GAF 변환을 사용한 딥 러닝 기반 단일 리드 ECG 신호에서의 수면 무호흡 감지

Sleep apnea detection from a single-lead ECG signal with GAF transform feature-extraction through deep learning

  • 주우 (한양대학교 컴퓨터공학과 바이오인공지능융합전공) ;
  • 이승은 (한양대학교 컴퓨터공학과 바이오인공지능융합전공) ;
  • 강경태 (한양대학교 인공지능용합학과)
  • Zhou, Yu (Dept. of Computer Science and Engineering, Major in Bio Artificial Intelligence, Hanyang University) ;
  • Lee, Seungeun (Dept. of Computer Science and Engineering, Major in Bio Artificial Intelligence, Hanyang University) ;
  • Kang, Kyungtae (Dept. of Applied Artificial Intelligence, Hanyang University)
  • 발행 : 2022.07.13

초록

Sleep apnea (SA) is a common chronic sleep disorder that disrupts breathing during sleep. Clinically, the standard for diagnosing SA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the availability of SA diagnoses in public health sectors. Therefore, ECG-based methods for SA detection have been proposed to automate the PSG procedure and reduce its discomfort. We propose a preprocessing method to convert the one-dimensional time series of ECG into two-dimensional images using the Gramian Angular Field (GAF) algorithm, extract temporal features, and use a two-dimensional convolutional neural network for classification. The results of this study demonstrated that the proposed method can perform SA detection with specificity, sensitivity, accuracy, and area under the curve (AUC) of 88.89%, 81.50%, 86.11%, and 0.85, respectively. Our experimental results show that SA is successfully classified by extracting preprocessing transforms with temporal features.

키워드

과제정보

This research was supported in part by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2020-0-01343, Artificial Intelligence Convergence Research Center(Hanyang University ERICA)) and in part by the Commercializations Promotion Agency for R&D Outcomes(COMPA) grant funded by the Korean Government(Miinistery of Science and ICT)" (2022).