Nidhi Garg ¹, Nikita Babbar ¹

Affiliations
1 UIET, Panjab University, Chandigarh - 160014, Punjab, IN

Abstract

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 Signal.

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Nidhi Garg ¹, Amandeep Bisht ¹

Affiliations
1 UIET, Panjab University, Chandigarh, IN

Abstract

Background/Objectives: Quantification of Wrist Pulse Signals is helpful to take benefit of ancient approach i.e. Pulse Diagnosis. The objective of this paper is to effectively segregate outliers present within wrist pulses. Methods/Statistical Analysis: This work presents modification in Dynamic Time Warping (DTW) algorithm. The existing DTW algorithm searches for an optimal path using squared Euclidean distance to measure the local distance between segments. Here, we are discussing and integrating different local distance measures such as Correlation Distance, Manhattan Distance, Kendall’s τ Distance and Canberra Distance with DTW. All the discussed local distance measures were compared with existing Euclidean based DTW algorithm on the basis of Similarity Index parameter. Findings: Results shown that Manhattan Distance and Canberra Distance based DTW algorithm was efficient in optimal path selection and segregation of segments which lose their shape characteristics. In euclidean based DTW, outlier segregation was difficult as all values lied between 0 to 1.Correlation distance and Kendall’s tau distance algorithm were inappropriate in detecting outliers as results were not matched with visual observations. It was noticed that combination of Manhattan Distance and Canberra Distance based DTW algorithm were giving better outlier finding.

Keywords

Canberra Distance, DTW, Euclidean Distance, Manhattan Distance, Similarity Index, Wrist Pulse outliers.

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