Data accuracy aware mobile healthcare applications
Introduction
Mobile health [[1], [2]] can be broadly defined as the use of information and communication technologies that are accessible to people or healthcare providers through mobile devices such as mobile phones, patient monitoring devices other wireless devices to support medical and public healthcare practices. mHealth plays a significant role in delivering remote healthcare services while empowering people to better manage their own health state and improve the ability to track diseases. It aims at reducing socio-economic costs [3] and even surmount organizational barriers [2].
Mobile healthcare systems are rapidly growing and evolving. The architecture of such system contains three domains. In the device domain, a patient is equipped with a set of wearable and/or implantable sensors that constantly sense vital signs. Sensors are interconnected via a medical body area network. The central point of the device domain is the application hosting device. It aggregates vital sign measurements and transmits them via the network domain to the application domain. Mobile health applications cover several areas to include disease management (such as chronic disease management [4], women's health and pregnancy [5] and elderly people monitoring [6]), as well as, wellness management [7].
Particularly, mobile health applications highly depend on continuous sensor readings to provide high-quality healthcare services and improve the quality of patient supervision [8]. However, sensor readings introduce critical accuracy challenges [9] due to several internal and external factors. Internal factors are related to the performance variation of sensors involved in the application such as energy depletion, hardware faults, calibration, etc., and other external environmental-related ones.
To achieve its full potential, a mHealth application needs to deal with medical data accuracy challenges.
In the last few years, there has been a growing interest in proposing approaches dealing with anomaly detection and isolation for mhealth applications. The proposed approaches are divided into two main classes including supervised [[10], [11], [12]] and unsupervised approaches [[13], [14], [15], [16], [17]].
Despite the tremendous research effort around approaches for anomaly detection and isolation in mHealth applications, there are still several issues preventing their deployment in real time healthcare systems. Classification-based supervised approaches [[10], [12]] rely on trustable training dataset. However, this assumption may not be conventional in real time mHealth applications, where anomalies are not known in advance. Furthermore, training data often contains abnormal measurements which lead to overfitted classification or regression models. A majority of existing unsupervised approaches [[14], [13], [16]] cannot be applicable in high-dimensional data and can generate additional complexity in terms of time and space. Indeed, in real time mHealth applications, wearable devices such as smart vests [18] and other implantable sensors like Implantable Cardioverter Defibrillator [19] increase the amount of multi-dimensional medical data by being able to send vital signs at any time and any place.
In this paper, we address these issues by proposing an unsupervised anomaly detection and isolation approach for mHealth applications. The proposed approach combines two key characteristics. First, it couples a dimension reduction algorithm based on the spatial and temporal correlation among monitored vital signs with anomaly detection and isolation methods. Second, it uses lightweight and robust unsupervised techniques in both steps. Dimension reduction is based on robust incremental principal component analysis which is not sensitive to anomalies and precludes the necessity of training using a trustable dataset, a strong advantage from the operational point of view. Multivariate anomaly detection is based on squared prediction errors and adequate threshold. Once multivariate anomaly is noticed, an isolation step is carried out using contribution plots [20] to identify suspicious vital signs.
Simulations in real medical record are firstly conducted to choose the suitable principal component analysis for dimensionality reduction. Second, our simulation results prove that our proposed approach achieves good detection performances and demonstrate the importance of dimension reduction in reducing computational time and memory. Finally, they validate that our approach outperforms other related approaches [[11], [13]].
The rest of the paper is organized into four sections: Section 2 outlines related work. The proposed approach is discussed in Section 3. Section 4 shows the experimental results of this work. Finally, Section 5 includes a summary and gives some directions for the future.
Section snippets
Literature review
Various mobile healthcare applications for periodic vital signs monitoring have been proposed, such as CodeBlue [21], HealthGear [22], SmartVest [18], MEDiSN [23], PIaIMoS [24], and so on. Their main features are summarized in Table 1.
Recent surveys of mobile healthcare applications are available in [[24], [25]].
Unfortunately, real-time vital sign measurements often contain inconsistencies and errors due to diverse internal and external factors. The internal factors are, especially, related to
Proposed approach
In real-time mobile health scenario, a patient performs his own vital sign measurements (respiration rate, pulse rate, blood pressure, SpO2, etc.) using M implantable and wearable medical sensors such as Pulse oximeter (as depicted in Fig. 1). Each sensor is used to sense one or many vital signs and transmit data to an Application Hosting Device (e.g. Smartphone) via Bluetooth technology. The application hosting device allows monitoring medical data from sensors and transferring it via internet
Simulations
In order to evaluate the proposed approach, we achieve simulations. In the first part, we begin by demonstrating the importance of robustness on PCA during the offline regime. Then, we perform a comparative study between two online PCAs to choice the suitable algorithm for updating model in the online regime. The second part provides an explanation of how our monitor module succeeds in detecting multivariate anomalies and identifying adequate symptoms. We denote the third part to compare the
Conclusion and future research
This paper proposes a lightweight anomaly detection and isolation approach for mobile healthcare applications, where anomalous vital signs measurements have a significant impact on the effectiveness of such applications and consequently affects the patient's life. The proposed approach is based on Principal Component Analysis for dimension reduction, squared prediction errors for multivariate anomaly detection and contribution plots for anomaly isolation. It is suitable for real time mobile
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