Influence-Based Nano Fuzzy Swarm Oxygen Deficiency Detection and Therapy

Abstract

Oxygen deficiency is a serious health problem that may occur as a result of many diseases. In this article, we present an influence-based nano fuzzy swarm (INFS) for oxygen deficiency detection and therapy using a swarm of oxygen carrier nanomachines operating in three cognitive fields of control, influence, and interest. In particular, we propose a long short-term memory (LSTM) deep neural network for detecting apnea by analyzing the irregular peripheral oxygen saturation (SpO2) signal. Using the proposed sleep-in-the-loop strategy and the desaturated blood biomarkers, including oxygen and hydrogen ion concentrations, an in-silico multithreshold nano fuzzy swarm noninvasive therapeutic method is then performed. We also analytically prove the stability of the INFS using swarm control theory. We apply our strategy to sleep apnea, as one of the most common instances of oxygen deficiency. Furthermore, we compare the accuracy of INSF by using LSTM, bidirectional LSTM (BiLSTM), multilayer perceptron (MLP), convolutional neural network (CNN), and support vector machines (SVM). The detection and therapy results are then compared with other apnea detection methods. The input variables and structure of INSF, i.e., the number of rules and width of membership functions, are studied in terms of robustness to noise. As the results show, the proposed artificial intelligence (AI)-based noninvasive nano detection and therapy method could outperform the competing approaches in treating oxygen deficiency emergencies such as apnea.