Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis: A machine learning approach
Article
Sharifrazi, Danial, Javed, Nouman, Alizadehsani, Roohallah, Paradkar, Prasad N., Acharya, U. Rajendra and Bhatti, Asim. 2024. "Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis: A machine learning approach." Biomedical Signal Processing and Control. 96 (Part B). https://doi.org/10.1016/j.bspc.2024.106594
| Article Title | Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis: A machine learning approach |
|---|---|
| ERA Journal ID | 3391 |
| Article Category | Article |
| Authors | Sharifrazi, Danial, Javed, Nouman, Alizadehsani, Roohallah, Paradkar, Prasad N., Acharya, U. Rajendra and Bhatti, Asim |
| Journal Title | Biomedical Signal Processing and Control |
| Journal Citation | 96 (Part B) |
| Article Number | 106594 |
| Number of Pages | 11 |
| Year | 2024 |
| Publisher | Elsevier |
| ISSN | 1746-8094 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bspc.2024.106594 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S1746809424006529 |
| Abstract | Mosquito-borne diseases present considerable risks to the health of both animals and humans. Aedes aegypti mosquitoes are the primary vectors for numerous medically important viruses such as dengue, Zika, yellow fever, and chikungunya. To characterize this mosquito neural activity, it is essential to classify the generated electrical spikes. However, no open-source neural spike classification method is currently available for mosquitoes. Our work presented in this paper provides an innovative artificial intelligence-based method to classify the neural spikes in uninfected, dengue-infected, and Zika-infected mosquitoes. Aiming for outstanding performance, the method employs a fusion of variance calculation, dimension reduction, and normalization for the preprocessing and combines convolutional neural network and extra gradient boosting (XGBoost) for classification. The method uses the electrical spiking activity data of mosquito neurons recorded by microelectrode array technology. We used data from 0, 1, 2, 3, and 7 days post-infection, containing over 15 million samples, to analyze the method’s performance. The performance of the proposed method was evaluated using accuracy, precision, recall, and the F1 scores. The results obtained from the method highlight its remarkable performance in differentiating infected vs uninfected mosquito samples, achieving an average of 95.3 %. The performance was also compared with 8 other machine learning algorithms to further assess the method’s capability. The method outperformed all other machine learning algorithms’ performance. Overall, this research serves as an efficient method to classify the neural spikes of Aedes aegypti mosquitoes and can assist in unraveling the complex interactions between pathogens and mosquitoes. |
| Keywords | Aedes aegypti; Dengue; Zika; Classification; Machine Learning ; Deep Learning |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 420308. Health informatics and information systems |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Deakin University |
| Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
| School of Mathematics, Physics and Computing | |
| Centre for Health Research |
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