Medical Healthcare System Based on Wireless Body Area Networks: The Importance of Anomaly Detection

Hayder Jassim Hassaballah, ,Rashid Ali,Bushra Abdulkareem Abdulazeez Alhayali

doi:10.35940/ijeat.c5945.029320

Abstract

In this research paper, a modern framework is presented to detect anomaly in medical wireless body sensor network systems that are incorporated in distant observation of patient’s vital signs. The suggested framework effects analysis of data in a sequential manner using a mini gateway utilized as a root station to discover abnormal alterations and to deal with inaccurate computations in gathered medical information minus advance awareness of irregular occurrences or consistent data patterns. The suggested perspective relies on Principal Component Analysis (PCA) utilized in spatial analysis and dimension reduction for gathered medical details. The key goal is distinguishing defective calculations from clinical dangers for reduction of false alarms prompted by incorrect computations or ill-behaved sensors. The result from the experiments on real medical datasets reveal that the suggested PCA perspective is able to attain good discovery perfection with lesser falt alarm rate when contrasted with other perspectives that fail to minimize the excessive dimension of gathered more information so in multivariate Wireless Body Sensor Networks (WBSN) implementations, and dynamic streaming nature of sensor data, mostly in medical implementations.

Highlights

Wireless sensor networks (WSNs) are networks of tiny, low cost, low energy, and multifunctional sensors which are densely deployed to monitor a phenomenon, track an object, or control a process [1, 2]
This study evaluates the medical health care implementations of wireless sensing systems and utilizes Wireless Body Sensor Networks (WBSN) instead of WSN
This paper suggested an unsupervised procedure to detect anomaly in medical WBNSs where incorrect computations and added data could endanger the life of the tracked victim

Summary

Introduction

Wireless sensor networks (WSNs) are networks of tiny, low cost, low energy, and multifunctional sensors which are densely deployed to monitor a phenomenon, track an object, or control a process [1, 2]. WSNs are used in many application domains, which include: personal applications such as home automation; business applications such as sales tracking; industrial applications such as architectural and control. Sensor data analysis is of high importance to decision makers. It was reported by Zhang [5], that the purpose of using WSN is to collect data from the field of deployment but more importantly the analysis of this data at timely manner that leads for making some important decisions. The data quality is the main concern since it reflects the real state of monitored environments

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Conclusion