Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 47, Pages: 1-6
Nidhi Garg and Nikita Babbar*
UIET, Panjab University, Chandigarh - 160014, Punjab, India; [email protected], [email protected]
*Author for correspondence
UIET, Panjab University, Chandigarh - 160014, Punjab, India; [email protected]
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.
Keywords: Feature Extraction, Gabor Spectrogram, Mean Instantaneous Frequency, Mean Instantaneous Bandwidth, Non-Stationary Signal, Time-Frequency Distribution, Wrist Pulse Signa
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