Seizure episodes detection via smart medical sensing system

Syed Shah, Dou Fan, Aifeng Ren, Nan Zhao, Xiaodong Yang, Shujaat Ali Khan Tanoli

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Cyber-physical systems (CPS) consist of seamless network of sensors and actuators integrated with physical processes related to human activities. The CPS exploits sensors and actuators to monitor and control different physical process that can affect the computations of the devices. This paper presents the monitoring of physical activities exploiting wireless devices as sensors used in medical cyber-physical systems. Patients undergoing epileptic seizures experience involuntary body movements such as jerking, muscle twitching, falling, and convulsions. The proposed method exploits S-Band sensing used in medical CPS that leverage wireless devices such as omni-directional antenna at the transmitter side, four-beam patch antenna at the receiver side, RF signal generator and vector signal analyzer that perform signal conditioning by providing amplitude and raw phase data. The method uses wireless monitoring and recording system for measurement and classification of a clinical condition (epileptic seizures) versus normal daily routine activities. The data acquired that are perturbations of the radio signal is analyzed as amplitude, phase information, and statistical models. Extracting the statistical features, we leverage various machine learning algorithms such as support vector machine, random forest, and K-nearest neighbor that classify the data to differentiate patient’s various activities such as press-ups, walking, sitting, squatting, and seizure episodes. The performance parameters used in three machine learning algorithms are accuracy, precision, recall, Cohen’s Kappa coefficient, and F-measure. The values obtained using five performance parameters provide the accuracy of more than 90%.
Original languageEnglish
Pages (from-to)(In-press)
JournalJournal of Ambient Intelligence and Humanized Computing
Volume(In-press)
Early online date23 Nov 2018
DOIs
Publication statusE-pub ahead of print - 23 Nov 2018
Externally publishedYes

Keywords

  • Smart medical sensing system
  • Machine learning
  • Internet of things

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