Signal-To-Noise Ratio Calculations to Validate Sensor Positioning for Facial Muscle Assessment Using Noninvasive Facial Electromyography

2021 ◽  
Author(s):  
Konstantin Frank ◽  
Nicholas Moellhoff ◽  
Antonia Kaiser ◽  
Michael Alfertshofer ◽  
Robert H. Gotkin ◽  
...  
Keyword(s):  
Facial Expression ◽  
Root Mean Square ◽  
Muscle Activity ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Facial Muscle ◽  
Signal To Noise ◽  
Facial Electromyography ◽  
Baseline Noise ◽  
Noise Ratio

AbstractThe evaluation of neuromodulator treatment outcomes can be performed by noninvasive surface-derived facial electromyography (fEMG) which can detect cumulative muscle fiber activity deep to the skin. The objective of the present study is to identify the most reliable facial locations where the motor unit action potentials (MUAPs) of various facial muscles can be quantified during fEMG measurements. The study population consisted of five males and seven females (31.0 [12.9] years, body mass index of 22.15 [1.6] kg/m2). Facial muscle activity was assessed in several facial regions in each patient for their respective muscle activity utilizing noninvasive surface-derived fEMG. Variables of interest were the average root mean square of three performed muscle contractions (= signal) (µV), mean root mean square between those contraction with the face in a relaxed facial expression (= baseline noise) (µV), and the signal to noise ratio (SNR). A total of 1,709 processed fEMG signals revealed one specific reliable location in each investigated region based on each muscle's anatomy, on the highest value of the SNR, on the lowest value for the baseline noise, and on the practicability to position the sensor while performing a facial expression. The results of this exploratory study may help guiding future researchers and practitioners in designing study protocols and measuring individual facial MUAP when utilizing fEMG. The locations presented herein were selected based on the measured parameters (SNR, signal, baseline noise) and on the practicability and reproducibility of sensor placement.

Understanding Facial Muscle Aging: A Surface Electromyography Study

2021 ◽  
Author(s):  
Sebastian Cotofana ◽  
Shirin Assemi-Kabir ◽  
Samir Mardini ◽  
Riccardo E Giunta ◽  
Robert H Gotkin ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Muscle Activity ◽  
Signal To Noise Ratio ◽  
Facial Muscle ◽  
Signal To Noise ◽  
Facial Aging ◽  
Non Invasive ◽  
Facial Muscles ◽  
Baseline Noise ◽  
Increased Activity

Abstract Background Facial aging is a multi-factorial process which involves all tissues of the face including skin, muscles, fat, ligaments, and bone. Whereas robust evidence is available for age-related changes of bone and facial fat, the influence of age on facial muscle activity is poorly understood. Objectives The objective of this study was to investigate the motor unit action potential (MUAP) of facial muscles by utilizing surface derived, non-invasive electromyography in young and old healthy volunteers. Methods The study investigated a total of 32 healthy volunteers with a mean age of 42.6 (19.6) years [range: 21 – 82] and a mean body mass index (BMI) of 23.9 (2.7) kg/m 2 [range: 18.5 – 29.7] utilizing surface derived, non-invasive facial electromyography. A total of 9 facial muscles were investigated bilaterally resulting in a total of 1632 measurements of the signal, baseline noise and signal-to-noise ratio of the nine muscles. Results The results of the study revealed that age does not significantly influence the signal (p = 0.234), the baseline noise (p = 0.225) or the signal-to-noise ratio (SNR; p = 0.432) when younger individuals (< 30 years) were compared to older individuals (> 50 years) in a gender and BMI matched statistical model. Exceptions were the (reduced muscle activity), procerus (increased activity), and corrugator supercilii (increased activity) muscles. Conclusions The results of this facial EMG study may help to increase the understanding of facial aging. Future studies need to reproduce the results presented herein to further increase our understanding of facial aging.

scholarly journals An Image Steganography Algorithm using Fractional Discrete Wavelet Transform with Advanced Encryption System

2020 ◽  
Vol 9 (5) ◽  
pp. 541-546
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

The research constitutes a distinctive technique of steganography of image. The procedure used for the study is Fractional Random Wavelet Transform (FRWT). The contrast between wavelet transform and the aforementioned FRWT is that it comprises of all the benefits and features of the wavelet transform but with additional highlights like randomness and partial fractional value put up into it. As a consequence of the fractional value and the randomness, the algorithm will give power and a rise in the surveillance layers for steganography. The stegano image will be acquired after administrating the algorithm which contains not only the coated image but also the concealed image. Despite the overlapping of two images, any diminution in the grade of the image is not perceived. Through this steganographic process, we endeavor for expansion in surveillance and magnitude as well. After running the algorithm, various variables like Mean Square Error (MSE) and Peak Signal to Noise ratio (PSNR) are deliberated. Through the intended algorithm, a rise in the power and imperceptibility is perceived and it can also support diverse modification such as scaling, translation and rotation with algorithms which previously prevailed. The irrefutable outcome demonstrated that the algorithm which is being suggested is indeed efficacious.

scholarly journals Analysis and Implementation of Enhanced Image Inpainting method using adjustable patch sizes

2021 ◽  
Vol 9 (3) ◽  
pp. 310-317
Keyword(s):  
Mean Square Error ◽  
Patch Size ◽  
Signal To Noise Ratio ◽  
Image Inpainting ◽  
Absolute Difference ◽  
Mean Square ◽  
Patch Selection ◽  
Signal To Noise ◽  
Distance Method ◽  
Noise Ratio

Image inpainting is the process of reconstruction of the damaged image and removal of unwanted objects in an image. In the image inpainting process patch priority andselection of best patch playsa major role. The patch size is also considered for producing good results in the image inpainting. In this paper patch priority is obtained by introducing a regularization factor (ɷ). The best patch selection is acquired by using the Sum of Absolute Difference (SAD) distance method. The results of inpainting are investigated with adjustable patch sizes of 5×5, 7×7, 9×9, 11×11, and 13×13 for the proposed method. The performance of these adjustable patch sizes is observed by using Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The best suitable patch size for good inpainting is announced based on the values of PSNR and MSE.

Method of Ultrasonic Signal De-Noising Based on Lifting Wavelet Improved Threshold

2014 ◽  
Vol 513-517 ◽  
pp. 3818-3821
Author(s):  
Zhou Yang Bi ◽  
Jian Hui Chen ◽  
Wen Jie Ju ◽  
Ming Wang ◽  
Ji Chen Li
Keyword(s):  
Signal To Noise Ratio ◽  
Ultrasonic Signal ◽  
Threshold Function ◽  
Time Signal ◽  
Mean Square ◽  
Signal To Noise ◽  
Ultrasonic Flaw ◽  
Lifting Wavelet ◽  
Noise Ratio ◽  
The Mathematical Model

The article established the mathematical model of ultrasonic flaw echo signals. First, the basic theory of wavelet transform is introduced, the principle of the wavelet threshold de-noising method is analyzed; Then on the basis of soft and hard threshold function, the paper proposes a method based on lifting wavelet de-noising. And from two aspects of signal-to-noise ratio (SNR) and mean square error (MSE) the de-noising performance is analysed. The results show that the method improved the shortcomings of soft and hard threshold de-noising method, and got a better de-noising performance and higher signal-to-noise ratio. So in real-time signal de-noising aspect the lifting wavelet has a very good application prospect.

scholarly journals Performance of Adaptive Noise Cancellation with Normalized Last-Mean-Square Based on the Signal-to-Noise Ratio of Lung and Heart Sound Separation

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Noman Q. Al-Naggar ◽  
Mohammed H. Al-Udyni
Keyword(s):  
Heart Sound ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Mean Square ◽  
Adaptive Noise Cancellation ◽  
Signal To Noise ◽  
Step Size ◽  
Nlms Algorithm ◽  
Noise Ratio ◽  
Adaptive Noise

The adaptive algorithm satisfies the present needs on technology for diagnosis biosignals as lung sound signals (LSSs) and accurate techniques for the separation of heart sound signals (HSSs) and other background noise from LSS. This study investigates an improved adaptive noise cancellation (ANC) based on normalized last-mean-square (NLMS) algorithm. The parameters of ANC-NLMS algorithm are the filter length Lj parameter, which is determined in 2n sequence of 2, 4, 8, 16, … , 2048, and the step size (μn), which is automatically randomly identified using variable μn (VSS) optimization. Initially, the algorithm is subjected experimentally to identify the optimal μn range that works with 11 Lj values as a specific case. This case is used to study the improved performance of the proposed method based on the signal-to-noise ratio and mean square error. Moreover, the performance is evaluated four times for four μn values, each of which with all Lj to obtain the output SNRout matrix (4 × 11). The improvement level is estimated and compared with the SNRin prior to the application of the proposed algorithm and after SNRouts. The proposed method achieves high-performance ANC-NLMS algorithm by optimizing VSS when it is close to zero at determining Lj, at which the algorithm shows the capability to separate HSS from LSS. Furthermore, the SNRout of normal LSS starts to improve at Lj of 64 and Lj limit of 1024. The SNRout of abnormal LSS starts from a Lj value of 512 to more than 2048 for all determined μn. Results revealed that the SNRout of the abnormal LSS is small (negative value), whereas that in the normal LSS is large (reaches a positive value). Finally, the designed ANC-NLMS algorithm can separate HSS from LSS. This algorithm can also achieve a good performance by optimizing VSS at the determined 11 Lj values. Additionally, the steps of the proposed method and the obtained SNRout may be used to classify LSS by using a computer.

scholarly journals PERBANDINGAN METODE UNTUK PERBAIKAN KUALITAS CITRA MAMMOGRAM

2016 ◽  
Vol 7 (2) ◽  
pp. 657
Author(s):  
Hanifah Rahmi Fajrin
Keyword(s):  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Region Of Interest ◽  
Contourlet Transform ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Kanker payudara merupakan pembunuh nomor satu pada wanita di seluruh dunia. Sejauh ini, deteksi dari kanker payudara hanya menggunakan mata telanjang dan berdasarkan jam terbang (pengalaman) dari dokter dan radiologis. Terdapat beberapa kelemahan dalam menganalisis mammogram payudara guna mendeteksi keberadaan kanker payudara. Hal ini bisa diakibatkan oleh sel kanker yang tertutup oleh noise, kontras citra yang rendah dan faktor manusiawi lainnya seperti : kelelahan, mood, dan lainnya. Untuk meminimalisir hal tersebut dibutuhkan suatu metode yang dapat membantu dokter dalam menganalisis citra mammogram payudara. Pada penelitian ini, dilakukan suatu proses yang bertujuan untuk meningkatkan kualitas mammogram agar lebih memudahkan dokter dalam mendiagnosis kelainan pada payudara. Citra yang diolah merupakan citra mammogram yang tidak dipangkas dan tidak disegmentasi pada bagian Region of Interest (ROI), melainkan keseluruhan citra payudara setelah dihilangkan background yang berlebihan. Tahapan pada proses peningkatan kuliatas citra mammogram ini (pra pengolahan) terdiri dari : menghilangkan label atau artifak yang ditemukan pada mammogram, meng-crop citra pada bagian payudara saja (menghilangkan background), memperbaiki kontras citra dengan membandingkan beberapa metode yaitu: CLAHE, Non Subsampled Contourlet Transform (NSCT), Contras Stretching (CS) dan selanjutnya dilakukan smoothing dengan menggunakan filter median. Kinerja dari setiap metode dihitung dengan mencari nilai Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR) citra. Dari nilai MSE dan PSNR yang didapatkan, ditemukan nilai MSE dan PSNR terbaik pada metode NSCT dengan nilai 50.20878 db 31.75332 db. Kata kunci: CLAHE, NSCT, CS, median filter.

scholarly journals Steganographic Techniques Using Modified Least Significant Bit and Modification Reshape Transposition Methods

2021 ◽  
Vol 13 (1) ◽  
pp. 67-77
Author(s):  
Guntoro Barovih ◽  
Fadhila Tangguh Admojo ◽  
Yoda Hersaputra
Keyword(s):  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Lossless Compression ◽  
Mean Square ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
Image File ◽  
Modification Technique ◽  
Noise Ratio

A message is a form of conveying information. Various ways are used to secure the information conveyed in the form of messages either in encrypted form or in the form of applying a password in the message. Messages can also be encrypted and embedded in other media such as images (steganography). This research aimed to insert a message into the form of an image by combining the Modified Least Significant Bit (MLSB) method in encrypting messages and reshape modification technique to determine at which position the message encryption will be embedded in the image. Tests were carried out to obtain the quality of the encryption process using the parameters of Fidelity, mean square error, peak signal to noise ratio, testing on file type, robustness, and comparison of message contents. The results of the tests showed that the files that can be used are files with the image file type in the lossless compression category, the rotation can be done at 90, 180, 270 without destroying the message in it, and changing the pixel in the image file will destroy the message inside

scholarly journals PEMILIHAN METODE PENGURANGAN NOISE PADA CITRA ULTRASONOGRAFI OVARIUM

2020 ◽  
Vol 15 (1) ◽  
pp. 13
Author(s):  
Eliyani Eliyani ◽  
Ahmad Riyadi Maulana
Keyword(s):  
Noise Reduction ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Median Filtering ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Pengurangan noise merupakan upaya untuk memperbaiki kualitas citra yang akan memudahkan tahapan selanjutnya dalam pengolahan citra. Noise Reduction atau mengurangi noise untuk menghasilkan citra lebih baik sehingga informasi data citra tidak hilang dan citra dapat diintepretasikan oleh mata manusia. Penelitian ini menggunakan data gambar ultrasonografi ovarium untuk membantu menganalisa kondisi kesehatan ovarium perempuan. Gambar ultrasonografi ovarium biasanya terdapat noise, metode pengurangan noise yang akan digunakan pada penelitian ini adalah Median Filtering dan Adaptive Median Filtering. Hasil filtering dari 2 metode tersebut akan dibandingkan menggunakan Mean Square Error(MSE) dan Peak Signal To Noise Ratio(PNSR). Ukuran kernel untuk Median Filtering dan Adaptive Median Filtering dipilih sebagai 3x3, 5x5, dan 7x7. Penelitian ini menghasilkan metode filtering dengan kinerja terbaik yaitu Adaptive Median Filtering dengan ukuran window 5x5 yang ditunjukan dari nilai Mean Square Error dan Peak Signal To Noise Ratio .

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