Matthew W. Kay

3.7k citations
87 papers · 1.9k indexed · h-index 28

Matthew W. Kay

85 papers receiving 1.9k citations

Peers

Matthew W. Kay
Comparison fields: 5 of 138
  • Cardiology and Cardiovascular Medicine 848
  • Endocrine and Autonomic Systems 175
  • Cellular and Molecular Neuroscience 419
  • Biophysics 105
  • Health, Toxicology and Mutagenesis 245
Replace Heimo Ehmke with:
Heimo Ehmke Germany
Caryl E. Hill Australia
Kyoichi Ono Japan
Joshua I. Goldhaber United States
Vadim V. Fedorov United States
Oscar U. Scremin United States
Kathryn A. Yamada United States
Lai‐Hua Xie United States
Vladimir V. Matchkov Denmark
Kassandra Kisler United States
Matthew W. Kay relative to Heimo Ehmke Germany Heimo Ehmke's profile →
Citations per field
00.5×6.6×
Heimo Ehmke · 1×
Citations per year

Countries citing papers authored by Matthew W. Kay

Since Specialization
Citations

This map shows the geographic impact of Matthew W. Kay'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 Matthew W. Kay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew W. Kay more than expected).

Fields of papers citing papers by Matthew W. Kay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew W. Kay. 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 Matthew W. Kay. The network helps show where Matthew W. Kay may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Matthew W. Kay, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Matthew W. Kay Line = papers co-authored together Matthew W. Kay links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20251
3 202413
4 20242
5 20232
6 202216
7 2020108
8 202021
9 201931
10 20177
11 201537
12 201465
13 20137
14 20121
15 201287
16 20087
17 200815
18 200610
19 200629
20 200646

About Matthew W. Kay

Matthew W. Kay is a scholar working on Endocrine and Autonomic Systems, Cardiology and Cardiovascular Medicine and Biophysics, having authored 87 papers that have together received 1.9k indexed citations. Recurring topics across this work include Cardiac electrophysiology and arrhythmias (31 papers), Neuroscience and Neural Engineering (15 papers), Neuroscience of respiration and sleep (13 papers), Advanced MRI Techniques and Applications (12 papers), Heart Rate Variability and Autonomic Control (10 papers), Cardiac Arrhythmias and Treatments (9 papers), Neuroendocrine regulation and behavior (8 papers) and Cardiac Ischemia and Reperfusion (8 papers). The work is most often cited by research in Cardiology and Cardiovascular Medicine (848 citations), Endocrine and Autonomic Systems (175 citations) and Cellular and Molecular Neuroscience (419 citations). Matthew W. Kay has collaborated with scholars based in United States, Canada and Chile. Frequent co-authors include Narine Sarvazyan, Luther Swift, Nikki Gillum Posnack, Rafael Jaimes, Jack M. Rogers, Emilia Entcheva, Sarah Kuzmiak‐Glancy, David Mendelowitz, Huda Asfour and Sharon B. Melnick. Their work appears in journals such as PLoS ONE, Circulation Research and The Journal of Physiology.

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.

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