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Mosquito-borne diseases present considerable risks to the health of bothanimals and humans. Aedes aegypti mosquitoes are the primary vectors fornumerous medically important viruses such as dengue, Zika, yellow fever, andchikungunya. To characterize this mosquito neural activity, it is essential toclassify the generated electrical spikes. However, no open-source neural spikeclassification method is currently available for mosquitoes. Our work presentedin this paper provides an innovative artificial intelligence-based method toclassify the neural spikes in uninfected, dengue-infected, and Zika-infectedmosquitoes. Aiming for outstanding performance, the method employs a fusion ofnormalization, feature importance, and dimension reduction for thepreprocessing and combines convolutional neural network and extra gradientboosting (XGBoost) for classification. The method uses the electrical spikingactivity data of mosquito neurons recorded by microelectrode array technology.We used data from 0, 1, 2, 3, and 7 days post-infection, containing over 15million samples, to analyze the method's performance. The performance of theproposed method was evaluated using accuracy, precision, recall, and the F1scores. The results obtained from the method highlight its remarkableperformance in differentiating infected vs uninfected mosquito samples,achieving an average of 98.1%. The performance was also compared with 6 othermachine learning algorithms to further assess the method's capability. Themethod outperformed all other machine learning algorithms' performance.Overall, this research serves as an efficient method to classify the neuralspikes of Aedes aegypti mosquitoes and can assist in unraveling the complexinteractions between pathogens and mosquitoes.

Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis