The DOI matches its Crossref record.
Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network
Abstract
We have developed a method to discriminate life-threatening ventricular arrhythmias by observing the QRS complex of the electrocardiogram (ECG) in each heartbeat. Changes in QRS complexes due to rhythm origination and conduction path were observed with the Fourier transform, and three kinds of rhythms were discriminated by a neural network. In this paper, the potential of our method for clinical uses and real-time detection was examined using human surface ECG's and intracardiac electrograms (EGM's). The method achieved high sensitivity and specificity (> or = 0.98) in discrimination of supraventricular rhythms from ventricular ones. We also present a hardware implementation of the algorithm on a commercial single-chip CPU.
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
Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals
Ye, C., Kumar, B. V. K. V., & Coimbra, M. T. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2213253 · Cited by 522
Convolutional Neural Networks for patient-specific ECG classification
Kiranyaz, S., Ince, T., Hamila, R., & Gabbouj, M. (2015) · 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318926 · Cited by 309
Explainable AI for time series via Virtual Inspection Layers
Vielhaben, J., Lapuschkin, S., Montavon, G., & Samek, W. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110309 · Cited by 38
Metadata
- Authors
- Minami, K., Nakajima, H., & Toyoshima, T.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 1999
- Volume
- 46
- Issue
- 2
- Pages
- 179-185
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2117624000
- Cited by
- 465
- References
- 0
- FWCI
- 2.26
- Type
- journal article
- 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: Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. https://app.cochat.ai/papers/10.1109/10.740880
- The paper
- Minami, K., Nakajima, H., & Toyoshima, T. (1999). Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.740880
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.