Software defined radio frequency sensing framework for intelligent monitoring of sleep apnea syndrome.

Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well. Sleep apnea syndrome is a critical breathing disorder that occurs during sleeping when breathing stops suddenly and starts when awake, causing sleep disturbance. If it is not treated timely, it can produce loud snoring and drowsiness or causes more acute health problems such as high blood pressure or heart attack. The accepted standard for diagnosing sleep apnea syndrome is full-night polysomnography. However, its limitations include a high cost and inconvenience. This article aims to develop an intelligent monitoring framework for detecting breathing events based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing sleep apnea syndrome. We extract the wireless channel state information (WCSI) for breathing motion using channel frequency response (CFR) recorded in time at every instant at the receiver. The proposed approach simplifies the receiver structure with the added functionality of communication and sensing together. Initially, simulations are conducted to test the feasibility of the SDRF sensing design for the simulated wireless channel. Then, a real-time experimental setup is developed in a lab environment to address the challenges of the wireless channel. We conducted 100 experiments to collect the dataset of 25 subjects for four breathing patterns. SDRF sensing system accurately detected breathing events during sleep without subject contact. The developed intelligent framework uses machine learning classifiers to classify sleep apnea syndrome and other breathing patterns with an acceptable accuracy of 95.9%. The developed framework aims to build a non-invasive sensing system to diagnose patients conveniently suffering from sleep apnea syndrome. Furthermore, this framework can easily be further extended for E-health applications.

RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames.

Brain tumors are one of the most fatal cancers. Magnetic Resonance Imaging (MRI) is a non-invasive method that provides multi-modal images containing important information regarding the tumor. Many contemporary techniques employ four modalities: T1-weighted (T1), T1-weighted with contrast (T1c), T2-weighted (T2), and fluid-attenuation-inversion-recovery (FLAIR), each of which provides unique and important characteristics for the location of each tumor. Although several modern procedures provide decent segmentation results on the multimodal brain tumor image segmentation benchmark (BraTS) dataset, they lack performance when evaluated simultaneously on all the regions of MRI images. Furthermore, there is still room for improvement due to parameter limitations and computational complexity. Therefore, in this work, a novel encoder-decoder-based architecture is proposed for the effective segmentation of brain tumor regions. Data pre-processing is performed by applying N4 bias field correction, z-score, and 0 to 1 resampling to facilitate model training. To minimize the loss of location information in different modules, a residual spatial pyramid pooling (RASPP) module is proposed. RASPP is a set of parallel layers using dilated convolution. In addition, an attention gate (AG) module is used to efficiently emphasize and restore the segmented output from extracted feature maps. The proposed modules attempt to acquire rich feature representations by combining knowledge from diverse feature maps and retaining their local information. The performance of the proposed deep network based on RASPP, AG, and recursive residual (R2) block termed RAAGR2-Net is evaluated on the BraTS benchmarks. The experimental results show that the suggested network outperforms existing networks that exhibit the usefulness of the proposed modules for "fine" segmentation. The code for this work is made available online at: https://github.com/Rehman1995/RAAGR2-Net.

DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes.

N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays a critical function in a variety of biological processes. Although experimental approaches for identifying m6A sites have been developed and deployed, they are currently expensive for transcriptome-wide m6A identification. Some computational strategies for identifying m6A sites have been presented as an effective complement to the experimental procedure. However, their performance still requires improvement. In this study, we have proposed a novel tool called DL-m6A for the identification of m6A sites in mammals using deep learning based on different encoding schemes. The proposed tool uses three encoding schemes which give the required contextual feature representation to the input RNA sequence. Later these contextual feature vectors individually go through several neural network layers for shallow feature extraction after which they are concatenated to a single feature vector. The concatenated feature map is then used by several other layers to extract the deep features so that the insight features of the sequence can be used for the prediction of m6A sites. The proposed tool is firstly evaluated on the tissue-specific dataset and later on a full transcript dataset. To ensure the generalizability of the tool we assessed the proposed model by training it on a full transcript dataset and test on the tissue-specific dataset. The achieved results by the proposed model have outperformed the existing tools. The results demonstrate that the proposed tool can be of great use for the biology experts and therefore a freely accessible web-server is created which can be accessed at: http://nsclbio.jbnu.ac.kr/tools/DL-m6A/.

BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder-Decoder Network.

Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions. These small-scale tumor regions are unable to be identified, the reason being their tiny size and the huge difference between area occupancy by different tumor classes. In previous state-of-the-art neural network models, the biggest problem was that the location information along with spatial details gets lost in deeper layers. To address these problems, we have proposed an encoder-decoder based model named BrainSeg-Net. The Feature Enhancer (FE) block is incorporated into the BrainSeg-Net architecture which extracts the middle-level features from low-level features from the shallow layers and shares them with the dense layers. This feature aggregation helps to achieve better performance of tumor identification. To address the problem associated with imbalance class, we have used a custom-designed loss function. For evaluation of BrainSeg-Net architecture, three benchmark datasets are utilized: BraTS2017, BraTS 2018, and BraTS 2019. Segmentation of Enhancing Core (EC), Whole Tumor (WT), and Tumor Core (TC) is carried out. The proposed architecture have exhibited good improvement when compared with existing baseline and state-of-the-art techniques. The MR brain tumor segmentation by BrainSeg-Net uses enhanced location and spatial features, which performs better than the existing plethora of brain MR image segmentation approaches.

Multimodal Imaging after Sudden Cardiac Arrest in an 18-Year-Old Athlete.

We report the case of a previously healthy 18-year-old male athlete who twice presented with sudden cardiac arrest. Our use of electrocardiography, echocardiography, cardiac magnetic resonance, coronary angiography, coronary computed tomographic angiography, and nuclear stress testing enabled the diagnoses of apical hypertrophic cardiomyopathy and anomalous origin of the right coronary artery. We discuss the patient's treatment and note the useful role of multiple cardiovascular imaging methods in cases of sudden cardiac arrest.