Biomedizinische Technik. Biomedical engineering最新文献

Objectives: To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task.

Methods: The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics.

Results: A superior sensitivity of 0.939 is noted for HC + SMA-SSA that was higher over HC with no optimization and proposed with traditional ST.

Conclusions: The proposed method achieved effective classification performance in terms of performance measures.

Objectives: To evaluate tissue expansion during cryoablation, the displacement of markers in ex vivo kidney tissue was determined using computed tomographic (CT) imaging.

Methods: CT-guided cryoablation was performed in nine porcine kidneys over a 10 min period. Markers and fiber optic temperature probes were positioned perpendicular to the cryoprobe shaft in an axial orientation. The temperature measurement was performed simultaneously with the acquisitions of the CT images in 5 s intervals. The distance change of the markers to the cryoprobe was determined in each CT image and equated to the measured temperature at the marker.

Results: The greatest increase in the distance between the markers and the cryoprobe was observed in the initial phase of cryoablation. The maximum displacement of the markers was determined to be 0.31±0.2 mm and 2.8±0.02 %, respectively.

Conclusions: The mean expansion of ex vivo kidney tissue during cryoablation with a single cryoprobe is 0.31±0.2 mm. The results can be used for identification of basic parameters for optimization of therapy planning.

Objectives: The present study is designed to explore the process of epileptic patterns' automatic detection, specifically, epileptic spikes and high-frequency oscillations (HFOs), via a selection of machine learning (ML) techniques. The primary motivation for conducting such a research lies mainly in the need to investigate the long-term electroencephalography (EEG) recordings' visual examination process, often considered as a time-consuming and potentially error-prone procedure, requiring a great deal of mental focus and highly experimented neurologists. On attempting to resolve such a challenge, a number of state-of-the-art ML algorithms have been evaluated and compare in terms of performance, to pinpoint the most effective algorithm fit for accurately extracting epileptic EEG patterns.

Content: Based on intracranial as well as simulated EEG data, the attained findings turn out to reveal that the randomforest (RF) method proved to be the most consistently effective approach, significantly outperforming the entirety of examined methods in terms of EEG recordings epileptic-pattern identification. Indeed, the RF classifier appeared to record an average balanced classification rate (BCR) of 92.38 % in regard to spikes recognition process, and 78.77 % in terms of HFOs detection.

Summary: Compared to other approaches, our results provide valuable insights into the RF classifier's effectiveness as a powerful ML technique, fit for detecting EEG signals born epileptic bursts.

Outlook: As a potential future work, we envisage to further validate and sustain our major reached findings through incorporating a larger EEG dataset. We also aim to explore the generative adversarial networks (GANs) application so as to generate synthetic EEG signals or combine signal generation techniques with deep learning approaches. Through this new vein of thought, we actually preconize to enhance and boost the automated detection methods' performance even more, thereby, noticeably enhancing the epileptic EEG pattern recognition area.

Second-generation headless compression screws (HCSs) are commonly used for the fixation of small bones and articular fractures. However, there is a lack of biomechanical data regarding the application of such screws to radial head fractures. This study evaluated the mechanical properties of the fixation of radial head fractures using a single oblique HCS compared with those obtained using a standard locking radial head plate (LRHP) construct and a double cortical screw (DCS) construct. Radial synbone models were used for biomechanical tests of HCS, LRHP, and DCS constructs. All specimens were first cyclically loaded and then loaded to failure. The stiffness for the LRHP group was significantly higher than that for the other two groups, and that for the HCS group was significantly higher than that for the DCS group. The LRHP group had the greatest strength, followed by the HCS group and then the DCS group. The HCS construct demonstrated greater fixation strength than that of the commonly used cortical screws, although the plate group was the most stable. The present study revealed the feasibility of using a single oblique HCS, which has the advantages of being buried, requiring limited wound exposure, and having relatively easy operation, for treating simple radial head fractures.

The automatic segmentation of the abnormality region from the head MRI is a challenging task in the medical science domain. The abnormality in the form of the tumor comprises the uncontrolled growth of the cells. The automatic identification of the affected cells using computerized software systems is demanding in the past several years to provide a second opinion to radiologists. In this paper, a new clustering approach is introduced based on the machine learning aspect that clusters the tumor region from the input MRI using disjoint tree generation followed by tree merging. Further, the proposed algorithm is improved by introducing the theory of joint probabilities and nearest neighbors. Later, the proposed algorithm is automated to find the number of clusters required with its nearest neighbors to do semantic segmentation of the tumor cells. The proposed algorithm provides good semantic segmentation results having the DB index-0.11 and Dunn index-13.18 on the SMS dataset. While the experimentation with BRATS 2015 dataset yields Dice _complete=80.5 %, Dice _core=73.2 %, and Dice _enhanced=62.8 %. The comparative analysis of the proposed approach with benchmark models and algorithms proves the model's significance and its applicability to do semantic segmentation of the tumor cells with the average increment in the accuracy of around ±2.5 % with machine learning algorithms.

Objectives: Numerous studies indicate that deep breathing (DB) enhances wellbeing. Multiple deep breathing methods exist, but few employ audio to reach similar results. This study developed audio-guided DB and evaluated its immediate impacts on healthy population via self-created auditory Go/No-Go task, tidal volume changes, and psychological measures.

Methods: Audio-guided DB with natural sounds to guide the DB was developed. Meanwhile, audio-based Go/No-Go paradigm with Arduino was built to measure the attention level. Thirty-two healthy young adults (n=32) were recruited. Psychological questionnaires (Rosenberg's Self-Esteem Scale (RSES), Cognitive and Affective Mindfulness Scale-Revised (CAMS-R), Perceived Stress Scale (PSS)), objective measurements with tidal volume and attention level with auditory Go/No-Go task were conducted before and after 5 min of DB.

Results: Results showed a significant increment in tidal volume and task reaction time from baseline (p=0.003 and p=0.033, respectively). Significant correlations were acquired between (1) task accuracy with commission error (r=-0.905), (2) CAMS-R with task accuracy (r=-0.425), commission error (r=0.53), omission error (r=0.395) and PSS (r=-0.477), and (3) RSES with task reaction time (r=-0.47), task accuracy (r=-0.362), PSS (r=-0.552) and CAMS-R (r=0.591).

Conclusions: This pilot study suggests a link between it and young adults' wellbeing and proposes auditory Go/No-Go task for assessing attention across various groups while maintaining physical and mental wellness.

Objectives: Fatigue has a considerable impact on the driver's vehicle and even the driver's own operating ability.

Methods: An intelligent algorithm is proposed for the problem that it is difficult to classify the degree of drowsiness generated by the driver during the driving process. By studying the driver's electrocardiogram (ECG) during driving, two models were established to jointly classify the ECG signals as awake, stress, and fatigue or drowsiness states for drowsiness levels. Firstly, the deep learning method was used to establish the model_1 to predict the drowsiness of the original ECG, and model_2 was developed using the combination of principal component analysis (PCA) and weighted K-nearest neighbor (WKNN) algorithm to classify the heart rate variability characteristics. Then, the drowsiness prediction results of the two models were weighted according to certain rules, and the hybrid learning model combining dilated convolution and bidirectional long short-term memory network with PCA and WKNN algorithm was established, and the mixed model was denoted as DiCNN-BiLSTM and PCA-WKNN (DBPW). Finally, the validity of the DBPW model was verified by simulation of the public database.

Results: The experimental results show that the average accuracy, sensitivity and F1 score of the test model in the dataset containing multiple drivers are 98.79, 98.81, and 98.79 % respectively, and the recognition accuracy for drowsiness or drowsiness state is 99.33 %.

Conclusions: Using the proposed algorithm, it is possible to identify driver anomalies and provide new ideas for the development of intelligent vehicles.

Objectives: Arrhythmia is an important component of cardiovascular disease, and electrocardiogram (ECG) is a method to detect arrhythmia. Arrhythmia detection is often paroxysmal, and ECG signal analysis is time-consuming and expensive. We propose a model and device for convenient monitoring of arrhythmia at any time.

Methods: This work proposes a model combining residual block and bidirectional long-term short-term memory network (BiLSTM) to detect and classify ECG signals. Residual blocks can extract deep features and avoid performance degradation caused by convolutional networks. Combined with the feature of BiLSTM to strengthen the connection relationship of the local window, it can achieve a better classification and prediction effect.

Results: Model optimization experiments were performed on the MIT-BIH Atrial Fibrillation Database (AFDB) and MIT-BIH Arrhythmia Database (MITDB). The accuracy simulation results on both long and short signal was higher than 99 %. To further demonstrate the applicability of the model, validation experiments were conducted on MIT-BIH Normal Sinus Rhythm Database (NSRDB) and the Long-Term AF Database (LTAFDB) datasets, and the related recognition accuracy were 99.830 and 91.252 %, respectively. Additionally, we proposed a portable household detection system including an ECG and a blood pressure detection module. The detection accuracy was higher than 98 % using the collected data as testing set.

Conclusions: Hence, we thought our system can be used for practical application.

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both microbiologist and Radiologist and can lead to miss-diagnosis. The main objective of this study is to addressed these challenges by employing Computer Aided Detection (CAD) using Artificial Intelligence-driven models which learn features based on convolution and result in an output with high accuracy. In this paper, we described automated discrimination of X-ray and microscopic slide images of tuberculosis into positive and negative cases using pretrained AlexNet Models. The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East university hospital and Kaggle repository. For classification of tuberculosis and healthy microscopic slide using AlexNet+Softmax, the model achieved accuracy of 98.14%. For classification of tuberculosis and healthy microscopic slide using AlexNet+SVM, the model achieved 98.73% accuracy. For classification of tuberculosis and healthy chest X-ray images using AlexNet+Softmax, the model achieved accuracy of 98.19%. For classification of tuberculosis and healthy chest X-ray images using AlexNet+SVM, the model achieved 98.38% accuracy. The result obtained has shown to outperformed several studies in the current literature. Future studies will attempt to integrate Internet of Medical Things (IoMT) for the design of IoMT/AI-enabled platform for detection of Tuberculosis from both X-ray and Microscopic slide images.

The utilization of nanotechnology in regenerative medicine has been globally proven to be the main solution to many issues faced with tissue engineering today, and the theoretical and empirical investigations of the association of nanomaterials with stem cells have made significant progress as well. For their ability to self-renew and differentiate into a variety of cell types, stem cells have become popular candidates for cell treatment in recent years, particularly in cartilage and Ocular regeneration. However, there are still several challenges to overcome before it may be used in a wide range of therapeutic contexts. This review paper provides a review of the various implications of nanomaterials in tissue and cell regeneration, the stem cell and scaffold application in novel treatments, and the basic developments in stem cell-based therapies, as well as the hurdles that must be solved for nanotechnology to be used in its full potential. Due to the increased interest in the continuously developing field of nanotechnology, demonstrating, and pinpointing the most recognized and used applications of nanotechnology in regenerative medicine became imperative to provide students, researchers, etc. who are interested.