Aimed at the problem of large localization error based on indoor received signal strength indication (RSSI), a RBF neural network (RBFNN) localization algorithm is proposed optimized by improved particle swarm optimization (PSO). Combined with resource allocation network (RAN), the number of nodes in hidden layer increase dynamically to determine the center of RBFNN, the number of nodes in hidden layer and spread constant. The inertia weight of PSO is improved to advance the global search ability of PSO and optimize the output weight of RBFNN. Finally, the optimized RBFNN is used for indoor RSSI positioning. Simulation and experimental results show that the improved localization algorithm has higher positioning accuracy.

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<abstract> <sec><title>Purpose</title><p>Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD.</p> </sec> <sec><title>Aim</title><p>To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD.</p> </sec> <sec><title>Methods</title><p>Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (<italic>SampEn</italic>), approximate entropy (<italic>ApEn</italic>), and complexity index (<italic>CI</italic>) derived from multiscale entropy were extracted from ST-T segments of each lead in ECGs and VCGs. The most effective entropy subset was determined using the sequential backward selection algorithm under the intra-patient and inter-patient schemes, separately. Then, the corresponding optimal model was selected from eight machine learning models for each entropy feature based on five-fold cross-validations. Finally, the classification performance of <italic>SampEn</italic>-based, <italic>ApEn</italic>-based, and <italic>CI</italic>-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme.</p> </sec> <sec><title>Results</title><p><italic>ApEn-</italic>based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, <italic>ApEn</italic>-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8.</p> </sec> <sec><title>Conclusions</title><p>Entropies derived from ECGs and VCGs can effectively detect CMD under both intra-patient and inter-patient schemes. Our proposed models may provide the possibility of an ECG-based tool for non-invasive detection of CMD.</p> </sec> </abstract>