AIMS Bioengineering最新文献

Robust QRS detection is crucial for accurate diagnosis and monitoring of cardiovascular diseases. During the detection process, various types of noise and artifacts in the electrocardiogram (ECG) can degrade the accuracy of algorithm. Previous QRS detectors have employed various filtering methods to minimize the negative impact of noise. However, their performance still significantly deteriorates in large-noise environments. To further enhance the robustness of QRS detectors on noisy electrocardiograms (ECGs), we proposed a QRS detection algorithm based on an underdamped. This method utilizes the period nonlinearity-induced stochastic resonance to enhance QRS complexes while suppressing noise and non-QRS components in the ECG. In contrast to neural network-based algorithms, our proposed algorithm does not rely on large datasets or prior knowledge. Through testing on three widely used ECG datasets, we demonstrated that the proposed algorithm achieves state-of-the-art detection performance. Furthermore, compared to traditional stochastic resonance-based method, our algorithm has increased noise robustness by 25% to 100% across various real-world environments. This enables the proposed method to maintain its optimal performance within a certain range even in the presence of additional injected noise, thus providing an excellent approach for robust QRS detection in noisy ECGs.

In this paper, we introduce a multi-modal sensor fusion system designed for biomedical engineering, specifically geared toward optimizing physical training by collecting detailed body movement data. This system employs inertial measurement units, flex sensors, electromyography sensors, and Microsoft's Kinect V2 to generate an in-depth analysis of an individual's physical performance. We incorporate a gated recurrent unit- recurrent neural network algorithm to achieve highly accurate body and hand motion estimation, thus surpassing the performance of traditional machine learning algorithms in terms of accuracy, precision, recall, and F1 score. The system's integration with the PICO 4 VR environment creates a rich, interactive experience for physical training. Unlike conventional motion capture systems, our sensor fusion system is not limited to a fixed workspace, allowing users to engage in exercise within a flexible, free-form environment.

Heart disease, globally recognized as a leading cause of death, has seen its impact magnified by the emergence of COVID-19. The heightened demand for early detection and diagnosis of heart disease has forced the development of innovative, intelligent systems. This research offers a novel approach by leveraging extended short-term memory networks (LSTM) and including COVID-19 as a significant parameter in cardiac arrest analysis. A comparative study is conducted between LSTM and other prevalent techniques, such as support vector machines (SVM), linear regression (LR), and artificial neural networks (ANN), focusing on accuracy and other prognostic criteria for heart disease. We aim to develop an intelligent system powered by LSTM to predict heart disease, thereby assisting healthcare professionals in making well-informed decisions about heart disease management, stroke prevention, and patient monitoring. Additionally, hyperparameter tuning has been performed to optimize the LSTM model's performance in cardiac arrest prediction. The results underscore that LSTM, especially when trained with COVID-19 as an input parameter, surpasses other established techniques in prediction accuracy. The proposed model underwent experimental testing, showcasing its proficiency in predicting cardiovascular disease.

The primary purpose of this study was to determine the accuracy of micro-computed tomography (micro-CT) as a novel tool for the 3D analysis of bone density around dental implants in tibia rabbits. Six male New Zealand rabbits were used in our evaluation. One Copa SKY® (Bredent Medical GmbH & Co. K.G.) with a 3.5 mm diameter by 8.0 mm in length was placed within 12 tibia rabbits divided into two experimental groups: Group A (crestal placement) and Group B (sub-crestal placement). The animals were sacrificed at four weeks. Micro-CT evaluations showed a high amount of bone around all implants in the tibia rabbit bone. There was an increased formation of bone around the Copa SKY implants, mainly in the implants that were placed crestally. The most frequent density found in most implants was a medullary bone formation surrounding the implant; the density three (D3) was the most common type in all implants. The 3D model analysis revealed a mean bone volume (B.V.) of 31.24 ± 1.24% in crestal implants compared with the 43.12 ± 0.43% in sub-crestal implants. The mean actual contact implant to bone (B.I.C.) in the sub-crestal group was 51.76 ± 0.86%, compared to the 42.63 ± 0.75% in the crestal group. Compared to crestal implants, the Copa Sky implant placed sub-crestally allows for the formation of bone on top of the neck, thereby stimulating bone growth in tibia rabbits.

Lung cancer is a predominant cause of global cancer-related mortality, highlighting the urgent need for enhanced diagnostic and therapeutic modalities. With the integration of artificial intelligence (AI) into clinical practice, a new horizon in lung cancer care has emerged, characterized by precision in both diagnosis and treatment. This review delves into AI's transformative role in this domain. We elucidate AI's significant contributions to imaging, pathology, and genomic diagnostics, underscoring its potential to revolutionize early detection and accurate categorization of the disease. Shifting the focus to treatment, we spotlight AI's synergistic role in tailoring patient-centric therapies, predicting therapeutic outcomes, and propelling drug research and development. By harnessing the combined prowess of AI and clinical expertise, there's potential for a seismic shift in the lung cancer care paradigm, promising more precise, individualized interventions, and ultimately, improved survival rates for patients.