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Feature Extraction of Wrist Pulse Signals using Gabor Spectrogram
Background/Objectives: The practice of Ayurveda has been long followed in India which is based on the analysis of wrist pulse signal for diagnosing human health. The pulse signal is a non-stationary signal. A signal can be analyzed in either time domain, frequency domain or joint time-frequency domain. This paper highlights the need of joint time-frequency analysis for a non-stationary signal and provides an approach of analyzing wrist pulse signals in time-frequency domain using Gabor Spectrogram. Methods/Statistical Analysis: The work in this paper has been done using Virtual Instruments (VI’s) in ®LabVIEW. In this paper, band pass filter and wavelet de-noising technique has been used for pre-processing of raw pulse signals. Segmentation and Normalization has been performed on the noise free pulse signals. For representing the pulse signals in t-f space, Gabor Spectrogram has been used. The Gabor Spectrogram is a time-frequency distribution technique that offers good time-frequency resolution and negligible cross term interference. The performance has been analyzed using a parameter Mean Square Error (MSE). Findings: Various features: Mean Instantaneous Frequency (MIF), Mean Instantaneous Bandwidth (MIB), Frequency Marginal Integral (FMI), Time Marginal Integral (TMI) and Group Delay (GD) have been extracted in t-f domain for pulse signals from healthy subjects and correlation has been performed. The extracted features for different healthy subjects show similarity in variation pattern even though the actual signals differ in time domain, illustrated through correlation, which marks a successful attempt towards the significance of time-frequency distribution for pulse signals. Application/Improvement: These features can prove very significant in health diagnosis of human beings.
Feature Extraction, Gabor Spectrogram, Mean Instantaneous Frequency, Mean Instantaneous Bandwidth, Non-Stationary Signal, Time-Frequency Distribution, Wrist Pulse Signal.
- Abhinav, Sareen M, Kumar M, Santhosh J, Salhan A, Anand S. Nadi Yantra : A robust system design to capture the signals from the radial artery for assessment of the autonomic nervous system non-invasively. J Biomedical Science and Engineering. 2009; 2:471–9.
- Tirumala Rao P, Koteswarao Rao S, Manikanta G, Ravi Kumar S. Distinguishing Normal and Abnormal ECG Signal. Indian Journal of Science and Technology. 2016 Mar; 9(10).
- Zhang DY, Zuo WM, Zhang D, Li NM. Wrist blood flow signal based computerized pulse diagnosis using spatial and spectrum features. J Biomed Sci Eng. 2010; 3:361.
- Rangaprakash D, Narayana Dutt D. Study of Wrist Pulse Signals using Time-Domain Spatial Features. Computers and Electrical Engineering. 2015.
- Chen Y, Zhang L, Zhang D, Zhang D. Computerized wrist pulse signal diagnosis using modified auto-regressive models.Springer Science + Business Media, LLC, 2009.
- Parikh K, Thakker B. Wrist Pulse Classification System for Healthy and Unhealthy Subjects. International Journal of Computer Applications. 2015; 124(15).
- Babbar N, Garg N. Progress and advancement in wrist pulse signal. International Conference on Advancement in Engineering and Technology (ICAET). 2016.
- Wu T et al. Instantaneous Frequency-Time Analysis of Physiology Signals: The Application of Pregnant Women’s Radial Artery Pulse Signals. Physica A: Statistical Mechanics and its Applications. 2008 Jan.
- Mahmoud SS, Hussain ZM, Cosic I, Fang Q. TimeFrequency Analysis of Normal and Abnormal Biological Signals. Biomedical Signal Processing and Control. 2006; 1(1):33–43.
- Mahmoud SS, Fang Q, Davidovic DM, Cosic I. A TimeFrequency Approach for the Analysis of Normal and Arrhythmia Cardiac Signals. IEEE. 2006.
- Tirtom H, Engin M, Engin EZ. Enhancement of TimeFrequency Properties of ECG for Detecting Micropotentials by Wavelet Transform based Method. Expert Systems with Applications. 2008; 746–53.
- Boashash B, Azemi G, Khan NA. Priciples of TimeFrequency Feature Extraction for Change Detection in Non-Stationary Signals: Applications to newborn EEG Abnormality Detection. Pattern Recognition. 2015; 616–27.
- Wang P, Hou S, Zang H, Zuo W, Zhang D. Wrist Pulse Diagnosis using Complex Network. International Conference on Medical Biometrics. 2014.
- Boashash B. Estimating and interpreting the instantaneous frequency of a signal-part 1: fundamentals. Proceedings of the IEEE. 1992 Apr; 80(4).
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