Research Excellence Award
| Daniel Suárez Hernández |
| Affiliation |
Hospital Orihuela |
| Country |
Spain |
| Subject Area |
Data Science |
| Scopus Profile |
7102276862 |
| Documents |
33 |
| Citations |
1361 |
| h-index |
13 |
| ORCID |
ORCID Profile |
| Event |
Global CSE Awards |
Daniel Suárez Hernández is a researcher affiliated with Hospital Orihuela, Spain, whose stated academic subject area is Data Science. Data Science involves the use of analytical methods, computational techniques, and data-driven approaches to identify patterns, interpret information, and support evidence-based decision-making. The supplied publications associated with his research profile focus on photoplethysmography (PPG) signal quality, reliable heartbeat detection in wearable PPG signals, and wavelet-based denoising using accelerometer features. [1] [2] [3]
Abstract
This article presents the academic research profile of Daniel Suárez Hernández, affiliated with Hospital Orihuela, Spain, in the subject area of Data Science. The supplied research publications address the analysis and processing of photoplethysmography (PPG) signals, with particular attention to signal quality under ambient light interference, reliable heartbeat detection in wearable devices, and signal denoising through wavelet methods and clustered accelerometer features. According to the supplied Scopus information, the researcher has 33 documents, 1,361 citations, and an h-index of 13. [1] [2] [3]
Keywords
Data Science; Photoplethysmography; PPG Signals; Signal Processing; Wearable Health Monitoring; Heartbeat Detection; Signal Quality Assessment; Ambient Light Interference; Wavelet Denoising; Accelerometer Features; Physiological Signals; Data Analysis; Machine Learning; Learning-Driven Methods; Biomedical Signal Processing; Wearable Technology.
Introduction
Data Science is an interdisciplinary field that combines data analysis, computational methods, statistical techniques, and information processing to extract meaningful insights from complex datasets. It supports research across multiple domains by providing methods to identify patterns, improve analytical accuracy, and develop data-driven solutions. In healthcare-related applications, Data Science can contribute to the analysis of physiological signals and the development of methods for monitoring and interpreting health-related information. [1] [2] [3]
Research Profile
Daniel Suárez Hernández is affiliated with Hospital Orihuela, Spain. His stated subject area is Data Science, and the supplied publication titles indicate research interests involving photoplethysmography signal analysis, wearable monitoring, heartbeat detection, and signal-processing techniques. The publications provided for this profile focus on methods for assessing PPG signal quality and improving the reliability of physiological signal interpretation.The supplied Scopus details indicate 33 scholarly documents, 1,361 citations, and an h-index of 13.[1] [2] [3]
Research Contributions
One of the supplied publications examines a spectral approach to evaluating PPG signal quality under ambient light interference. Ambient light can affect optical measurements and introduce interference into photoplethysmography recordings. Evaluating signal quality under these conditions is important for understanding the reliability of collected data. Collectively, the supplied publications cover three connected aspects of PPG signal analysis: signal quality evaluation, heartbeat detection, and noise reduction. [1] [2] [3]
Publications
The following publications were provided as references for the research profile of Daniel Suárez Hernández. These works address photoplethysmography signal quality, heartbeat detection in wearable PPG, and wavelet-based signal denoising using accelerometer features. [1] [2] [3]
Research Impact
Research into PPG signal quality contributes to the understanding of how external conditions influence optical physiological measurements. Ambient light interference can affect the characteristics of recorded signals, making signal quality evaluation an important part of wearable monitoring research. Methods for assessing signal quality can help researchers examine the reliability of collected measurements and understand the conditions under which PPG signals can be interpreted.Together, the three publications provide research perspectives on signal quality, heartbeat detection, and denoising in wearable PPG applications. [1] [2] [3]
Award Suitability
The Research Excellence Award recognizes researchers whose academic work demonstrates research productivity, scholarly contribution, subject expertise, and engagement with the advancement of knowledge. In Data Science, relevant research contributions may include the development and application of analytical methods, computational techniques, data-processing approaches, and innovative solutions to complex research problems. Daniel Suárez Hernández’s supplied academic profile identifies his affiliation with Hospital Orihuela, Spain, and his subject area as Data Science. The provided Scopus information records 33 documents, 1,361 citations, and an h-index of 13. [1] [2] [3]
Conclusion
Daniel Suárez Hernández’s academic profile is associated with Hospital Orihuela, Spain, and the subject area of Data Science. The supplied Scopus information includes 33 documents, 1,361 citations, and an h-index of 13. The provided research publications examine spectral evaluation of PPG signal quality under ambient light interference, template-based and learning-driven heartbeat detection in wearable PPG, and context-aware wavelet denoising using clustered accelerometer features. [1] [2] [3]
External Links
References
- A Spectral Approach to Evaluating PPG Signal Quality Under Ambient Light Interference.
https://doi.org/10.1007/978-3-032-24045-3_17
- A Template-Based and Learning-Driven Method for Reliable Heartbeat Detection in Wearable PPG.
https://doi.org/10.1007/978-3-032-24045-3_21
- Context-Aware Wavelet Denoising of PPG Signals Using Clustered Accelerometer Features.
https://doi.org/10.1007/978-3-032-24045-3_18