Ten lectures on wavelets
An introduction to wavelets
Survey of utilisation of fuzzy technology in medicine and healthcare
Fuzzy Sets and Systems - Special issue on clustering and learning
Multi-Level Shape Recognition Based on Wavelet-Transform Modulus Maxima
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter
Computers in Biology and Medicine
An improved wavelet-based corner detection technique
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Approximate entropy based pulse variability analysis
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Analysis of singularities from modulus maxima of complex wavelets
IEEE Transactions on Information Theory
The long-range saliency of edge- and corner-based salient points
IEEE Transactions on Image Processing
Classification of pulse waveforms using edit distance with real penalty
EURASIP Journal on Advances in Signal Processing
Pulse waveform classification using ERP-Based difference-weighted KNN classifier
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Accurate cirrhosis identification with wrist-pulse data for mobile healthcare
Proceedings of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare
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A methodology for the automated time-domain characteristic parameter extraction of human pulse signals is presented. Due to the subjectivity and fuzziness of pulse diagnosis, the quantitative methods are needed. Up to now, the characteristic parameters are mostly obtained by labeling manually and reading directly from the pulse signal, which is an obstacle to realize the automated pulse recognition. To extract the parameters of pulse signals automatically, the idea is to start with the detection of characteristic points of pulse signals based on wavelet transform, and then determine the number of pulse waves based on chain code to label the characteristics. The time-domain parameters, which are endowed with important physiological significance by specialists of traditional Chinese medicine (TCM), are computed based on the labeling result. The proposed methodology is testified by applying it to compute the parameters of five hundred pulse signal samples collected from clinic. The results are mostly in accord with the expertise, which indicate that the method we proposed is feasible and effective, and can extract the features of pulse signals accurately, which can be expected to facilitate the modernization of pulse diagnosis.