임상 데이터 분석의 질적 향상을 위한 모션센서 기반 무선 네트워크 시스템
In-bed Motion Sensor-based Wireless Network System for Improved Clinical Data Analysis
- 주제(키워드) Clinical Decision Support System , Wireless Sensor Network , Health Care Information Systems , Motion Sensing , Noise Filter
- 발행기관 아주대학교
- 지도교수 이석원
- 발행년도 2020
- 학위수여년월 2020. 2
- 학위명 석사
- 학과 및 전공 일반대학원 컴퓨터공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000029456
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Recently, the advancement in data analytics including various machine learning methods lead to designing effective clinical decision support systems (CDSS) in the clinical sector. However, a CDSS including noisy clinical data can provide unhelpful (if not misleading) decision support to clinical staff as well as clinically dangerous decisions to already suffering patients. Therefore, in order to design a better and effective CDSS, such noisy data caused by patient movements and clinical protocols should be identified and filtered from the inputs in in the training dataset for CDSS development. For this purpose, we propose MediSense a system designed to identify and classify different patient motions on the bed and filter out physiological signal data points collected when patient motion occurs using sensor-based motion classification results. Essentially, MediSense can be considered as ``glasses" for the third eye in accurate-sensitive clinical domain is an intelligent embedded wireless sensing system for supporting a CDSS and consists of a motion classifier, a wireless network and localization techniques. To evaluate our system, we deploy MediSense in intensive care units (ICUs) at the Ajou University Hospital Trauma Center, a major hospital facility located in Suwon, South Korea, and evaluate its each system component's performance from real patient traces collected at these ICUs through a 4-month pilot study. Our results show that MediSense successfully classifies patient motions on the bed with >90% accuracy, shows 100% reliability in determining the locations of beds within the ICU, and each bed-attached sensor achieves a lifetime of more than 33 days, which satisfies the application-level requirements suggested by our clinical partners. Furthermore, a simple case-study with arrhythmia patient data shows that MediSense can help improve the clinical diagnosis accuracy.
more목차
List of Figures
List of Tables
1. Introduction
2. Motivation and Problem Statement
2.1 Preliminary Studies
2.2 System Requirements
3. Main System Components
3.1 Physiological signal Repository
3.2 Hardware Platforms in MediSense
3.2.1 In-Bed Sensor Node
3.2.2 Location-Beacon Node
3.2.3 Gateway
4. Patient Motion Classification
4.1 Physiological Signal Noise-causing Motions
4.2 Sensor Selection and Configuration
4.3 Peak Detection & Classification
4.4 Filter Evaluations
4.4.1 Impact on Sampling Rate
4.4.2 Application-level Evaluation
5. System Architecture of MediSense
5.1 Device-to-Bed Localization
5.2 Routing
5.3 Time Synchronization
5.4 Power and Energy
