The DOI matches its Crossref record.
Biomedical Signal Analysis
Abstract
The book will help assist a reader in the development of techniques for analysis of biomedical signals and computer aided diagnoses with a pedagogical examination of basic and advanced topics accompanied by over 350 figures and illustrations. Wide range of filtering techniques presented to address various applications. 800 mathematical expressions and equations. Practical questions, problems and laboratory exercises. Includes fractals and chaos theory with biomedical applications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System
Haag, A., Goronzy, S., Schaich, P., & Williams, J. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24842-2_4 · Cited by 451
Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains
Kabir, M. A., & Shahnaz, C. (2012) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2011.11.003 · Cited by 444
Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction
Sharma, L. N., Tripathy, R. K., & Dandapat, S. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2405134 · Cited by 296
Cardiac arrhythmia detection using deep learning
Isin, A., & Ozdalili, S. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.11.238 · Cited by 267
Autonomic specificity of basic emotions: Evidence from pattern classification and cluster analysis
Stephens, C. L., Christie, I. C., & Friedman, B. H. (2010) · Biological Psychology · doi:10.1016/j.biopsycho.2010.03.014 · Cited by 209
Metadata
- Authors
- Rangayyan, R. M.
- Venue
- Institute of Electrical and Electronics Engineers eBooks
- Publisher
- IEEE
- Published
- 2001
- OpenAlex
- W2476797758
- Cited by
- 422
- References
- 0
- FWCI
- 3.38
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Biomedical Signal Analysis. https://app.cochat.ai/papers/10.1109/9780470544204
- The paper
- Rangayyan, R. M. (2001). Biomedical Signal Analysis. Institute of Electrical and Electronics Engineers eBooks. https://doi.org/10.1109/9780470544204
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
