Multi-Class Seizure Type Classification Using Features Extracted from the EEG.

In this study, we classify the seizure types using feature extraction and machine learning algorithms. Initially, we pre-processed the electroencephalogram (EEG) of focal non-specific seizure (FNSZ), generalized seizure (GNSZ), tonic-clonic seizure (TCSZ), complex partial seizure (CPSZ) and absence seizure (ABSZ). Further, 21 features from time (9) and frequency (12) domain were computed from the EEG signals of different seizure types. XGBoost classifier model was built for individual domain features and combination of time and frequency features and validated the results using 10-fold cross-validation. Our results revealed that the classifier model with combination of time and frequency features performed well followed by the time and frequency domain features. We obtained a highest multi-class accuracy of 79.72% for the classification of five types of seizure while using all the 21 features. The band power between 11-13 Hz was found to be the top feature in our study. The proposed study can be used for the seizure type classification in clinical applications.

Low Valence Low Arousal Stimuli: An Effective Candidate for EEG-Based Biometrics Authentication System.

Electroencephalography (EEG) has recently gained popularity in user authentication systems since it is unique and less impacted by fraudulent interceptions. Although EEG is known to be sensitive to emotions, understanding the stability of brain responses to EEG-based authentication systems is challenging. In this study, we compared the effect of different emotion stimuli for the application in the EEG-based biometrics system (EBS). Initially, we pre-processed audio-visual evoked EEG potentials from the 'A Database for Emotion Analysis using Physiological Signals' (DEAP) dataset. A total of 21 time-domain and 33 frequency-domain features were extracted from the considered EEG signals in response to Low valence Low arousal (LVLA) and High valence low arousal (HVLA) stimuli. These features were fed as input to an XGBoost classifier to evaluate the performance and identify the significant features. The model performance was validated using leave-one-out cross-validation. The pipeline achieved high performance with multiclass accuracy of 80.97% and a binary-class accuracy of 99.41% with LVLA stimuli. In addition, it also achieved recall, precision and F-measure scores of 80.97%, 81.58% and 80.95%, respectively. For both the cases of LVLA and LVHA, skewness was the stand-out feature. We conclude that boring stimuli (negative experience) that fall under the LVLA category can elicit a more unique neuronal response than its counterpart the LVHA (positive experience). Thus, the proposed pipeline involving LVLA stimuli could be a potential authentication technique in security applications.

Correlation between gray values of cone-beam computed tomograms and Hounsfield units of computed tomograms: A systematic review and meta-analysis.

Purpose: The aim of this review was to systematically analyze the available literature on the correlation between the gray values (GVs) of cone-beam computed tomography (CBCT) and the Hounsfield units (HUs) of computed tomography (CT) for assessing bone mineral density. Materials and Methods: A literature search was carried out in PubMed, Cochrane Library, Google Scholar, Scopus, and LILACS for studies published through September 2021. In vitro, in vivo, and animal studies that analyzed the correlations GVs of CBCT and HUs of CT were included in this review. The review was prepared according to the PRISMA checklist for systematic reviews, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A quantitative analysis was performed using a fixed-effects model. Results: The literature search identified a total of 5,955 studies, of which 14 studies were included for the qualitative analysis and 2 studies for the quantitative analysis. A positive correlation was observed between the GVs of CBCT and HUs of CT. Out of the 14 studies, 100% had low risks of bias for the domains of patient selection, index test, and reference standards, while 95% of studies had a low risk of bias for the domain of flow and timing. The fixed-effects meta-analysis performed for Pearson correlation coefficients between CBCT and CT showed a moderate positive correlation (r=0.669; 95% CI, 0.388 to 0.836; P<0.05). Conclusion: The available evidence showed a positive correlation between the GVs of CBCT and HUs of CT.

Automated Diagnosis of Autism Spectrum Disorder Condition Using Shape Based Features Extracted from Brainstem.

Alterations to the brainstem can hamper cognitive functioning, including audiovisual and behavioral disintegration, leading to individuals with Autism Spectrum Disorder (ASD) face challenges in social interaction. In this study, a process pipeline for the diagnosis of ASD has been proposed, based on geometrical and Zernike moments features, extracted from the brainstem of ASD subjects. The subjects considered for this study are obtained from publicly available data base ABIDE (300 ASD and 300 typically developing (TD)). Distance regularized level set (DRLSE) method has been used to segment the brainstem region from the midsagittal view of MRI data. Similarity measures were used to validate the segmented images against the ground truth images. Geometrical and Zernike moments features were extracted from the segmented images. The significant features were used to train Support vector machine (SVM) classifier to perform classification between ASD and TD subjects. The similarity results show high matching between DRLSE segmented brainstem and ground truth with high similarity index scores of Pearson Heron-II (PH II) = 0.9740 and Sokal and Sneath-II (SS II) = 0.9727. The SVM classifier achieved 70.53% accuracy to classify ASD and TD subjects. Thus, the process pipeline proposed in this study is able to achieve good accuracy in the classification of ASD subjects.

Pain Perception Associated with Mini-Implants and Interventions for Pain Management: A Cross-Sectional Questionnaire-Based Survey.

BACKGROUND: Orthodontists use mini-implants temporarily as an effective mode of skeletal anchorage devices. The placement of mini-implants can cause pain and discomfort to the patients. Patients often develop swelling, and the pain could interfere with their daily activities. Practitioners tend to prescribe antibiotics and pain medication for management. OBJECTIVES: The main objectives of this study are to evaluate the pain perception and discomfort due to mini-implant placement experienced by the patient and evaluate the interventions for pain management commonly practiced among orthodontists. MATERIALS AND METHODS: The study was designed as a questionnaire-based cross-sectional study. A total of 271 patients were assessed, for whom 625 mini-implants (ranging from 1.2 to 2 mm diameter and length 8-14 mm) were placed. Pain scores were assessed using the VAS and the "Faces" pain rating scale to collect data about discomfort in daily activity and function. Data was collected from 244 patients. A total of 155 orthodontists were questioned regarding the prescription of medications and the interventions for managing pain and adverse effects. RESULTS: Average pain score among female subjects was 16.71 and among men was 13.5. The highest pain scores were recorded for palatal mini-implants with an average score of 36.29 and the least for interradicular mini-implants with an average score of 9.02. Among the subjects, 47.9% of them took analgesics, and the most commonly prescribed analgesics were paracetamol (39%). Swelling at the site is where the mini-implants were placed, and ulceration due to implants were commonly dealt with the excision of the surrounding soft tissue, composite placement, and palliative care with oral analgesic gels. CONCLUSION: Female subjects had more mini-implants placed, and female subjects had also given more pain scores than their male counterparts. Palatal mini-implants caused the highest pain, followed by mini-implants placed at the infrazygomatic crest and the buccal shelf region. Palatal mini-implants caused maximum discomfort during speech and eating, followed by the mini-implant in the buccal shelf and the infrazygomatic crest region that also caused difficulty in yawning and laughing. Infiltration anesthesia was commonly given for the placement of interradicular implants and extra-alveolar mini-implants. Paracetamol was the most prescribed by the orthodontists, and more than half the doctors did not regularly prescribe antibiotics.

Efficacy of zirconium oxide nanoparticles coated on stainless steel and nickel titanium wires in orthodontic treatment.

It is of particular intrigue to synthesize, analyze anti-bacterial, anti-inflammatory activity, cytotoxicity effect of clove and cardamom reinforced zirconium oxide nanoparticles to coat the orthodontic archwires and study its ramifications. Characterization of nanoparticles was done using Transmission electron microscopic analysis (TEM). Antimicrobial activity was assessed using agar well diffusion method. Cytotoxic effect was assessed using Brine Shrimp Assay. Anti-inflammatory activity was completed using Bovine Serum Albumin (BSA). A Digital magnetic stirrer with a hot plate was used to coat orthodontic arch wires such as NiTi and SS. TEM spherical shape was of size 5 -20 nm. Minimal cytotoxicity was observed at 50 µL. Anti-inflammatory property was fair. Antimicrobial activity against Lactobacillus species, streptococcus mutans staphylococcus aureus and Candida albicans was recorded. NiTi and SS showed a colour shift from silver to orange red with a uniform surface coating on wires. Thus, green synthesized zirconium oxide nanoparticles have potent antimicrobial, anti-inflammatory properties with minimal cytotoxicity for further consideration as nano-coatings on orthodontic archwires such as NiTi and Stainless Steel.