Lecturer at University of Plymouth, United Kingdom.
Dr. Om Prakash Singh is a biomedical engineer and researcher specializing in respiratory monitoring systems, machine learning-based diagnostics, and medical signal processing. With a strong academic foundation in biomedical and electronics engineering, he has led the development of innovative tools for non-invasive cardiorespiratory assessment, including CO₂ waveform analysis for asthma detection and AI-driven classification models for COVID-19 diagnostics. His multidisciplinary work bridges engineering, clinical medicine, and artificial intelligence, making significant contributions to the field of digital health technologies.
Publication Profile
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Education
Dr. Om Prakash Singh holds a Ph.D. in Biomedical Engineering, with his doctoral research focusing on non-invasive respiratory monitoring and machine learning for disease diagnosis. He obtained his Master’s degree in Biomedical Engineering, where he specialized in medical instrumentation and physiological signal analysis. His foundational training includes a Bachelor’s degree in Electronics and Communication Engineering, laying the groundwork for his interdisciplinary expertise in biomedical technology and computational analysis.
Professional Experience
Dr. Singh has extensive academic and research experience in the fields of biomedical instrumentation, respiratory physiology, and artificial intelligence in healthcare. He has served as a faculty member and researcher at various reputed institutions, actively contributing to teaching, clinical device development, and collaborative health technology projects. His work has led to the development of real-time respiration monitoring systems, automated asthma diagnostic tools, and COVID-19 classification algorithms using machine learning. He has collaborated with clinical experts and engineers, bridging the gap between engineering innovation and medical application. Dr. Singh is also actively involved in peer reviewing for international journals and has served as a mentor for several graduate students.
Research Interest
- Biomedical signal processing (especially capnography and ECG)
- Non-invasive respiratory monitoring systems
- Machine learning and AI in medical diagnosis
- Carbon dioxide sensing and capnogram analysis
- Clinical decision support systems
- Digital health and wearable biosensors
- Respiratory disease classification (e.g., asthma, COVID-19)
Author Metrics
- Total Publications: 25+
- Citations: 200+
- h-index: 7
- i10-index: 5
- Most Cited Paper: “Real-time human respiration carbon dioxide measurement device for cardiorespiratory assessment” (56 citations)
- ResearchGate Score / Google Scholar Profile: Available upon request or institutional access
Top Noted Publication
1. Real-time human respiration carbon dioxide measurement device for cardiorespiratory assessment
Authors: OP Singh, TA Howe, MB Malarvili
Published in: Journal of Breath Research, Vol. 12, Issue 2, 026003, 2018
Citations: 56
Summary:
This paper presents the development and validation of a real-time carbon dioxide (CO₂) measurement device for assessing human respiration. The system utilizes infrared CO₂ sensors to non-invasively monitor exhaled breath and produce a capnogram—a visual representation of CO₂ levels during the respiratory cycle. The device aims to assist in cardiorespiratory health assessments, including applications in clinical diagnostics, athletic monitoring, and telemedicine.
2. Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine
Authors: OP Singh, R Palaniappan, MB Malarvili
Published in: IEEE Access, Vol. 6, pp. 55245–55256, 2018
Citations: 33
Summary:
This study introduces a machine learning-based approach for classifying asthma and non-asthma patients using features derived from capnography (CO₂ waveform). The Support Vector Machine (SVM) classifier was trained on extracted waveform features to differentiate between the two conditions. Results indicated a high classification accuracy, demonstrating the potential for non-invasive asthma monitoring using breath analysis and machine learning techniques.
3. Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms
Authors: OP Singh, M Vallejo, IM El-Badawy, A Aysha, J Madhanagopal, et al.
Published in: Computers in Biology and Medicine, Vol. 136, 104650, 2021
Citations: 31
Summary:
This paper explores the use of machine learning models to classify SARS-CoV-2 (COVID-19) and non-SARS-CoV-2 cases based on clinical and laboratory parameters. A variety of algorithms, including decision trees, random forests, and neural networks, were tested on real-world datasets. The study highlights the feasibility of AI-assisted diagnosis to support pandemic response efforts and early detection of COVID-19 infections.
4. Apini and Meliponini foraging activities influence the phenolic content of different types of Malaysian honey
Authors: NI Ismail, MR Abdul Kadir, NH Mahmood, OP Singh, N Iqbal, RM Zulkifli
Published in: Journal of Apicultural Research, Vol. 55, Issue 2, pp. 137–150, 2016
Citations: 27
Summary:
The research examines how the foraging behavior of different bee species—Apini (honeybees) and Meliponini (stingless bees)—influences the phenolic content of Malaysian honey. Through spectroscopic and biochemical analyses, the study finds variation in antioxidant activity based on bee species and floral sources, contributing to the understanding of honey quality and nutritional value linked to pollinator behavior.
5. Review of Infrared Carbon-Dioxide Sensors and Capnogram Features for Developing Asthma-Monitoring Device
Authors: OMPS Singh, MB Malarvili
Published in: Journal of Clinical & Diagnostic Research, Vol. 12, Issue 10, 2018
Citations: 20
Summary:
This review article summarizes the state-of-the-art in infrared CO₂ sensors and capnogram feature extraction for the development of asthma-monitoring devices. It provides insights into the technology, signal processing techniques, and clinical relevance of CO₂ monitoring. The review also discusses challenges and potential directions for portable asthma diagnostic systems, bridging sensor technology and healthcare applications.
Conclusion
Dr. Om Prakash Singh is highly suitable for the Best Researcher Award in Biomedical Engineering.
His research is innovative, clinically relevant, and technologically advanced, especially in respiratory diagnostics and digital health. While expanding global reach and pushing for translational milestones would strengthen his portfolio, his current academic and applied research accomplishments make him a strong candidate for this honor.