Biomechanical Comparison of a Closed-Loop Double Endobutton to a Lag Screw in Fixation of Posterior Malleolar Fractures.

Anteroposterior (AP) lag screw, posteroanterior (PA) lag screw, or posterior buttress plate are usually performed for posterior malleolar fixation, but the biomechanically strongest technique is unclear. The aim of our study was to biomechanically compare 3 different fixation methods for posterior malleolar fractures; AP lag screw, PA lag screw, and closed-loop double endobutton. Fracture models were created using a thin blade power saw after drawing the fracture line. The resultant fracture involved 30% of the joint on the distal tibial joint surface and extends with an angulation of approximately 50 degrees using 15 tibia composite bone samples. After anatomical reduction, fixation was achieved with 3.5 mm cortical screw in PA direction and in AP direction for group PA and AP, respectively. In Group DL, fixation was achieved with a closed-loop double endobutton (double lift loop, Orthomed, Turkey). The highest compression force to generate all displacement amounts was required for the double loop group (Group DL). The strongest fixation against compression was a double loop. The PA group was the second strongest fixation, and the AP group was the biomechanically weakest among these 3 fixation techniques. The closed-loop double endobutton technique was found biomechanically superior to anterior to posterior or posterior to anterior screw fixation techniques for posterior malleolar fracture.

A biomechanical investigation of miniscrews placed on a mandible for temporary anchorage; a patient-specific finite element analysis.

Miniscrews are temporary skeletal anchorage devices that are widely used in orthodontic treatment, and their success depends on the placement area, angle, technique, and screw dimensions. This study aimed to investigate the effects of miniscrew lengths, insertion angles, and force directions on a mandible model consisting of teeth, cortical and cancellous bones. One Dental Volumetric Tomography (DVT) scan from a patient who had miniscrews were used for mandibular bone modeling to perform finite element analysis. The model variables included miniscrew lengths (6, 8, and 10 mm), insertion angles (-15°, 45°, 60°, and 90°), and force directions (30°, 45°, and 60°). The minimum and maximum stresses were calculated as 18.61 and 37.11 MPa at 6 mm and 10 mm, respectively. According to the insertion angles, the lowest stress was observed at 60°, while the highest stress was found at 15° in the ventral direction. At force directions, the lowest stress was at 60°, and the highest stress was at 45°. However, there were no significant differences in insertion angles and force directions. A statistically significant difference was determined in miniscrew length. As a result, the best result was calculated to be 6 mm inserted at a 60° angle, which could induce the lowest stress. Increasing the miniscrew length will increase the stress on the mandible. In addition, because of the higher force direction, stress decreases with shorter power arms.

Investigation of Different Miniscrew Head Designs by Finite Element Analysis.

Objective: To determine the optimum miniscrew head design in orthodontic treatments for primary stability and compare stress distribution on a representative bone structure. Methods: Miniscrews with cross heads, mushroom-shaped heads, button heads, bracket heads, and through-hole heads were compared using finite element analysis. Miniscrews, whose three-dimensional drawings were completed using the SolidWorks computer-aided software package, were inserted in the bone block. Orthodontic force was applied to the head, and stress distributions, strains, and total deformations were investigated. Results: The lowest von Mises stress of 5.67 MPa was obtained using the bracket head. On the other hand, the highest von Mises stress of 22.4 MPa was found with the button head. Through mesh convergence analysis, the most appropriate mesh size was determined to be 0.5 mm; approximately 230,000 elements were formed for each model. Conclusion: Because the need for low stress is substantial for the primary stability of the miniscrew, this study demonstrated that the bracket head miniscrew is the optimal head design. In addition, it is posited that the success rate of orthodontic anchorage treatments will increase when bracket head miniscrews are used.

EEG-based finger movement classification with intrinsic time-scale decomposition.

Introduction: Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals. Methods: In this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not. Results: As a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems. Discussion: When compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).

Central Auditory Changes Associated with Age-related Hearing Loss.

Objective. This study aimed to investigate age-related changes in cortical auditory evoked potentials (CAEPs) while considering three crucial factors: aging, high-frequency hearing loss and sensation level of the CAEP stimulus. Method. The electrophysiological and audiometric data of 71 elderly participants were analyzed using multiple regression analysis to investigate the association of CAEPs with the factors of aging, high-frequency hearing loss and sensation level of the CAEP test stimulus. Results. Aging was significantly associated with prolonged N1 and P2 latencies and reduced P2 amplitude. Elevated thresholds related to the sensation level of the CAEP stimulus were significantly associated with increased N1 and P2 amplitudes and decreased N1 latency. A significant relationship was detected between high-frequency hearing thresholds and the shortening of P2 latencies and the reduction of P2 amplitudes. Conclusion. The results of this study highlight the complex interplay of aging, high-frequency hearing loss and the sensation level of the CAEP stimulus on CAEP components in elderly people. These factors should be considered in future research using CAEPs to enhance overall understanding of auditory processing in the aging population.

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs.

In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require effective feature extraction methods and classification algorithms to acquire discriminative features from EEG signals due to the non-linear and non-stationary structure of EEG signals. This study investigates the effect of statistical significance-based feature selection on binary and multi-class Motor Imagery EEG signal classifications. In the feature extraction process performed 24 different time-domain features, 15 different frequency-domain features which are energy, variance, and entropy of Fourier transform within five EEG frequency subbands, 15 different time-frequency domain features which are energy, variance, and entropy of Wavelet transform based on five EEG frequency subbands, and 4 different Poincare plot-based non-linear parameters are extracted from each EEG channel. A total of 1,364 Motor Imagery EEG features are supplied from 22 channel EEG signals for each input EEG data. In the statistical significance-based feature selection process, the best one among all possible combinations of these features is tried to be determined using the independent t-test and one-way analysis of variance (ANOVA) test on binary and multi-class Motor Imagery EEG signal classifications, respectively. The whole extracted feature set and the feature set that contain statistically significant features only are classified in this study. We implemented 6 and 7 different classifiers in multi-class and binary (two-class) classification tasks, respectively. The classification process is evaluated using the five-fold cross-validation method, and each classification algorithm is tested 10 times. These repeated tests provide to check the repeatability of the results. The maximum of 61.86 and 47.36% for the two-class and four-class scenarios, respectively, are obtained with Ensemble Subspace Discriminant among all these classifiers using selected features including only statistically significant features. The results reveal that the introduced statistical significance-based feature selection approach improves the classifier performances by achieving higher classifier performances with fewer relevant components in Motor Imagery task classification. In conclusion, the main contribution of the presented study is two-fold evaluation of non-linear parameters as an alternative to the commonly used features and the prediction of multiple Motor Imagery tasks using statistically significant features.

A Novel Plate for Vertical Shear Fractures of Medial Malleolus: A Biomechanical Study.

BACKGROUND: This study aims to evaluate and compare stiffness and the load to failure values of our novel medial malleolus compression plate (MP) and 3,5mm 1/3 tubular plate (TP) in the treatment of vertical shear fractures of medial malleolar fractures. METHODS: Fourteen identical synthetic third generation composite polyurethane bone models of right distal tibia were randomly separated into two groups. Fracture models were created with a custom-made osteotomy guide to provide the same fracture characteristics in every sample (AO OTA type 44A2). Fractures were reduced and novel medial malleolus compression plate was applied to bone models in MP group and tubular plate was applied to TP group. All samples were evaluated biomechanically, force/displacement and the load to failure values were recorded. RESULTS: The force required to create displacement in MP group was twice of that of the TP group. There was a significant difference between two groups in all amounts of displacement (p = .006, p = .005, p = .007 and .015 for 0.5, 1.0, 1.5, and 2.0 mm, respectively). CONCLUSIONS: In the treatment of vertical shear fractures of the medial malleolus, the strength of fixation with the novel medial malleolar compression plate is biomechanically higher than the one-third semi-tubular plate.

The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis.

In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist of statistical time-domain measures, frequency-domain measures (power spectral density estimations from Fourier transform and Lomb-Scargle methods), time-frequency HRV measures (Wavelet transform), and nonlinear HRV measures (Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy). All these HRV features are the classifiers' inputs. We repeated the study ten times using the first one to the first 10 principal components from PCA instead of all HRV features. Nine different classifiers, namely logistic regression, Naive Bayes, k-nearest neighbors, decision tree, AdaBoost, support vector machines, stochastic gradient descent, random forest, and artificial neuronal network (multilayer perceptron) are examined. The proposed study results in the 100% accuracy, 100% specificity, and 100% sensitivity after utilizing PCA (with the first eight principal components) using the Random Forest classifier where the maximum classifier performances are the 86% accuracy, 79% specificity, and 86% sensitivity before PCA. In conclusion, PCA is beneficial in the diagnosis of patients with CHF. In addition, we experienced the online Python-based visual machine learning tool, Orange, which can implement well-known machine learning algorithms.

Efficiency evaluation of rapid maxillary expansion treatment on nasal septal deviation using tortuosity ratio from cone-beam computer tomography images.

BACKGROUND AND OBJECTIVE: This study aims to assess the effect of Rapid Maxillary Expansion (RME) on Nasal Septal Deviation (NSD) changes from three-dimensional (3D) images. METHODS: In this study, cone-beam computed tomography (CBCT) images from 15 patients with maxillary constriction (mean age 12 ± 1.6 years) were included. RME treatment with Hyrax appliance was performed in all patients. CBCT scans were taken at three different times; before appliance insertion (T0), after active expansion (T1) and 3 months after appliance insertion (T2). We developed a novel Matlab-based application to quantify NSD based on the tortuosity ratio by dividing the actual length of the septum by the ideal length in the mid-sagittal plane by using this application. RESULTS: Tortuosity ratio (TR) values were found as 1.03 ± 0.03 (T0), 1.02 ± 0.02 (T1), and 1.02 ± 0.02 (T2). Differences of TR values among these groups were evaluated using the statistical method of ANOVA (ANalysis Of VAriance) for repeated measures with the significance level of p ≤ .05. Results showed significant reductions in TR values between T0-T1 (p ≤ .05) and between T0-T2 (p ≤ .05). Nonetheless, a significant difference between T1-T2 was not determined (p > .05). CONCLUSIONS: As a result, we can conclude that the NSD degree is affected by the RME treatment. The developed application can be used for both educational and research purposes.

Comparison of Cortical Auditory Evoked Potential Findings in Presbycusis with Low and High Word Recognition Score.

BACKGROUND: Deteriorated speech understanding is a common complaint in elderly people, and behavioral tests are used for routine clinical assessment of this problem. Cortical auditory evoked potentials (CAEPs) are frequently used for assessing speech detection and discrimination abilities of the elderly, and give promise for differential diagnosis of speech understanding problems. PURPOSE: The aim of the study was to compare the P1, N1, and P2 CAEP latencies and amplitudes in presbycusis with low and high word recognition score (WRS). RESEARCH DESIGN: A cross-sectional study design was used forthe study. Two groups were formed from the patients with presbycusis based on their scores on the speech recognition test. STUDY SAMPLE: Fifty-seven elderly volunteers participated in the study. The first group composed of 27 participants with high WRS, the other group composed of 30 participants with low WRS. DATA COLLECTION AND ANALYSIS: The CAEP waves were recorded from these participants using speech signals. Latencies and amplitudes of P1 -N1-P2 waves of the two groups were compared with the f-test statistic. RESULTS: There were significant prolongation of P1 and N1 latencies in presbycusis with low WRS when compared with presbycusis with a relatively high word score (p < 0.05). CONCLUSION: According to the result of the research, P1 and N1 latencies of presbycusis with low WRS were longer than the participants with high WRS. Factors affecting peripheral auditory system, such as stimulus sensation level, might be responsible for P1 and N1 latency prolongation of the low WRS group.

Comparison of Cortical Auditory Evoked Potential Findings in Presbycusis with Low and High Word Recognition Score.

BACKGROUND: Deteriorated speech understanding is a common complaint in elderly people, and behavioral tests are used for routine clinical assessment of this problem. Cortical auditory evoked potentials (CAEPs) are frequently used for assessing speech detection and discrimination abilities of the elderly, and give promise for differential diagnosis of speech understanding problems. PURPOSE: The aim of the study was to compare the P1, N1, and P2 CAEP latencies and amplitudes in presbycusis with low and high word recognition score (WRS). RESEARCH DESIGN: A cross-sectional study design was used for the study. Two groups were formed from the patients with presbycusis based on their scores on the speech recognition test. STUDY SAMPLE: Fifty-seven elderly volunteers participated in the study. The first group composed of 27 participants with high WRS, the other group composed of 30 participants with low WRS. DATA COLLECTION AND ANALYSIS: The CAEP waves were recorded from these participants using speech signals. Latencies and amplitudes of P1-N1-P2 waves of the two groups were compared with the t-test statistic. RESULTS: There were significant prolongation of P1 and N1 latencies in presbycusis with low WRS when compared with presbycusis with a relatively high word score (p < 0.05). CONCLUSION: According to the result of the research, P1 and N1 latencies of presbycusis with low WRS were longer than the participants with high WRS. Factors affecting peripheral auditory system, such as stimulus sensation level, might be responsible for P1 and N1 latency prolongation of the low WRS group.

A software for simulating steady-state properties of passive dendrites based on the cable theory.

In this study, a computer software, CableTeo, is introduced for simulating the steady-state electrical properties of passive dendrite based on the cable theory. The cable theory for dendritic neurons addresses to current-voltage relations in a continuous passive dendritic tree. It is briefly summarized that the cable theory related to passive cables and dendrites, which is a useful approximation and an important reference for excitable cases. The proposed software can be used to construct user-defined dendritic tree model. The user can define the model in detail, display the constructed dendritic tree, and examine the basic electrical properties of the dendritic tree using transfer impedance approach. The software addresses to ones who want to run simple simulations of the cable theory without need to any programming language skills or expensive software. It can also be used for educational purposes.

Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure.

In this study, best combination of short-term heart rate variability (HRV) measures are sought for to distinguish 29 patients with congestive heart failure (CHF) from 54 healthy subjects in the control group. In the analysis performed, in addition to the standard HRV measures, wavelet entropy measures are also used. A genetic algorithm is used to select the best ones from among all possible combinations of these measures. A k-nearest neighbor classifier is used to evaluate the performance of the feature combinations in classifying these two groups. The results imply that two combinations of all HRV measures, both of which include wavelet entropy measures, have the highest discrimination power in terms of sensitivity and specificity values.

Discrimination of systolic and diastolic dysfunctions using multi-layer perceptron in heart rate variability analysis.

In this study, the heart rate variability (HRV) analysis is used to distinguish patients with systolic congestive heart failure (CHF) from patients with diastolic CHF. In the analysis performed, the best accuracy performances of short-term HRV measures are compared. These measures are calculated in four different ways with optional normalization methods of heart rate and data. The nearest neighbor and the multi-layer perceptron (MLP) are used to evaluate the performances in discriminating these two groups. The results point out that using both data and heart rate normalizations enhances the classifier performance. The maximum accuracy is obtained as 96.43% with MLP classifier.

A computer software for simulating single-compartmental model of neurons.

In this paper, a new computer software package, Yalzer, is introduced for simulating single-compartmental model of neurons. Passive or excitable membranes with voltage-gated ion channels can be modeled, and current clamp and voltage clamp experiments can be simulated. In the Yalzer, first-order differential equations used to define the dynamics of the gate variables and the membrane potential are solved by two separate integration methods with variable time steps: forward Euler and exponential Euler methods. Outputs of the simulation are shown on a spreadsheet template for allowing flexible data manipulation and can be graphically displayed. The user can define the model in detail, and examine the excitability of the model and the dynamics of voltage-gated ion channels. The software package addresses to ones who want to run simple simulations of neurons without need to any programming language skills or expensive software. It can also be used for educational purposes.

Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance.

In this study, the best combination of short-term heart rate variability (HRV) measures was investigated to distinguish 29 patients with congestive heart failure from 54 healthy subjects in the control group. In the analysis performed, wavelet packet transform based frequency-domain measures and several non-linear parameters were used in addition to standard HRV measures. The backward elimination and unpaired statistical analysis methods were used to select the best one among all possible combinations of these measures. Five distinct typical classifiers with different parameters were evaluated in discriminating these two groups using the leave-one-out cross validation method. Each algorithm was tested 30 times to determine the repeatability of the results. The results imply that the backward elimination method gives better performance when compared to the statistical significance method in the feature selection stage. The best performance (82.75%, 96.29%, and 91.56% for the sensitivity, specificity, and accuracy) was obtained by using the SVM classifier with 27 selected features including non-linear and wavelet-based measures.