Radial artery pulse waveform analysis based on curve fitting using discrete Fourier series. (June 2019)

• Record Type: Journal Article
• Authors: Jiang, Zhixing; Zhang, David; Lu, Guangming
• Is Part Of: Computer methods and programs in biomedicine. Volume 174(2019)
• Page Start: 25
• Page End: 31
• Publication Date: 2019-06
• Subjects: Radial artery -- Pressure waveform analysis -- Diseases diagnosis -- Curve fitting -- Discrete fourier series; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.04.019
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose a fitting method to model the pressure pulse waveform. We extract the coefficients of the proposed model as feature. The fitting residuals of proposed method are smaller than the traditional methods. The classification performance of the proposed feature is higher than the previous features. Abstract: Background and objectives: Radial artery pulse diagnosis has been playing an important role in traditional Chinese medicine (TCM). For its non-invasion and convenience, the pulse diagnosis has great significance in diseases analysis of modern medicine. The practitioners sense the pulse waveforms in patients' wrist to make diagnoses based on their non-objective personal experience. With the researches of pulse acquisition platforms and computerized analysis methods, the objective study on pulse diagnosis can help the TCM to keep up with the development of modern medicine. Methods: In this paper, we propose a new method to extract feature from pulse waveform based on discrete Fourier series (DFS). It regards the waveform as one kind of signal that consists of a series of sub-components represented by sine and cosine (SC) signals with different frequencies and amplitudes. After the pulse signals are collected and preprocessed, we fit the average waveform for each sample using discrete Fourier series by least squares. The feature vector is comprised by the coefficients of discrete Fourier series function. Results: Compared with the fitting method using Gaussian mixture function, the fitting errors of proposed method are smaller, which indicate that our method can represent the original signal better. The classification performance of proposed feature is superior to the other features extracted from waveform, liking auto-regression model and Gaussian mixture model. Conclusions: The coefficients of optimized DFS function, who is used to fit the arterial pressure waveforms, can obtain better performance in modeling the waveforms and holds more potential information for distinguishing different psychological states.