The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Quantum inspired improved AI computing for the sensors of cardiac mechano-biology
Open accessAbstract
Recent advancements in the field of quantum mechanics have opened the research avenues for the accurate mapping of the heart’s conductivity with the aid of quantum sensors.The heart rate variability can now be accessed to better levels with the aid of recent noninvasive approaches. This novel idea can be improved with the aid of transfer learning where a smart machine learning algorithm, the “improved dynamic time wrapping” can be used, after selecting the important attributes such as the cardiac action potential. During this research, a two step approach is used for the dynamical analysis of the atrial fibrillation cardiac amyloid, since the Atrial-fibrillation (AF) is more common in patients with the “cardiac amyloidosis”, resulting from the deposition of amyloid protein. During the first step, inspired from the experimental studies in the domain of cardiac amyloidosis, an electric heart model is simulated for different amyloid deposition levels, and during the second step, the transfer learning algorithm is applied to explore the time series data of the electric potential and its divergence. This AI- synchronised divergence can be used to cross verify the electric potential divergence, detected by novel quantum sensors, in ongoing and future research projects.
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“Recently, Sohail and Ashiq have tried to use a model for quantum-inspired AI for the sensor development of a cardiac problem, like cardiac amyloidosis.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [85]
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“Quantum computing is also being applied in mechano-biology.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [85]
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Cites
AI for next generation computing: Emerging trends and future directions
Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Rami Bahsoon, A., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H., Abraham, A., et al. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100514 · Cited by 775
A Novel Hybrid Particle Swarm Optimization Algorithm for Path Planning of UAVs
Yu, Z., Si, Z., Li, X., Wang, D., & Song, H. H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3182798 · Cited by 358
Transfer learning for ECG classification
Weimann, K., & Conrad, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-84374-8 · Cited by 239
Pathophysiology and treatment of cardiac amyloidosis
Gertz, M. A., Dispenzieri, A. A., & Sher, T. (2014) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2014.165 · Cited by 170
Drezner, J. A., Asif, I. M., Owens, D. S., Prutkin, J. M., Salerno, J. C., Fean, R., Rao, A. L., Stout, K., & Harmon, K. G. (2012) · British Journal of Sports Medicine · doi:10.1136/bjsports-2012-090612 · Cited by 107
Naïve Bayes Classifier for ECG Abnormalities Using Multivariate Maximal Time Series Motif
Padmavathi, S. V., & Ramanujam, E. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.03.201 · Cited by 69
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Quantum computing for chemical and biomolecular product design
Andersson, M. P., Jones, M. N., Mikkelsen, K. V., You, F., & Mansouri, S. S. (2022) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2021.100754 · Cited by 66
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)
(2019) · doi:10.1109/biocas45198.2019 · Cited by 65
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Pal, S., Bhattacharya, M., Lee, S.-S., & Chakraborty, C. (2024) · Molecular Biotechnology · doi:10.1007/s12033-023-00765-4 · Cited by 103
“It is a good example of quantum computing in the field of mechano-biology [85].”
Metadata
- Authors
- Sohail, A., & Ashiq, U.
- Affiliations
- COMSATS University Islamabad (PK)
- Corresponding author
- Sohail, Ayesha
- ORCID
- Sohail, Ayesha
- Venue
- Sensors International
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 4
- Pages
- 100212
- ISSN
- 2666-3511 (ISSN-L)
- OpenAlex
- W4313574194
- Cited by
- 8
- References
- 19
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 4
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors International (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- quantum sensors, heart rate variability, atrial fibrillation, cardiac amyloidosis, amyloid deposition, cardiac action potential, transfer learning, dynamic time warping, non-invasive cardiac monitoring
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