DeepAI AI Chat
Log In Sign Up

Pattern Recognition in Vital Signs Using Spectrograms

Spectrograms visualize the frequency components of a given signal which may be an audio signal or even a time-series signal. Audio signals have higher sampling rate and high variability of frequency with time. Spectrograms can capture such variations well. But, vital signs which are time-series signals have less sampling frequency and low-frequency variability due to which, spectrograms fail to express variations and patterns. In this paper, we propose a novel solution to introduce frequency variability using frequency modulation on vital signs. Then we apply spectrograms on frequency modulated signals to capture the patterns. The proposed approach has been evaluated on 4 different medical datasets across both prediction and classification tasks. Significant results are found showing the efficacy of the approach for vital sign signals. The results from the proposed approach are promising with an accuracy of 91.55 and 91.67


Bag of Recurrence Patterns Representation for Time-Series Classification

Time-Series Classification (TSC) has attracted a lot of attention in pat...

When Ramanujan meets time-frequency analysis in complicated time series analysis

To handle time series with complicated oscillatory structure, we propose...

Student-t Networks for Melody Estimation

Melody estimation or melody extraction refers to the extraction of the p...

Bio-Signals-based Situation Comparison Approach to Predict Pain

This paper describes a time-series-based classification approach to iden...

Circulant Singular Spectrum Analysis: A new automated procedure for signal extraction

Sometimes, it is of interest to single out the fluctuations associated t...

Ballistocardiogram Signal Processing: A Literature Review

Time-domain algorithms are focused on detecting local maxima or local mi...