Biomedical Physics & Engineering Express最新文献

Accurate detection of cardiac arrhythmias is crucial for preventing premature deaths. The current study employs a dual-stage Discrete Wavelet Transform (DWT) and a median filter to eliminate noise from ECG signals. Subsequently, ECG signals are segmented, and QRS regions are extracted for further preprocessing. The study considers five cardiac arrhythmias: normal beats, Premature Ventricular Contractions (PVC), Premature Atrial Contractions (PAC), Right Bundle Branch Block (R-BBB), and Left Bundle Branch Block (L-BBB) for classification. Nine distinct temporal features are extracted from the segmented QRS complex. These features are then applied to six different classifiers for arrhythmia classification. The classifiers' performance is evaluated using the MIT-BIH Arrhythmia Database (MIT-BIH AD). Support Vector Machine (SVM) and Ensemble Tree classifiers demonstrate superior performance in classifying the five different classes. Particularly, the Support Vector Machine classifier achieves high sensitivity (97.44%), specificity (99.36%), positive predictive value (97.44%), and accuracy (98.97%) with a Gaussian kernel. This comprehensive approach, integrating preprocessing, and feature extraction, holds promise for improving automatic cardiac arrhythmia classification in clinical trials.

Objective.Clinical adoption of innovative EEG technology is contingent on the non-inferiority of the new devices relative to conventional ones. We present the four key results from testing the signal quality of Zeto's WR 19 EEG system against a conventional EEG system conducted on patients in a clinical setting.Methods.We performed 30 min simultaneous recordings using the Zeto WR 19 (zEEG) and a conventional clinical EEG system (cEEG) in a cohort of 15 patients. We compared the signal quality between the two EEG systems by computing time domain statistics, waveform correlation, spectral density, signal-to-noise ratio and signal stability.Results.All statistical comparisons resulted in signal quality non-inferior relative to cEEG. (i) Time domain statistics, including the Hjorth parameters, showed equivalence between the two systems, except for a significant reduction of sensitivity to electric noise in zEEG relative to cEEG. (ii) The point-by-point waveform correlation between the two systems was acceptable (r > 0.6; P < 0.001). (iii) Each of the 15 datasets showed a high spectral correlation (r > 0.99; P < 0.001) and overlapping spectral density across all electrode positions, indicating no systematic signal distortion. (iv) The mean signal-to-noise ratio (SNR) of the zEEG system exceeded that of the cEEG by 4.82 dB, equivalent to a 16% improvement. (v) The signal stability was maintained through the recordings.Conclusion.In terms of signal quality, the zEEG system is non-inferior to conventional clinical EEG systems with respect to all relevant technical parameters that determine EEG readability and interpretability. Zeto's WR 19 wireless dry electrode system has signal quality in the clinical EEG space at least equivalent to traditional cEEG recordings.

Accurate detection of cardiac arrhythmias is crucial for preventing premature deaths. The current study employs a dual-stage Discrete Wavelet Transform (DWT) and a median filter to eliminate noise from ECG signals. Subsequently, ECG signals are segmented, and QRS regions are extracted for further preprocessing. The study considers five cardiac arrhythmias: normal beats, Premature Ventricular Contractions (PVC), Premature Atrial Contractions (PAC), Right Bundle Branch Block (R-BBB), and Left Bundle Branch Block (L-BBB) for classification. Nine distinct temporal features are extracted from the segmented QRS complex. These features are then applied to six different classifiers for arrhythmia classification. The classifiers' performance is evaluated using the MIT-BIH Arrhythmia Database (MIT-BIH AD). Support Vector Machine (SVM) and Ensemble Tree classifiers demonstrate superior performance in classifying the five different classes. Particularly, the Support Vector Machine classifier achieves high sensitivity (97.44%), specificity (99.36%), positive predictive value (97.44%), and accuracy (98.97%) with a Gaussian kernel. This comprehensive approach, integrating preprocessing, and feature extraction, holds promise for improving automatic cardiac arrhythmia classification in clinical trials.

Objective Applying carbon ion beams, which have high linear energy transfer and low scatter within the human body, to Spatially Fractionated Radiation Therapy (SFRT) could benefit the treatment of deep-seated or radioresistant tumors. This study aims to simulate the dose distributions of spatially fractionated beams (SFB) to accurately determine the delivered dose and model the cell survival rate following SFB irradiation. Approach Dose distributions of carbon ion beams are calculated using the Triple Gaussian Model. The sensitive volume of the detector used in measurements was also considered. If the measurements and simulations show good agreement, the dose distribution and absolute dose delivered by SFB can be accurately estimated. Three types of dose distributions were delivered to human salivary gland cells (HSGc-C5): uniform dose distribution (UDD), and one-dimensional (1D) grid-like dose distributions (GDD) with 6 mm and 8 mm spacing. These provided high (Peak-to-Valley Dose Ratio, PVDR=4.0) and low (PVDR=1.64) dose differences between peak and valley doses, respectively. Linear-Quadratic (LQ) model parameters for HSGc-C5 were derived from the UDD and cell survival fractions (SF) were simulated for 1D GDD using these values. Main results Good agreement was observed between measurements and simulations when accounting for detector volume. However, the TPS results overestimated dose in steep gradient region, likely due to the 2.0 mm calculation grid size. LQ parameters for HSGc-C5 were α = 0.34 and β = 0.057. The 1D GDD with 6 mm spacing showed good agreement between simulations and experiments, but the 8.0 mm spacing resulted in lower experimental cell survival. Significance We successfully simulated grid-like dose distributions and conducted SF simulations. The results suggest potential cell-killing effects due to high-dose differences in SFB. .

Objective assessments of shoulder motion are paramount for effective rehabilitation and evaluation of surgical outcomes. Inertial Measurement Units (IMU) have demonstrated promise in providing unbiased movement data. This study is dedicated to evaluating the concurrent construct validity and accuracy of a wearable IMU-based sensor system, called 'Motion Shirt', for the assessment of humero-thoracic motion arcs in patients awaiting shoulder replacement surgery. This evaluation was conducted by comparing Motion Shirt data with the Dartfish Motion Analyzer software during the Functional Impairment Test-Hand and Neck/Shoulder/Arm (FIT-HaNSA) test. Thirteen patients (age > 50), who were awaiting shoulder replacement surgery, were recruited. The Motion Shirt was employed to measure angular humero-thoracic movements in two planes during the FIT-HaNSA test. Simultaneously, two cameras recorded the participants' movements to provide reference data. Bland-Altman plots were generated to visualize agreement between the Motion Shirt and the reference data obtained from the Dartfish Motion Analyzer software. The data analysis on Bland-Altman plots revealed a substantial level of agreement between the Motion Shirt and Dartfish analysis in measuring humero-thoracic motion. In Task-1, no significant systematic errors were exhibited, with only 3.27% and 2.18% of points exceeding the limits of agreement (LOA) in both elevation and the Plane of Elevation (POE), signifying a high level of concordance. In Task-2, a high level of agreement was also observed in Elevation, with only 3.8% of points exceeding the LOA. However, 5.98% of points exceeded LOA in POE for Task-2. In Task-3, focused on sustained overhead activity, the Motion Shirt showed strong agreement with Dartfish in Elevation (2.44% points exceeded LOA), but in POE, 7.32% points exceeded LOA. The Motion Shirt demonstrated a robust concordance with Dartfish Motion Analyzer system in assessing humerothoracic motion during the FIT-HaNSA test. These results affirm the Motion Shirt's suitability for objective motion analysis in patients awaiting shoulder replacement surgery.

Clinical research in boron neutron capture therapy (BNCT) has been conducted worldwide. Currently, the Monte Carlo (MC) method is the only dose calculation algorithm implemented in the treatment planning system for the clinical treatment of BNCT. We previously developed the MC-RD calculation method, which combines the MC method and the removal-diffusion (RD) equation, for fast dose calculation in BNCT. This study aimed to verify the partial-MC-RD calculation method, which utilizes the MC-RD calculation method for a portion of the entire neutron energy range, in terms of calculation accuracy and time as the dose calculation method. We applied the partial-MC-RD calculation method to calculate the total dose for head phantom, comprising soft tissue, brain tissue, and bone. The calculation time and accuracy were evaluated based on the full-MC method. Our accuracy verifications indicated that the partial-MC-RD calculation was mostly comparable with full-MC calculation in the accuracy. However, the assumptions and approximation used in the RD calculation mainly occurred the discrepancy from the full-MC calculation result. Additionally, the partial-MC-RD calculation reduced the time required to approximately 45% for the irradiation to the top and cheek region of head phantom, compared to the full-MC calculation. In conclusion, the MC-RD calculation method can be the basis of a fast dose calculation method in BNCT.

Characterization of the electroencephalography (EEG) signals related to motor activity, such as alpha- and beta-band motor event-related desynchronizations (ERDs), is essential for Brain Computer Interface (BCI) development. Determining the best electrode combination to detect the ERD is crucial for the success of the BCI. Considering that the EEG signals are bipolar, this involves the choice of the main and reference electrodes. So far, no strategy to guarantee signals free of the activity from the reference electrode has achieved consensus among the scientific community. Therefore, mapping the ERD in terms of the spatial distribution of the main and reference electrodes can provide additional perspectives for the BCI field. The goal of this work is to identify subject-specific channels where ERD is temporally coupled to the initiation of an upper-limb motor task. We defined a criterion to determine the presence of the ERD linked to the movement onset and searched, separately for each subject, for the single channel with the most prominent ERD. The search was conducted over all available channels composed by a pair of electrodes, and the selected signals were analyzed according to their temporal and spatial characteristics. We found that alpha- and beta-band ERD temporarily linked to movement onset can be detected in atypical channels (pairs of electrodes) across the scalp. The selected channels were different across subjects. Four ERD temporal patterns were observed in terms of the initiation instant of the ERD. These patterns revealed that the M1 cortex seems to be related to later ERDs. Moreover, they were also associated to different cortical processes related to the motor task. To the best of our knowledge, this is the first time these findings are reported. Aiming at BCI development, further experiments with more subjects and with motor-imagery tasks are desirable for more robustness and applicability of these findings.

Fracture-related infections are burdensome conditions that affect both a patient's health and financial well-being. Preventing an infection and stabilizing the fracture are critical aspects in a care plan that rely on antibiotics and orthopedic implants, both which need to be improved. Bacteriophage or phage are viruses that specifically kill bacteria and are a promising alternative/companion to antibiotics while enhanced orthopedic implants that are osteoinductive and biodegradable are needed for bone healing. In this work we report the inhibitory effectiveness of three phages Ø K, Ø 0146, and Ø 104023 alone and in combination against a strain of methicillin-resistantStaphylococcus aureus. Single phage and cocktails were mixed with bacteria at multiplicities of infection of 5 and 2.5 and growth was measured using optical density over 48 h. Ø K alone and Ø K + Ø 0146 were able to completely inhibit bacterial growth. We also present and the ability of Ø K to bind to and be released from a biodegradable and biocompatible orthopedic carbon scaffold. The carbon scaffold was soaked in a solution of Ø K, washed, and then incubated in sequential buffer baths while samples were removed at timepoints up to seven days to calculate phage elution. At every timepoint measured including seven days, phages were found to still be eluting from the scaffold. These results indicate that the studied phages are effective bacterial inhibitors and could be used to prevent infections. Furthermore, orthopedic implants such as a carbon scaffold can be coated with phage to provide long-term protection.In vivoinfection experiments on phage loaded scaffold that test bacterial clearance, phage persistence in tissue, resolution of inflammation, and bone regrowth with an active infection are needed to further this work.

Orthopedic surgery relies on bone drills to create tunnels for fracture fixation, bone fusion, or tendon repair. Traditional rigid and straight bone drills often pose challenges in accessing the desired entry points without risking damage to the surrounding anatomical structures, especially in minimal invasive procedures. In this study, we explore the use of hydraulic pressure waves in a flexible bone design to facilitate bone drilling. The HydroFlex Drill includes a handle for generating a hydraulic pressure wave in the flexible, fluid-filled shaft to transmit an impulse to the hammer tip, enabling bone drilling. We evaluated seven different hammer tip shapes to determine their impact on drilling efficiency. Subsequently, the most promising tip was implemented in the HydroFlex Drill. The HydroFlex Drill Validation demonstrated the drill's ability to successfully transfer the impulse generated in the handle to the hammer tip, with the shaft in different curves. This combined with the drill's ability to create indentations in bone phantom material is a promising first step towards the development of a flexible or even steerable bone drill. With ongoing research to enhance the drilling efficiency, the HydroFlex Drill opens possibilities for a range of orthopedic surgical procedures where minimally invasive drilling is essential.

Malignant thyroid nodules are closely linked to cancer, making the precise classification of thyroid nodules into benign and malignant categories highly significant. However, the subtle differences in contour between benign and malignant thyroid nodules, combined with the texture features obscured by the inherent noise in ultrasound images, often result in low classification accuracy in most models. To address this, we propose a Bidirectional Interaction Directional Variance Attention Model based on Increased-Transformer, named IFormer-DVNet. This paper proposes the Increased-Transformer, which enables global feature modeling of feature maps extracted by the Convolutional Feature Extraction Module (CFEM). This design maximally alleviates noise interference in ultrasound images. The Bidirectional Interaction Directional Variance Attention module (BIDVA) dynamically calculates attention weights using the variance of input tensors along both vertical and horizontal directions. This allows the model to focus more effectively on regions with rich information in the image. The vertical and horizontal features are interactively combined to enhance the model's representational capability. During the model training process, we designed a Multi-Dimensional Loss function (MD Loss) to stretch the boundary distance between different classes and reduce the distance between samples of the same class. Additionally, the MD Loss function helps mitigate issues related to class imbalance in the dataset. We evaluated our network model using the public TNCD dataset and a private dataset. The results show that our network achieved an accuracy of 76.55% on the TNCD dataset and 93.02% on the private dataset. Compared to other state-of-the-art classification networks, our model outperformed them across all evaluation metrics.