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

Baig, Mirza Mansoor; GholamHosseini, Hamid; Connolly, Martin; Kashfi, Ghodsi

doi:10.1109/bhi.2014.6864376

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…Due to the humongous volume of such alerts, they are difficult to manage even in the case of hospital in-patient settings, let alone for a much larger number of remotely monitored patients. Starting from some of the initial attempts reported in [ 2 ], to more recent works such as [ 3 – 5 ], and [ 6 ], the severity detection and alert generation is typically based either on predefined thresholds, or based on training of thresholds using machine learning followed by online classification of multi-sensor data. Very similar techniques of machine learning have also been used in fall detection [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Data to diagnosis in global health: a 3P approach

Pathinarupothi

Durga

Rangan³

2018

BMC Med Inform Decis Mak

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BackgroundWith connected medical devices fast becoming ubiquitous in healthcare monitoring there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge.MethodsTo address this challenge, we present a 3P approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is Physician Assist Filters (PAF) that transform unwieldy multi-sensor time series data into summarized patient/disease specific trends in steps of progressive precision as demanded by the doctor for patient’s personalized condition at hand and help in identifying and subsequently predictively alerting the onset of critical conditions. The output of PAFs is a clinically useful, yet extremely succinct summary of a patient’s medical condition, represented as a motif, which could be sent to remote doctors even over SMS, reducing the need for data bandwidths. We evaluate the clinical validity of these techniques using SVM machine learning models measuring both the predictive power and its ability to classify disease condition. We used more than 16,000 min of patient data (N=70) from the openly available MIMIC II database for conducting these experiments. Furthermore, we also report the clinical utility of the system through doctor feedback from a large super-speciality hospital in India.ResultsThe results show that the RASPRO motifs perform as well as (and in many cases better than) raw time series data. In addition, we also see improvement in diagnostic performance using optimized sensor severity threshold ranges set using the personalization PAF severity quantizer.ConclusionThe RASPRO-PAF system and the associated techniques are found to be useful in many healthcare applications, especially in remote patient monitoring. The personalization, precision, and prevention PAFs presented in the paper successfully shows remarkable performance in satisfying the goals of 3Ps, thereby providing the advantages of three A’s: availability, affordability, and accessibility in the global health scenario.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0658-y) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Data to diagnosis in global health: a 3P approach

Pathinarupothi

Durga

Rangan³

2018

BMC Med Inform Decis Mak

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“…To solve this problem, a lot of health care systems have been proposed [1,2,3,6,9], which improved the health level of elderly to some extent. However, there still are two main shortcomings in existing systems that significantly limit the quality of health care, as explained in the following sections.…”

Section: Introductionmentioning

confidence: 99%

“…Data with lots of noise * The work is partially supported by the National Natural Science Zhu Wang (wangzhu@nwpu.edu.cn) is with is with the Shaanxi Key Lab of Embedded System in Northwestern Polytechnical University, Xi'an 710129 CHN may result in inaccurate diagnosis, leading to low treatment effects, or worse consequences. However, under the condition of limited resources, e.g., computation ability, most existing systems [2,4,5,9] don't have a data preprocessing module, and the raw data is directly used for health analysis. Even through there are several systems that integrate the data preprocessing function [1,3], they usually deal with all the data with the same multi-stage amplifier circuit and filter circuit, which ignores the inherent characteristics of different physiological data.…”

Section: Introductionmentioning

confidence: 99%

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A light-weight data preprocessing and integrative scheduling framework for health monitoring

Liu

Zhou

Wang

et al. 2016

2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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The aging of population presents a severe challenge to the traditional "face-to-face" diagnosis mode. Household health monitoring systems play a more and more important role in identifying health conditions, diagnosing diseases, assisting daily lives, etc. However, most existing health monitoring systems are incomplete. They always leave out some function, e.g., data preprocessing and scheduling mechanism, which decrease the accuracy and efficiency of health services. In this paper, we propose a multi-function health monitoring system which integrates data acquisition, wireless data transmission, data storage, and data visualization. Moreover, we put forward a light-weight data preprocessing algorithm (LDPA) and an integrative scheduling mechanism (ISM) based on the amount of data and the semaphore. The LDPA can identify and eliminate abnormal data, while the ISM is able to significantly reduce the total time consuming. Experimental results based on 97 elderlies approved the high performance of LDPA in identifying abnormal breathing waves (the precision and the recall are 92.14% and 93.26% respectively) and the significant time reduction of ISM (saving 34.24% to 56.30% time overhead), compared with three other scheduling mechanisms.

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“…Irrespective of the medical condition, age and size of a patient, a few vital signs and physiological parameters are common that require constant monitoring. These vital signs and physiological parameters include Blood Pressure (BP), Body Temperature (TEMP), Heart Rate (HR), Pulse Rate (PR), Electrocardiogram (ECG) and Respiratory Rate (RR) [3][4][5] . As collaborative advancement hits through multidisciplinary fields such as information technology, computer science, electrical and electronics engineering, biomedical engineering and medicine, healthcare systems are experiencing enormous drift from the traditional hospital centered care to patient centered healthcare delivery systems commonly referred to as telemedicine or telehealth 4,6,7 .…”

Section: Introductionmentioning

confidence: 99%

Real Time Telemedical Health Care Systems with Wearable Sensors

Fajingbesi

Olanrewaju

Pampori

et al. 2017

AJPRHC

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The time between detection and response to chronic diseases could go a long way in saving lives. The current trend in health monitoring systems is to move from the hospital centered device to eventually portable personal devices. Hence, Telemedical health care involves the remote delivery of medical care service to either out-of-hospital or admitted patients through wireless network and computer information technology. This paper systematically reviews the most recent works in telemedical health care system to propose a more efficient model. The focus is more on wearable sensors and devices with most attention given to cardiovascular patients in recent times. The huge literature available reflects the size of activity and attention given to telemedicine. The reviewed works are published within the last five years. Furthermore, the proposed systems are compared in terms of their connectivity, targeted application, type of sensor used, etc. Our study reveals Telemedicine to be a profound field with researchers from multidisciplinary sectors. However, there are still many gaps that need to be filled before maturity. Factors such as efficient wireless transmission, cyber data security, sensor design and integration, device miniaturization and intelligent algorithm for multi parameter data fusion require further considerations.

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

Cited by 16 publications

References 15 publications

Data to diagnosis in global health: a 3P approach

Data to diagnosis in global health: a 3P approach

A light-weight data preprocessing and integrative scheduling framework for health monitoring

Real Time Telemedical Health Care Systems with Wearable Sensors

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