scholarly journals Pathological Electromyogram (EMG) Signal Analysis Parameters

2021 ◽  
Vol 4 (13) ◽  
pp. 01-14
Author(s):  
S. M. Debbal
Keyword(s):  
Signal Processing ◽  
Signal Analysis ◽  
Neuromuscular Diseases ◽  
Temporal Analysis ◽  
Clinical Analysis ◽  
Emg Signal ◽  
Wide Range ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Signal Treatment

Clinical analysis of the electromyogram is a powerful tool for diagnosis of neuromuscular diseases. There fore, the detection and the analysis of electromyogram signals has he attracted much attention over the years. Several methods based on modern signal Processing techniques such as temporal analysis, spectro-temporel analysis ..., have been investigated for electromyogram signal treatment. However, many of these analysis methods are not highly successful due to their complexity and non-stationarity. The aim of this study is to analyse the EMGs signals using nonlinear analysis. This analysis can provide a wide range of information’s related to the type of signal (normal and pathological).

scholarly journals Signal Processing Techniques for Removal of Various Artifacts from Obstructive Sleep Apnea Signals

2019 ◽  
Vol 8 (12) ◽  
pp. 430-435
Keyword(s):  
Signal Processing ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Body Position ◽  
Well Being ◽  
Motion Artifacts ◽  
Obstructive Sleep ◽  
Wide Range ◽  
Processing Techniques ◽  
Signal Processing Techniques

Sleep is judgmental to health and well-being. Deficient quality sleep is similar with a wide range of negative outcomes varies from schizophrenia to cardiovascular disorders. Obstructive sleep apnea is one of the sleep disorders. In order to identify the various syndromes the signals are need to record by using the sensors. Sleep signals are recorded by using the polysomnography (PSG) labs which is the old traditional and gold standard for recording the sleep signals. PhysioNet is a large online medical database that consists of a large collection of recordings of various physiological signals. PhysioNet database consist of sleep apnea database available. Physionet website is a universal service, physionet resource supported by the national institute of health’s National Institute of Biomedical Imaging and Bioengineering (NIBIB) and National Institute of General Medical Sciences (NIGMS). This survey paper aims to bring the different Signal Processing Techniques for Removal of Various Artifacts from Obstructive Sleep Apnea Signals to identify sleep apnea syndrome, because pre-processing is most effective and efficient to reduce unwanted signals from the original sleep signals. While recording the sleep apnea signals various artifacts records along with raw signals either directly or indirectly due to the internal and external sources like Power line interference, Muscle contractions, Electrode contact noise, Motion Artifacts, Baseline wandering, Noise generated by electronic circuits, while breathing and coughing, body position movements etc, and they need to be eliminated in order to acquire genuine health information. So in order to remove there artificats from the sleep signals the signal processing techniques (filtering techniques) are predominantly used for pre-processing of the sleep signals and have been executed in a wide variety of systems for analysis. Filtering of the sleep signal is contingent and should be implemented only when the required statistics remains cryptic

EVALUATION OF SIGNAL PROCESSING TECHNIQUES IN DISCRIMINATING ECG SIGNALS OF MEN AND WOMEN DURING REST CONDITION AND EMOTIONAL STATES

2018 ◽  
Vol 30 (04) ◽  
pp. 1850028 ◽  
Author(s):  
Ateke Goshvarpour ◽  
Atefeh Goshvarpour ◽  
Ataollah Abbasi
Keyword(s):  
Gender Differences ◽  
Signal Processing ◽  
Emotional States ◽  
Men And Women ◽  
Wide Range ◽  
Significant Difference ◽  
Rest Condition ◽  
Processing Techniques ◽  
The Impact ◽  
Signal Processing Techniques

Great range of electrocardiogram (ECG) signal processing methods can be found in the literature. In addition, the importance of gender differences in physiological activities was also identified in various conditions. This article aims to provide a comprehensive evaluation of linear and nonlinear ECG parameters to indicate suitable signal processing approaches which can show significant differences between men and women. These differences were investigated in two conditions: (i) during rest condition, and (ii) during the affective image inducements. A wide range of parameters from time-, frequency-, wavelet-, and nonlinear-techniques were examined. Applying the Wilcoxon rank sum test, significant differences between two genders were inspected. The analysis was performed on 47 college students at rest condition and while subjects watching four types of affective pictures, including sadness, happiness, fear, and peacefulness. The impact of these emotions on the results was also investigated. The results indicated that 72.95% and 72.61% of all features were significantly different between male and female in rest condition and affective inducements, respectively. In addition, the highest percentage of the significant difference between ECG parameters of men and women was achieved using nonlinear characteristics. Considering all features together, the highest significant difference between two genders was achieved for negative emotions, including sadness and fear. In conclusion, the results of this study emphasized the importance of gender role in cardiac responses during rest condition and different emotional states. Since these gender differences are well manifested by nonlinear signal processing techniques, dynamical gender-specific ECG system may improve the automatic emotion recognition accuracies.

scholarly journals Discrimination of Parkinsonian Tremor From Essential Tremor by Voting Between Different EMG Signal Processing Techniques

2014 ◽  
Vol 10 (2) ◽  
pp. 11
Author(s):  
A Hossen ◽  
Z Al-Hakim ◽  
M Muthuraman ◽  
J Raethjen ◽  
G Deuschl ◽  
...  
Keyword(s):  
Signal Processing ◽  
Essential Tremor ◽  
Test Group ◽  
The Body ◽  
Soft Decision ◽  
Test Set ◽  
Parkinsonian Tremor ◽  
Emg Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

 Parkinson's disease (PD) and essential tremor (ET) are the two most common disorders that cause involuntary muscle shaking movements, or what is called "tremor”. PD is a neurodegenerative disease caused by the loss of dopamine receptors which control and adjust the movement of the body. On the other hand, ET is a neurological movement disorder which also causes tremors and shaking, but it is not related to dopamine receptor loss; it is simply a tremor. The differential diagnosis between these two disorders is sometimes difficult to make clinically because of the similarities of their symptoms; additionally, the available tests are complex and expensive. Thus, the objective of this paper is to discriminate between these two disorders with simpler, cheaper and easier ways by using electromyography (EMG) signal processing techniques. EMG and accelerometer records of 39 patients with PD and 41 with ET were acquired from the Hospital of Kiel University in Germany and divided into a trial group and a test group. Three main techniques were applied: the wavelet-based soft-decision technique, statistical signal characterization (SSC) of the spectrum of the signal, and SSC of the amplitude variation of the Hilbert transform. The first technique resulted in a discrimination efficiency of 80% on the trial set and 85% on the test set. The second technique resulted in an efficiency of 90% on the trial set and 82.5% on the test set. The third technique resulted in an 87.5% efficiency on the trial set and 65.5% efficiency on the test set. Lastly, a final vote was done to finalize the discrimination using these three techniques, and as a result of the vote, accuracies of 92.5%, 85.0% and 88.75% were obtained on the trial data, test data and total data, respectively. 

scholarly journals An Overview of Signal Processing Techniques for Terahertz Communications

2020 ◽  
Author(s):  
Hadi Sarieddeen ◽  
Mohamed-Slim Alouini ◽  
Tareq Y. Al-Naffouri
Keyword(s):  
Signal Processing ◽  
Channel Coding ◽  
Communication Systems ◽  
Channel Model ◽  
Mimo Systems ◽  
Future Generation ◽  
Data Detection ◽  
Wide Range ◽  
Processing Techniques ◽  
Signal Processing Techniques

Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advancements in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and the corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress paves the way for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques with an emphasis on ultra-massive multiple-input multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, which are vital to overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.

scholarly journals An Overview of Signal Processing Techniques for Terahertz Communications

2020 ◽  
Author(s):  
Hadi Sarieddeen ◽  
Mohamed-Slim Alouini ◽  
Tareq Y. Al-Naffouri
Keyword(s):  
Signal Processing ◽  
Channel Coding ◽  
Communication Systems ◽  
Channel Model ◽  
Mimo Systems ◽  
Future Generation ◽  
Data Detection ◽  
Wide Range ◽  
Processing Techniques ◽  
Signal Processing Techniques

Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advancements in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and the corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress paves the way for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques with an emphasis on ultra-massive multiple-input multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, which are vital to overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.

scholarly journals A Microprocessor Based Laser-Doppler Vibration Probe

1984 ◽  
Author(s):  
A. D. Brown ◽  
R. A. Cookson
Keyword(s):  
Signal Processing ◽  
Mechanical Vibration ◽  
Laser Doppler ◽  
Microprocessor System ◽  
Doppler Probe ◽  
Wide Range ◽  
Fibre Optic Probe ◽  
Optic Probe ◽  
Processing Techniques ◽  
Signal Processing Techniques

The laser-Doppler, fibre-optic probe which has been developed at Cranfield and which can be used for the non-intrusive detection and measurement of mechanical vibration, has been improved optically and by the inclusion of a microprocessor system to replace the previously employed frequency tracker and Bragg cells. These improvements facilitate the manufacture of a laser-Doppler probe which is more compact and considerably cheaper than the previous version, and which has potential for the application of a wide range of signal processing techniques.

Sign in / Sign up

Export Citation Format

Share Document