Biomedical Signal Analysis: A Case‐Study Approach. Author(s). Rangaraj M. Rangayyan. First published December Print ISBN ENEL BIOMEDICAL SIGNAL ANALYSIS Rangaraj M. Rangayyan. Pages·· MB· Downloads. 3. 0. 1 s(n). Time in seconds. ENEL BIOMEDICAL SIGNAL ANALYSIS Rangaraj M. Rangayyan. Pages · Downloads·New! ronaldweinland.info Food Mark Hyman.
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and my father Sri Srinivasan Mandayam Rangayyan, and to all of my field of biomedical signal analysis has advanced to the stage of practical application of signal likelihood function of class Ci or state-conditional PDF of 2 pulses per . –1– cс R.M. Rangayyan, IEEE/Wiley biomedical signal processing and analysis. –3– cс R.M. Random process η characterized by PDF pη(η). Mean µη. Biomedical Signal Analysis, 2. Editor(s). Rangaraj M. Rangayyan. First published April Print ISBN |Online ISBN:
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About this book The development of techniques to analyze biomedical signals, such as electro-cardiograms, has dramatically affected countless lives by making possible improved noninvasive diagnosis, online monitoring of critically ill patients, and rehabilitation and sensory aids for the handicapped. Rangaraj Rangayyan supplies a practical, hands-on field guide to this constantly evolving technology in Biomedical Signal Analysis , focusing on the diagnostic challenges that medical professionals continue to face.
Rangayyan applies a problem-solving approach to his study. Each chapter begins with the statement of a different biomedical signal problem, followed by a selection of real-life case studies and the associated signals.
Signal processing, modeling, or analysis techniques are then presented, starting with relatively simple "textbook" methods, followed by more sophisticated research approaches. The chapter concludes with one or more application solutions; illustrations of real-life biomedical signals and their derivatives are included throughout.
Computer-aided analysis of knee joint VAG signals is very useful for screening and monitoring of articular cartilage disorders at an early stage [ 11 — 13 ]. Based on the noninvasive detection results, the computational algorithms may effectively help the medical experts make an accurate decision, so that the frequency of the diagnostic open surgery with arthroscope can be reduced [ 8 , 14 — 16 ].
Adaptive filtering techniques based on the least-mean-square LMS and recursive least-squares lattice RLSL algorithms were used to remove muscle contraction interference present in VAG signals [ 17 ]. Tavathia et al.
Jiang et al. In order to simplify the procedures of signal processing and decision making, Rangayyan and Wu [ 11 , 12 ] proposed the statistical parameters including form factors, skewness, kurtosis, probability density function entropy, variance of mean-squared values, and turns count with adaptive threshold, for the screening of VAG signals based on the radial-basis function network RBFN.
Mu et al. In this paper, the number of atoms derived from the wavelet MP decomposition and the turns count detected with the fixed threshold in the waveform variability analysis are extracted as features, and a classifier fusion system based on the dynamic weighted fusion DWF method is proposed for the classification of the VAG signals.
Each subject was requested to sit on a rigid table in a relaxed position with the leg being tested freely and suspended in midair. The knee joint vibration was measured by placing a miniature accelerometer at the middle position of the patella [ 8 ].
Each VAG signal was conditioned by an isolation preamplifiers to prevent the aliasing effects.
Auscultation of the knee joint using a stethoscope was also performed, and a qualitative description of the sound intensity and type was recorded. In the present study, we used a total of 89 VAG signals recorded from 51 healthy volunteers and 38 subjects with knee joint pathologies , the same as investigated in a few previous related studies [ 11 , 12 , 14 ].