Mirza Mansoor Baig

Smart Health Monitoring Systems: An Overview of Design and Modeling

Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.

A comprehensive survey of wearable and wireless ECG monitoring systems for older adults.

Wearable health monitoring is an emerging technology for continuous monitoring of vital signs including the electrocardiogram (ECG). This signal is widely adopted to diagnose and assess major health risks and chronic cardiac diseases. This paper focuses on reviewing wearable ECG monitoring systems in the form of wireless, mobile and remote technologies related to older adults. Furthermore, the efficiency, user acceptability, strategies and recommendations on improving current ECG monitoring systems with an overview of the design and modelling are presented. In this paper, over 120 ECG monitoring systems were reviewed and classified into smart wearable, wireless, mobile ECG monitoring systems with related signal processing algorithms. The results of the review suggest that most research in wearable ECG monitoring systems focus on the older adults and this technology has been adopted in aged care facilitates. Moreover, it is shown that how mobile telemedicine systems have evolved and how advances in wearable wireless textile-based systems could ensure better quality of healthcare delivery. The main drawbacks of deployed ECG monitoring systems including imposed limitations on patients, short battery life, lack of user acceptability and medical professional’s feedback, and lack of security and privacy of essential data have been also discussed.

Mobile healthcare applications: system design review, critical issues and challenges

Mobile phones are becoming increasingly important in monitoring and delivery of healthcare interventions. They are often considered as pocket computers, due to their advanced computing features, enhanced preferences and diverse capabilities. Their sophisticated sensors and complex software applications make the mobile healthcare (m-health) based applications more feasible and innovative. In a number of scenarios user-friendliness, convenience and effectiveness of these systems have been acknowledged by both patients as well as healthcare providers. M-health technology employs advanced concepts and techniques from multidisciplinary fields of electrical engineering, computer science, biomedical engineering and medicine which benefit the innovations of these fields towards healthcare systems. This paper deals with two important aspects of current mobile phone based sensor applications in healthcare. Firstly, critical review of advanced applications such as; vital sign monitoring, blood glucose monitoring and in-built camera based smartphone sensor applications. Secondly, investigating challenges and critical issues related to the use of smartphones in healthcare including; reliability, efficiency, mobile phone platform variability, cost effectiveness, energy usage, user interface, quality of medical data, and security and privacy. It was found that the mobile based applications have been widely developed in recent years with fast growing deployment by healthcare professionals and patients. However, despite the advantages of smartphones in patient monitoring, education, and management there are some critical issues and challenges related to security and privacy of data, acceptability, reliability and cost that need to be addressed.

A Systematic Review of Wearable Patient Monitoring Systems --- Current Challenges and Opportunities for Clinical Adoption

The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and ‘silo’ solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.

A remote monitoring system with early diagnosis of hypertension and Hypotension

Intelligent systems deliver meaningful information of patients physiological data and provide essential decision support, on patients status, to medical professionals. This study focuses on two important areas of healthcare; real-time remote patient monitoring and decision support systems for diagnosing of Hypertension and Hypotension. The proposed real-time, wireless vital sign monitoring system with two-way audio/video conferencing enables medical professionals to provide much enhanced healthcare when compared to the traditional methods. Moreover, the development of a universal diagnostic module for early warning/alert of two physiological disorders (events) including Hypertension and Hypotension will be presented. The proposed system will be tested in real-time hospital environment while the simulation/offline result shows the system has achieved acceptable accuracy, sensitivity, specificity and predictability.

Wireless remote patient monitoring in older adults

Wireless patient monitoring systems are emerging as a low cost, reliable and accurate way of healthcare delivery. In this paper we present a wireless remote vital sign monitoring system with audio/video data transmission. Vital signs include; blood pressure (systolic and diastolic), heart rate, pulse, oxygen saturation, body temperature, lungs air volume and blood glucose level. In addition, a two-way audio/video communication link connects patients to their healthcare providers. The proposed system employs a computer-based software application that effectively incorporates current data with electronic medical record in order to enhance patient care. We evaluated this system with 10 individuals for assessing its acceptability by the users and its compatibility with other medical devices. A clinical trial with more than 30 participants aged over 65 years is also in progress at a local hospital.

Anaesthesia monitoring using fuzzy logic

ObjectiveHumans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians’ performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration.MethodThe proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS’ diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of off-line tests. The training and testing data set are selected randomly from 30 sets of patients’ data.ResultsThe accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s and FLMS’s diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures.ConclusionThe diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.

Real-time vital signs monitoring and interpretation system for early detection of multiple physical signs in older adults

Advanced engineering, communication and information technologies combined with medical and clinical knowledge enable the possibility of remote, wireless, continuous physiological monitoring. These technologies facilitate the implementation of patient monitoring and diagnostic systems virtually anywhere: home, hospital and outdoors (on the move). Physiological parameters are considered as critical information to assess health condition and the type of possible illness of patients. In this work, vital signs are collected using wireless medical devices and fed to a computerised decision support system consist of a diagnostic model. The proposed vital signs monitoring system is able to help clinicians by illustrating the trace of critical physiological parameters, generating early warning/alerts and indicating any significant changes to the data. Moreover, it can assist patients to monitor their health status and communicate their concerns with the healthcare providers. The system was validated with different set of collected data from 20 hospitalised older adults and achieved an accuracy of 95.83%, sensitivity of 100%, specificity of 93.15%, and predictability of 90.38% in compare with a clinician assessment for tachycardia, hypertension, hypotension, hypoxemia and hypothermia.

Detection and classification of hypovolaemia during anaesthesia

In recent years, there has been a rapid growth in patient monitoring and medical data analysis using decision support systems, smart alarm monitoring, expert systems and many other computer aided protocols. The main goal of this study was to enhance the developed diagnostic alarm system for detecting critical events during anaesthesia. The proposed diagnostic alarm system is called Fuzzy logic monitoring system-2 (FLMS-2). The performance of the system was validated through a series of off-line tests. When detecting hypovolaemia a substantial level of agreement was observed between FLMS-2 and the human expert and it is shown that system has a better performance with sensitivity of 94%, specificity of 90% and predictability of 72%.

Falls Risk Assessment for Hospitalised Older Adults: A Combination of Motion Data and Vital Signs

Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way healthcare is currently delivered. Currently hospital falls are a major healthcare concern worldwide because of the ageing population. Current observational data and vital signs give the critical information related to the patient’s physiology, and motion data provide an additional tool in falls risk assessment. These data combined with the patient’s medical history potentially may give the interpretation model high information accessibility to predict falls risk. This study aims to develop a robust falls risk assessment system, in order to avoid falls and its related long-term disabilities in hospitals especially among older adults. The proposed system employs real-time vital signs, motion data, falls history and other clinical information. The falls risk assessment model has been tested and evaluated with 30 patients. The results of the proposed system have been compared with and evaluated against the hospital’s falls scoring scale.