Daniel Suárez Hernández | Data Science | Research Excellence Award

Published on by Global CSE Awards

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]

References

  1. A Spectral Approach to Evaluating PPG Signal Quality Under Ambient Light Interference.
    https://doi.org/10.1007/978-3-032-24045-3_17
  2. 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
  3. Context-Aware Wavelet Denoising of PPG Signals Using Clustered Accelerometer Features.
    https://doi.org/10.1007/978-3-032-24045-3_18

Daniel Suárez | Data Science | Excellence in Data Science Achievement Award

Published on by Global CSE Awards

Excellence in Data Science Achievement Award

Daniel Suárez
Affiliation Conselleria de Sanidad
Country Spain
Subject Area Data Science
Scopus ID 7102276862
Documents 32
Citations 1,361
h-index 13
ORCID 0000-0002-1273-5077
Event Global CSE Awards

Daniel Suárez is a researcher affiliated with Conselleria de Sanidad, Spain, whose supplied academic profile is associated with Data Science. His research profile includes work connected to biomedical signal analysis, wearable monitoring technologies, and data-driven methods for improving the interpretation of physiological measurements. The supplied publication references focus on photoplethysmography (PPG) signal quality assessment, heartbeat detection in wearable PPG systems, and wavelet-based denoising using accelerometer information. These topics demonstrate the relevance of data science techniques to healthcare technology, physiological signal processing, and intelligent health-monitoring systems. [1] [2] [3]

Abstract

This article presents the academic research profile of Daniel Suárez, affiliated with Conselleria de Sanidad, Spain, with Data Science as the stated subject area. Data Science combines statistical analysis, computational methods, and data-driven techniques to extract meaningful information from complex datasets. In healthcare, these methods are particularly valuable for analysing physiological signals, supporting wearable monitoring systems, and improving the reliability of health-related measurements. The supplied Scopus profile reports 32 documents, 1,361 citations, and an h-index of 13. [1] [2] [3]

Keywords

Data Science;Biomedical Data Analysis; Photoplethysmography; PPG Signals; Signal Processing; Wearable Health Monitoring; Heartbeat Detection; Wavelet Denoising; Accelerometer Features; Healthcare Technology; Physiological Signal Analysis; Digital Health; Scopus; Research Impact.

Introduction

Data Science has become an important discipline for extracting knowledge from complex information and supporting evidence-based decisions across scientific and professional fields. By combining statistical methods, computational techniques, machine learning, and data visualisation, it enables researchers to identify patterns, evaluate relationships, and develop practical solutions to real-world challenges. In healthcare, data science plays a growing role in analysing patient information, interpreting physiological measurements, and supporting the development of intelligent monitoring technologies.[1] [2] [3]

Research Profile

Daniel Suárez is affiliated with Conselleria de Sanidad in Spain. The supplied academic profile identifies Data Science as the subject area and includes a Scopus author identifier and an ORCID record for researcher identification. The Scopus profile provides bibliometric information, including document count, citation count, and h-index, while the ORCID identifier supports the identification of scholarly activities across academic platforms.According to the supplied Scopus information, the research profile includes 32 documents, 1,361 citations, and an h-index of 13. These indicators provide a quantitative overview of the bibliometric information supplied for the researcher.[1] [2] [3]

Research Contributions

Data Science contributes to biomedical research by providing methods for analysing complex physiological datasets, identifying meaningful patterns, and improving the reliability of measurement systems. In wearable healthcare technology, data-driven signal processing can help address challenges associated with noisy measurements, environmental interference, and variations in physiological signals. The supplied publications provide examples of research themes involving PPG signal assessment, heartbeat detection, and signal denoising.[1] [2] [3]

Publications

The following three references were supplied for this academic profile. Their titles and DOI links are reproduced as provided. The works address PPG signal quality assessment, heartbeat detection in wearable monitoring systems, and context-aware denoising of physiological signals using accelerometer features.Context-aware analysis can help distinguish movement-related artefacts from physiological signal patterns, supporting more robust processing approaches. These research directions are relevant to the continuing development of wearable devices, remote monitoring systems, and data-driven healthcare applications. The supplied publications provide context for these broader areas of research.[1] [2] [3]

Research Impact

Data science methods applied to biomedical signals can contribute to more reliable health-monitoring technologies and improved interpretation of physiological measurements. Signal quality assessment helps identify conditions that may affect the accuracy of sensor readings. Reliable heartbeat detection supports the extraction of cardiovascular information, while denoising methods can reduce interference and improve the usability of wearable PPG data.[1] [2] [3]

Award Suitability

The Excellence in Data Science Achievement Award recognizes research and academic contributions that demonstrate the effective use of data-driven methods, analytical techniques, and computational approaches to address important challenges. Daniel Suárez’s supplied profile identifies Conselleria de Sanidad, Spain, as the institutional affiliation and Data Science as the subject area. The provided Scopus information includes 32 documents, 1,361 citations, and an h-index of 13.[1] [2] [3]

Conclusion

Daniel Suárez’s academic profile is associated with Conselleria de Sanidad, Spain, and the research subject of Data Science. The supplied Scopus information reports 32 documents, 1,361 citations, and an h-index of 13. The provided publications address spectral evaluation of PPG signal quality under ambient light interference, learning-driven heartbeat detection in wearable PPG systems, and context-aware wavelet denoising using accelerometer features. These research topics highlight the importance of data-driven methods in biomedical signal processing and wearable healthcare technologies. The Scopus and ORCID profiles provide additional researcher identification resources for academic reference. [1] [2] [3]

References

  1. A Spectral Approach to Evaluating PPG Signal Quality Under Ambient Light Interference.
    https://doi.org/10.1007/978-3-032-24045-3_17
  2. 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
  3. Context-Aware Wavelet Denoising of PPG Signals Using Clustered Accelerometer Features.
    https://doi.org/10.1007/978-3-032-24045-3_18