Abstract
This paper analyzes the variability of pulse waveforms by means of approximate entropy (ApEn) and classifies three group objects using support vector machines (SVM). The subjects were divided into three groups according to their cardiovascular conditions. Firstly, we employed ApEn to analyze three groups’ pulse morphology variability (PMV). The pulse waveform’s ApEn of a patient with cardiovascular disease tends to have a smaller value and its variation’s spectral contents differ greatly during different cardiovascular conditions. Then, we applied a SVM to discriminate cardiovascular disease patients from non-cardiovascular disease controls. The specificity and sensitivity for clinical diagnosis of cardiovascular system is 85% and 93% respectively. The proposed techniques in this paper, from a long-term PMV analysis viewpoint, can be applied to a further research on cardiovascular system.
Supported by the National Natural Science Foundation of China under Grant No.90209020.
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Wang, K., Wang, L., Wang, D., Xu, L. (2004). SVM Classification for Discriminating Cardiovascular Disease Patients from Non-cardiovascular Disease Controls Using Pulse Waveform Variability Analysis. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_10
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DOI: https://doi.org/10.1007/978-3-540-30549-1_10
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