Efficient and lightweight detection of PPG onset and systolic peaks using implementable time-domain strategies. (15th August 2022)

• Record Type: Journal Article
• Authors: Chakraborty, Abhishek; Sadhukhan, Deboleena; Mitra, Madhuchhanda
• Is Part Of: Measurement. Volume 200(2022)
• Publication Date: 2022-08-15
• Subjects: Automated analysis of bio-signals -- Photoplethysmography (PPG) -- PPG onset and systolic peaks -- PPG-derivative -- Time-domain strategies; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111628
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A lightweight, efficient and automated algorithm is proposed for the detection of PPG systolic peak and onset points without amplitude thresholding. A modified version of PPG signal derivative via simple mathematical technique is employed for initial enhancement of the signal morphology. Highly implementable trigonometric area of the curve (TAOC) and interval-based criteria are used to facilitate accurate detection of PPG systolic peak and onset points with minimal error. Extensive evaluation of the method is carried out over ∼ 0.2 million signal beats with wide pathophysiological variety, including arrhythmic beats. In terms of its overall efficacy, accuracy and execution speed, the technique outperforms all the other existing methods. Abstract: A majority of clinically reliable Photoplethysmogram (PPG) signal features necessitate exact location of PPG onset and systolic peaks. So far, state-of-the-art researches in the field of PPG peak detection is lagging due to the use of amplitude thresholding, complicated techniques or pathophysiologically limited datasets. In this paper, a robust algorithm is developed that primarily uses a novel combination of PPG-derivative, trigonometric area of the curve (TAOC), slope reversal and time-domain based lightweight methodologies to uncover the precise location of PPG onset and systolic peaks. Rigorous evaluation of the algorithm is conducted over 2, 16, 374 beats, collected from standard databases together with the PPG signal acquired from healthy and cardiac subjects. Superiority of the algorithm is presented by high average accuracy, sensitivity, predictivity and error as 99.69%, 99.77%, 99.92% and 0.31%. The adopted lightweight methodologies and high-speed execution over a wide pathophysiological variety presents immense promises for implementation in standard healthcare applications.