Fernando Andreotti

2.8k total citations · 2 hit papers
27 papers, 1.9k citations indexed

About

Fernando Andreotti is a scholar working on Cognitive Neuroscience, Cardiology and Cardiovascular Medicine and Biomedical Engineering. According to data from OpenAlex, Fernando Andreotti has authored 27 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Cognitive Neuroscience, 13 papers in Cardiology and Cardiovascular Medicine and 6 papers in Biomedical Engineering. Recurrent topics in Fernando Andreotti's work include EEG and Brain-Computer Interfaces (14 papers), ECG Monitoring and Analysis (11 papers) and Sleep and Wakefulness Research (6 papers). Fernando Andreotti is often cited by papers focused on EEG and Brain-Computer Interfaces (14 papers), ECG Monitoring and Analysis (11 papers) and Sleep and Wakefulness Research (6 papers). Fernando Andreotti collaborates with scholars based in United Kingdom, Germany and United States. Fernando Andreotti's co-authors include Maarten De Vos, Navin Cooray, Huy Phan, Oliver Y. Chén, Sebastian Zaunseder, Julien Oster, Joachim A. Behar, Gari D. Clifford, Hagen Malberg and Christine Lo and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Fernando Andreotti

27 papers receiving 1.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

2019 375 citations Huy Phan, Fernando Andreotti et al. IEEE Transactions on Neural Systems and Rehabilitation Engineering profile →
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

2018 329 citations Huy Phan, Fernando Andreotti et al. IEEE Transactions on Biomedical Engineering profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Fernando Andreotti United Kingdom	16	1.2k	733	465	412	349	27	1.9k
Alpo Värri Finland	22	1.1k 0.9×	555 0.8×	227 0.5×	637 1.5×	310 0.9×	105	1.9k
H. Nazeran United States	25	654 0.6×	760 1.0×	343 0.7×	872 2.1×	215 0.6×	108	2.0k
Luay Fraiwan Jordan	22	567 0.5×	206 0.3×	335 0.7×	291 0.7×	117 0.3×	64	1.7k
Pedro Fonseca Netherlands	24	859 0.7×	556 0.8×	144 0.3×	857 2.1×	673 1.9×	95	1.7k
Mohammad Bagher Shamsollahi Iran	25	1.4k 1.2×	1.7k 2.4×	795 1.7×	916 2.2×	58 0.2×	132	2.7k
Carolina Varon Belgium	21	614 0.5×	772 1.1×	98 0.2×	758 1.8×	465 1.3×	129	1.6k
Oh Shu Lih Singapore	12	1.0k 0.9×	1.3k 1.8×	123 0.3×	537 1.3×	54 0.2×	14	2.1k
Yaniv Zigel Israel	19	231 0.2×	251 0.3×	442 1.0×	487 1.2×	511 1.5×	72	1.5k
Sebastian Zaunseder Germany	20	527 0.5×	1.2k 1.6×	295 0.6×	799 1.9×	33 0.1×	78	1.8k
Manuel Blanco–Velasco Spain	22	343 0.3×	1.1k 1.6×	806 1.7×	620 1.5×	312 0.9×	79	2.2k

Countries citing papers authored by Fernando Andreotti

Since Specialization

Citations

This map shows the geographic impact of Fernando Andreotti's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fernando Andreotti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando Andreotti more than expected).

Fields of papers citing papers by Fernando Andreotti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fernando Andreotti. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fernando Andreotti. The network helps show where Fernando Andreotti may publish in the future.

Co-authorship network of co-authors of Fernando Andreotti

This figure shows the co-authorship network connecting the top 25 collaborators of Fernando Andreotti. A scholar is included among the top collaborators of Fernando Andreotti based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fernando Andreotti. Fernando Andreotti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Heldt, Frank S., Marcela P. Vizcaychipi, Fernando Andreotti, et al.. (2021). Early risk assessment for COVID-19 patients from emergency department data using machine learning. Scientific Reports. 11(1). 4200–4200. 63 indexed citations

Cooray, Navin, et al.. (2021). Proof of concept: Screening for REM sleep behaviour disorder with a minimal set of sensors. Clinical Neurophysiology. 132(4). 904–913. 11 indexed citations

Vinci, Ramona, Daniela Pedicino, Fernando Andreotti, et al.. (2020). From angiotensin-converting enzyme 2 disruption to thromboinflammatory microvascular disease: A paradigm drawn from COVID-19. International Journal of Cardiology. 326. 243–247. 14 indexed citations

Phan, Huy, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, & Maarten De Vos. (2019). SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 27(3). 400–410. 375 indexed citations breakdown →

Cooray, Navin, et al.. (2019). Detection of REM sleep behaviour disorder by automated polysomnography analysis. Clinical Neurophysiology. 130(4). 505–514. 60 indexed citations

Andreotti, Fernando, et al.. (2019). Low-Cost IoT Surveillance System Using Hardware-Acceleration and Convolutional Neural Networks. 931–936. 10 indexed citations

Phan, Huy, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, & Maarten De Vos. (2018). Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification. IEEE Transactions on Biomedical Engineering. 66(5). 1285–1296. 329 indexed citations breakdown →

Andreotti, Fernando, Huy Phan, & Maarten De Vos. (2018). Visualising Convolutional Neural Network Decisions in Automatic Sleep Scoring. Kent Academic Repository (University of Kent). 70–81. 15 indexed citations

Phan, Huy, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, & Maarten De Vos. (2018). DNN Filter Bank Improves 1-Max Pooling CNN for Single-Channel EEG Automatic Sleep Stage Classification. PubMed. 2018. 453–456. 66 indexed citations

10.

Prince, John, Fernando Andreotti, & Maarten De Vos. (2018). Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data. IEEE Transactions on Biomedical Engineering. 66(5). 1402–1411. 59 indexed citations

11.

Phan, Huy, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, & Maarten De Vos. (2018). Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks. PubMed. 2018. 1452–1455. 99 indexed citations

12.

Behar, Joachim A., et al.. (2017). The use of non-invasive fetal electrocardiography in diagnosing second-degree fetal atrioventricular block. SHILAP Revista de lepidopterología. 3(1). 14–14. 7 indexed citations

13.

Andreotti, Fernando, et al.. (2017). Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG. Computing in cardiology. 44. 124 indexed citations

14.

Andreotti, Fernando, Felix Gräßer, Hagen Malberg, & Sebastian Zaunseder. (2017). Non-invasive Fetal ECG Signal Quality Assessment for Multichannel Heart Rate Estimation. IEEE Transactions on Biomedical Engineering. 64(12). 2793–2802. 55 indexed citations

15.

Behar, Joachim A., Fernando Andreotti, Sebastian Zaunseder, Julien Oster, & Gari D. Clifford. (2016). A practical guide to non-invasive foetal electrocardiogram extraction and analysis. Physiological Measurement. 37(5). R1–R35. 105 indexed citations

16.

Andreotti, Fernando, Joachim A. Behar, Sebastian Zaunseder, Julien Oster, & Gari D. Clifford. (2016). An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms. Physiological Measurement. 37(5). 627–648. 135 indexed citations

17.

Johnson, Alistair E. W., Joachim A. Behar, Fernando Andreotti, Gari D. Clifford, & Julien Oster. (2015). Multimodal heart beat detection using signal quality indices. Physiological Measurement. 36(8). 1665–1677. 78 indexed citations

18.

Andreotti, Fernando, Maik Riedl, Daniel Wedekind, et al.. (2014). Robust fetal ECG extraction and detection from abdominal leads. Physiological Measurement. 35(8). 1551–1567. 81 indexed citations

19.

Behar, Joachim A., Fernando Andreotti, Sebastian Zaunseder, et al.. (2014). An ECG simulator for generating maternal-foetal activity mixtures on abdominal ECG recordings. Physiological Measurement. 35(8). 1537–1550. 79 indexed citations

20.

Andreotti, Fernando, Maik Riedl, Daniel Wedekind, et al.. (2013). Maternal signal estimation by Kalman filtering and Template Adaptation for fetal heart rate extraction. OPUS (Augsburg University). 193–196. 25 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact