Dongmei Hao

Classification of Surface EMGs Using Wavelet Packet Energy Analysis and a Genetic Algorithm-Based Support Vector Machine

The aim of our study was to recognize results of surface electromyography (sEMG) recorded under conditions of a maximum voluntary contraction (MVС) and fatigue states using wavelet packet transform and energy analysis. The sEMG signals were recorded in 10 young men from the right upper limb with a handgrip. sEMG signals were decomposed by wavelet packet transform, and the corresponding energies of certain frequencies were normalized as feature vectors. A back-propagation neural network, a support vector machine (SVM), and a genetic algorithm-based SVM (GA-SVM) worked as classifiers to distinguish muscle states. The results showed that muscle fatigue and MVC could be identified by level-4 wavelet packet transform and GA-SVM more accurately than when using other approaches. The classification correct rate reached 97.3% with seven fold cross-validation. The proposed method can be used to adequately reflect the muscle activity.

Effects of Force Load, Muscle Fatigue, and Magnetic Stimulation on Surface Electromyography during Side Arm Lateral Raise Task: A Preliminary Study with Healthy Subjects

The aim of this study was to quantitatively investigate the effects of force load, muscle fatigue, and extremely low-frequency (ELF) magnetic stimulation on surface electromyography (SEMG) signal features during side arm lateral raise task. SEMG signals were recorded from 18 healthy subjects on the anterior deltoid using a BIOSEMI ActiveTwo system during side lateral raise task (with the right arm 90 degrees away from the body) with three different loads on the forearm (0 kg, 1 kg, and 3 kg; their order was randomized between subjects). The arm maintained the loads until the subject felt exhausted. The first 10 s recording for each load was regarded as nonfatigue status and the last 10 s before the subject was exhausted was regarded as fatigue status. The subject was then given a five-minute resting between different loads. Two days later, the same experiment was repeated on every subject, and this time the ELF magnetic stimulation was applied to the subjects deltoid muscle during the five-minute rest period. Three commonly used SEMG features, root mean square (RMS), median frequency (MDF), and sample entropy (SampEn), were analyzed and compared between different loads, nonfatigue/fatigue status, and ELF stimulation and no stimulation. Variance analysis results showed that the effect of force load on RMS was significant (p < 0.001) but not for MDF and SampEn (both p > 0.05). In comparison with nonfatigue status, for all the different force loads with and without ELF stimulation, RMS was significantly larger at fatigue (all p < 0.001) and MDF and SampEn were significantly smaller (all p < 0.001).

Modeling and Simulation of Rat Head Exposed to Mobile Phone Electromagnetic Field

Considering that electromagnetic radiation has a potential hazard to human body, rats is usually used to do long-time exposure experiments instead of human. It is necessary to investigate the distribution of electromagnetic field inside rats in order to guide experiment. The electromagnetic field simulation software HFSS was used in the paper to analyze the distribution of electromagnetic field and the specific absorption rate (SAR) inside rats’ head. Results showed that most of the electric energy was absorbed by the skin and skull on the side near the antenna, a quarter of them penetrated into the brain. SAR distribution was similar to that of electromagnetic field, and decreased with the distance to antenna. The results suggest that rat model and simulation can help to understand the specific distribution of mobile phone electromagnetic fields inside the rat head.

Muscle Extremely Low Frequency Magnetic Stimulation Eliminates the Effect of Fatigue on EEG-EMG Coherence during the Lateral Raise Task: A Pilot Quantitative Investigation

The aim of this study was to quantitatively investigate the effects of force load, muscle fatigue, and extremely low frequency (ELF) magnetic stimulation on electroencephalography- (EEG-) electromyography (EMG) coherence during right arm lateral raise task. Eighteen healthy male subjects were recruited. EEG and EMG signals were simultaneously recorded from each subject while three different loads (0, 1, and 3kg) were added on the forearm. ELF magnetic stimulation was applied to the subjects deltoid muscle between tasks during the resting period. Univariate ANOVA showed that all EEG-EMG coherence areas of C3, C4, CP5, and CP6 were not significantly affected by the force load (all p>0.05) and that muscle fatigue led to statistically significant reductions on the coherence area of gamma band in C3 (p=0.014) and CP5 (p=0.019). More interestingly, these statistically significant reductions disappeared with the application of muscle ELF magnetic stimulation, indicating its potential application to eliminate the effect of fatigue.

Effects of force load, muscle fatigue and extremely low frequency magnetic stimulation on EEG signals during side arm lateral raise task

OBJECTIVE This study was to quantitatively investigate the effects of force load, muscle fatigue and extremely low frequency (ELF) magnetic stimulation on electroencephalography (EEG) signal features during side arm lateral raise task. APPROACH EEG signals were recorded by a BIOSEMI Active Two system with Pin-Type active-electrodes from 18 healthy subjects when they performed the right arm side lateral raise task (90° away from the body) with three different loads (0 kg, 1 kg and 3 kg; their order was randomized among the subjects) on the forearm. The arm maintained the loads until the subject felt exhausted. The first 10 s recording for each load was regarded as non-fatigue status and the last 10 s before the subject was exhausted as fatigue status. The subject was then given a 5 min resting between different loads. Two days later, the same experiment was performed on each subject except that ELF magnetic stimulation was applied to the subjects deltoid muscle during the 5 min resting period. EEG features from C3 and C4 electrodes including the power of alpha, beta and gamma and sample entropy were analyzed and compared between different loads, non-fatigue/fatigue status, and with/without ELF magnetic stimulation. MAIN RESULTS The key results were associated with the change of the power of alpha band. From both C3-EEG and C4-EEG, with 1 kg and 3 kg force loads, the power of alpha band was significantly smaller than that from 0 kg for both non-fatigue and fatigue periods (all p  <  0.05). However, no significant difference of the power in alpha between 1 kg and 3 kg was observed (p  >  0.05 for all the force loads except C4-EEG with ELF simulation). The power of alpha band at fatigue status was significantly increased for both C3-EEG and C4-EEG when compared with the non-fatigue status (p  <  0.01 for all the force loads except 3 kg force from C4-EEG). With magnetic stimulation, the powers of alpha from C3-EEG and C4-EEG were significantly decreased than without stimulation (all p  <  0.05), and the difference in the power of alpha between fatigue and non-fatigue status disappeared with 1 kg and 3 kg force loads, The powers of beta and gamma bands and SampEn were not significantly different between different force loads, between fatigue and non-fatigue status, and between with and without ELF magnetic stimulation (all p  >  0.05, except between non-fatigue and fatigue with magnetic stimulation in gamma band of C3-EEG at 1 kg, and in the SampEn at 1 kg and 3 kg force loads from C4-EEG). SIGNIFICANCE Our study comprehensively quantified the effects of force, fatigue and the ELF magnetic stimulation on EEG features with difference forces, fatigue status and ELF magnetic stimulation.

Computerized analysis of acceleration parameter for the non-stress test normal and potentially abnormal fetuses

Abstract Non-stress testing (NST) is the primary method of determining fetal condition during the perinatal period, and as such, has high specificity. However, short-term monitoring and visual inspection of the cardiotocogram demonstrates several limitations in understanding fetal status which can be mistaken as predictors of neonatal asphyxia. Fetal electrocardiography (FECG) is a novel, long-term monitoring method which can reflect more objective and accurate fetal information. This article presents experimental results of four fetal heart rate (FHR) acceleration features of 44 fetuses extracted from FECG. The novelty of this approach lies in its combined use of parameters which can express both duration and amplitude of heart rate acceleration. Results demonstrate that most parameters significantly differ between normal fetuses and fetuses with suspected abnormalities. Results are promising for the identification of a set or parameters which may be used as classifiers to improve the success rate when distinguishing between normal and abnormal fetuses.

916 MHz electromagnetic field exposure affects rat behavior and hippocampal neuronal discharge

Wistar rats were exposed to a 916 MHz, 10 W/m2 mobile phone electromagnetic field for 6 hours a day, 5 days a week. Average completion times in an eight-arm radial maze were longer in the exposed rats than control rats after 4–5 weeks of exposure. Error rates in the exposed rats were greater than the control rats at 6 weeks. Hippocampal neurons from the exposed rats showed irregular firing patterns during the experiment, and they exhibited decreased spiking activity 6–9 weeks compared with that after 2–5 weeks of exposure. These results indicate that 916 MHz electromagnetic fields influence learning and memory in rats during exposure, but long-term effects are not obvious.

Fatigue muscle detection using time-frequency methods

In order to detect muscle fatigue effectively, we recorded surface electromyographic (sEMG) signals on the right upper limbs of ten young men while they were implementing handgrip tasks. Wavelet packet transform and back propagation neural network were designed to extract features of sEMG and recognize the muscle states. 7-fold cross-validation was used to test the results. Our results showed a very efficient fatigue recognition using these methods even if a larger scale analysis would have been better. The study indicates that muscle fatigue could be detected by analyzing the sEMG signals, which allow us to consider a promising future for practical applications.

Cellular Canceration Induced by Mobile Phone Radiation

With the rapid development of communication technology, there is a widespread use of mobile telephone, whose effect on human has been getting more and more attention. The electromagnetic wave can penetrate biosystems and interacts with biological tissue at different biological levels. Although many researchers have been studying biological effects of electromagnetic radiation for years, their results are not consistent and need to be confirmed by reliable and repeatable experiments. Since the energy of electromagnetic radiation of mobile phone is much low and the organism has an adaptability to external stimulation, short-time radiation may not induce obvious effect on organism. To avoid other inductive factors in the environment, we used in vitro cell culture technology and exposure experiment to study the activating effect of 900 MHz electromagnetic fields (EMF) on NIH/3T3 cells. NIH/3T3 cells are sensitive to sarcoma virus focus formation and leukaemia virus propagation, and therefore are often used for DNA transformation studies. The NIH/3T3 cells were divided into four groups, the control group with sham exposure and the three exposure groups exposed to 10W/m2, 50W/m2 and 90W/m2 916MHz EMF respectively. The exposure lasted 2 hours a day. After exposure we found the cells morphology changed and had the characteristics of epithelial cells. The Soft agar assay showed that all the exposure groups formed colonies in soft agar while the control group did not form colonies. The results in this study suggest that 916MHz electromagnetic waves can promote malignant transformation of NIH/3T3 cells and induce cellular canceration .

Changes of Arterial Pulse Waveform Characteristics with Gestational Age during Normal Pregnancy

Arterial pulse waveform analysis has been widely used to reflect physiological changes in the cardiovascular system. This study aimed to comprehensively investigate the changes of waveform characteristics of both photoplethysmographic (PPG) and radial pulses with gestational age during normal pregnancy. PPG and radial pulses were simultaneously recorded from 130 healthy pregnant women at seven gestational time points. After normalizing the arterial pulse waveforms, the abscissa of notch point, the total pulse area and the reflection index were extracted and compared between different measurement points and between the PPG and radial pulses using post-hoc multiple comparisons with Bonferrioni correction. The results showed that the effect of gestational age on all the three waveform characteristics was significant (all p < 0.001) after adjusting for maternal age, heart rate and blood pressures. All the three waveform characteristics demonstrated similar changing trends with gestational age, and they were all significantly different between the measurements from gestational week 12–15 and the others (all p < 0.05, except for the PPG total pulse area between the first and second measurement points). In conclusion, this study has comprehensively quantified similar changes of both PPG and radial pulse waveform characteristics with gestational age.