Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Systematic Review

ABSTRACT Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as extracellular recording, which can offer scientists valuable information about diseases and neuron activities. One of the difficulties researchers confront while evaluating these signals is the existence of large volumes of spike data. Spikes are significant components of signal data that can happen as a consequence of vital biomarkers or physical issues such as electrode movements. Hence, distinguishing types of spikes is essential. From this spot, the spike classification concept commences. Previously, researchers classified spikes manually. The manual classification was not precise enough, as it involved extensive analysis. Consequently, Artificial Intelligence (AI) was introduced into neuroscience to assist clinicians in classifying spikes correctly. Recognizing noises from spikes produced by neural activity causes the spike classification task to bear a significant demand. Classifying spikes accurately and quickly reveals the role of AI in the scope of spike classification. This review provides an in‐depth discussion of the importance and use of AI in spike classification. This work organizes materials in the spike classification field for future studies and fully describes how spikes are recognized. Therefore, the existing datasets are described first. The topic of spike classification is then separated into three major components: preprocessing, classification, and evaluation. Each of these sections introduces existing methods and determines their importance. Having been summarized and compared, more efficient algorithms are highlighted. The primary goal of this work is to provide a perspective on spike classification for future research, as well as a thorough grasp of the methodologies and issues involved. In this work, numerous studies were extracted from various databases. The PRISMA‐related research guidelines were then used to choose papers. Then, research studies based on spike classification using machine learning and deep learning approaches with effective preprocessing were selected. Although there are research papers on spike sorting using the keyword spike, the primary focus of this study is on spike classification. Finally, 47 papers were selected for in‐depth review. First, useful information on the datasets for these papers is supplied. In addition, preprocessing approaches, classification methods, and ultimate performance are investigated in each of these studies. The material is then summarized. Furthermore, the fundamental concerns regarding spike classification raised in the opening of this paper are thoroughly addressed throughout the review. Our reviewing outcomes illustrate that support vector machine and clustering‐based algorithms drastically influence machine learning methods in terms of high accuracy and many uses. Moreover, convolutional neural networks, spiky neural networks, and attention‐based techniques can classify spikes with considerable functionality among deep learning methods. Various preprocessing and classification techniques have been used practically to classify extracted signal data from patients in medical institutions. Our review emphasizes the importance of classifying spikes in neuroscience applications with machine learning and deep learning models. This can provide precious insights and hands‐on solutions for using AI to classify real‐world medical data. This article is categorized under: Technologies > Artificial Intelligence Technologies > Machine Learning Algorithmic Development > Biological Data Mining