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首页> 外文期刊>International journal of e-health and medical communications. >Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models
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Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

机译：基于SVM和线性回归模型的医疗无线传感器网络异常检测。

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This paper details the architecture and describes the preliminary experimentation with theproposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors 'application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reportingfaulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.
机译：本文详细介绍了该体系结构，并介绍了用于医疗无线人体局域网中异常检测的提议框架的初步实验，该网络用于无处不在的患者和医疗保健监控。该架构将新颖的数据挖掘和机器学习算法与现代传感器融合技术集成在一起。知道无线传感器网络容易因其局限性而导致故障（即有限的能源和计算能力），使用此框架，作者可以区分受监视患者的生理参数的不规则变化和错误的传感器数据，以确保可靠的操作以及来自智能设备的实时全局监控。传感器节点用于测量患者的特征，感测到的数据存储在本地处理单元上。只要授权用户保持与启用了应用程序的智能设备的连接性，他们就可以远程访问该患者数据。传感器节点损坏或恶意外部造成的异常或错误的测量数据可能导致患者误诊甚至死亡。作者的应用程序使用支持向量机对传入传感器数据中的异常实例进行分类。如果找到，作者将对异常实例应用定期重建的回归预测模型，并确定患者是否进入危急状态或传感器是否报告错误读数。在我们的实验中使用真实的患者数据，结果验证了我们提出的框架的稳健性。作者进一步讨论了所提出方法的实验分析，该方法表明该方法能够快速识别传感器异常，并且与其他几种算法相比，它保持了较高的真阳性率和较低的假阴性率。

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