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The estimation of heart rate (HR) based on wearable devices is of interest in fitness. Photoplethysmography (PPG) is a promising approach to estimate HR due to low cost; however, it is easily corrupted by motion artifacts (MA). In this work, a robust approach based on random forest is proposed for accurately estimating HR from the photoplethysmography signal contaminated by intense motion artifacts, consisting of two stages. Stage 1 proposes a hybrid method to effectively remove MA with a low computation complexity, where two MA removal algorithms are combined by an accurate binary decision algorithm whose aim is to decide whether or not to adopt the second MA removal algorithm. Stage 2 proposes a random forest-based spectral peak-tracking algorithm, whose aim is to locate the spectral peak corresponding to HR, formulating the problem of spectral peak tracking into a pattern classification problem. Experiments on the PPG datasets including 22 subjects used in the 2015 IEEE Signal Processing Cup showed that the proposed approach achieved the average absolute error of 1.65 beats per minute (BPM) on the 22 PPG datasets. Compared to state-of-the-art approaches, the proposed approach has better accuracy and robustness to intense motion artifacts, indicating its potential use in wearable sensors for health monitoring and fitness tracking.
Yalan Ye; Wenwen He; Yunfei Cheng; Wenxia Huang; Zhilin Zhang. A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts. Sensors 2017, 17, 385 .
AMA StyleYalan Ye, Wenwen He, Yunfei Cheng, Wenxia Huang, Zhilin Zhang. A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts. Sensors. 2017; 17 (2):385.
Chicago/Turabian StyleYalan Ye; Wenwen He; Yunfei Cheng; Wenxia Huang; Zhilin Zhang. 2017. "A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts." Sensors 17, no. 2: 385.
The objective of this work is to respond to the question: can quantitative electroencephalography (EEG) distinguish among Alzheimer’s Disease (AD) patients, mild cognitive impaired (MCI) subjects and elderly healthy controls? In other words, are there nonlinear indexes extracted from raw EEG data that are able to manifest the background difference among EEG? The response we give here is that a synthetic index of entropic complexity (Permutation Entropy, PE) as well as a measure of compressibility of the EEG can be used to discriminate among classes of subjects. An experimental database has been analyzed to make these measurements and the results we achieved are encouraging also in terms of disease evolution. Indeed, it is clearly shown that the condition of MCI has intermediate properties with respect to the analyzed markers: thus, these markers could in principle be used to evaluate the probability of transition from MCI to mild AD.
Domenico Labate; Fabio La Foresta; Isabella Palamara; Giuseppe Morabito; Alessia Bramanti; Zhilin Zhang; Francesco Carlo Morabito. EEG Complexity Modifications and Altered Compressibility in Mild Cognitive Impairment and Alzheimer’s Disease. Blockchain Technology and Innovations in Business Processes 2014, 26, 163 -173.
AMA StyleDomenico Labate, Fabio La Foresta, Isabella Palamara, Giuseppe Morabito, Alessia Bramanti, Zhilin Zhang, Francesco Carlo Morabito. EEG Complexity Modifications and Altered Compressibility in Mild Cognitive Impairment and Alzheimer’s Disease. Blockchain Technology and Innovations in Business Processes. 2014; 26 ():163-173.
Chicago/Turabian StyleDomenico Labate; Fabio La Foresta; Isabella Palamara; Giuseppe Morabito; Alessia Bramanti; Zhilin Zhang; Francesco Carlo Morabito. 2014. "EEG Complexity Modifications and Altered Compressibility in Mild Cognitive Impairment and Alzheimer’s Disease." Blockchain Technology and Innovations in Business Processes 26, no. : 163-173.
Zhi-Lin Zhang; Yalan Ye. Extended Barros's extraction algorithm with its application in fetal ECG extraction. 2005 International Conference on Neural Networks and Brain 2006, 1 .
AMA StyleZhi-Lin Zhang, Yalan Ye. Extended Barros's extraction algorithm with its application in fetal ECG extraction. 2005 International Conference on Neural Networks and Brain. 2006; ():1.
Chicago/Turabian StyleZhi-Lin Zhang; Yalan Ye. 2006. "Extended Barros's extraction algorithm with its application in fetal ECG extraction." 2005 International Conference on Neural Networks and Brain , no. : 1.