LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals.

Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam's spiral and Sophia Germain's prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time-frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time-frequency representation is saved as a time-frequency image, and a non-overlapping n × n sliding window is applied to this image for patch extraction. An n × n Ulam's spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain's primes are located in Ulam's spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children's neurological disorders.

Comparison of the effectiveness and safety of the "endoscope-assisted polypropylene mesh lift" and "gliding brow lift" techniques for the treatment of lateral brow ptosis.

BACKGROUND: There are many techniques used to treat lateral brow ptosis. This study compared two techniques that are used for lateral brow rejuvenation in terms of effectiveness and safety-namely, endoscope-assisted polypropylene mesh lift (EAML) and gliding brow lift (GBL). METHOD: Eighty-six patients who underwent brow lift surgery between March 2018 and June 2020 were included in this retrospective study. Forty-four patients were operated on using the EAML technique, whereas 42 patients were operated on using the GBL technique. The measurement of defined distances in photographs was carried out using a software, and the Brow Positioning Grading Scale (BPGS) and Global Aesthetic Improvement Scale (GAIS) were applied in the pre and postoperative periods. RESULTS: The measurement results obtained in the postoperative period were better than those obtained in the preoperative period for both the techniques, whereas the results obtained at postoperative month 3 were found to be better than those obtained at month 12 (p < 0.05). The results were similar between the measurements at postoperative months 3 and 12 for both the techniques. The loss of brow height from postoperative months 3-12 was greater in the GBL group (p < 0.05). The postoperative scores on the BPGS were found to be better in both techniques than the preoperative scores (p < 0.05). The GAIS score at postoperative month 12 was found to be better in the EAML group. The two groups had similar rates of complications. CONCLUSION: The two techniques were found to have similar effectiveness and safety profiles for brow rejuvenation.

Automated Hypertension Detection Using ConvMixer and Spectrogram Techniques with Ballistocardiograph Signals.

Blood pressure is the pressure exerted by the blood in the veins against the walls of the veins. If this value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). This health problem which often referred to as the "silent killer" reduces the quality of life and causes severe damage to many body parts in various ways. Besides, its mortality rate is very high. Hence, rapid and effective diagnosis of this health problem is crucial. In this study, an automatic diagnosis of HPT has been proposed using ballistocardiography (BCG) signals. The BCG signals were transformed to the time-frequency domain using the spectrogram method. While creating the spectrogram images, parameters such as window type, window length, overlapping rate, and fast Fourier transform size were adjusted. Then, these images were classified using ConvMixer architecture, similar to vision transformers (ViT) and multi-layer perceptron (MLP)-mixer structures, which have attracted a lot of attention. Its performance was compared with classical architectures such as ResNet18 and ResNet50. The results obtained showed that the ConvMixer structure gave very successful results and a very short operation time. Our proposed model has obtained an accuracy of 98.14%, 98.79%, and 97.69% for the ResNet18, ResNet50, and ConvMixer architectures, respectively. In addition, it has been observed that the processing time of the ConvMixer architecture is relatively short compared to these two architectures.

Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset.

Emotion identification is an essential task for human-computer interaction systems. Electroencephalogram (EEG) signals have been widely used in emotion recognition. So far, there have been several EEG-based emotion recognition datasets that the researchers have used to validate their developed models. Hence, we have used a new ICBrainDB EEG dataset to classify angry, neutral, happy, and sad emotions in this work. Signal processing-based wavelet transform (WT), tunable Q-factor wavelet transform (TQWT), and image processing-based histogram of oriented gradients (HOG), local binary pattern (LBP), and convolutional neural network (CNN) features have been used extracted from the EEG signals. The WT is used to extract the rhythms from each channel of the EEG signal. The instantaneous frequency and spectral entropy are computed from each EEG rhythm signal. The average, and standard deviation of instantaneous frequency, and spectral entropy of each rhythm of the signal are the final feature vectors. The spectral entropy in each channel of the EEG signal after performing the TQWT is used to create the feature vectors in the second signal side method. Each EEG channel is transformed into time-frequency plots using the synchrosqueezed wavelet transform. Then, the feature vectors are constructed individually using windowed HOG and LBP features. Also, each channel of the EEG data is fed to a pretrained CNN to extract the features. In the feature selection process, the ReliefF feature selector is employed. Various feature classification algorithms namely, k-nearest neighbor (KNN), support vector machines, and neural networks are used for the automated classification of angry, neutral, happy, and sad emotions. Our developed model obtained an average accuracy of 90.7% using HOG features and a KNN classifier with a tenfold cross-validation strategy.

Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds.

The polymerase chain reaction (PCR) test is not only time-intensive but also a contact method that puts healthcare personnel at risk. Thus, contactless and fast detection tests are more valuable. Cough sound is an important indicator of COVID-19, and in this paper, a novel explainable scheme is developed for cough sound-based COVID-19 detection. In the presented work, the cough sound is initially segmented into overlapping parts, and each segment is labeled as the input audio, which may contain other sounds. The deep Yet Another Mobile Network (YAMNet) model is considered in this work. After labeling, the segments labeled as cough are cropped and concatenated to reconstruct the pure cough sounds. Then, four fractal dimensions (FD) calculation methods are employed to acquire the FD coefficients on the cough sound with an overlapped sliding window that forms a matrix. The constructed matrixes are then used to form the fractal dimension images. Finally, a pretrained vision transformer (ViT) model is used to classify the constructed images into COVID-19, healthy and symptomatic classes. In this work, we demonstrate the performance of the ViT on cough sound-based COVID-19, and a visual explainability of the inner workings of the ViT model is shown. Three publically available cough sound datasets, namely COUGHVID, VIRUFY, and COSWARA, are used in this study. We have obtained 98.45%, 98.15%, and 97.59% accuracy for COUGHVID, VIRUFY, and COSWARA datasets, respectively. Our developed model obtained the highest performance compared to the state-of-the-art methods and is ready to be tested in real-world applications.

Capsular contracture around silicone miniimplants following bacterial contamination: an in vivo comparative experimental study between textured and polyurethane implants.

BACKGROUND: Capsular contracture remains a problem following breast implant surgery. Although impact of biofilm and implant surface on capsule formation has been demonstrated, interaction of microorganisms with different surface types has not been clarified yet. We aimed to compare the ability of biofilm formation of implants with different surfaces, under standard conditions and to demonstrate its impact on capsular contracture. METHODS: Twenty-four rats were divided into four groups. Mini-implants with three different surfaces (fine-textured, rough-textured and polyurethane) were placed on the dorsum of each rat. In Group-1, sterile implants were placed in submuscular pockets. In Group-2, implants were incubated in Staphylococcus epidermidis medium before implantation. In Group-3, before implantation, implants were immersed in Rifamycin solution following bacterial contamination. In Group-4, sterile implants were immersed in Rifamycin solution before implantation, and served as the control group. Rats were sacrificed at three months. Clinical, microbiological, histological and immunohistochemical evaluations were performed. RESULTS: Capsule contracture developed only on infected rough-textured implants. Rough-textured and polyurethane implants showed more biofilm formation than fine-textured implants. Capsule thickness and inflammatory cell density were higher on rough-textured implants compared to fine-textured implants (p = 0.004). Actin sequence was parallel and concentric on fine-textured and rough-textured implants; but was in irregular array on polyurethane implants. CONCLUSION: In presence of bacterial contamination, rough-textured implants have the most propensity of developing capsular contracture comparing to fine-textured and polyurethane implants at three months after implantation. Despite high bacterial load and biofilm formation, polyurethane implants are resistant to capsule contracture due to surface characteristics.

Transfer learning based histopathologic image classification for breast cancer detection.

Breast cancer is one of the leading cancer type among women in worldwide. Many breast cancer patients die every year due to the late diagnosis and treatment. Thus, in recent years, early breast cancer detection systems based on patient's imagery are in demand. Deep learning attracts many researchers recently and many computer vision applications have come out in various environments. Convolutional neural network (CNN) which is known as deep learning architecture, has achieved impressive results in many applications. CNNs generally suffer from tuning a huge number of parameters which bring a great amount of complexity to the system. In addition, the initialization of the weights of the CNN is another handicap that needs to be handle carefully. In this paper, transfer learning and deep feature extraction methods are used which adapt a pre-trained CNN model to the problem at hand. AlexNet and Vgg16 models are considered in the presented work for feature extraction and AlexNet is used for further fine-tuning. The obtained features are then classified by support vector machines (SVM). Extensive experiments on a publicly available histopathologic breast cancer dataset are carried out and the accuracy scores are calculated for performance evaluation. The evaluation results show that the transfer learning produced better result than deep feature extraction and SVM classification.