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The vigorous development of electronic health (e-health) breeds many healthcare applications, such as telemedicine and healthcare monitoring. Yet these applications confront generic challenges of network dependence and medical personnel necessity which dramatically hinders the popularization and universality of e-health services. This paper presents a versatile distributed e-home healthcare system...
For purpose of detecting cardiovascular diseases (CVDs) hierarchically via hemodynamic parameters (HDPs) derived from sphygmogram, a fused hierarchical neural networks (FHNNs) scheme is proposed, which provides a noninvasive way to detect CVDs. To deduce conclusion via FHNNs, method of variance analysis is used to categorize HDPs. The categorized HDP sets are then inhaled by different sub neural networks...
A fused hierarchical neural networks (FHNNs) is proposed for applications mainly related to diagnosis and fault detection. The benefit of such a model is well demonstrated by applying FHNNs for cardiovascular disease (CVD) diagnosis hierarchically using hemodynamic parameters (HDPs) derived from non-invasive sphygmogram (SPG). Patients' medical records with diagnostic results confirmed by doctors...
For purpose of detecting cardiovascular diseases (CVDs) hierarchically via hemodynamic parameters (HDPs) derived from sphygmogram, a hierarchical fuzzy neural networks (HFNNs) scheme is proposed, which provides a non-invasive way to detect CVDs. To deduce conclusion via HFNNs using HDPs as evidences, method of variance analysis is used to categorize and reduce the dimension of feature space. A unique...
For the goal of cardiovascular disease risk detection, the statistic analysis is used to reduce the dimension of feature space and normalize each input feature; the modified Takagi-Sugeno model fuzzy neural networks are applied to realize the nonlinear mapping relationship between hemodynamic parameters and conclusions, which does not require predefining rules and subjective defuzzification. The preliminary...
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