Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions

Abstract Masseter is a type of muscle which involves in the process of chewing. Any disorder in the masseter muscle creates dysfunction of the entire mastication process and leads to neuromuscular problems. Electromyograms (EMGs) act as a diagnostic tool in detecting any abnormalities present in the...
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Autor*in:	Arockia Sukanya, S. [verfasserIn] Kamalanand, K. [verfasserIn] Thayumanavan, B. [verfasserIn] Emmanuel, C. [verfasserIn] Rajnikanth, V. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Mastication Masseter muscle Electromyograms Surface electrodes Fast Fourier transform Hjorth parameters

Übergeordnetes Werk:	Enthalten in: Network modeling analysis in health informatics and bioinformatics - Wien : Springer, 2012, 9(2020), 1 vom: 12. März
Übergeordnetes Werk:	volume:9 ; year:2020 ; number:1 ; day:12 ; month:03

Links:	Volltext

DOI / URN:	10.1007/s13721-020-0224-2

Katalog-ID:	SPR03907563X

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520			\|a Abstract Masseter is a type of muscle which involves in the process of chewing. Any disorder in the masseter muscle creates dysfunction of the entire mastication process and leads to neuromuscular problems. Electromyograms (EMGs) act as a diagnostic tool in detecting any abnormalities present in the nervous and muscular system. The objective of this work is to record and analyze the electrical activity of the masseter muscle of healthy subjects during resting and chewing conditions. In this work, the electrical activity of the masseter muscle was recorded form healthy volunteers of different age, using non invasive surface electrodes. The EMG signals are recorded from left as well as right sides in resting and chewing conditions. The characteristics of the EMG signals in resting and chewing conditions are analyzed using various statistical measures, Fast Fourier Transforms (FFT) and Hjorth parameters such as complexity, activity, and mobility. Results demonstrate that the EMG signals acquired at resting conditions of different subjects have equal peak frequencies in the FFT. However, there is a significant shift in the peak frequency of the EMG signals during chewing activity. Furthermore, it is observed that there are variations in both the Hjorth parameters as well as the statistical parameters of EMG signals recorded during resting and chewing conditions. Results show that there is a significant change in the electrical activity of masseter muscle in the resting and chewing conditions. This work appears to be of high clinical significance, since the electrical activity of masseter muscle can provide diagnostic information for the analysis of various diseases in the oral structures.
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650		4	\|a Masseter muscle \|7 (dpeaa)DE-He213
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650		4	\|a Surface electrodes \|7 (dpeaa)DE-He213
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700	1		\|a Emmanuel, C. \|e verfasserin \|4 aut
700	1		\|a Rajnikanth, V. \|e verfasserin \|4 aut
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allfields	10.1007/s13721-020-0224-2 doi (DE-627)SPR03907563X (SPR)s13721-020-0224-2-e DE-627 ger DE-627 rakwb eng 004 610 570 ASE 44.32 bkl Arockia Sukanya, S. verfasserin aut Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Masseter is a type of muscle which involves in the process of chewing. Any disorder in the masseter muscle creates dysfunction of the entire mastication process and leads to neuromuscular problems. Electromyograms (EMGs) act as a diagnostic tool in detecting any abnormalities present in the nervous and muscular system. The objective of this work is to record and analyze the electrical activity of the masseter muscle of healthy subjects during resting and chewing conditions. In this work, the electrical activity of the masseter muscle was recorded form healthy volunteers of different age, using non invasive surface electrodes. The EMG signals are recorded from left as well as right sides in resting and chewing conditions. The characteristics of the EMG signals in resting and chewing conditions are analyzed using various statistical measures, Fast Fourier Transforms (FFT) and Hjorth parameters such as complexity, activity, and mobility. Results demonstrate that the EMG signals acquired at resting conditions of different subjects have equal peak frequencies in the FFT. However, there is a significant shift in the peak frequency of the EMG signals during chewing activity. Furthermore, it is observed that there are variations in both the Hjorth parameters as well as the statistical parameters of EMG signals recorded during resting and chewing conditions. Results show that there is a significant change in the electrical activity of masseter muscle in the resting and chewing conditions. This work appears to be of high clinical significance, since the electrical activity of masseter muscle can provide diagnostic information for the analysis of various diseases in the oral structures. Mastication (dpeaa)DE-He213 Masseter muscle (dpeaa)DE-He213 Electromyograms (dpeaa)DE-He213 Surface electrodes (dpeaa)DE-He213 Fast Fourier transform (dpeaa)DE-He213 Hjorth parameters (dpeaa)DE-He213 Kamalanand, K. verfasserin aut Thayumanavan, B. verfasserin aut Emmanuel, C. verfasserin aut Rajnikanth, V. verfasserin aut Enthalten in Network modeling analysis in health informatics and bioinformatics Wien : Springer, 2012 9(2020), 1 vom: 12. März (DE-627)684967553 (DE-600)2649488-7 2192-6670 nnns volume:9 year:2020 number:1 day:12 month:03 https://dx.doi.org/10.1007/s13721-020-0224-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.32 ASE AR 9 2020 1 12 03
spelling	10.1007/s13721-020-0224-2 doi (DE-627)SPR03907563X (SPR)s13721-020-0224-2-e DE-627 ger DE-627 rakwb eng 004 610 570 ASE 44.32 bkl Arockia Sukanya, S. verfasserin aut Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Masseter is a type of muscle which involves in the process of chewing. Any disorder in the masseter muscle creates dysfunction of the entire mastication process and leads to neuromuscular problems. Electromyograms (EMGs) act as a diagnostic tool in detecting any abnormalities present in the nervous and muscular system. The objective of this work is to record and analyze the electrical activity of the masseter muscle of healthy subjects during resting and chewing conditions. In this work, the electrical activity of the masseter muscle was recorded form healthy volunteers of different age, using non invasive surface electrodes. The EMG signals are recorded from left as well as right sides in resting and chewing conditions. The characteristics of the EMG signals in resting and chewing conditions are analyzed using various statistical measures, Fast Fourier Transforms (FFT) and Hjorth parameters such as complexity, activity, and mobility. Results demonstrate that the EMG signals acquired at resting conditions of different subjects have equal peak frequencies in the FFT. However, there is a significant shift in the peak frequency of the EMG signals during chewing activity. Furthermore, it is observed that there are variations in both the Hjorth parameters as well as the statistical parameters of EMG signals recorded during resting and chewing conditions. Results show that there is a significant change in the electrical activity of masseter muscle in the resting and chewing conditions. This work appears to be of high clinical significance, since the electrical activity of masseter muscle can provide diagnostic information for the analysis of various diseases in the oral structures. Mastication (dpeaa)DE-He213 Masseter muscle (dpeaa)DE-He213 Electromyograms (dpeaa)DE-He213 Surface electrodes (dpeaa)DE-He213 Fast Fourier transform (dpeaa)DE-He213 Hjorth parameters (dpeaa)DE-He213 Kamalanand, K. verfasserin aut Thayumanavan, B. verfasserin aut Emmanuel, C. verfasserin aut Rajnikanth, V. verfasserin aut Enthalten in Network modeling analysis in health informatics and bioinformatics Wien : Springer, 2012 9(2020), 1 vom: 12. März (DE-627)684967553 (DE-600)2649488-7 2192-6670 nnns volume:9 year:2020 number:1 day:12 month:03 https://dx.doi.org/10.1007/s13721-020-0224-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.32 ASE AR 9 2020 1 12 03
allfields_unstemmed	10.1007/s13721-020-0224-2 doi (DE-627)SPR03907563X (SPR)s13721-020-0224-2-e DE-627 ger DE-627 rakwb eng 004 610 570 ASE 44.32 bkl Arockia Sukanya, S. verfasserin aut Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Masseter is a type of muscle which involves in the process of chewing. Any disorder in the masseter muscle creates dysfunction of the entire mastication process and leads to neuromuscular problems. Electromyograms (EMGs) act as a diagnostic tool in detecting any abnormalities present in the nervous and muscular system. The objective of this work is to record and analyze the electrical activity of the masseter muscle of healthy subjects during resting and chewing conditions. In this work, the electrical activity of the masseter muscle was recorded form healthy volunteers of different age, using non invasive surface electrodes. The EMG signals are recorded from left as well as right sides in resting and chewing conditions. The characteristics of the EMG signals in resting and chewing conditions are analyzed using various statistical measures, Fast Fourier Transforms (FFT) and Hjorth parameters such as complexity, activity, and mobility. Results demonstrate that the EMG signals acquired at resting conditions of different subjects have equal peak frequencies in the FFT. However, there is a significant shift in the peak frequency of the EMG signals during chewing activity. Furthermore, it is observed that there are variations in both the Hjorth parameters as well as the statistical parameters of EMG signals recorded during resting and chewing conditions. Results show that there is a significant change in the electrical activity of masseter muscle in the resting and chewing conditions. This work appears to be of high clinical significance, since the electrical activity of masseter muscle can provide diagnostic information for the analysis of various diseases in the oral structures. Mastication (dpeaa)DE-He213 Masseter muscle (dpeaa)DE-He213 Electromyograms (dpeaa)DE-He213 Surface electrodes (dpeaa)DE-He213 Fast Fourier transform (dpeaa)DE-He213 Hjorth parameters (dpeaa)DE-He213 Kamalanand, K. verfasserin aut Thayumanavan, B. verfasserin aut Emmanuel, C. verfasserin aut Rajnikanth, V. verfasserin aut Enthalten in Network modeling analysis in health informatics and bioinformatics Wien : Springer, 2012 9(2020), 1 vom: 12. März (DE-627)684967553 (DE-600)2649488-7 2192-6670 nnns volume:9 year:2020 number:1 day:12 month:03 https://dx.doi.org/10.1007/s13721-020-0224-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.32 ASE AR 9 2020 1 12 03
allfieldsGer	10.1007/s13721-020-0224-2 doi (DE-627)SPR03907563X (SPR)s13721-020-0224-2-e DE-627 ger DE-627 rakwb eng 004 610 570 ASE 44.32 bkl Arockia Sukanya, S. verfasserin aut Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Masseter is a type of muscle which involves in the process of chewing. Any disorder in the masseter muscle creates dysfunction of the entire mastication process and leads to neuromuscular problems. Electromyograms (EMGs) act as a diagnostic tool in detecting any abnormalities present in the nervous and muscular system. The objective of this work is to record and analyze the electrical activity of the masseter muscle of healthy subjects during resting and chewing conditions. In this work, the electrical activity of the masseter muscle was recorded form healthy volunteers of different age, using non invasive surface electrodes. The EMG signals are recorded from left as well as right sides in resting and chewing conditions. The characteristics of the EMG signals in resting and chewing conditions are analyzed using various statistical measures, Fast Fourier Transforms (FFT) and Hjorth parameters such as complexity, activity, and mobility. Results demonstrate that the EMG signals acquired at resting conditions of different subjects have equal peak frequencies in the FFT. However, there is a significant shift in the peak frequency of the EMG signals during chewing activity. Furthermore, it is observed that there are variations in both the Hjorth parameters as well as the statistical parameters of EMG signals recorded during resting and chewing conditions. Results show that there is a significant change in the electrical activity of masseter muscle in the resting and chewing conditions. This work appears to be of high clinical significance, since the electrical activity of masseter muscle can provide diagnostic information for the analysis of various diseases in the oral structures. Mastication (dpeaa)DE-He213 Masseter muscle (dpeaa)DE-He213 Electromyograms (dpeaa)DE-He213 Surface electrodes (dpeaa)DE-He213 Fast Fourier transform (dpeaa)DE-He213 Hjorth parameters (dpeaa)DE-He213 Kamalanand, K. verfasserin aut Thayumanavan, B. verfasserin aut Emmanuel, C. verfasserin aut Rajnikanth, V. verfasserin aut Enthalten in Network modeling analysis in health informatics and bioinformatics Wien : Springer, 2012 9(2020), 1 vom: 12. März (DE-627)684967553 (DE-600)2649488-7 2192-6670 nnns volume:9 year:2020 number:1 day:12 month:03 https://dx.doi.org/10.1007/s13721-020-0224-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.32 ASE AR 9 2020 1 12 03
allfieldsSound	10.1007/s13721-020-0224-2 doi (DE-627)SPR03907563X (SPR)s13721-020-0224-2-e DE-627 ger DE-627 rakwb eng 004 610 570 ASE 44.32 bkl Arockia Sukanya, S. verfasserin aut Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Masseter is a type of muscle which involves in the process of chewing. Any disorder in the masseter muscle creates dysfunction of the entire mastication process and leads to neuromuscular problems. Electromyograms (EMGs) act as a diagnostic tool in detecting any abnormalities present in the nervous and muscular system. The objective of this work is to record and analyze the electrical activity of the masseter muscle of healthy subjects during resting and chewing conditions. In this work, the electrical activity of the masseter muscle was recorded form healthy volunteers of different age, using non invasive surface electrodes. The EMG signals are recorded from left as well as right sides in resting and chewing conditions. The characteristics of the EMG signals in resting and chewing conditions are analyzed using various statistical measures, Fast Fourier Transforms (FFT) and Hjorth parameters such as complexity, activity, and mobility. Results demonstrate that the EMG signals acquired at resting conditions of different subjects have equal peak frequencies in the FFT. However, there is a significant shift in the peak frequency of the EMG signals during chewing activity. Furthermore, it is observed that there are variations in both the Hjorth parameters as well as the statistical parameters of EMG signals recorded during resting and chewing conditions. Results show that there is a significant change in the electrical activity of masseter muscle in the resting and chewing conditions. This work appears to be of high clinical significance, since the electrical activity of masseter muscle can provide diagnostic information for the analysis of various diseases in the oral structures. Mastication (dpeaa)DE-He213 Masseter muscle (dpeaa)DE-He213 Electromyograms (dpeaa)DE-He213 Surface electrodes (dpeaa)DE-He213 Fast Fourier transform (dpeaa)DE-He213 Hjorth parameters (dpeaa)DE-He213 Kamalanand, K. verfasserin aut Thayumanavan, B. verfasserin aut Emmanuel, C. verfasserin aut Rajnikanth, V. verfasserin aut Enthalten in Network modeling analysis in health informatics and bioinformatics Wien : Springer, 2012 9(2020), 1 vom: 12. März (DE-627)684967553 (DE-600)2649488-7 2192-6670 nnns volume:9 year:2020 number:1 day:12 month:03 https://dx.doi.org/10.1007/s13721-020-0224-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.32 ASE AR 9 2020 1 12 03
language	English
source	Enthalten in Network modeling analysis in health informatics and bioinformatics 9(2020), 1 vom: 12. März volume:9 year:2020 number:1 day:12 month:03
sourceStr	Enthalten in Network modeling analysis in health informatics and bioinformatics 9(2020), 1 vom: 12. März volume:9 year:2020 number:1 day:12 month:03
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Mastication Masseter muscle Electromyograms Surface electrodes Fast Fourier transform Hjorth parameters
dewey-raw	004
isfreeaccess_bool	false
container_title	Network modeling analysis in health informatics and bioinformatics
authorswithroles_txt_mv	Arockia Sukanya, S. @@aut@@ Kamalanand, K. @@aut@@ Thayumanavan, B. @@aut@@ Emmanuel, C. @@aut@@ Rajnikanth, V. @@aut@@
publishDateDaySort_date	2020-03-12T00:00:00Z
hierarchy_top_id	684967553
dewey-sort	14
id	SPR03907563X
language_de	englisch
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author	Arockia Sukanya, S.
spellingShingle	Arockia Sukanya, S. ddc 004 bkl 44.32 misc Mastication misc Masseter muscle misc Electromyograms misc Surface electrodes misc Fast Fourier transform misc Hjorth parameters Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions
authorStr	Arockia Sukanya, S.
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topic_title	004 610 570 ASE 44.32 bkl Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions Mastication (dpeaa)DE-He213 Masseter muscle (dpeaa)DE-He213 Electromyograms (dpeaa)DE-He213 Surface electrodes (dpeaa)DE-He213 Fast Fourier transform (dpeaa)DE-He213 Hjorth parameters (dpeaa)DE-He213
topic	ddc 004 bkl 44.32 misc Mastication misc Masseter muscle misc Electromyograms misc Surface electrodes misc Fast Fourier transform misc Hjorth parameters
topic_unstemmed	ddc 004 bkl 44.32 misc Mastication misc Masseter muscle misc Electromyograms misc Surface electrodes misc Fast Fourier transform misc Hjorth parameters
topic_browse	ddc 004 bkl 44.32 misc Mastication misc Masseter muscle misc Electromyograms misc Surface electrodes misc Fast Fourier transform misc Hjorth parameters
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title	Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions
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title_full	Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions
author_sort	Arockia Sukanya, S.
journal	Network modeling analysis in health informatics and bioinformatics
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author_browse	Arockia Sukanya, S. Kamalanand, K. Thayumanavan, B. Emmanuel, C. Rajnikanth, V.
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author-letter	Arockia Sukanya, S.
doi_str_mv	10.1007/s13721-020-0224-2
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title_sort	time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions
title_auth	Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions
abstract	Abstract Masseter is a type of muscle which involves in the process of chewing. Any disorder in the masseter muscle creates dysfunction of the entire mastication process and leads to neuromuscular problems. Electromyograms (EMGs) act as a diagnostic tool in detecting any abnormalities present in the nervous and muscular system. The objective of this work is to record and analyze the electrical activity of the masseter muscle of healthy subjects during resting and chewing conditions. In this work, the electrical activity of the masseter muscle was recorded form healthy volunteers of different age, using non invasive surface electrodes. The EMG signals are recorded from left as well as right sides in resting and chewing conditions. The characteristics of the EMG signals in resting and chewing conditions are analyzed using various statistical measures, Fast Fourier Transforms (FFT) and Hjorth parameters such as complexity, activity, and mobility. Results demonstrate that the EMG signals acquired at resting conditions of different subjects have equal peak frequencies in the FFT. However, there is a significant shift in the peak frequency of the EMG signals during chewing activity. Furthermore, it is observed that there are variations in both the Hjorth parameters as well as the statistical parameters of EMG signals recorded during resting and chewing conditions. Results show that there is a significant change in the electrical activity of masseter muscle in the resting and chewing conditions. This work appears to be of high clinical significance, since the electrical activity of masseter muscle can provide diagnostic information for the analysis of various diseases in the oral structures.
abstractGer	Abstract Masseter is a type of muscle which involves in the process of chewing. Any disorder in the masseter muscle creates dysfunction of the entire mastication process and leads to neuromuscular problems. Electromyograms (EMGs) act as a diagnostic tool in detecting any abnormalities present in the nervous and muscular system. The objective of this work is to record and analyze the electrical activity of the masseter muscle of healthy subjects during resting and chewing conditions. In this work, the electrical activity of the masseter muscle was recorded form healthy volunteers of different age, using non invasive surface electrodes. The EMG signals are recorded from left as well as right sides in resting and chewing conditions. The characteristics of the EMG signals in resting and chewing conditions are analyzed using various statistical measures, Fast Fourier Transforms (FFT) and Hjorth parameters such as complexity, activity, and mobility. Results demonstrate that the EMG signals acquired at resting conditions of different subjects have equal peak frequencies in the FFT. However, there is a significant shift in the peak frequency of the EMG signals during chewing activity. Furthermore, it is observed that there are variations in both the Hjorth parameters as well as the statistical parameters of EMG signals recorded during resting and chewing conditions. Results show that there is a significant change in the electrical activity of masseter muscle in the resting and chewing conditions. This work appears to be of high clinical significance, since the electrical activity of masseter muscle can provide diagnostic information for the analysis of various diseases in the oral structures.
abstract_unstemmed	Abstract Masseter is a type of muscle which involves in the process of chewing. Any disorder in the masseter muscle creates dysfunction of the entire mastication process and leads to neuromuscular problems. Electromyograms (EMGs) act as a diagnostic tool in detecting any abnormalities present in the nervous and muscular system. The objective of this work is to record and analyze the electrical activity of the masseter muscle of healthy subjects during resting and chewing conditions. In this work, the electrical activity of the masseter muscle was recorded form healthy volunteers of different age, using non invasive surface electrodes. The EMG signals are recorded from left as well as right sides in resting and chewing conditions. The characteristics of the EMG signals in resting and chewing conditions are analyzed using various statistical measures, Fast Fourier Transforms (FFT) and Hjorth parameters such as complexity, activity, and mobility. Results demonstrate that the EMG signals acquired at resting conditions of different subjects have equal peak frequencies in the FFT. However, there is a significant shift in the peak frequency of the EMG signals during chewing activity. Furthermore, it is observed that there are variations in both the Hjorth parameters as well as the statistical parameters of EMG signals recorded during resting and chewing conditions. Results show that there is a significant change in the electrical activity of masseter muscle in the resting and chewing conditions. This work appears to be of high clinical significance, since the electrical activity of masseter muscle can provide diagnostic information for the analysis of various diseases in the oral structures.
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title_short	Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions
url	https://dx.doi.org/10.1007/s13721-020-0224-2
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author2	Kamalanand, K. Thayumanavan, B. Emmanuel, C. Rajnikanth, V.
author2Str	Kamalanand, K. Thayumanavan, B. Emmanuel, C. Rajnikanth, V.
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doi_str	10.1007/s13721-020-0224-2
up_date	2024-07-03T21:48:46.002Z
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Time domain analysis on myoelectric activity of masseter muscles in resting and chewing conditions

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