콘텐츠 본문
논문 해외 국제전문학술지(SCI급) 1-Dimensional Convolutional Neural Network Based Blood Pressure Estimation with Photo plethysmography Signals and Semi-Classical Signal Analysis
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2022-05
- 저자명 정의림공동(교신),김성현
- 학술지명 International Journal of Electrical and Electronics Research
- 발행처명 Forex Publication
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 2
논문 초록 (Abstract)
Continuous blood pressure (BP) measurement is vital in monitoring patients’ health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood volume, peripheral resistance, respiration, and emotional behavior. Yet, traditional BP measurement methods in continuously estimating the BP are cumbersome and inefficient. This paper presents a novel hybrid model by integrating a convolutional neural network (CNN) as a trainable feature extractor and support vector regression (SVR) as a regression model. This model can automatically extract features from the electrocardiogram (ECG) and photoplethysmography (PPG) signals and continuously estimates the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The CNN takes the correct topology of input data and establishes the relationship between ECG and PPG features and BP. A total of 120 patients with available ECG, PPG, SBP, and DBP data are selected from the MIMIC III database to evaluate the performance of the proposed model. This novel model achieves an overall Mean Absolute Error (MAE) of 1.23 ± 2.45 mmHg (MAE ± STD) for SBP and 3.08 ± 5.67 for DBP, all of which comply with the accuracy requirements of the AAMI SP10 standard.
