A comparative study of denoising sEMG signals

Başpinar, Ulvi; Senyurek, Volkan; Doğan, Buket; Varol, H. Selçuk

doi:10.3906/elk-1210-4

Cited by 11 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overall rate of the classification was 99.07% for DB4 and 99.54% for HAAR mother wavelets, respectively. It can be seen that with both preferred mother wavelets for EMGs, 48 the results were promising. Besides, since the attentional focusing types could be differentiated at EMG signal level, the attentional focus preferences might be used as alternate inputs to control robotic mechanisms.…”

Section: Discussionmentioning

confidence: 88%

The performance of an electromyography‐based deep neural network classifier for external and internal focus instructions

Yıldız

2022

Concurrency and Computation

View full text Add to dashboard Cite

External focusing (directs focus to apparatus, environment or the effect of the action) enhances motor skill learning and reduces muscular activity compared to internal focusing (leads focus to the movement or the action itself). Nevertheless, in case of electromyography (EMG) based systems such as prosthetic arms, robotic controllers, and robot-assisted musculoskeletal rehabilitation, low EMG activity is not preferred. This present study was based on classifying attentional focus-based EMG signals using deep neural networks. Here DB4 and HAAR wavelet coefficients of the participants' signals were extracted to be used as inputs to the proposed DNNs. The accuracy rate results found to be for DB4 and HAAR 99.07% and 99.54%, respectively. Although in previous studies, attentional focusing types could be classified at EMG activity level using artificial neural networks, our DNN results would be more reliable to be used as alternate inputs to EMG-based control mechanisms. Besides, this study also provides the potential advantages of internal focusing especially in musculoskeletal rehabilitation sessions. Verbal instructions via internal focus might lead physicians to plan effective muscular rehabilitation treatment for patients who has suffered a stroke or a disorder of lower or upper limb extremities.

show abstract

Section: Discussionmentioning

confidence: 88%

The performance of an electromyography‐based deep neural network classifier for external and internal focus instructions

Yıldız

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…sEMG signals are often contaminated by noise due to interference from the surrounding environment and the circuitry of the acquisition device, which can sometimes overpower the sEMG signal. To obtain the true sEMG signal, noise reduction measures must be taken [ 10 , 11 , 12 ].…”

Section: Experimental Methodsmentioning

confidence: 99%

High-Performance Surface Electromyography Armband Design for Gesture Recognition

Zhang

Hong

Zhang

et al. 2023

Sensors

View full text Add to dashboard Cite

Wearable surface electromyography (sEMG) signal-acquisition devices have considerable potential for medical applications. Signals obtained from sEMG armbands can be used to identify a person’s intentions using machine learning. However, the performance and recognition capabilities of commercially available sEMG armbands are generally limited. This paper presents the design of a wireless high-performance sEMG armband (hereinafter referred to as the α Armband), which has 16 channels and a 16-bit analog-to-digital converter and can reach 2000 samples per second per channel (adjustable) with a bandwidth of 0.1–20 kHz (adjustable). The α Armband can configure parameters and interact with sEMG data through low-power Bluetooth. We collected sEMG data from the forearms of 30 subjects using the α Armband and extracted three different image samples from the time–frequency domain for training and testing convolutional neural networks. The average recognition accuracy for 10 hand gestures was as high as 98.6%, indicating that the α Armband is highly practical and robust, with excellent development potential.

show abstract

“…This distortion must be filtered out of the PuPG signal before the boundary allocation of the region of interest. To avoid the experimental tweaking of filter or filter banks, Discrete Wavelet Transform (DWT) [38,39] was used to remove this odd signature. components and high-frequency resolution for low-frequency components [40].…”

Section: Preprocessing -Wavelet Denoisingmentioning

confidence: 99%

A novel pulse plethysmograph signal analysis method for identification of myocardial infarction, dilated cardiomyopathy, and hypertension

Khan¹,

Aziz²

2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Cardiac diseases (CDs) are one of the leading causes of the growing global mortality rate. Early detection of CDs is necessary to avoid a high increase in the mortality rate. Machine learning-based computer-aided diagnosis of CDs using various physiological signals has recently been used by researchers. Since Pulse Plethysmograph (PuPG) signal contains a wealth of information about cardiac pathologies, therefore, this paper presents an expert system design for the automatic diagnosis of cardiac disorders like Hypertension, Dilated Cardiomyopathy and Myocardial Infarction using a novel fingertip PuPG signal analysis. The proposed system first performs signal de-noising of raw PuPG sensor data using Discrete Wavelet Transform (DWT). After signal segmentation, it extracts discriminant and simplest timedomain features, which are used to perform the detection of normal and abnormal subjects through a support vector machine (SVM) classifier. The proposed detection and classification systems are tested using 10-fold cross-validation which yielded an average accuracy of 98.90%, sensitivity of 100.00%, and specificity of 98.02% for detection (normal vs abnormal) experiments with only four features and an average accuracy of 97.57% for the multiclass problem using five computationally inexpensive features. Comparative analysis with existing methods based on Electrocardiogram, Photoplethysmograph, and Phonocardiogram revealed that the proposed system has high efficiency in terms of CD detection with very low computational complexity. The findings of this work provide insights into the contribution of PuPG signal analysis towards accurate detection of cardiac disorders through innovative, low cost, and non-invasive methods. Such a system with mobile cardiac health monitoring could be used as a counterpart or second opinion with clinical diagnoses and provide patients with additional but subtle indicators of varying heart dynamics.

show abstract

A comparative study of denoising sEMG signals

Cited by 11 publications

References 30 publications

The performance of an electromyography‐based deep neural network classifier for external and internal focus instructions

The performance of an electromyography‐based deep neural network classifier for external and internal focus instructions

High-Performance Surface Electromyography Armband Design for Gesture Recognition

A novel pulse plethysmograph signal analysis method for identification of myocardial infarction, dilated cardiomyopathy, and hypertension

Contact Info

Product

Resources

About