In this, study, an attempt is made to differentiate muscle nonfatigue and fatigue condition using signal complexity metrics derived from phase space network features. A total of 55 healthy adult volunteers performed dynamic contraction of the biceps brachii muscle. The first and last curl are segmented and are considered as nonfatigue and fatigue condition respectively. A weighted phase space network is constructed and reduced to a binary network based on various radii. The mean and median degree centrality features are extracted from these networks and are used for classification. The results of the classification indicate that these features are capable of differentiating nonfatigue and fatigue condition with 91% accuracy. This method of analysis can be extended to applications such as diagnosis of neuromuscular disorder where fatigue is a symptom. © 2020 European Federation for Medical Informatics (EFMI) and IOS Press.