Amir Dehsarvi

Amir Dehsarvi

I am a researcher interested in the application of machine learning techniques for the analysis of medical data.

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Pages

Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

publications

Classification of resting-state fMRI for olfactory dysfunction in Parkinson's disease using evolutionary algorithms.

Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18, 2018

This paper develops automatic methods for detecting brain imaging preclinical biomarkers for olfactory dysfunction in early stage Parkinson's disease (PD) by considering the novel application of evolutionary algorithms. Classification will be applied to PD patients with severe hyposmia, patients with no/mild hyposmia, and healthy controls.

Recommended citation: Dehsarvi, A., & Smith, S. L. (2018). "Classification of resting-state fMRI for olfactory dysfunction in Parkinson's disease using evolutionary algorithms." Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18. New York, New York, USA: ACM Press. https://doi.org/10.1145/3205651.3205681

Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease

Published in arXiv, 2019

This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson's disease (PD) by considering the novel application of evolutionary algorithms. A fundamental novel element of this work is the use of evolutionary algorithms to both map and predict the functional connectivity in patients using resting state functional MRI data taken from the PPMI to identify PD progression biomarkers. Specifically, Cartesian Genetic Programming was used to classify DCM data as well as time-series data.

Recommended citation: Dehsarvi, A., & Smith, S. L. (under review). "Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease." arXiv. https://arxiv.org/abs/1910.05378

Towards Monitoring Parkinson's Disease Following Drug Treatment: CGP Classification of rs-MRI Data.

Published in arXiv, 2019

This research develops automatic clinical monitoring techniques for PD, following treatment, using the novel application of EAs. Specifically, the research question addressed was: Can accurate monitoring of PD be achieved using EAs on rs-fMRI data for patients prescribed Modafinil (typically prescribed for PD patients to relieve physical fatigue)?

Recommended citation: Dehsarvi, A., South Palomares, J. K., & Smith, S. L. (under review). "Towards Monitoring Parkinson's Disease Following Drug Treatment: CGP Classification of rs-MRI Data." arXiv. https://arxiv.org/abs/1911.03439

Cognitive and structural correlates of conversational speech timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer's Disease: relevance for early detection approaches.

Published in Frontiers in Aging Neuroscience, 2021

This study examines whether temporal characteristics in a collaborative referencing task are associated with cognitive function and the volumes of brain regions involved in speech production and known to be reduced in mild cognitive impairment and Alzheimer's pathology. We also explored the discriminative ability of the temporal speech measures for the classification of mild cognitive impairement and Alzheimer's disease.

Recommended citation: De Looze, C., Dehsarvi A., Crosby, L., Vourdanou, A., Coen, R. F., Lawlor, B., & Reilly, R. B. (2021). "Cognitive and structural correlates of conversational speech timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer's Disease: relevance for early detection approaches."; Frontiers in Aging Neuroscience. 13:637404. doi: 10.3389/fnagi.2021.637404. https://doi.org/10.3389/fnagi.2021.637404

Towards Automated Monitoring of Parkinson's Disease Following Drug Treatment

Published in Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13364. Springer, Cham., 2022

This paper reports an automated approach to the clinical monitoring of Parkinson's disease (PD) by applying Evolutionary Algorithms (EAs) to resting-state functional magnetic imaging (rs-fMRI) data.

Recommended citation: Dehsarvi, A., South Palomares, J.K., Smith, S.L. (2022). Towards Automated Monitoring of Parkinson's Disease Following Drug Treatment. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13364. Springer, Cham. doi: 10.1007/978-3-031-09282-4_17. https://doi.org/10.1007/978-3-031-09282-4_17

Structural Correlates of Overt Sentence Reading in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer's Disease

Published in Current Alzheimer Research, 2022

This preliminary case-control study investigates whether the temporal organization of speech is associated with the volume of brain regions involved in overt sentence reading and explores the discriminative ability of temporal speech parameters and standard volumetric MRI measures for the classification of MCI and Alzheimer's disease.

Recommended citation: De Looze, C., Dehsarvi A., Crosby, L., Vourdanou, A., Coen, R. F., Lawlor, B., & Reilly, R. B. (2022). "Structural Correlates of Overt Sentence Reading in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer's Disease"; Current Alzheimer Research. 19. doi: 10.2174/1567205019666220805110248. https://doi.org/10.2174/1567205019666220805110248

Artificial Intelligence for Dementia Research Methods Optimization

Published in arXiv, 2023

This research summarizes and critically evaluates current applications of ML in dementia research and highlight directions for future research. Results: We presented an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials.

Recommended citation: Bucholc, M., James, C., Khleifat, A. al, Badhwar, A., Clarke, N., Dehsarvi, A., Madan, C. R., Marzi, S. J., Shand, C., Schilder, B. M., Tamburin, S., Tantiangco, H. M., Lourida, I., Llewellyn, D. J., Ranson, J. M. (2023). Artificial Intelligence for Dementia Research Methods Optimization. http://arxiv.org/abs/2303.01949 https://doi.org/10.48550/arXiv.2303.01949

talks

The Application of Evolutionary Algorithms for the Analysis of Biomedical Data

Published:

I talked about my work on how I have used different machine learning techniques for the analysis of biomedical data in order to identify different approaches and methods to help with the diagnosis and treatment of different diseases, including chronic diseases with fatigue as a symptom, and neurodegenerative diseases, such as Alzheimer's disease, and Parkinson's disease.

Towards Automated Monitoring of Parkinson's Disease Following Drug Treatment

Published:

I talked about an automated approach to the clinical monitoring of Parkinson's disease (PD) by applying Evolutionary Algorithms (EAs) to resting-state functional magnetic imaging (rs-fMRI) data. The novel application of EAs to both map and predict the functional connectivity is considered in patients receiving the drug Modafinil versus placebo.

teaching

Lab Leader and Postgraduate Teaching Assistant

Workshop, University of York, Department of Electronic Engineering, 2017

Teaching, demonstrating, and marking for undergraduate and postgraduate students, Department of Electronic Engineering. BSc and MSc modules include: Electronics for Medicine, Maths and C Programming, Digital & Analogue Electronics Labs, Computer Architecture Workshops, Electronics for Music Technology, among others.