Nonlinear Analysis of Surface EMG Signals

Lei, Min; Meng, Guang

doi:10.5772/49986

Cited by 9 publications

(12 citation statements)

References 68 publications

(88 reference statements)

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“…In order to evaluate the performance of the developed system and compare it with a simple method of direct signal maxima detection, the influence of added noise on the R-wave detection accuracy was examined. The idea was to achieve the effect of the occurrence of muscle artifacts (EMG) [ 26 ] and other disturbances. For each user a 30-s good quality ECG signal was selected and then a noise was added to it.…”

Section: Resultsmentioning

confidence: 99%

System for automatic heart rate calculation in epileptic seizures

Kołodziej

Majkowski

Rak

et al. 2017

Australas Phys Eng Sci Med

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This article presents a comprehensive system for automatic heart rate (HR) detection. The system is robust and resistant to disturbances (noise, interferences, artifacts) occurring mainly during epileptic seizures. ECG signal filtration (IIR) and normalization due to skewness and standard deviation were used as preprocessing steps. A key element of the system is a reference QRS complex pattern calculated individually for each ECG recording. Next, a cross-correlation of the reference QRS pattern with short, normalized ECG windows is calculated and the maxima of the correlation are found (R-wave locations). Determination of the RR intervals makes possible calculation of heart rate changes and also heart rate variability (HRV). The algorithm was tested using a simulation in which a noise of an amplitude several times higher than ECG standard deviation levels was added. The proposed algorithm is characterized by high QRS detection accuracy, and high sensitivity and specificity. The algorithm proved to be useful in clinical practice, where it was used to automatically determine HR for ECG signals recorded before and during 58 focal seizures in 56 adult patients with intractable temporal lobe epilepsy.

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Section: Resultsmentioning

confidence: 99%

System for automatic heart rate calculation in epileptic seizures

Kołodziej

Majkowski

Rak

et al. 2017

Australas Phys Eng Sci Med

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“…These features are extensively utilised in medical and technical research. The time-domain features treat the signal as stationary, which overcomes the drawback of EMG signals that have non-stationary characteristics [25]. Although these features may be affected by the dynamic movement noise, especially features that involve energy characteristics [26], the high classification performance in low noise environments and low computational complexity made it the extensive features for EMG signal analysis.…”

Section: Time-domain Featuresmentioning

confidence: 99%

Dual-Layer Ranking Feature Selection Method Based on Statistical Formula for Driver Fatigue Detection of EMG Signals

Naim¹,

Mustafa²,

Sulaiman³

et al. 2022

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Electromyography (EMG) signals are one of the most studied inputs for driver drowsiness detection systems. As the number of EMG features available can be daunting, finding the most significant and minimal subset features is desirable. Hence, a simplified feature selection method is necessary. This work proposed a dual-layer ranking feature selection algorithm based on statistical formula f EMG signals for driver fatigue detection. In the beginning, in the first layer, 21 filter algorithms were calculated to rank 47 sets of EMG features (25 time-domain and 9 frequency-domain) and applied to six classifiers. Then, in the second layer, all the ranks were re-ranked based on the statistical formula (average, median, mode and variance). The classification performance of all rankings was compared along with the number of features. The highest classification accuracy achieved was 95% for 12 features using the Average Statistical Rank (ASR) and LDA classifier. It is conclusive that a combination of features from the time domain and frequency domain can deliver better performance compared to a single domain feature. Concurrently, the statistical rank ASR performed better than the single filter rank by reducing the number of features. The proposed model can be a benchmark for the enhanced feature selection method for EMG driver fatigue signal.

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“…Detailed methods have been elaborated to explore the intrinsic properties of the observed phenomenon by distinguishing between nonlinear deterministic dynamics and noisy dynamics from a time series [2] [3]. EMG is apparently a complex signal, highly corrupted by noise but really governed by chaotic and fractal dynamics [4]. Consequently, in the attempt to obtain valuable information by EMG analysis, the methods of the nonlinear analysis must be employed.…”

Section: Introductionmentioning

confidence: 99%

“…The aim to use nonlinear chaotic deterministic methodologies as well as RQA in EMG analysis is not new here. In references [4]- [8], we report the indication of some previous studies that were conducted with excellent results.…”

Section: Introductionmentioning

confidence: 99%

Chaos, Fractal and Recurrence Quantification Analysis of Surface Electromyography in Muscular Dystrophy

Conte¹,

Ware²,

Marvulli³

et al. 2015

WJNS

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We analyze muscular dystrophy recorded by sEMG and use standard methodologies and nonlinear chaotic methods here including the RQA. We reach sufficient evidence that the sEMG signal contains a large chaotic component. We have estimated the correlation dimension (fractal measure), the largest Lyapunov exponent, the LZ complexity and the %Rec and %Det of the RQA demonstrating that such indexes are able to detect the presence of repetitive hidden patterns in sEMG which, in turn, senses the level of MU synchronization within the muscle. The results give also an interesting methodological indication in the sense that it evidences the manner in which nonlinear methods and RQA must be arranged and applied in clinical routine in order to obtain results of clinical interest. We have studied the muscular dystrophy and evidence that the continuous regime of chaotic transitions that we have in muscular mechanisms may benefit in this pathology by the use of the NPT treatment that we have considered in detail in our previous publications.

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Nonlinear Analysis of Surface EMG Signals

Cited by 9 publications

References 68 publications

System for automatic heart rate calculation in epileptic seizures

System for automatic heart rate calculation in epileptic seizures

Dual-Layer Ranking Feature Selection Method Based on Statistical Formula for Driver Fatigue Detection of EMG Signals

Chaos, Fractal and Recurrence Quantification Analysis of Surface Electromyography in Muscular Dystrophy

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