S. Raghu

1.4k citations

21 papers · 1.1k indexed · 1 hit paper · h-index 14

Cognitive Neuroscience top 2%
Signal Processing top 1%
Cardiology and Cardiovascular Medicine top 10%
Artificial Intelligence top 10%
Psychiatry and Mental health top 10%

Co-authors: N. Sriraam Pieter Kubben Shyam Vasudeva Rao Yasin Temel Alangar S. Hegde Mark Elshaw Erik D. Gommer Mukundhan Srinivasan
Topics: EEG and Brain-Computer Interfaces (19 papers)Blind Source Separation Techniques (16 papers)Epilepsy research and treatment (7 papers)
Cited by: Signal Processing Cognitive Neuroscience Neurology
Journals: Expert Systems with Applications IEEE Transactions on Biomedical Engineering Neurocomputing
Partner nations: India Netherlands United Kingdom

In The Last Decade

S. Raghu

20 papers receiving 1.1k citations

Hit Papers

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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.

EEG based multi-class seizure type classification using convolutional neural network and transfer learning

2020 266 citations S. Raghu, N. Sriraam et al. Neural Networks profile →

Peers

Countries citing papers authored by S. Raghu

Since Specialization

Citations

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

Fields of papers citing papers by S. Raghu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. Raghu. 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 S. Raghu. The network helps show where S. Raghu may publish in the future.

Co-authorship network of co-authors of S. Raghu

This figure shows the co-authorship network connecting the top 25 collaborators of S. Raghu. A scholar is included among the top collaborators of S. Raghu 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 S. Raghu. S. Raghu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation 2023 ·PubMed ·N. Sriraam,S. Raghu,Erik D. Gommer,(unknown),Yasin Temel,Shyam Vasudeva Rao,(unknown),Pieter Kubben	1
2	Complexity analysis and dynamic characteristics of EEG using MODWT based entropies for identification of seizure onset 2020 ·Journal of Biomedical Research ·S. Raghu,N. Sriraam,Yasin Temel,Shyam Vasudeva Rao,(unknown),Pieter Kubben	10
3	Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction 2020 ·Clinical Neurophysiology ·S. Raghu,N. Sriraam,Erik D. Gommer,(unknown),Yasin Temel,Shyam Vasudeva Rao,Alangar S. Hegde,Pieter Kubben	13
4	EEG based multi-class seizure type classification using convolutional neural network and transfer learningbreakdown → 2020 ·Neural Networks ·S. Raghu,N. Sriraam,Yasin Temel,Shyam Vasudeva Rao,Pieter Kubben	266
5	Adaptive median feature baseline correction for improving recognition of epileptic seizures in ICU EEG 2020 ·Neurocomputing ·S. Raghu,N. Sriraam,Erik D. Gommer,(unknown),Yasin Temel,Shyam Vasudeva Rao,Pieter Kubben	8
6	Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier 2019 ·Computers in Biology and Medicine ·S. Raghu,N. Sriraam,Yasin Temel,Shyam Vasudeva Rao,Alangar S. Hegde,Pieter Kubben	83
7	Automated detection of epileptic seizures using DWT based features and SVM classifier 2019 ·Research Publications (Maastricht University) ·N. Sriraam,S. Raghu,Yasin Temel,(unknown),(unknown),(unknown),Pieter Kubben	2
8	Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG 2019 ·Neural Computing and Applications ·S. Raghu,N. Sriraam,Shyam Vasudeva Rao,(unknown),Pieter Kubben	48
9	A convolutional neural network based framework for classification of seizure types 2019 ·PubMed ·S. Raghu,N. Sriraam,Yasin Temel,Shyam Vasudeva Rao,Pieter Kubben	35
10	Multichannel EEG based inter-ictal seizures detection using Teager energy with backpropagation neural network classifier 2018 ·Australasian Physical & Engineering Sciences in Medicine ·N. Sriraam,(unknown),(unknown),(unknown),S. Raghu,(unknown),(unknown)	14
11	Automated epileptic seizures detection using multi-features and multilayer perceptron neural network 2018 ·Brain Informatics ·N. Sriraam,S. Raghu,(unknown),(unknown),(unknown),(unknown),(unknown)	32
12	Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms 2018 ·Expert Systems with Applications ·S. Raghu,N. Sriraam	172
13	A Novel Approach for Real-Time Recognition of Epileptic Seizures Using Minimum Variance Modified Fuzzy Entropy 2018 ·IEEE Transactions on Biomedical Engineering ·S. Raghu,N. Sriraam,(unknown),Alangar S. Hegde	47
14	Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier 2017 ·Journal of Medical Systems ·N. Sriraam,S. Raghu	78
15	Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures 2017 ·Expert Systems with Applications ·S. Raghu,N. Sriraam	72
16	Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier 2016 ·Cognitive Neurodynamics ·S. Raghu,N. Sriraam,(unknown)	66
17	Features ranking for the classification of epileptic seizure from temporal EEG 2016 ·S. Raghu,N. Sriraam,(unknown)	8
18	Effect of Wavelet Packet Log Energy Entropy on Electroencephalogram (EEG) Signals 2015 ·S. Raghu,N. Sriraam,(unknown)	16
19	Comparative Study on Hidden Markov Model Versus Support Vector Machine: A Component-Based Method for Better Face Recognition 2013 ·Mukundhan Srinivasan,S. Raghu	1
20	Emotional recognition from the speech signal for a virtual education agent 2013 ·Journal of Physics Conference Series ·(unknown),S. Raghu,Mark Elshaw	16

About S. Raghu

S. Raghu is a scholar working on Signal Processing, Cognitive Neuroscience and Psychiatry and Mental health, having authored 21 papers that have together received 1.1k indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (19 papers), Blind Source Separation Techniques (16 papers) and Epilepsy research and treatment (7 papers). The work is most often cited by research in Signal Processing (468 citations), Cognitive Neuroscience (812 citations) and Neurology (93 citations). S. Raghu has collaborated with scholars based in India, Netherlands and United Kingdom. Frequent co-authors include N. Sriraam, Pieter Kubben, Shyam Vasudeva Rao, Yasin Temel, Alangar S. Hegde, Mark Elshaw, Erik D. Gommer and Mukundhan Srinivasan. Their work appears in journals such as Expert Systems with Applications, IEEE Transactions on Biomedical Engineering and Neurocomputing.

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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