Journal of Biomedical Engineering and Technology

ISSN (Print): 2373-129X

ISSN (Online): 2373-1303

Editor-in-Chief: Ahmed Al-Jumaily

Website: http://www.sciepub.com/journal/JBET

   

Article

Design of Microfluidic Sensing and Transport Device

1Department of Engineering, UPM, Serdang, Malaysia

2Department of Engineering, University of Sunderland U.K


Journal of Biomedical Engineering and Technology. 2015, 3(1), 15-20
doi: 10.12691/jbet-3-1-3
Copyright © 2016 Science and Education Publishing

Cite this paper:
Masoumeh Asgharighajari, Nurul Amziah, Nasri Sulaiman, Sherif Adebayo Sodeinde. Design of Microfluidic Sensing and Transport Device. Journal of Biomedical Engineering and Technology. 2015; 3(1):15-20. doi: 10.12691/jbet-3-1-3.

Correspondence to: Masoumeh  Asgharighajari, Department of Engineering, UPM, Serdang, Malaysia. Email: marinaasghari@yahoo.com

Abstract

This study presents detection of microfluidic droplet using an impedance measurement, study carried out with available commercial material such as copper. Model of channel and electrode, geometry a sweep from different point for sensor optimization and simulation. The study focus on admittance measurement for modeling compare to other available design with capacitive measurement. The model become more easy and in-expensive as less circuit required for electronics model. The study indicates how peak voltage can implement to measure speed of a fluid in a channel.

Keywords

References

[1]  A.Rasmussen, C. Mavriplis, M.E. Zaghloul, O. Mikulchenko, K. Mayaram. “Simulation and optimization of a microfuidic flow sensor.” Sensors and Actuators A 88 (2001): 121-132.
 
[2]  Caglar Elbuken, Tomasz, Glawdel, Danny, Chan, Carolyn L. Ren. “Detection of Micro Droplet Size and Speed Using Capacitive Sensors”, Sensors and Actuators A: Physical. Sensors and Actuators A: Physical (Elservier), 2011: 8.
 
[3]  “Droplet Operation.” Lab Chip 10, 2010.
 
[4]  F.Wang, M.A. Burns. “Multiphase Bioreaction Microsystem with Automated Onchip.” n.d.
 
[5]  George M. Whitesides. “The origins and the future of microfluidics.” 442 (2006): 1-2.
 
Show More References
[6]  John Doe, Richard Miles, Richard Roe and Claus Santa. “Electroosmotic Flow (DC), Electroosmosis, Electro-osmotic flow, Electrokinetic flow, Electroendosmosis.” n.d. ftp://ftp.springer.de/pub/tex/latex/cyclop/sample/example.pdf (accessed April 20, 2013).
 
[7]  Meysam Rahmat. “Geometric Optimization for a Thermal Microfluidic Chip.” (Mc Grill) 2007: 109.
 
[8]  Reza Nosrati1, Mohammad Hadigol, Arian Jafari, Mehrdad Raisee and Ahamad Nourbakhs. “Numerical Investigation of Electroosmotic Mixing in Microchannels with Heterogeneous Zeta Potential.” (American Scientific Publishers) 3 (2011): 1-9.
 
Show Less References

Article

Influence of Gender on the Activity of Agonist-Antagonist Muscles during Maximum Knee and Ankle Contractions

1Biomedical Engineering Department, North Eastern Hill University (NEHU), Shillong, Meghalaya, India

2Department of Electrical & Electronics Engineering, Graphic Era University, Dehradun, Uttrakhand, India


Journal of Biomedical Engineering and Technology. 2016, 4(1), 1-6
doi: 10.12691/jbet-4-1-1
Copyright © 2016 Science and Education Publishing

Cite this paper:
Manvinder Kaur, Shilpi Mathur, Dinesh Bhatia, Deepak Joshi. Influence of Gender on the Activity of Agonist-Antagonist Muscles during Maximum Knee and Ankle Contractions. Journal of Biomedical Engineering and Technology. 2016; 4(1):1-6. doi: 10.12691/jbet-4-1-1.

Correspondence to: Dinesh  Bhatia, Biomedical Engineering Department, North Eastern Hill University (NEHU), Shillong, Meghalaya, India. Email: bhatiadinesh@rediffmail.com

Abstract

Muscle mechanical energy expenditure reflects the neuro-motor strategies employed by the nervous system to analyze human locomotion tasks and is directly related to its efficiency. The purpose of this study was to investigate the influence of gender on the activity of agonist-antagonist muscles during maximum knee and ankle contraction in males (n1=10) and females (n2=10) adult population. Different movements of knee and ankle used for the maximum contractions were knee flexion and extension, ankle plantar flexion and dorsiflexion. The agonist-antagonist muscles considered for the study were Rectus femoris (Quadriceps Muscle group), Biceps femoris (Hamstring Muscle group), Tibialis Anterior and Soleus. The statistical analysis applied was post hoc analysis to determine least significant differences among the male and female groups. The different groups for classifying these movements were Female Dominant Leg (FDL), Female Non Dominant Leg (FNDL), Male Dominant Leg (MDL) and Male Non Dominant Leg (MNDL). The results showed no significant differences (p≥0.1) in the muscle energy expenditure for different lower limb activities among gender. In addition to this, knee flexion was found to be the activity with minimum energy expenditure in healthy males and females. Active agonist-antagonist muscle pairs during knee and ankle contractions were found to have minimum mechanical energy expenditure. This study is a part of a larger intervention study that is being carried out for designing feedback based FES devices.

Keywords

References

[1]  Albert WJ, Wrigler AT, McLean RB, Sleivert GG. Sex Differences in the Rate of Fatigue Development and Recovery. Dynamic Medicine: BioMed Central,10:1-10, 2005.
 
[2]  Aleshinsky, SY. An energy sources and fractions approach to the mechanical energy expenditure problem-I. Basic concepts, description of the model, analysis of a one-link system movement. J. Biomechanics. 19:281-293, 1986a.
 
[3]  Aleshinsky, SY. An energy sources and fractions approach to the mechanical energy expenditure problem-II. Movement of the multi-link chain model, J. Biomechanics, 19:287-293, 1986b.
 
[4]  Basmajian JV, De Luca CJ. Muscle Alive: Their Function Revealed by Electromyography. Baltimore: Willians & Wilkins, pp.201-222, 1985.
 
[5]  Bilodeau M, Schindler-Ivens SJ, Williams DM, Chandran R and Sharma SS. EMG frequency content changes with increasing force and during fatigue in the quadriceps femoris muscle of men and women. J. Electromyogr. Kinesiol., 13(1):83-92, 2003.
 
Show More References
[6]  Blake OM & Wakeling JM. Estimating changes in metabolic power from EMG. Springer Plus, 2, 2013.
 
[7]  Browning RC, Baker EA, Herron JA, Kram R. Effects of obesity and sex on the energetic cost and preferred speed of walking. Journal of Applied Physiology, 100:390-398, 2005.
 
[8]  Garfinkel S, Cafarelli E. Relative changes in maximal force, EMG, and muscle cross-sectional area after isometric training. Medice and Science in Sports and Exercise, 24:1220-27, 1992.
 
[9]  Gerdle B, Karlsson S, Crenshaw AG, Elert J, Fride J. The influences of muscle fibre proportions and areas upon EMG during maximal dynamic knee extensions. Eur J Appl Physiol, 81: 2-10, 2000.
 
[10]  Gough JV, Ladley G. An investigation into the effectiveness of various forms of quadriceps exercises. Physiotherapy, 57:356-361, 1971.
 
[11]  Kent-Braun JA, Ng AV, Doyle JW, Towse TF. Human skeletal muscle responses vary with age and gender during fatigue due to incremental isometric exercise. Journal of Applied Physiology, 93(5): 1813-1823, 2002.
 
[12]  Konard P. The ABC of EMG: A Practical Introduction to Kinesiological Electromyography. Noraxon Inc. USA, version 1.0 April 2005.
 
[13]  Lehmann M, Fourneir A. et. al. Inactivation of Rho signaling pathway promotes CNS axon regeneration. Journal of Neuroscience, 19: 7537-7547, 1999.
 
[14]  Mathur S, Eng JJ, MacIntyre DL. Reliability of surface EMG during sustained contractions of the quadriceps. Journal of Electromyography and Kinesiology, 15:102-110, 2005.
 
[15]  Morio B, Beaufrere B, Montaurier C. et. al. Gender differences in energy expended during activities and in daily energy expenditure of elderly people. American Journal of Physiology, 273:321-327, 1997.
 
[16]  Pincivero DM, Campy RM, Salfetnikov Y. et. al. Influence of contraction intensity, muscle, and gender on median frequency of the quadriceps femoris. J. Appl. Physiol., 90(3):804-810, 2001.
 
[17]  Rodrigo S, Gracia I, Franco M. et. al. Energy expenditure during human gait II- Role of muscle groups. IEEE Engineering in Medicine and Biology Society, 4858-4861, 2010.
 
[18]  Sasaki K, Neptune RR. Muscle mechanical work and elastic energy utilization during walking and running near the preferred gait transition speed. Gait & Posture, 23: 383–390, 2006.
 
[19]  Seniam: European recommendations for surface electromyography. Roessingh Research and Development, Enschede, Holland, 1999.
 
[20]  Signorile JF, Kacsik D, Perry A, Roberson B, Williams R. The effect of knee and foot position on the electromyographical activity of the superficial quadriceps. J. Orthop Sport Phys Ther, 22:2-9, 1995.
 
[21]  Sparrow WA & Newell KM. Metabolic energy expenditure and the regulation of movement economy. Psychonomic Bulletin and Review, 5:173-196, 1998.
 
[22]  Winter D. Biomechanics and motor control of human movement. New Jersey: John Wiley & Sons, 4th Ed., 2009.
 
[23]  Zelik KE, Kuo AD. Mechanical work as an indirect measure of subjective costs influencing human movement. PLoS One, 7, 2012.
 
Show Less References

Article

Power Spectrum Density Analysis of EEG Signals in Spastic Cerebral Palsy Patients by Inducing r-TMS Therapy

1Department of Biomedical Engineering North Eastern Hill University, Shillong-793022, Meghalaya, India

2UDAAN-for the differently abled, Lajpat Nagar, New Delhi-110024, India

3Computer Centre, North Eastern Hill University, Shillong-793022, Meghalaya, India


Journal of Biomedical Engineering and Technology. 2016, 4(1), 7-11
doi: 10.12691/jbet-4-1-2
Copyright © 2017 Science and Education Publishing

Cite this paper:
Bablu Lal Rajak, Meena Gupta, Dinesh Bhatia, Arun Mukherjee, Sudip Paul, Tapas Kumar Sinha. Power Spectrum Density Analysis of EEG Signals in Spastic Cerebral Palsy Patients by Inducing r-TMS Therapy. Journal of Biomedical Engineering and Technology. 2016; 4(1):7-11. doi: 10.12691/jbet-4-1-2.

Correspondence to: Dinesh  Bhatia, Department of Biomedical Engineering North Eastern Hill University, Shillong-793022, Meghalaya, India. Email: bhatiadinesh@rediffmail.com

Abstract

Cerebral palsy (CP) is a non-progressive neurological motor disorder affecting children that are often accompanied by disturbances of sensation, perception, epilepsy and secondary musculoskeletal problems. These problems arise due to disturbed cortical or subcortical excitability leading to abnormal electrophysiological brain activity in these children. In order to control the abnormal brain activity, repetitive Transcranial magnetic stimulation (r-TMS) therapy was employed. The present work analyzes the electroencephalogram (EEG) signal before and after r-TMS therapy of spastic CP children and compared it with the power spectrum of normal healthy children. EEG recording was performed on all the twenty selected subjects using four electrodes placed on pathway known for motor control and planning, namely C3-C4 and F3-F4. The artifact-free EEG signals of 15 minutes duration was extracted for spectral analysis using Fast Fourier Transformation (FFT) algorithm to obtain power density spectrum (PSD). The PSD revealed high power peak at frequency of 50 Hz and smaller or none at 100 Hz, for all healthy subjects. In case of spastic CP children, peak at 100 Hz were prominent prior to r-TMS therapy and at 50 Hz it was found to be quite low to none. After therapy, there was a shift in the high intensity peak from 100Hz to 50Hz with the peak at 100Hz being significantly reduced. 50Hz peak obtained in CP patients matched with those observed in normal children, thus showing the effectiveness of r-TMS therapy in controlling abnormal brain activity in spastic CP patients.

Keywords

References

[1]  Rosenbaum, P., Paneth, N., Leviton, A., Goldstein, M., Bax, M., Damiano, D., et al.. “A report: the definition and classification of cerebral palsy April 2006”, Developmental Medicine & Child Neurology, vol. 109, no. suppl 109, pp. 8-14, 2007.
 
[2]  Data and Statistics for Cerebral Palsy, Jan 12, 2015. [Online] available: http://www.cdc.gov/ncbddd/cp/data.html, accessed May 10, 2016.
 
[3]  S. Al-Rajeh, O. Bademosi, A. Awada, H. Ismail, S. Al-Shammasi, A. Dawodu. “Cerebral palsy in Saudi Arabia: a case-control study of risk factors”. Developmental Medicine & Child Neurology, vol. 33, no. 12, pp. 1048-52, 1991.
 
[4]  M. Bax, M. Goldstein, P. Rosenbaum, A. Leviton, N. Paneth, B. Dan, et al.. “Proposed definition and classification of cerebral palsy, April 2005”, Developmental Medicine & Child Neurology, vol. 47, no. 8, pp. 571-6, 2005.
 
[5]  M. Yeargin-Allsopp, K. V. Braun, N. S. Doernberg, R. E. Benedict, R. S. Kirby, M. S. Durkin. “Prevalence of cerebral palsy in 8-year-old children in three areas of the United States in 2002: a multisite collaboration”. Pediatrics, vol. 121, no. 3, pp. 547-54, 2008.
 
Show More References
[6]  M. A. Perlstein, E. L. Gibbs, F. A. Gibbs. “The electroencephalogram in infantile cerebral palsy”, American Journal of Physical Medicine & Rehabilitation, vol. 34, no. 4, pp. 477-96, 1955.
 
[7]  D. Lindsley and M. Jones. “Clinical and EEG correlations in cerebral palsy children and adults”, Electroencephalography and Clinical Neurophysiology, vol. 8, no. 1, pp. 168-168, 1956.
 
[8]  K. A. Melin. “EEG and epilepsy in cerebral palsy”, Developmental Medicine & Child Neurology, vol. 4, no. 2, pp. 180-3, 1962.
 
[9]  A. Al-Sulaiman. “Electroencephalographic findings in children with cerebral palsy: a study of 151 patients”, Functional Neurology, vol. 16, no. 4, pp. 325-28, 2001
 
[10]  E. Niedermeyer and F. L. da Silva, Electroencephalography: basic principles, clinical applications, and related fields, Lippincott Williams & Wilkins, 2005.
 
[11]  V. Srinivasan, C. Eswaran, A. N. Sriraam. “Artificial neural network based epileptic detection using time-domain and frequency-domain features”, Journal of Medical Systems, vol. 29, no. 6, pp. 647-60, 2005.
 
[12]  K. J. Stam, D. L. Tavy, B. Jelles, H. A. Achtereekte, J. P. Slaets, R. W. Keunen. “Non-linear dynamical analysis of multichannel EEG: clinical applications in dementia and Parkinson's disease”, Brain topography, vol. 7, no. 2, pp. 141-50, 1994.
 
[13]  R. Wang, J. Wang, H. Li, Y. Chen. “Power spectral density and high order bispectral analysis of Alzheimer's EEG”, in Proceedings of 27th Chinese IEEE Conference on Control and Decision (CCDC) 2015, pp. 1822-1826, 2015.
 
[14]  I. Lesný. “EEG Study in Different Forms of Cerebral Palsy”, Developmental Medicine & Child Neurology, vol. 5, no. 6, pp. 593-602, 1963.
 
[15]  H. Adeli, Z. Zhou, N. Dadmehr. “Analysis of EEG records in an epileptic patient using wavelet transform”, Journal of neuroscience methods, vol. 123, no. 1, pp. 69-87, 2003.
 
[16]  G. Dumermuth and H. Flühler. “Some modern aspects in numerical spectrum analysis of multichannel electroencephalographic data”, Medical and biological engineering, vol. 5, no. 4, pp. 319-31, 1967.
 
[17]  A. Valero-Cabre and A. Pascual-Leone. “Impact of TMS on the primary motor cortex and associated spinal systems”, IEEE Engineering in Medicine and Biology Magazine, vol. 24, no. 1, pp. 29-35, 2005.
 
[18]  E. M. Wassermann and S. H. Lisanby. “Therapeutic application of transcranial magnetic stimulation: a review”, Clinical Neurophysiology, vol. 112, no. 8, pp. 1367-77, 2001.
 
[19]  P. M. Rossini, A. T. Barker, A. Berardelli, et al.. “Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: basic principles and procedures for routine clinical application. Report of an IFCN committee.” Electroencephalography and clinical neurophysiology, vol. 91, no. 2, pp. 79-92, 1994.
 
[20]  G. H. Klem, H. O. Lüders, H. H. Jasper, C. Elger. “The ten-twenty electrode system of the International Federation”, Electroencephalogr Clin Neurophysiol, vol. 52, no. 3, pp. 3-6, 1999.
 
[21]  O. Dressler, G. Schneider, G. Stockmanns, E. F. Kochs. “Awareness and the EEG power spectrum: analysis of frequencies”, British journal of anaesthesia, vol. 93, no. 6, pp. 806-809, 2004.
 
[22]  P. D. Welch. “The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms”, IEEE Transactions on audio and electroacoustics, vol. 15, no. 2, pp. 70-3, 1967.
 
[23]  E. M. Khedr, M. A. Ahmed, N. Fathy, J. C. Rothwell. “Therapeutic trial of repetitive transcranial magnetic stimulation after acute ischemic stroke”, Neurology, vol. 65, pp. 466-468, 2005.
 
[24]  E. J. Plautz, S. Barbay, S. B. Frost, K. M. Friel, N. Dancause, et al., “Post-infarct cortical plasticity and behavioral recovery using concurrent cortical stimulation and rehabilitative training: a feasibility study in primates”, Neurol Res, vol. 25, pp. 801-810, 2003.
 
[25]  M. Gupta, B. L. Rajak, D. Bhatia, A. Mukherjee. “Effect of r-TMS over standard therapy in decreasing muscle tone of spastic cerebral palsy patients”, Journal of Medical Engineering & Technology, vol. 40, no. 4, pp. 210-16, 2016.
 
[26]  F. P. De Lange, O. Jensen, M. Bauer, I Toni. “Interactions between posterior gamma and frontal alpha/beta oscillations during imagined actions”, Frontiers in human neuroscience, vol. 2, pp. 7, 2008.
 
[27]  R. T. Canolty, E. Edwards, S. S. Dalal, M. Soltani, S. S. Nagarajan, et al.. “High gamma power is phase-locked to theta oscillations in human neocortex”, Science, vol. 313, no. 5793, pp. 1626-1628, 2006.
 
Show Less References