, accuracy (ACC), false omission rate (FOR), sensitivity (SEN), false discovery rate (FDR), specificity (SPE), and threat score (TS), have been calculated to evaluate the developed model. Our developed system attained the classifications ACC of 98.91%, 98.68%, FOR of 1.5, 0.66, SEN of 98.94%, 99.31%, FDR of 0.80, 2.00, SPE of 98.87%, 98.08%, and TS 0.98, 0.97 on CAP and SDRC datasets, respectively. The developed model is less complex and more accurate than transfer-learning networks. The prototype is ready to be tested with a huge dataset from diverse centers. • First of its kind of intelligent system for insomnia identification using ECG and CNN. • The proposed INSOMNet architecture has proven to be highly effective in identifying insomnia by utilizing two sleep datasets. • The presented network outperforms benchmarked state-of-the-art approaches. • The prototype model developed can be deployed in wearables devices. [ABSTRACT FROM AUTHOR]

This manuscript presents a comparative study of different combination for the dual workfunction gate material, underlap and hetero gate dielectric tunnel field-effect transistors (TFET’s). Their performances have been analyzed in terms of ON-state current, ambipolar behaviour and RF response along with different drain doping profile. For this, the Dual work function of gate provides enhancement in ON-state current by reducing the tunnel barrier width at source/channel interface. Whereas, the underlap of gate is done near to the drain region, helps in reduction of ambipolar conduction by creating deficiency of hole for the conduction, which is major hurdle for TFET. Further, the combinations of the dual workfunction and underlap give combine advantages of both such as improve ON-state current and suppressed ambipolar current. Apart from this, the combination of hetero gate dielectric dual workfunction under lapping leads to superior device performance in terms of ON-state current and ambipolar behaviour. The use of hetero gate dielectric and Gaussian doping profile with gate underlap reduces the gate to drain capacitance that also improves the RF parameters of the device. [ABSTRACT FROM AUTHOR]

• Edge preserving optimal guided filtering-based image denoising is proposed. • Inclusion of Markov Random Field based Energy Minimization based cost function. • Behavioural/Adaptive guidance image is computed through unsharp masking. • Optimal guided filter designing is proposed by employing the Swarm intelligence. In this work, a novel and efficient approach for image denoising is proposed. More often, noise affecting the pixels in image is Gaussian in nature and uniformly deters information pixels in image irrespective of their intensity values. This behaviour of noise can also be identified as Additive White Gaussian Noise (AWGN). For restoration of AWGN affected images, the proposed denoising approach is inspired by image adaptive guided image filtering using modified cuckoo search algorithm. The guidance image is itself derived from the noisy image for this purpose. Bilateral filtering smoothed noisy image is sharpened by unsharp masking and then employed as guidance image for the proposed optimal guided filtering approach. Optimal evaluation of parameters like guided filter smoothing parameter (regularization parameter or degree of smoothing (DoS)) and guided filter's neighbourhood (kernel) size is done appropriately with the help of the modified cuckoo search algorithm. Two-dimensional search space is explored and exploited for deciding the behaviour of guided filtering adaptively as per the input image requirements. This guided image filter has a better behaviour at it acts as an edge preserving smoothing operator. It is considerably effective as its computational complexity is independent of filtering kernel size. A novel attempt is made by incorporating the Markov Random Field based Energy Minimization based objective/fitness function for imparting adaptive image denoising using metaheuristic intelligence. The proposed method is tested in terms of the performance metrics like peak signal to noise ratio, structural similarity index and mean square error. Performance of the proposed approach is compared with the already proposed image denoising techniques. For this comparison, only those methods are considered which were proposed for filtering of Gaussian Noise. Qualitative (visual) as well as quantitative (objective) results underlines the efficacy of the proposed method for filtering of Gaussian Noise. [ABSTRACT FROM AUTHOR]

Cardiotocography (CTG) is generally provided on printed traces, and digitization of CTG signal is important for forthcoming assessments. In this paper, a new algorithm relies on the box-counting method is offered for the digitization of the CTG signals from CTG printed traces. The introduced algorithm inputs the CTG printed traces and outputs the digital fetal heart rate (FHR) and uterine contraction (UC) signals. The proposed method initially extracts the CTG signal image and gridded background image. Retrieving of the FHR and UC signals on the gridded background disrupts the background grids. So, we employ an algorithm to fix the degraded lines in the gridded background. After the line fixing operation, the boxes in the horizontal and vertical axes are counted for determining the calibration parameters. A set of specific equations are used to determine the calibration parameters. The signal extraction is performed on by red-channel thresholding of input CTG printing images. An open-access CTG intrapartum database comprises 552 samples is used in the experiment. As a result, the average correlation coefficients of FHR and UC signals are 0.9811 ± 0.0251 and 0.9905 ± 0.0126, respectively. • A box-counting is presented for the digitization of the CTG signals. • The algorithm inputs the CTG printing images and outputs the digitalized CTG signals. • An open-access database contains 552 CTG recordings is employed. • The average correlation coefficient for FHR signals is 0.9811 ± 0.0251. • The average correlation coefficients for UC signals is 0.9905 ± 0.0126. [ABSTRACT FROM AUTHOR]

Automatic environmental sound classification (ESC) is prominent in various fields like robotics, security, and crime investigation. In this paper, optimum allocation sampling (OAS)-based empirical mode method (EMD) is proposed for automatic ESC. The OAS provides the reduced homogeneous length sequence of each long length sound signal, which is further decomposed into band-limited intrinsic mode functions (IMFs) using EMD. The features namely approximate entropy (AE), permutation entropy (PE), log energy entropy (LE), interquartile range (IQR), and zero cross rate (ZCR) are extracted from the IMFs. The OAS-EMD based features used as input to multi-class least squares support vector machine (MC-LS-SVM) and extreme learning machine (ELM) classifiers for evaluation the performance of proposed method. Experimental results show an accuracy of 87.25% and 77.61% with MC-LS-SVM and ELM classifiers, respectively. • Optimum allocation sampling based empirical mode method is proposed for automatic Environmental Sound Classification. • OAS provides the reduced homogeneous length sequence of each long length sound signal. • The OAS-EMD based features used as input to multi-class least squares support vector machine (MC-LS-SVM) and extreme learning machine (ELM) classifiers. • Experimental results show an accuracy of 87.25% and 77.61% with MC- LS-SVM and ELM classifiers. [ABSTRACT FROM AUTHOR]

Abstract: Crystallizing galactocele is an uncommon condition that produces a viscous, chalky substance on fine needle aspiration cytology. (FNAC). Both the diagnosis and the management of this illness include the use of FNAC. Here, we discuss the case of a 25-year-old nursing woman who experienced left breast edema lump for two years. The upper outer quadrant of the leftt breast was involved by the hard, small, non-tender, and movable enlargement. The lesion's FNAC produced a thick, milky, and chalky substance. Numerous semi-transparent crystals of various sizes and shapes with angulated edges could be seen in cytological smears against a background of granular and amorphous proteinaceous material. A diagnosis of crystallizing galactocele was made on the basis of the patient's clinical history of lactation and characteristic cytological findings. Due to the rarity of this condition-to the best of our knowledge, less than ten cases of crystallizing galactocele have been documented in medical literature., (Copyright © 2024 Copyright: © 2024 Indian Journal of Pathology and Microbiology.)

Technological advancements in healthcare, production, automobile, and aviation industries have shifted working styles from manual to automatic. This automation requires smart, intellectual, and safe machinery to develop an accurate and efficient brain-computer interface (BCI) system. However, developing such BCI systems requires effective processing and analysis of human physiology. Electroencephalography (EEG) is one such technique that provides a low-cost, portable, non-invasive, and safe solution for BCI systems. However, the non-stationary and nonlinear nature of EEG signals makes it difficult for experts to perform accurate subjective analyses. Hence, there is an urgent need for the development of automatic mental state detection. This paper presents the classification of three mental states using an ensemble of the tunable Q wavelet transform, the multilevel discrete wavelet transform, and the flexible analytic wavelet transform. Various features are extracted from the subbands of EEG signals during focused, unfocused, and drowsy states. Separate and fused features from ensemble decomposition are classified using an optimized ensemble classifier. Our analysis shows that the fusion of features results in a dimensionality reduction. The proposed model obtained the highest accuracies of 92.45% and 97.8% with ten-fold cross-validation and the iterative majority voting technique. The proposed method is suitable for real-time mental state detection to improve BCI systems.

Non-invasive fetal electrocardiography (NI-fECG) is a promising technique for continuous fetal heart rate (fHR) monitoring. However, the weak amplitude of the fetal electrocardiogram (fECG), and the presence of the dominant maternal ECG (mECG), makes it highly challenging to detect the fetal QRS (fQRS) complex, which is needed to obtain the fHR. This paper proposes a new method for automated fQRS detection from single-channel NI-fECG signals, without cancelling out the mECG. The proposed method leverages the different spectral behaviour exhibited by mECG and fECG signals. Fetal R-peaks are detected using a hybrid combination of k-means clustering with time and time-frequency features extracted from pre-processed NI-fECG recordings. The performance of our method is evaluated using real and synthetic signals from publicly available datasets, achieving a best of 96.3% sensitivity and 90.4% F1 score. The results obtained demonstrates the effectiveness of the proposed method for the detection of fQRS complexes with high sensitivity and low computational complexity.

Objective. Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging. Approach. The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model. Results. The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model. Significance. The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios., (Creative Commons Attribution license.)

Introduction: The term monoclonal gammopathy of renal significance (MGRS) has been described to include patients with renal manifestations associated with circulating monoclonal proteins with or without a clonal lymphoproliferation (B-cell or plasma cell) and not meeting diagnostic criteria for an overt hematological malignancy. A host of MGRS-associated lesions have been described that involve various renal compartments. Our study describes the histomorphological spectrum of MGRS cases at our center in the last 5 years and description as per the classification system of the International Kidney and Monoclonal Gammopathy Research Group (IKMG)., Material and Methods: Retrospective analysis was carried out of all the renal biopsies with characteristic monoclonal immunoglobulin lesions for histopathological diagnosis between years 2015 and 2020 and reviewed by two independent pathologists., Results: Most patients in the study belonged to the fifth decade, with a median age of 50 years (mean 50.14 ± 10.43) range (24-68 years) with a male preponderance. Most patients presented with proteinuria as the sole manifestation (66.6%). Many of the patients (48%) had an M spike by serum protein electrophoresis or urinary protein electrophoresis with an abnormal serum free light chain assay (60.8%). AL amyloidosis was the most common diagnosis observed on histopathological evaluation (68.7%), followed by light chain deposition disease (10.4%)., Conclusion: MGRS lesions are infrequently encountered in the practice of nephropathology and pose a diagnostic challenge due to the limitation of a congruent clinical or hematological picture. A thorough histological examination with immunofluorescence and electron microscopy often precipitates in the right diagnosis and prompts timely management., Competing Interests: The authors have no conflicts of interest to declare., (Copyright © 2022 by The Author(s). Published by S. Karger AG, Basel.)

Digital medical images contain important information regarding patient's health and very useful for diagnosis. Even a small change in medical images (especially in the region of interest (ROI)) can mislead the doctors/practitioners for deciding further treatment. Therefore, the protection of the images against intentional/unintentional tampering, forgery, filtering, compression and other common signal processing attacks are mandatory. This manuscript presents a multipurpose medical image watermarking scheme to offer copyright/ownership protection, tamper detection/localization (for ROI (region of interest) and different segments of RONI (region of non-interest)), and self-recovery of the ROI with 100% reversibility. Initially, the recovery information of the host image's ROI is compressed using LZW (Lempel-Ziv-Welch) algorithm. Afterwards, the robust watermark is embedded into the host image using a transform domain based embedding mechanism. Further, the 256-bit hash keys are generated using SHA-256 algorithm for the ROI and eight RONI regions (i.e. RONI-1 to RONI-8) of the robust watermarked image. The compressed recovery data and hash keys are combined and then embedded into the segmented RONI region of the robust watermarked image using an LSB replacement based fragile watermarking approach. Experimental results show high imperceptibility, high robustness, perfect tamper detection, significant tamper localization, and perfect recovery of the ROI (100% reversibility). The scheme doesn't need original host or watermark information for the extraction process due to the blind nature. The relative analysis demonstrates the superiority of the proposed scheme over existing schemes., Competing Interests: Conflicts of interest/competing interestsThere is no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)

Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected after excluding the pectoral muscle from mammogram images. Hence, it is very significant to identify and segment the pectoral muscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetism optimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among the charges to develop the members of a population. Here, both Kapur's and Otsu based cost functions are employed with EMO separately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimal threshold levels can be identified for the considered mammographic image. The proposed methodology is applied on all the three twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentation of the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found to be robust for variations in the pectoral muscle., Competing Interests: Conflict of interestAll the authors declare that they have no conflict of interest., (© Korean Society of Medical and Biological Engineering 2019.)

Background and Objective: The recognition of emotional states is a crucial step in the development of a brain-computer interface (BCI) system. Emotion recognition system finds applications in medical science for the impaired and disabled people. Electroencephalography assesses the neurophysiology of the brain for recognition of different emotional states., Methods: The audio-video stimulus based experimental setup is arranged for the electroencephalogram (EEG) recordings of happy, fear, sad, and relax emotions and a two-stage filtering method is proposed for the recognition of emotion EEG signals. At the first stage, a correlation-criterion is suggested for removal of noisy intrinsic mode functions (IMFs) from the IMFs obtained by applying the empirical mode decomposition on the raw EEG signal. The noise-free IMFs are used to reconstruct the denoised EEG signal with improved stationarity characteristics. The denoised EEG signal is further decomposed into modes using the variational mode decomposition (VMD). At the second stage, the instantaneous-frequency based filtering of VMD modes is performed and filtered modes are retained for the reconstruction of denoised EEG signal with the desired frequency range. After two-stage filtering, the non-linear measures of filtered EEG signals are used as input features to multi-class least squares support vector machine (MC-LS-SVM) classifier for emotion recognition., Results: The different kernel functions are tested in MC-LS-SVM classifier for emotion recognition. The Morlet wavelet (MW) kernel function provides the best individual classification accuracies for happy, fear, sad, and relax emotions as 92.79%, 87.62%, 88.98%, and 93.13%, respectively. The MW-kernel function also obtained the best overall accuracy of 90.63%, F1-score 0.9064, and kappa value 0.8751., Conclusions: The Audio-video stimulus based emotion EEG-dataset is recorded. A new filtering method is proposed for EEG signals. The proposed method provides better emotion recognition performance as compared to the state-of-the-art methods and classifies emotions using single-bipolar EEG channel, which can greatly reduce the complexity of emotion-recognition based BCI systems., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In this paper, we present a new method for classification of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) method. The intrinsic mode functions (IMFs) generated by EMD method can be considered as a set of amplitude and frequency modulated (AM-FM) signals. The Hilbert transformation of IMFs provides an analytic signal representation of the IMFs. The two bandwidths, namely amplitude modulation bandwidth (B(AM)) and frequency modulation bandwidth (B(FM)), computed from the analytic IMFs, have been used as an input to least squares support vector machine (LS-SVM) for classifying seizure and non-seizure EEG signals. The proposed method for classification of EEG signals based on the bandwidth features (B(A M) and B (FM)) and the LS-SVM has provided better classification accuracy than the method of Liang et. al [20]. The experimental results with the recorded EEG signals from a published dataset are included to show the effectiveness of the proposed method for EEG signal classification.