scholarly journals Managing electromyogram contamination in scalp recordings: an approach identifying reliable beta and gamma EEG features of psychoses or other disorders

2021 ◽  
Author(s):  
Kenneth J Pope ◽  
Trent W Lewis ◽  
Sean P Fitzgibbon ◽  
Azin S Janani ◽  
Tyler S Grummett ◽  
...  
Keyword(s):  
High Frequency ◽  
Healthy Subjects ◽  
Eeg Signals ◽  
Gamma Frequency ◽  
Free Data ◽  
Eeg Data ◽  
Recorded Data ◽  
Frequency Power ◽  
Eeg Features ◽  
Pruning Methods

Objective: In publications on the electroencephalographic (EEG) features of psychoses and other disorders, various methods are utilised to diminish electromyogram (EMG) contamination. The extent of residual EMG contamination using these methods has not been recognised. Here, we seek to emphasise the extent of residual EMG contamination of EEG. Methods: We compared scalp electrical recordings after applying different EMG-pruning methods with recordings of EMG-free data from 6 fully-paralysed healthy subjects. We calculated the ratio of the power of pruned, normal scalp electrical recordings in the 6 subjects, to the power of unpruned recordings in the same subjects when paralysed. We produced contamination graphs for different pruning methods. Results: EMG contamination exceeds EEG signals progressively more as frequencies exceed 25 Hz and with distance from the vertex. In contrast, Laplacian signals are spared in central scalp areas, even to 100 Hz. Conclusion: Given probable EMG contamination of EEG in psychiatric and other studies, few findings on beta- or gamma-frequency power can be relied upon. Based on the effectiveness of current methods of EEG de-contamination, investigators should be able to re-analyse recorded data, re-evaluate conclusions from high frequency EEG data and be aware of limitations of the methods.

scholarly journals Cross-Spectral Analysis of Electrocardiographic and Nostril Airflow Signals Identifies Two Respiratory Frequencies of Heart Rate Modulation

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Wan-An Lu ◽  
Jui-Feng Lin ◽  
Chen-Hsu Wang ◽  
Yung-Sheng Chen ◽  
Ying-Hua Shieh ◽  
...  
Keyword(s):  
Heart Rate ◽  
Spectral Analysis ◽  
High Frequency ◽  
Healthy Subjects ◽  
Low Frequency ◽  
High Frequency Power ◽  
Very Low Frequency ◽  
The Cross ◽  
Low Frequency Power ◽  
Frequency Power

Respiration is known to be a significant modulator of heart rate, and the high-frequency component in the power spectrum of heart rate variability (HRV) is believed to be caused mainly by respiration. To investigate the effect of respiration on heart rate, cross-spectral analysis of electrocardiographic (ECG) and nostril airflow signals was performed in healthy subjects to find the common features of ECG and respiration. Forty-two healthy subjects were included in this study. The autospectra of respective ECG and nostril airflow signals and the cross-spectra of ECG and nostril airflow signals were obtained and compared with the corresponding conventional HRV measures. We found that there were two spectral peaks at around 0.03 Hz and 0.3 Hz in the autospectrum of nostril airflow and the cross-spectrum of ECG and nostril airflow. In addition, the cross-spectral normalized high-frequency power (nHFPcs) was significantly larger than that of conventional HRV, while the cross-spectral normalized very low-frequency power (nVLFPcs), normalized low-frequency power (nLFPcs), and low-/high-frequency power ratio (LHRcs) were significantly lower than those of the conventional HRV. The cross-spectral nLFPcs and LHRcs had positive correlations with their corresponding HRV measures. We conclude that cross-spectral analysis of ECG and nostril airflow signals identifies two respiratory frequencies at around 0.03 Hz and below and around 0.3 Hz and can yield significantly enhanced nHFPcs and significantly suppressed nVLFPcs, as compared to their counterparts in conventional HRV. Both very low-frequency and high-frequency components of HRV are caused in part or mainly by respiration.

A Novel Stacked Cell Layout for High Frequency Power Applications

2021 ◽  
pp. 1-1
Author(s):  
Ferdinando Costanzo ◽  
Anna Piacibello ◽  
Marco Pirola ◽  
Paolo Colantonio ◽  
Vittorio Camarchia ◽  
...  
Keyword(s):  
High Frequency ◽  
High Frequency Power ◽  
Frequency Power

scholarly journals Effect of short-term exposure to particulate air pollution on heart rate variability in normal-weight and obese adults

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Luyi Li ◽  
Dayu Hu ◽  
Wenlou Zhang ◽  
Liyan Cui ◽  
Xu Jia ◽  
...  
Keyword(s):  
Heart Rate ◽  
Air Pollution ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Normal Weight ◽  
High Frequency Power ◽  
Short Term ◽  
Particulate Air Pollution ◽  
Short Term Exposure ◽  
Frequency Power

Abstract Background The adverse effects of particulate air pollution on heart rate variability (HRV) have been reported. However, it remains unclear whether they differ by the weight status as well as between wake and sleep. Methods A repeated-measure study was conducted in 97 young adults in Beijing, China, and they were classified by body mass index (BMI) as normal-weight (BMI, 18.5–24.0 kg/m2) and obese (BMI ≥ 28.0 kg/m2) groups. Personal exposures to fine particulate matter (PM2.5) and black carbon (BC) were measured with portable exposure monitors, and the ambient PM2.5/BC concentrations were obtained from the fixed monitoring sites near the subjects’ residences. HRV and heart rate (HR) were monitored by 24-h Holter electrocardiography. The study period was divided into waking and sleeping hours according to time-activity diaries. Linear mixed-effects models were used to investigate the effects of PM2.5/BC on HRV and HR in both groups during wake and sleep. Results The effects of short-term exposure to PM2.5/BC on HRV were more pronounced among obese participants. In the normal-weight group, the positive association between personal PM2.5/BC exposure and high-frequency power (HF) as well as the ratio of low-frequency power to high-frequency power (LF/HF) was observed during wakefulness. In the obese group, personal PM2.5/BC exposure was negatively associated with HF but positively associated with LF/HF during wakefulness, whereas it was negatively correlated to total power and standard deviation of all NN intervals (SDNN) during sleep. An interquartile range (IQR) increase in BC at 2-h moving average was associated with 37.64% (95% confidence interval [CI]: 25.03, 51.51%) increases in LF/HF during wakefulness and associated with 6.28% (95% CI: − 17.26, 6.15%) decreases in SDNN during sleep in obese individuals, and the interaction terms between BC and obesity in LF/HF and SDNN were both statistically significant (p <  0.05). The results also suggested that the effects of PM2.5/BC exposure on several HRV indices and HR differed in magnitude or direction between wake and sleep. Conclusions Short-term exposure to PM2.5/BC is associated with HRV and HR, especially in obese individuals. The circadian rhythm of HRV should be considered in future studies when HRV is applied. Graphical abstract

scholarly journals Transcranial Evoked Potentials Can Be Reliably Recorded with Active Electrodes

2021 ◽  
Vol 11 (2) ◽  
pp. 145
Author(s):  
Marco Mancuso ◽  
Valerio Sveva ◽  
Alessandro Cruciani ◽  
Katlyn Brown ◽  
Jaime Ibáñez ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Magnetic Stimulation ◽  
Primary Motor Cortex ◽  
The Other ◽  
Concordance Correlation Coefficient ◽  
Concordance Correlation ◽  
Eeg Signals ◽  
Active Electrode ◽  
Viable Solution ◽  
Space Projection

Electroencephalographic (EEG) signals evoked by transcranial magnetic stimulation (TMS) are usually recorded with passive electrodes (PE). Active electrode (AE) systems have recently become widely available; compared to PE, they allow for easier electrode preparation and a higher-quality signal, due to the preamplification at the electrode stage, which reduces electrical line noise. The performance between the AE and PE can differ, especially with fast EEG voltage changes, which can easily occur with TMS-EEG; however, a systematic comparison in the TMS-EEG setting has not been made. Therefore, we recorded TMS-evoked EEG potentials (TEPs) in a group of healthy subjects in two sessions, one using PE and the other using AE. We stimulated the left primary motor cortex and right medial prefrontal cortex and used two different approaches to remove early TMS artefacts, Independent Component Analysis and Signal Space Projection—Source Informed Recovery. We assessed statistical differences in amplitude and topography of TEPs, and their similarity, by means of the concordance correlation coefficient (CCC). We also tested the capability of each system to approximate the final TEP waveform with a reduced number of trials. The results showed that TEPs recorded with AE and PE do not differ in amplitude and topography, and only few electrodes showed a lower-than-expected CCC between the two methods of amplification. We conclude that AE are a viable solution for TMS-EEG recording.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

Heart rate variability and baroreflex sensitivity are reduced in chronically undernourished, but otherwise healthy, human subjects

2003 ◽  
Vol 104 (3) ◽  
pp. 295-302 ◽  
Author(s):  
Mario VAZ ◽  
A.V. BHARATHI ◽  
S. SUCHARITA ◽  
D. NAZARETH
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Baroreflex Sensitivity ◽  
Human Subjects ◽  
Normal Weight ◽  
Autonomic Nerve ◽  
Total Power ◽  
Healthy Human ◽  
Frequency Power

Alterations in autonomic nerve activity in subjects in a chronically undernourished state have been proposed, but have been inadequately documented. The present study evaluated heart rate and systolic blood pressure variability in the frequency domain in two underweight groups, one of which was undernourished and recruited from the lower socio-economic strata [underweight, undernourished (UW/UN); n = 15], while the other was from a high class of socio-economic background [underweight, well nourished (UW/WN); n = 17], as well as in normal-weight controls [normal weight, well nourished (NW/WN); n = 27]. Baroreflex sensitivity, which is a determinant of heart rate variability, was also assessed. The data indicate that total power (0–0.4Hz), low-frequency power (0.04–0.15Hz) and high-frequency power (0.15–0.4Hz) of RR interval variability were significantly lower in the UW/UN subjects (P<0.05) than in the NW/WN controls when expressed in absolute units, but not when the low- and high-frequency components were normalized for total power. Baroreflex sensitivity was similarly lower in the UW/UN group (P<0.05). Heart rate variability parameters in the UW/WN group were generally between those of the UW/UN and NW/WN groups, but were not statistically different from either. The mechanisms that contribute to the observed differences between undernourished and normal-weight groups, and the implications of these differences, remain to be elucidated.

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