智林的科学之路分享 http://blog.sciencenet.cn/u/oliviazhang Sparse Signal Recovery, Bayesian Learning, Biomedical Signal Processing, Smart-Watch, Smart-Home, Health Monitoring

博文

利用PPG信号检测心率的文章被IEEE T-BME 接收了

已有 8429 次阅读 2014-9-19 12:00 |系统分类:论文交流

这篇文章从投稿到接收非常快,只用了50多天(其中经历了3轮审稿)。感谢副主编和审稿人的高效率。文章如下:

Zhilin Zhang, Zhouyue Pi, Benyuan Liu, TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic (PPG) Signals During Intensive Physical Exercise, IEEE Transactions on Biomedical Engineering, accepted in 2014, DOI: 10.1109/TBME.2014.2359372

preprint 下载:http://arxiv.org/abs/1409.5181

这篇文章所用的12组数据将用来做为2015 Signal Processing Cup(http://icassp2015.org/signal-processing-cup-2015/)的训练数据    

摘要:

Heart rate monitoring using wrist-type photoplethysmographic (PPG) signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this work, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/hour showed that the average absolute error of heart rate estimation was 2.34 beat per minute (BPM), and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.992. This framework is of great values to wearable devices such as smart-watches which use PPG signals to monitor heart rate for fitness.





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