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A Novel Approach for Removing ECG Interferences from Surface EMG signals Using a Combined ANFIS and Wavelet
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-8294-861X
Amirkabir University of Technology, Tehran, Iran.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
Auckland University of Technology, Auckland, New Zealand.
2015 (English)In: Journal of Electromyography & Kinesiology, ISSN 1050-6411, E-ISSN 1873-5711, Vol. 26, p. 52-59Article in journal (Refereed) Published
Abstract [en]

In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).

Place, publisher, year, edition, pages
2015. Vol. 26, p. 52-59
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-32779DOI: 10.1016/j.jelekin.2015.11.003ISI: 000370187700008PubMedID: 26643795Scopus ID: 2-s2.0-84960226613OAI: oai:DiVA.org:mdh-32779DiVA, id: diva2:955414
Projects
ESS-H - Embedded Sensor Systems for Health Research ProfileAvailable from: 2016-08-25 Created: 2016-08-24 Last updated: 2018-12-17Bibliographically approved
In thesis
1. Electromyogram Signal Enhancement and Upper-Limb Myoelectric Pattern Recognition
Open this publication in new window or tab >>Electromyogram Signal Enhancement and Upper-Limb Myoelectric Pattern Recognition
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Losing a limb causes difficulties in our daily life. To regain the ability to live an independent life, artificial limbs have been developed. Hand prostheses belong to a group of artificial limbs that can be controlled by the user through the activity of the remnant muscles above the amputation. Electromyogram (EMG) is one of the sources that can be used for control methods for hand prostheses. Surface EMGs are powerful, non-invasive tools that provide information about neuromuscular activity of the subjected muscle, which has been essential to its use as a source of control for prosthetic limbs. However, the complexity of this signal introduces a big challenge to its applications. EMG pattern recognition to decode different limb movements is an important advancement regarding the control of powered prostheses. It has the potential to enable the control of powered prostheses using the generated EMG by muscular contractions as an input. However, its use has yet to be transitioned into wide clinical use. Different algorithms have been developed in state of the art to decode different movements; however, the challenge still lies in different stages of a successful hand gesture recognition and improvements in these areas could potentially increase the functionality of powered prostheses. This thesis firstly focuses on improving the EMG signal’s quality by proposing novel and advanced filtering techniques. Four efficient approaches (adaptive neuro-fuzzy inference system-wavelet, artificial neural network-wavelet, adaptive subtraction and automated independent component analysis-wavelet) are proposed to improve the filtering process of surface EMG signals and effectively eliminate ECG interferences. Then, the offline performance of different EMG-based recognition algorithms for classifying different hand movements are evaluated with the aim of obtaining new myoelectric control configurations that improves the recognition stage. Afterwards, to gain proper insight on the implementation of myoelectric pattern recognition, a wide range of myoelectric pattern recognition algorithms are investigated in real time. The experimental result on 15 healthy volunteers suggests that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) outperform other classifiers. The real-time investigation illustrates that in addition to the LDA and MLE, multilayer perceptron also outperforms the other algorithms when compared using classification accuracy and completion rate.

Abstract [sv]

Att förlora en extremitet orsakar svårigheter i vår vardag. För att återfå förmågan till ett självständigt liv har artificiella händer och ben utvecklats. Handproteser kan kontrolleras av användaren genom aktiviteten hos återstående muskler ovanför amputationen. Elektromyogram (EMG) är en av de källor som kan användas till kontrollmetoder för handproteser. Yt-EMG är kraftfulla icke-invasiva verktyg som ger information om neuromuskulär aktivitet hos en specifik muskel, vilket är avgörande för dess användning att styra proteser. Komplexiteten hos signalen utgör dock en stor utmaning. EMG-mönsterigenkänning för att avkoda olika handrörelser är ett viktigt framsteg när det gäller kontroll av motoriserade proteser. Denna metod har potential att möjliggöra styrning av proteser genom att använda EMG-signalerna från muskelkontraktioner som insignal. Denna metod har dock ännu inte fått någon stor klinisk spridning. Olika algoritmer har utvecklats inom området för att avkoda olika rörelser; men utmaningen att identifiera olika handrörelser i olika faser kvarstår, och förbättringar inom dessa områden kan komma att öka funktionaliteten hos motoriserade proteser. Denna avhandling undersöker flera aspekter kring detta, först hur kvaliteten hos EMG-signaler kan förbättras genom att nya och avancerade filtreringstekniker. Fyra effektiva tillvägagångssätt (adaptivt neuro-fuzzy inference system-wavelet, artificiellt neuralt nätverk-wavelet, adaptiv subtraktion och automatiserad oberoende komponentanalys-wavelet) presenteras för att förbättra filtreringsprocessen för yt-EMG-signaler och effektivt eliminera EKG-störningar. Även offline-prestanda för olika EMG-baserade igenkänningsalgoritmer undersöks, däribland förmågan att klassificera olika handrörelser med sikte på att erhålla nya myoelektriska kontrollkonfigurationer som förbättrar igenkänningen. För att undersöka hur väl de myoelektriska mönsterigenkänningssalgoritmerna fungerar i verkliga situationer, har ett brett spektrum av myoelektriska algoritmer undersökts i realtid. 15 friska frivilliga försökspersoner har använt systemet och resultaten tyder på att linjär diskriminantanalys (LDA) och maximal sannolikhetsbedömning (MLE) är bättre än de andra klassificeringsmetoderna. Realtidsundersökningen visar också att förutom LDA och MLE, så är algoritmerna med flerlagersperception bättre än de övriga algoritmerna då de jämförs med avseende på klassificeringsnoggrannhet och beräkningshastighet.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 283
National Category
Signal Processing
Research subject
Electronics
Identifiers
urn:nbn:se:mdh:diva-41669 (URN)978-91-7485-418-3 (ISBN)
Public defence
2019-02-22, Delta, Mälardalens högskola, Västerås, 09:30 (English)
Opponent
Supervisors
Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2019-01-02Bibliographically approved

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Abbaspour, SaraLindén, Maria

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