IEEE Access · 2018 · 24 citations · 33 references
ComputingBiomedical EngineeringComputer ScienceBiomedical Signal ProcessingCyber-physical SystemsSensor FusionAlerting SystemsHuman-cyber-physical InteractionMedical Cyber-physical SystemsSensor NetworksWearable Health TechnologiesWireless Sensor NetworksAnomaly Detection In Medical SystemsBody Area NetworksBody Sensor NetworksControl Of Cyber-physical SystemsPhysiological Signal MonitoringWearable DevicesWireless Body Area NetworksWireless Body Sensor Networks
Stroke is a fatal brain disease that can cause death within hours, but timely detection via intelligent health systems—such as cyber‑physical systems that integrate physical and computational worlds—can reduce mortality; extensive research has explored this through wireless sensor networks, body area networks, and wearable devices. The study proposes a CPS to detect strokes in high‑risk or post‑stroke patients, aiming to reduce mortality and address interdisciplinary gaps. The CPS acquires EEG data, applies pattern‑recognition algorithms, and transmits real‑time alerts to clinicians, integrating communication networks, software engineering, mathematics, and biomedical expertise.
Stroke is one of the fatal diseases that affect the brain and causes death within 3 to 10 h. However, most of the deaths caused by a stroke can be avoided with the identification of the nature of stroke and react to it in a timely manner by intelligent health systems. The state-of-the-art cyber-physical systems (CPS) enable interaction between physical and computational world to identify any anomaly in the physical world and respond to it. The response of CPS may vary depending upon the context of the physical world. Extensive research has been done in this area from the perspective of wireless sensor networks, body area networks, and wearable smart devices. This paper proposes a CPS for detecting the occurrence of stroke in patients, who have a high risk of stroke or have survived a stroke before. The developed CPS sends recorded data to the doctor and alerts him when the stroke occurs. The proposed system is operating on data acquired from electroencephalography sensors from patients' brain. This article aimed at decreasing human mortality rate due to stroke and will bridge the gaps in CPS due to interdisciplinary isolation. The disciplines involved in the development of a CPS include communication networks, pattern recognition, software engineering, mathematics, and biomedical.
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WEKA: a machine learning workbench
Geoffrey Holmes, A. Donkin, Ian H. Witten · 2002 · 936 citations · Full text