Majid Sarrafzadeh

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.

WANDA: an end-to-end remote health monitoring and analytics system for heart failure patients

Recent advances in wireless sensors, mobile technologies, and cloud computing have made continuous remote monitoring of patients possible. In this paper, we introduce the design and implementation of WANDA, an end-to-end remote health monitoring and analytics system designed for heart failure patients. The system consists of a smartphone-based data collection gateway, an Internet-scale data storage and search system, and a backend analytics engine for diagnostic and prognostic purposes. The system supports the collection of data from a wide range of sensory devices that measure patients vital signs as well as self-reported questionnaires. The main objective of the analytics engine is to predict future events by examining physiological readings of the patients.n We demonstrate the efficiency of the proposed analytics engine using the data gathered from a pilot study of 18 heart failure patients. In particular, our results show that the advanced analytic algorithms used in our system are capable of predicting the worsening of patients heart failure symptoms with up to 74% accuracy while improving the sensitivity performance by more than 45% compared to the commonly used thresholding algorithm based on daily weight change. Moreover, the accuracy attained by our system is only 9% lower than the theoretical upper bound. The proposed framework is currently deployed in a large ongoing heart failure study that targets 1500 congestive heart failure patients.

WANDA B.: Weight and activity with blood pressure monitoring system for heart failure patients

Heart failure is a leading cause of death in the United States, with around 5 million Americans currently suffering from congestive heart failure. The WANDA B. wireless health technology leverages sensor technology and wireless communication to monitor heart failure patient activity and to provide tailored guidance. Patients who have cardiovascular system disorders can measure their weight, blood pressure, activity levels, and other vital signs in a real-time automated fashion. The system was developed in conjunction with the UCLA Nursing School and the UCLA Wireless Health Institute for use on actual patients. It is currently in use with real patients in a clinical trial.

An automated vital sign monitoring system for congestive heart failure patients

Congestive heart failure (CHF) is a cardiovascular disorder that affects approximately 4.6 million Americans and is a leading cause of death in the United States. Current research shows that strategies to promote early recognition and treatment of symptoms and enhance self-care management behaviors reduce unnecessary hospitalizations. However, mechanisms to monitor patients health status and behaviors are limited by constraints imposed by the patients geography, infirmity, or resources. Remote monitoring supports a more dynamic connection between healthcare providers and patients, improves health promotion and patient care through monitoring of health data, communicates health reminders, and makes provisions for patient feedback. This paper will describe two versions of Weight and Activity with Blood Pressure Monitoring System (WANDA [22]) that leverages sensor technology and wireless communication to monitor health status of patients with CHF. The WANDA system is built on a three-tier architecture consisting of sensors, a web server, and back-end database tiers. 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 in a real-time automated fashion and allows health professionals to offer surveillance, advice, and continuity of care and triggers early implementation of strategies to enhance adherence behaviors. The small study has enabled patients to reduce or maintain the number of readings which are out of the acceptable range. For diastolic, systolic, and heart rate values, the t-test results show that the WANDA study is effective for patients with CHF.

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.

Nutrition Monitor: A Food Purchase and Consumption Monitoring Mobile System

The challenge of monitoring food intake can be facilitated by the truly transformational power of mobile phones. Mobile phones provide a pervasive and fairly ubiquitous infrastructure, which we leverage to provide cost-effective, high quality aids to behavior monitoring and modification. Additionally, the technology allows public health messages to reach certain target groups, such as youth and members of low-income communities, which may not otherwise be practical. Our system leverages the existing mobile phone infrastructure. We use the highly capable computational and data-gathering platform of mobile phones to facilitate the collection, transmission and processing of data for purposes of monitoring in the field, behavior and activity classification, and timely behavioral cuing. The nature of mobile phones coupled with a web-interface also allow for customization and personalization, retrieval of nutrition information on demand, as well as the ability to truly monitor the user’s consumption trends.

Interval training guidance system with music and wireless group exercise motivations

Interval training is a well known exercise protocol which helps strengthen and improve ones cardiovascular fitness. It interleaves high intensity exercises with rest periods. Despite the known benefits, proper scheduling and completion of interval training routines are not easy to perform. For example, without expensive equipment such as a treadmill, there is almost no way to figure out ones speed for proper imitation of a given exercise protocol, and thus interval training is heavily dependent on individual motivation levels. In this work, we use behavioral cueing using music and performance feedback to provide motivation during interval training exercise sessions. We have developed an application program on the popular iPhone platform. Our game-like and social networking application guides the user using exercise music. By measuring performance of the user through sensor readings, specifically accelerometers embedded in the iPhone, we are able to play the right song to match the users workout plan. A hybrid of a collaborative, content, and context-aware filtering algorithm incorporates the users music preferences and the exercise speed that will enhance performance. Additionally, adherence to an exercise protocol and the amount of calories burned is translated into a score that is sent to the users social network group.

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.

Optimizing Interval Training Protocols Using Data Mining Decision Trees

Interval training consists of interleaving high intensity exercises with rest periods. This training method is a well known exercise protocol which helps strengthen and improve one’s cardiovascular fitness. However, there is no known method for formulating and tailoring an optimized interval training protocol for a specific individual which maximizes the amount of work done while limiting fatigue. But by using data mining schemes with various attributes, conditions, and data gathered from an individual’s exercise session, we are able to efficiently formulate an optimized interval training method for an individual. Recent advances in wireless wearable sensors and smart phones have made available a new generation of fitness monitoring systems. With accelerometers embedded in an iPhone, a Bluetooth pulse oximeter, and the Weka data mining tool, we are able to formulate the optimized interval training protocols, which can increase the amount of calorie burned up to 29.54%, compared with the modified Tabata interval training protocol.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.