Objectives: Atrial Tachycardia (AT) and Left Atrial Enlargement (LAE) are atrial diseases that are significant precursors to Atrial Fibrillation (AF). There are ML models for ECG classification; clinical features-based classification is required. The suggested work aims to create stacked ML models that categorize Sinus Rhythm (SR), Sinus Tachycardia (ST), AT, and LAE signals based on clinical parameters for AF prognosis. Methods: The classification was based on thirteen clinical parameters, such as amplitude, time domain ECG aspects, and P-Wave Indices (PWI), such as the ratio of P-wave length and amplitude ((P (ms)/P (µV)), P-wave area (µV*ms), and P-wave terminal force (PTFV1(µV*ms). Apart from classifying the ECG signals, the stacked ML models prioritized the clinical features using a pie formula-based technique. Results: The Stack 1 model achieves 99% accuracy, sensitivity, precision, and F1 score, while the Stack 2 model achieves 91%, 91%, 94%, and 92% for identifying SR, ST, LAE, and AT, respectively. Both stack models obtained a computational time of 0.06 seconds. PTFV1 (µV*ms), P (ms)/P (µV)), and P-wave area (µV*ms) were ranked as crucial clinical features. Conclusion: Clinical feature-based stacking ML models may help doctors obtain insight into important clinical ECG aspects for early AF prediction.

The strength of zirconia ceramic materials used in restorations is dependent upon sintering. Varying sintering protocols may affect the biaxial flexural strength of zirconia materials. This in vitro study was conducted to investigate the effects of sintering parameters on the biaxial flexural strength of monolithic zirconia. Two different monoblock zirconia ceramics were used. Following coloration, samples of both types of ceramics were divided into groups according to whether or not biaxial flexural strength testing was performed directly after sintering or following thermocycling. Biaxial flexural strength data was analysed with a Shapiro Wilk normality test, followed by 1-way ANOVA, Tukey post hoc tests for inter-group comparisons, and paired samples t-tests for intra-group comparisons. A significant difference was found between the biaxial flexural strengths of Zircon X and Upcera ceramics before thermocycling (p<0.05). In both Zircon X and Upcera ceramic groups, the thermocycling process created a significant difference in the biaxial flexural strength values of the ceramic samples in Group 6 (p<0.05) which had the slowest heating rate and longest holding time. The zirconia ceramics have higher BFS at higher heating rates either before or after thermocycling. The holding time has significant effects on thermocycling and flexural strength. The zirconia achieved its optimum strength when it sintered at longer time regardless of heating rates.

Objectives: The identification of the intersegmental plane is a major interoperative challenges during pulmonary segmentectomies. The objective of this pilot study is to test the feasibility of lung perfusion assessment by Hyperspectral Imaging for identification of the intersegmental plane. Methods: A pilot study (clinicaltrials.org: NCT04784884) was conducted in patients with lung cancer. Measuring tissue oxygenation (StO₂; upper tissue perfusion), organ hemoglobin index (OHI), near-infrared index (NIR; deeper tissue perfusion) and tissue water index (TWI), the Hyperspectral Imaging measurements were carried out in inflated (P_vent) and deflated pulmonary lobes (P_nV) as well as in deflated pulmonary lobes with divided circulation (P_nVC) before dissection of the lobar bronchus. Results: A total of 341 measuring points were evaluated during pulmonary lobectomies. Pulmonary lobes showed a reduced StO2 (P_vent: 84.56% ± 3.92 vs. P_nV: 63.62% ± 11.62 vs. P_nVC: 39.20% ± 23.57; p<0.05) and NIR-perfusion (P_vent: 50.55 ± 5.62 vs. P_nV: 47.55 ± 3.38 vs. P_nVC: 27.60 ± 9.33; p<0.05). There were no differences of OHI and TWI between the three groups. Conclusions: This pilot study demonstrates that HSI enables differentiation between different ventilated and perfused pulmonary tissue as a precondition for HSI segment mapping.

Millions of people around the world are affected by different kinds of epileptic seizures. A deep brain stimulator is now claimed to be one of the most promising tools to control severe epileptic seizures. The present study proposes Hodgkin-Huxley (HH) model-based Active Fault Tolerant Deep Brain Stimulator (AFTDBS) for brain neurons to suppress epileptic seizures against ion channel conductance variations using a Deep Neural Network (DNN). The AFTDBS contains the following three modules: (i) Detection of epileptic seizures using black box classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), (ii) Prediction of ion channels conductance variations using Long Short-Term Memory (LSTM), and (iii) Development of Reconfigurable Deep Brain Stimulator (RDBS) to control epileptic spikes using Proportional Integral (PI) Controller and Model Predictive Controller (MPC). Initially, the synthetic data were collected from the HH model by varying ion channel conductance. Then, the seizure was classified into four groups namely, normal and epileptic due to variations in sodium ion-channel conductance, potassium ion-channel conductance, and both sodium and potassium ion-channel conductance. In the present work, current controlled deep brain stimulators were designed for epileptic suppression. Finally, the closed-loop performances and stability of the proposed control schemes were analyzed. The simulation results demonstrated the efficacy of the proposed DNN-based AFTDBS.

Objectives: Hyperspectral imaging is an emerging imaging modality that beginning to gain attention for medical research and has an important potential in clinical applications. Nowadays, spectral imaging modalities such as multispectral and hyperspectral have proven their ability to provide important information that can help to better characterize the wound. Oxygenation changes in the wounded tissue differ from normal tissue. This causes the spectral characteristics to be different. In this study, it is classified cutaneous wounds with neighbourhood extraction 3-dimensional convolutional neural network method. Methods: The methodology of hyperspectral imaging performed to obtain the most useful information about the wounded and normal tissue is explained in detail. When the hyperspectral signatures of wounded and normal tissues are compared on the hyperspectral image, it is revealed that there is a relative difference between them. By taking advantage of these differences, cuboids that also consider neighbouring pixels are generated, and a uniquely designed 3-dimensional convolutional neural network model is trained with the cuboids to extract both spatial and spectral information. Results: The effectiveness of the proposed method was evaluated for different cuboid spatial dimensions and training/testing rates. The best result with 99.69% was achieved when the training/testing rate was 0.9/0.1 and the cuboid spatial dimension was 17. It is observed that the proposed method outperforms the 2-dimensional convolutional neural network method and achieves high accuracy even with much less training data. The obtained results using the neighbourhood extraction 3-dimensional convolutional neural network method show that the proposed method highly classifies the wounded area. In addition, the classification performance and the2computation time of the neighbourhood extraction 3-dimensional convolutional neural network methodology were analyzed and compared with existing 2-dimensional convolutional neural network. Conclusions: As a clinical diagnostic tool, hyperspectral imaging, with neighbourhood extraction 3-dimensional convolutional neural network, has yielded remarkable results for the classification of wounded and normal tissues. Skin color does not play any role in the success of the proposed method. Since only the reflectance values of the spectral signatures are different for various skin colors. For different ethnic groups, The spectral signatures of wounded tissue and the spectral signatures of normal tissue show similar spectral characteristics among themselves.

Objectives: Several studies have revealed that after spinal cord injury (SCI), in acute and sub-acute phase the spinal cord neurons below the injury are alive and could stimulate by use of electrical pulses. Spinal cord electrical stimulation could generate movement for paralyzed limbs and is a rehabilitation strategy for paralyzed patients. An innovative idea for controlling spinal cord electrical stimulation onset time is presented in current study. Methods: In our method, the time of applying electrical pulse on the spinal cord is according to rat behavioral movement and two movements behaviors are recognized only based on rat EEG theta rhythm on the treadmill line. Briefly, 5 rats were placed on the treadmill and the animals experienced zero or 12 m/min speeds. Results: These speeds were recognized based on EEG signals and off-line periodogram analysis. Finally, the electrical stimulation pulses had been applied to the spinal cord if the results of the EEG analysis had detected running behavior. Conclusions: These findings may guide future research in utilizing theta rhythms for the recognition of animal motor behavior and designing electrical stimulation systems based on it.

Driver states are reported as one of the principal factors in driving safety. Distinguishing the driving driver state based on the artifact-free electroencephalogram (EEG) signal is an effective means, but redundant information and noise will inevitably reduce the signal-to-noise ratio of the EEG signal. This study proposes a method to automatically remove electrooculography (EOG) artifacts by noise fraction analysis. Specifically, multi-channel EEG recordings are collected after the driver experiences a long time driving and after a certain period of rest respectively. Noise fraction analysis is then applied to remove EOG artifacts by separating the multichannel EEG into components by optimizing the signal-to-noise quotient. The representation of data characteristics of the EEG after denoising is found in the Fisher ratio space. Additionally, a novel clustering algorithm is designed to identify denoising EEG by combining cluster ensemble and probability mixture model (CEPM). The EEG mapping plot is used to illustrate the effectiveness and efficiency of noise fraction analysis on the denoising of EEG signals. Adjusted rand index (ARI) and accuracy (ACC) are used to demonstrate clustering performance and precision. The results showed that the noise artifacts in the EEG were removed and the clustering accuracy of all participants was above 90%, resulting in a high driver fatigue recognition rate.

Objectives: Electroencephalogram (EEG) is often used to detect mental fatigue because of its real-time characteristic and objective nature. However, because of the individual variability of EEG among different individuals, tedious and time-consuming calibration sessions are needed. Methods: Therefore, we propose a multi-source domain adaptation network for inter-subject mental fatigue detection named FLDANN, which is short for focal loss based domain-adversarial training of neural network. As for mental state feature extraction, power spectrum density is extracted based on the Welch method from four sub-bands of EEG signals. The features of the source domain and target domain are fed into the FLDANN network. The contributions of FLDANN include: (1) It uses the idea of adversarial to reduce feature differences between the source and target domain. (2) A loss function named focal loss is used to assign weights to source and target domain samples Results: The experiment result shows that when the number of the source domains increases, the classification accuracy of domain-adversarial training of neural network (DANN) gradually decreases and finally tends to be stable. The proposed method achieves an accuracy of 84.10% ± 8.75% on the SEED-VIG dataset and 65.42% ± 7.47% on the self-designed dataset. In addition, the proposed method is compared with other domain adaptation methods and the results show that the proposed method outperforms those state-of-the-art methods. Conclusions: The result proves that the proposed method is able to solve the problem of individual differences across subjects and to solve the problem of low classification performance of multi-source domain transfer learning.

Objectives: Although the use of short implants is becoming more common for patients with atrophic alveolar ridges, their use is still quite limited. This is due to the lack of data of long-term survival compared to standard-length implants. The aim of this study was to determine the load in the bone and implant system with different superstructures. Methods: Three kinds of prosthetic restorations were created on short implants based on CT-Data. Two short implants with different macro-geometries were used. The implants were inserted in idealised posterior lower mandibular segments and afterwards restored with a crown, a double splinted crown, and a bridge. Results: The analysis was performed under load of 300 N either divided between a mesial and distal point or as a point load on the pontic/mesial crown. The different design of the implant systems had a noticeable influence on the stress in the cortical bone, in the implant system, and the displacement of the superstructure as well. Conclusions: Compared with implants of standard length, higher stresses were observed, which can lead early failure of the implant during the healing period or a late cervical bone resorption. Precise indications are essential for short implants to avoid the failure of short implants.

Heart diseases represent a serious medical condition that can be fatal. Therefore, it is critical to investigate the measures of its early prevention. The Mel-scale frequency cepstral coefficients (MFCC) feature has been widely used in the early diagnosis of heart abnormity and achieved promising results. During feature extraction, the Mel-scale triangular overlapping filter set is applied, which makes the frequency response more in line with the human auditory property. However, the frequency of the heart sound signals has no specific relationship with the human auditory system, which may not be suitable for processing of heart sound signals. To overcome this issue and obtain a more objective feature that can better adapt to practical use, in this work, we propose an equal scale frequency cepstral coefficients (EFCC) feature based on replacing the Mel-scale filter set with a set of equally spaced triangular overlapping filters. We further designed classifiers combining convolutional neural network (CNN), recurrent neural network (RNN) and random forest (RF) layers, which can extract both the spatial and temporal information of the input features. We evaluated the proposed algorithm on our database and the PhysioNet Computational Cardiology (CinC) 2016 Challenge Database. Results from ten-fold cross-validation reveal that the EFCC-based features show considerably better performance and robustness than the MFCC-based features on the task of classifying heart sounds from novel patients. Our algorithm can be further used in wearable medical devices to monitor the heart status of patients in real time with high precision, which is of great clinical importance.