Gleasonmaldonado5637

Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provided a promising tool to non-invasively characterize and analyze the synchronous neural firing. However, the topographical spectral information for each predominate microstate is still unclear during the switch of consciousness, such as sedation, and the practical usage of the EEG microstate is worth probing. Also, the mechanism behind the anesthetic-induced alternations of brain states remains poorly understood. In this study, a novel EEG microstate spectral analysis was utilized using multivariate empirical mode decomposition in Hilbert-Huang transform. The practicability was further investigated in scalp EEG recordings during the propofol-induced transition of consciousness. The process of transition from awake to moderate sedation was accompanied by apparent increases in microstate (A, B, and F) energy, especially in the whole-brain delta band, frontal alpha band and beta band. In comparison to other effective EEG-based parameters that commonly used to measure anesthetic depth, utilizing the selected spectral features reached better performance (80% sensitivity, 90% accuracy) to estimate the brain states during sedation. U0126 mouse The changes in microstate energy also exhibited high correlations with individual behavioral data during sedation. In a nutshell, the EEG microstate spectral analysis is an effective method to estimate brain states during propofol-induced sedation, giving great insights into the underlying mechanism. The generated spectral features can be promising markers to dynamically assess the consciousness level.Early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for guiding therapy decisions. In this work, we propose a deep learning based approach for the early NAC response prediction in ultrasound (US) imaging. We used transfer learning with deep convolutional neural networks (CNNs) to develop the response prediction models. The usefulness of two transfer learning techniques was examined. First, a CNN pre-trained on the ImageNet dataset was utilized. Second, we applied double transfer learning, the CNN pre-trained on the ImageNet dataset was additionally fine-tuned with breast mass US images to differentiate malignant and benign lesions. Two prediction tasks were investigated. First, a L1 regularized logistic regression prediction model was developed based on generic neural features extracted from US images collected before the chemotherapy (a priori prediction). Second, Siamese CNNs were used to quantify differences between US images collected before the treatment and after the first and second course of NAC. The proposed methods were evaluated using US data collected from 39 tumors. The better performing deep learning models achieved areas under the receiver operating characteristic curve of 0.797 and 0.847 in the case of the a priori prediction and the Siamese model, respectively. The proposed approach was compared with a method based on handcrafted morphological features. Our study presents the feasibility of using transfer learning with CNNs for the NAC response prediction in US imaging.The advent of Internet of Things (IoT) has escalated the information sharing among various smart devices by many folds, irrespective of their geographical locations. Recently, applications like e-healthcare monitoring has attracted wide attention from the research community, where both the security and the effectiveness of the system are greatly imperative. However, to the best of our knowledge none of the existing literature can accomplish both these objectives (e.g., existing systems are not secure against physical attacks). This paper addresses the shortcomings in existing IoT-based healthcare system. We propose an enhanced system by introducing a Physical Unclonable Function (PUF)-based authentication scheme and a data driven fault-tolerant decision-making scheme for designing an IoT-based modern healthcare system. Analyses show that our proposed scheme is more secure and efficient than existing systems. Hence, it will be useful in designing an advanced IoT-based healthcare system.The investigation of risk factors associated with hypertension patients has been extensively studied in the past decades. However, the pattern of natural progressive trajectories to hypertension from nonhypertensive states was rarely explored. In this study, we are interested in discovering the underlying transition patterns between different blood pressure states, namely normal state, elevated state, and hypertensive state among the working population in the United States. A multi-state Markov model was built based on 88,966 clinical records from 34,719 participants we collected during the worksite preventive screening from 2012 to 2018. We first investigated the various risk factors, and we found that body mass index (BMI) is the most critical factor for developing new-onset hypertension. The transition probabilities, survival probabilities, and sojourn time of each state were derived given different levels of BMI, age groups, and gender categories. We found the underweight participants are more likely to remain in the current nonhypertensive states within 3 years, while extremely obese participants have a higher probability of developing hypertension. We discovered the distinct transition patterns among male and female participants. On average, the sojourn time in the normal state for normal-weight participants is 4.33 years for females and 2.18 years for their male counterparts. For the extremely obese participants, the average sojourn time in the normal state is 1.38 years for females and 0.71 years for males. In the end, a web-based graphical user interface (GUI) application was developed for clinicians to visualize the impact of behavioral interventions on delaying the progression of hypertension. Our analysis can provide a unique insight into hypertension research and proactive interventions.In this paper, we propose an attention based convolutional neural network long short-term memory (CNN-LSTM) approach for sleep-wake detection with heterogeneous sensor data, i.e., acceleration and heart rate variability (HRV). Since the three-dimensional acceleration data was sampled with a high frequency, we firstly design a CNN-LSTM structure to effectively learn latent features from the acceleration. Meanwhile, considering the unique format of the HRV data, some effective features are extracted based on domain knowledge. Next, we design a unified architecture to efficiently merge the features learned by CNN-LSTM approach from the acceleration and the extracted features from the HRV, which enables us to make full use of all the available information from these two heterogeneous sources. Taking into consideration that these two heterogeneous sources may have distinct contributions for the sleep and wake states, we propose an attention network to dynamically adjust the importance of features from the two sources.