{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:39:45Z","timestamp":1753889985633,"version":"3.41.2"},"reference-count":41,"publisher":"IMR Press","issue":"3","license":[{"start":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T00:00:00Z","timestamp":1601424000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Integr. Neurosci."],"abstract":"<jats:p>The purpose is to estimate the effectiveness of electrocardiograms during resting and active participation by the differentiation between the electrical activity of the heart while standing and sitting in a resting state. The concern is to identify the electrocardiogram parameters that did not show significant changes within these positions. The electrocardiogram parameters can be considered to be a standard marker for medically compromised patients. The electrocardiogram is recorded in the standing and sitting positions focusing on healthy participants using standard electrode placement of lead-I. Combined lead-I patterns (camel-hump or ST-segment prolongation) are usually seen in neurologic injury or hypothermia patients. The pairwise comparisons of a year data are about 454,400 cycles of sitting and 493,470 cycles of standing data. Thus, it is essential to quantify the nature and magnitude of changes seen in the electrocardiogram with a change of posture from sitting to standing in a healthy individual. This makes the findings of electrocardiogram analysis in this paper interesting in which some parameters (i.e., camel-hump patterns in lead-I) are helpful for clinical interpretations and could be suggestive of neurologic injury.<\/jats:p>","DOI":"10.31083\/j.jin.2020.03.222","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T01:38:25Z","timestamp":1602639505000},"source":"Crossref","is-referenced-by-count":1,"title":["Clinical interpretations of the effectiveness of changes in body position during aerobic fitness after neurologic injury"],"prefix":"10.31083","volume":"19","author":[{"given":"Nur Fariza","family":"Izan","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering and Health Science, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheikh Hussain","family":"Salleh","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering and Health Science, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia"},{"name":"Centre for Biomedical engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia"},{"name":"Health Education and Lifestyle (HEAL ULTRA), 81300, Johor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chee-Ming","family":"Ting","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering and Health Science, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia"},{"name":"School of Information Technology, Monash University Malaysia, 47500, Bandar Sunway, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuad","family":"Noman","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering and Health Science, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadrina","family":"Sh-Hussain","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering and Health Science, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman R.","family":"Poznanski","sequence":"first","affiliation":[{"name":"Faculty of Computing and Informatics, Universiti Sultan Zainal Abidin, 22200, Beset, Terengganu, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u202aAhmad Zubaidi Abdul","family":"Latif","sequence":"first","affiliation":[{"name":"Neurosurgery, Faculty of Medicine, Universiti Sultan Zainal Abidin, Medical Campus, 20400, Kuala Terengganu, Terengganu, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16242","published-online":{"date-parts":[[2020,9,30]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Adams, M. 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(2016) ECG segmentation and fiducial point extraction using multi hidden Markov model. Computers in Biology and Medicine 79, 21-29.","DOI":"10.1016\/j.compbiomed.2016.09.004"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Bacharova, L., Szathmary, V., Potse, M. and Mateasik, A. (2013) Computer simulation of ECG manifestations of left ventricular electrical remodeling. Journal of Electrocardiology 45, 630-634.","DOI":"10.1016\/j.jelectrocard.2012.07.009"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Bacharova, L., Szathmary, V., Svehlikova, J., Mateasik, A. and Tysler, M. (2016) QRS complex waveform indicators of ventricular activation slowing: Simulation studies. Journal of Electrocardiology 49, 790-793.","DOI":"10.1016\/j.jelectrocard.2016.07.032"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Bashir, S., Bakhshi, A. D. and Maud, M. A. (2014) A template matched-filter based scheme for detection and estimation of t-wave alternans. 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(2005) Removal of power-line interference from the ECG: a review of the subtraction procedure. Biomedical Engineering Online 4, 50.","DOI":"10.1186\/1475-925X-4-50"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Lin, C., Mailhes, C. and Tourneret, J. (2010) P- and T-wave delineation in ECG signals using a bayesian approach and a partially collapsed Gibbs sampler. IEEE Transactions on Biomedical Engineering 57, 2840-2849.","DOI":"10.1109\/TBME.2010.2076809"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Lyon, A., Minchol\u00e9, A., Mart\u00ednez, J. P., Laguna, P. and Rodriguez, B. (2018) Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. Journal of the Royal Society Interface 15, 20170821.","DOI":"10.1098\/rsif.2017.0821"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Madeiro, J. P. V., Nicolson, W. B., Cortez, P. C., Marques, J. A. L., V\u00e1zquez-Seisdedos, C. 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Biomedical Signal Processing and Control 10, 41-49.","DOI":"10.1016\/j.bspc.2013.12.007"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"Thamizhvani, T. R., Joseph, J. E., Babu, B., Rithikka, U., Rohini, D. and Dhivya, A. J. (2018) Comparison of normal and abnormal conditions in ECG using RR variability and spectral density. International Journal of Engineering and Technology 7, 28.","DOI":"10.14419\/ijet.v7i2.25.12362"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Yochum, M., Renaud, C. and Jacquir, S. (2016) Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT. Biomedical Signal Processing and Control 25, 46-52.","DOI":"10.1016\/j.bspc.2015.10.011"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Zhu, H. and Dong, J. (2013) An R-peak detection method based on peaks of Shannon energy envelope. 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