Key result
A wearable detection microsystem using a classification and regression trees (CART) algorithm achieved an accuracy of 98.5% for blink artifact classification in photoplethysmograph signals.
Why the study?
Wearable health monitoring systems struggle to balance multimode physiological indicator monitoring with demands for low power consumption and small size.
A novel wearable detection system using a CART algorithm can accurately classify blink artifacts from PPG signals, improving resource utilization in health monitoring.
Single-sensor wearable microsystem may enable compact multimode monitoring; hypothesis-generating, requires prospective validation before clinical use.
With the rise of the concept of health care, wearable health monitoring system has also become a topic of great interest. However, it is hard to realize the demands on multimode physiological indicators monitoring and the requirements of wearable, low consumption, and small size at the same time. Thus, it is important to realize multiple indicators detection within a limited detection system. This article proposes a wearable detection microsystem with only a single sensor, which has the potential to realize dual-mode detection of the photoplethysmograph (PPG) signal and blink motion. The PPG signal has been widely utilized in monitoring systems, while blink artifacts always be seen as noise. This work points out the potential application of blink artifacts in wearable monitoring systems and combines it with PPG detection. A classification method based on the first-order differential features was proposed to capture the faint blink artifact signal in the PPG wave. A classification and regression trees (CART) algorithm was further introduced to raise the accuracy of blink artifact classification to 98.5%. Our detection system and algorithm reveal a novel design route to wearable health monitoring system that improves the wearability and resource utilization of the monitoring system by sufficiently mining the signal feature.
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Chen et al. (2023) studied this question. Wearable detection microsystem with CART algorithm was evaluated on Accuracy of blink artifact classification. A wearable detection microsystem using a classification and regression trees (CART) algorithm achieved an accuracy of 98.5% for blink artifact classification in photoplethysmograph signals.
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