인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Worldwide, Cardiovascular Diseases (CVDs) are the leading cause of death. Patients at high cardiovascular risk require long-term follow-up for early CVDs detection. Generally, cardiac arrhythmia detection through the electrocardiogram (ECG) signal has been the basis of many studies. This technique does not provide sufficient information in addition to a high false alarm potential. In addition, the electrodes used to record the ECG signal are not suitable for long-term monitoring. Recently, the photoplethysmogram (PPG) signal has attracted great interest among scientists as it provides a non-invasive, inexpensive, and convenient source of information related to cardiac activity. In this paper, the PPG signal (online database Physio Net Challenge 2015) is used to classify different cardiac arrhythmias, namely, tachycardia, bradycardia, ventricular tachycardia, and ventricular flutter/fibrillation. The PPG signals are pre-processed and analyzed utilizing various signal-processing techniques to eliminate noise and artifacts, which forms a stage of signal preparation prior to the feature extraction process. A set of 41 PPG features is used for cardiac arrhythmias' classification through the application of four machine-learning techniques, namely, Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNNs), and Ensembles. Principal Component Analysis (PCA) technique is used for dimensionality reduction and feature extraction while preserving the most important information in the data. The results show a high-throughput evaluation with an accuracy of 98.4% for the KNN technique with a sensitivity of 98.3%, 95%, 96.8%, and 99.7% for bradycardia, tachycardia, ventricular flutter/fibrillation, and ventricular tachycardia, respectively. The outcomes of this work provide a tool to correlate the properties of the PPG signal with cardiac arrhythmias and thus the early diagnosis and treatment of CVDs.
#Photoplethysmogram
#Ventricular tachycardia
#Bradycardia
#Artificial intelligence
#Pattern recognition (psychology)
#Ventricular fibrillation
#Support vector machine
#Tachycardia
#Cardiology
#Medicine
#Cardiac arrhythmia
#Computer science
#Atrial fibrillation
#Internal medicine
#Heart rate
#Computer vision
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
