4 research outputs found
Recommended from our members
Analyzing Heart Rate Variability Using a Photoplethysmographic Signal Measuring System
A heart rate variability (HRV) measuring system and its analysis method have been developed in this study. It is composed of a hardware measuring system based on a noninvasive photoplethysmographic (PPG) signal measuring device to acquire oxyhemoglobin saturation using pulse oximetry (SpO2) signals and a further software package including the methods used to filter and analyze the signals for heart rate variability. Firstly, an experiment is designed for measuring heartbeat using the system to observe whether the empirical mode decomposition(EMD) can really inhibit noise or not on one volunteer with 10 minutes repeated for 10 times. Then, the hardware system and analysis method are tested on another 10 volunteers before and after receiving cold face immersion. The results of the first experiment have no significant difference with commercial instrument (p > 0.05), but the results using EMD perform better when signals are contaminated by artifacts. The second part experiment is subdivided into two stages. The results show that HR values at each stage have no significant difference with commercial instrument (p > 0.05). The LF significantly decreases from 0.33 0.03 to 0.31 0.03, while HF significantly increases from 0.41 0.07 to 0.43 0.07 indicating cold face immersion can increase parasympathetic and decrease sympathetic actions. Hence, LF/HF changes significantly from high (0.85 0.17) to low (0.74 0.17) before and after adding stimulation. Due to the reasons above, it confirms that the developed system can measure heartbeat and observe the heart rate variability. So the findings of this research may be useful for developing a low-cost and a miniaturized pulse oxymeter system to continuously measure HR and HRV for the purpose of convenience, portability, and operability
A Time-Frequency approach for EEG signal segmentation
The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful tool in signal processing. Unlike traditional time-frequency approaches, HHT exploits the nonlinearity of the medium and non-stationarity of the EEG signals. In addition, we use singular spectrum analysis (SSA) in the pre-processing step as an effective noise removal approach. By using synthetic and real EEG signals, the proposed method is compared with wavelet generalized likelihood ratio (WGLR) as a well-known signal segmentation method. The simulation results indicate the performance superiority of the proposed method
Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine
Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group