Identification and analysis of stable breathing periods in electrical impedance tomography recordings. (29th June 2021)

• Record Type: Journal Article
• Authors: Haris, K; Vogt, B; Strodthoff, C; Pessoa, D; Cheimariotis, G-A; Rocha, B; Petmezas, G; Weiler, N; Paiva, R P; de Carvalho, P; Maglaveras, N; Frerichs, I
• Is Part Of: Physiological measurement. Volume 42:Number 6(2021)
• Publication Date: 2021-06-29
• Subjects: EIT -- electrical impedance tomography -- stable tidal breathing -- wearable sensors; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/ac08e5
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective . In this paper, an automated stable tidal breathing period (STBP) identification method based on processing electrical impedance tomography (EIT) waveforms is proposed and the possibility of detecting and identifying such periods using EIT waveforms is analyzed. In wearable chest EIT, patients breathe spontaneously, and therefore, their breathing pattern might not be stable. Since most of the EIT feature extraction methods are applied to STBPs, this renders their automatic identification of central importance. Approach . The EIT frame sequence is reconstructed from the raw EIT recordings and the raw global impedance waveform (GIW) is computed. Next, the respiratory component of the raw GIW is extracted and processed for the automatic respiratory cycle (breath) extraction and their subsequent grouping into STBPs. Main results . We suggest three criteria for the identification of STBPs, namely, the coefficient of variation of (i) breath tidal volume, (ii) breath duration and (iii) end-expiratory impedance. The total number of true STBPs identified by the proposed method was 294 out of 318 identified by the expert corresponding to accuracy over 90%. Specific activities such as speaking, eating and arm elevation are identified as sources of false positives and their discrimination is discussed. Significance . Simple and computationally efficient STBP detection and identification is a highly desirable component in the EIT processing pipeline. Our study implies that it is feasible, however, the determination of its limits is necessary in order to consider the implementation of more advanced and computationally demanding approaches such as deep learning and fusion with data from other wearable sensors such as accelerometers and microphones.