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Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the technique for monitoring different signals during the patient’s sleep cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to the high cost and inconvenience of polysomnography, the usefulness of ECG signals in detecting OSA is explored in this work, which proposes a two-dimensional convolutional neural network (2D-CNN) model for detecting OSA using ECG signals. A publicly available apnea ECG database from PhysioNet is used for experimentation. Further, a constant Q-transform (CQT) is applied for segmentation, filtering, and conversion of ECG beats into images. The proposed CNN model demonstrates an average accuracy, sensitivity and specificity of 91.34%, 90.68% and 90.70%, respectively. The findings obtained using the proposed approach are comparable to those of many other existing methods for automatic detection of OSA.

This paper presents a decagonal patch antenna loaded with graphene designed for terahertz (THz) frequency applications, with a specific emphasis on its potential for early breast cancer detection. The proposed antenna features a hybrid structure, integrating both copper and graphene materials. A decagonal graphene strip is intricately incorporated into the copper patch, yielding significant improvements in reflection coefficient, bandwidth, and gain. The antenna, with dimensions of 155µ m ×130µ m ×13µ m , is designed on a polyimide substrate, characterized by a dielectric constant of 3.5 and a loss tangent of 0.0027. To ensure relevance in medical contexts, the design is optimized to operate within the frequency range of 2.1 to 5.7 THz, a critical spectrum for medical applications. Simulation results validate the effectiveness of the proposed antenna, demonstrating S 11 <-10 d B within the frequency band of 2.1 to 5.7 THz (92.3% fractional bandwidth). The antenna exhibits an impressive bandwidth of 3.6 THz and a gain of 7.87 dBi at 4 THz. These findings establish the graphene-loaded decagonal patch antenna as a highly promising solution for breast cancer detection applications, showcasing its potential in the realm of medical diagnostics.

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., (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)

To propose an algorithm for automatic localization of 3D cephalometric landmarks on CBCT data, those are useful for both cephalometric and upper airway volumetric analysis. 20 landmarks were targeted for automatic detection, of which 12 landmarks exist on the mid-sagittal plane. Automatic detection of mid-sagittal plane from the volume is a challenging task. Mid-sagittal plane is detected by extraction of statistical parameters of the symmetrical features of the skull. The mid-sagittal plane is partitioned into four quadrants based on the boundary definitions extracted from the human anatomy. Template matching algorithm is applied on the mid-sagittal plane to identify the region of interest ROI, further the edge features are extracted, to form contours in the individual regions. The landmarks are automatically localized by using the extracted knowledge of anatomical definitions of the landmarks. The overall mean error for detection of 20 landmarks was 1.88 mm with a standard deviation of 1.10 mm. The cephalometric land marks on CBCT data were detected automatically with in the mean error less than 2 mm.