Jonathan Woodbridge

A Remote Patient Monitoring System for Congestive Heart Failure

Congestive heart failure (CHF) is a leading cause of death in the United States affecting approximately 670,000 individuals. Due to the prevalence of CHF related issues, it is prudent to seek out methodologies that would facilitate the prevention, monitoring, and treatment of heart disease on a daily basis. This paper describes WANDA (Weight and Activity with Blood Pressure Monitoring System); a study that leverages sensor technologies and wireless communications to monitor the health related measurements of patients with CHF. The WANDA system is a three-tier architecture consisting of sensors, web servers, and back-end databases. The system was developed in conjunction with the UCLA School of Nursing and the UCLA Wireless Health Institute to enable early detection of key clinical symptoms indicative of CHF-related decompensation. This study shows that CHF patients monitored by WANDA are less likely to have readings fall outside a healthy range. In addition, WANDA provides a useful feedback system for regulating readings of CHF patients.

Forelimb EMG-based trigger to control an electronic spinal bridge to enable hindlimb stepping after a complete spinal cord lesion in rats

BackgroundA complete spinal cord transection results in loss of all supraspinal motor control below the level of the injury. The neural circuitry in the lumbosacral spinal cord, however, can generate locomotor patterns in the hindlimbs of rats and cats with the aid of motor training, epidural stimulation and/or administration of monoaminergic agonists. We hypothesized that there are patterns of EMG signals from the forelimbs during quadrupedal locomotion that uniquely represent a signal for the “intent” to step with the hindlimbs. These observations led us to determine whether this type of “indirect” volitional control of stepping can be achieved after a complete spinal cord injury. The objective of this study was to develop an electronic bridge across the lesion of the spinal cord to facilitate hindlimb stepping after a complete mid-thoracic spinal cord injury in adult rats.MethodsWe developed an electronic spinal bridge that can detect specific patterns of EMG activity from the forelimb muscles to initiate electrical-enabling motor control ( eEmc) of the lumbosacral spinal cord to enable quadrupedal stepping after a complete spinal cord transection in rats. A moving window detection algorithm was implemented in a small microprocessor to detect biceps brachii EMG activity bilaterally that then was used to initiate and terminate epidural stimulation in the lumbosacral spinal cord. We found dominant frequencies of 180–220 Hz in the EMG of the forelimb muscles during active periods, whereas these frequencies were between 0–10 Hz when the muscles were inactive.Results and conclusionsOnce the algorithm was validated to represent kinematically appropriate quadrupedal stepping, we observed that the algorithm could reliably detect, initiate, and facilitate stepping under different pharmacological conditions and at various treadmill speeds.

Wireless health and the smart phone conundrum

This paper presents a study of the five best selling Smart Phones in terms of their applicability to Wireless Health. Smart Phones are generally used as central controlling units in Wireless Health applications. We carried out our investigation by implementing a wireless health application that performs sensor communication, data processing, and data visualization. Our overarching goal is to develop a plug-and-play Wireless Health software platform. Our task begins with an in depth study of Smart Phones: the central controller of Wireless health applications.

DeepDGA: Adversarially-Tuned Domain Generation and Detection

Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections. While there are many methods to pseudorandomly generate domains, we focus in this paper on detecting (and generating) domains on a per-domain basis which provides a simple and flexible means to detect known DGA families. Recent machine learning approaches to DGA detection have been successful on fairly simplistic DGAs, many of which produce names of fixed length. However, models trained on limited datasets are somewhat blind to new DGA variants. In this paper, we leverage the concept of generative adversarial networks to construct a deep learning based DGA that is designed to intentionally bypass a deep learning based detector. In a series of adversarial rounds, the generator learns to generate domain names that are increasingly more difficult to detect. In turn, a detector model updates its parameters to compensate for the adversarially generated domains. We test the hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs. We detail solutions to several challenges in training this character-based generative adversarial network. In particular, our deep learning architecture begins as a domain name auto-encoder (encoder + decoder) trained on domains in the Alexa one million. Then the encoder and decoder are reassembled competitively in a generative adversarial network (detector + generator), with novel neural architectures and training strategies to improve convergence.

Dynamic self-adaptive remote health monitoring system for diabetics

Diabetes is the seventh leading cause of death in the United States. In 2010, about 1.9 million new cases of diabetes were diagnosed in people aged 20 years or older. Remote health monitoring systems can help diabetics and their healthcare professionals monitor health-related measurements by providing real-time feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the remote health monitoring. This paper presents a task optimization technique used in WANDA (Weight and Activity with Blood Pressure and Other Vital Signs); a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. WANDA applies data analytics in real-time to improving the quality of care. The developed algorithm minimizes the number of daily tasks required by diabetic patients using association rules that satisfies a minimum support threshold. Each of these tasks maximizes information gain, thereby improving the overall level of care. Experimental results show that the developed algorithm can reduce the number of tasks up to 28.6% with minimum support 0.95, minimum confidence 0.97 and high efficiency.

MARHS: mobility assessment system with remote healthcare functionality for movement disorders

Due to the global trend of aging societies with increasing demand for low cost and high quality healthcare services, there has been extensive research and development directed toward wireless and remote healthcare technology that considers age-associated ailments. In this paper, we introduce Mobility Assessment and Remote Healthcare System (MARHS) that utilizes a force sensor in order to provide quantitative assessment of the mobility level of patients with movement disorder ailment, which is one common age-associated ailment. The proposed system also enables the remote healthcare services that allow patients to receive diagnoses from clinical experts without his/her presence. MARHS also contains a data analysis unit in order to provide information that summarizes the characteristics of symptoms of a group of patients (e.g., patients with a certain type of ailment) using a combination of feature ranking, feature selection, and classification algorithms. The results of the analyses on the data from a clinical trial show that the examination results of the proposed system can accurately recognize various groups of patients, such as, patients with (i) chronic obstructive pulmonary disease, (ii) hypertension, and (iii) cerebral vascular accident with an average accuracy of 90.05%, 82.60%, and 93.54%, respectively.

HIP: Health integration platform

This paper introduces a new software development platform specifically designed for wireless health applications. Wireless health applications follow a unique paradigm encompassing body sensor networks. These networks are controlled by a central processing unit (such as a cell phone or PDA) that provides connectivity to health care professionals over a wide area network. With such architectures, health monitoring becomes ubiquitous allowing patients a greater degree of freedom from conventional medical monitoring. This paper introduces HIP - a wireless health integration platform. HIP is a complete end to end software development platform. Wireless Health platforms previous to HIP have two main faults. First, these platforms lack generality and focus on few wireless health components. Second, they offer an extremely rigid platform that is difficult to integrate into preexisting research. HIP addresses these issues by providing a flexible and modularized plug and play architecture that seamlessly integrates into preexisting work.

Improving biomedical signal search results in big data case-based reasoning environments

Time series subsequence matching has importance in a variety of areas in healthcare informatics. These include case-based diagnosis and treatment as well as discovery of trends among patients. However, few medical systems employ subsequence matching due to high computational and memory complexities. This manuscript proposes a randomized Monte Carlo sampling method to broaden search criteria with minimal increases in computational and memory complexities over R-NN indexing. Information gain improves while producing result sets that approximate the theoretical result space, query results increase by several orders of magnitude, and recall is improved with no signi cant degradation to precision over R-NN matching.

Machine Learning-Based Adaptive Wireless Interval Training Guidance System

Interval training has been shown to improve the physical and psychological performance of users, in terms of fatigue level, cardiovascular build-up, hemoglobin concentration, and self-esteem. Despite the benefits, there is no known automated method for formulating and tailoring an optimized interval training protocol for a specific individual that maximizes the amount of calories burned while limiting fatigue. Additionally, an application that provides the aforementioned optimal training protocol must also provide motivation for repetitious and tedious exercises necessary to improve a patient’s adherence. This paper presents a system that efficiently formulates an optimized interval training method for each individual by using data mining schemes on attributes, conditions, and data gathered from individuals exercise sessions. This system uses accelerometers embedded within iPhones, a Bluetooth pulse oximeter, and the Weka data mining tool to formulate optimized interval training protocols and has been shown to increase the amount of calories burned by 29.54% as compared to the modified Tabata interval training protocol. We also developed a behavioral cueing system that uses music and performance feedback to provide motivation during interval training exercise sessions. By measuring a user’s performance through sensor readings, we are able to play songs that match the user’s workout plan. A hybrid collaborative, content, and context-aware filtering algorithm incorporates the user’s music preferences and the exercise speed to enhance performance.

Missing data imputation for remote CHF patient monitoring systems

Congestive heart failure (CHF) is a leading cause of death in the United States. WANDA is a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with CHF. The first pilot study of WANDA showed the systems effectiveness for patients with CHF. However, WANDA experienced a considerable amount of missing data due to system misuse, nonuse, and failure. Missing data is highly undesirable as automated alarms may fail to notify healthcare professionals of potentially dangerous patient conditions. In this study, we exploit machine learning techniques including projection adjustment by contribution estimation regression (PACE), Bayesian methods, and voting feature interval (VFI) algorithms to predict both non-binomial and binomial data. The experimental results show that the aforementioned algorithms are superior to other methods with high accuracy and recall. This approach also shows an improved ability to predict missing data when training on entire populations, as opposed to training unique classifiers for each individual.