Abstract
With the rise of the concept of smart cities and healthcare, artificial intelligence helps people pay increasing attention to the health of themselves. People can wear a variety of wearable devices to monitor their physiological conditions. The pulse wave is a kind of physiological signal which is widely applied in the physiological monitoring system. However, the pulse wave is susceptible to artifacts, which prevents its popularization. In this work, we propose a novel beat-to-beat artifact detection algorithm, which performs pulse wave segmentation based on wavelet transform and then detects artifacts beat by beat based on the decision list. We verified our method on data acquired from different databases and compared with experts’ annotations. The segmentation algorithm achieved an accuracy of 96.13%. When it is applied to detect main peaks, the performance achieved an accuracy of 99.11%. After the previous segmentation algorithm, the artifact detection algorithm can detect beat-to-beat pulse waves and artifacts with an accuracy of 98.11%. The result indicated that the proposed method is robust for pulse waves of different patterns and could effectively detect the artifact without the complex algorithm. In summary, our proposed algorithm is capable of annotating pulse waves of various patterns and determining pulse wave quality. Since our method is developed and evaluated on the transmission-mode PPG data, it is more suitable for the devices and applications inside the hospitals instead of reflectance-mode PPG.
Highlights
Smart cities aim to provide citizens with quality life and services, and smart healthcare is its essential part
Pulse wave contains vital physiological information about the cardiovascular system, which is commonly applied in smart healthcare. e acquisition of the above physiological parameters depends on two fiducial points
Fiducial Point Detection. e Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database contains five types of pulse waveforms, as shown in Figure 8. e identical subject can be counted in different kinds according to various types. e results of onset detection are listed in Table 2. e evaluation results show that the proposed method achieved SE ranging from 95.46% to 98.78% and positive predictive value (PP) ranging from 95.89% to 98.92% for five types of pulse waveforms
Summary
Smart cities aim to provide citizens with quality life and services, and smart healthcare is its essential part. Erefore, if artifacts are detected beat by beat, the normal pulse waves can be preserved as much as possible. Artifact reduction methods can distort normal signals while correcting disturbed signals. Erefore, recent studies usually divided the signal into fixed-time segments and discarded the disturbed segments as a whole after artifact detection [6], resulting in useful signals filtered out along with artifacts. For this purpose, we propose a beat-to-beat artifact detection algorithm for pulse wave
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