Details

Autor(en) / Beteiligte

• Sheikh, Shafa‐at Ali
• Gurel, Nil Z.
• Gupta, Shishir
• Chukwu, Ikenna V.
• Levantsevych, Oleksiy
• Alkhalaf, Mhmtjamil
• Soudan, Majd
• Abdulbaki, Rami
• Haffar, Ammer
• Vaccarino, Viola
• Inan, Omer T.
• Shah, Amit J.
• Clifford, Gari D.
• Bahrami Rad, Ali

Titel

Data‐driven approach for automatic detection of aortic valve opening: B point detection from impedance cardiogram

Ist Teil von

• Psychophysiology, 2022-12, Vol.59 (12), p.e14128-n/a

Ort / Verlag

Oxford: Blackwell Publishing Ltd

Erscheinungsjahr

2022

Link zum Volltext

Quelle

EBSCOhost Psychology and Behavioral Sciences Collection

Beschreibungen/Notizen

• Pre‐ejection period (PEP), an indicator of sympathetic nervous system activity, is useful in psychophysiology and cardiovascular studies. Accurate PEP measurement is challenging and relies on robust identification of the timing of aortic valve opening, marked as the B point on impedance cardiogram (ICG) signals. The ICG sensitivity to noise and its waveform's morphological variability makes automated B point detection difficult, requiring inefficient and cumbersome expert visual annotation. In this article, we propose a machine learning‐based automated algorithm to detect the aortic valve opening for PEP measurement, which is robust against noise and ICG morphological variations. We analyzed over 60 hr of synchronized ECG and ICG records from 189 subjects. A total of 3657 averaged beats were formed using our recently developed ICG noise removal algorithm. Features such as the averaged ICG waveform, its first and second derivatives, as well as high‐level morphological and critical hemodynamic parameters were extracted and fed into the regression algorithms to estimate the B point location. The morphological features were extracted from our proposed “variable” physiologically valid search‐window related to diverse B point shapes. A subject‐wise nested cross‐validation procedure was performed for parameter tuning and model assessment. After examining multiple regression models, Adaboost was selected, which demonstrated superior performance and higher robustness to five state‐of‐the‐art algorithms that were evaluated in terms of low mean absolute error of 3.5 ms, low median absolute error of 0.0 ms, high correlation with experts' estimates (Pearson coefficient = 0.9), and low standard deviation of errors of 9.2 ms. For reproducibility, an open‐source toolbox is provided. Accurate computation of pre‐ejection period (PEP), an index of sympathetic nervous system activity, depends upon reliable detection of aortic valve opening (B point on impedance cardiogram (ICG)). Due to ICG waveform morphological variability, automated B point detection is challenging and requires time‐consuming visual annotations. We present a machine learning‐based automated B point detection algorithm that addresses several limitations imposed by other methods and can calculate PEP in an efficient, fully automated, and reproducible manner.