Pattern Recognition in Vital Signs Using Spectrograms

TL;DR

This paper introduces a novel frequency modulation technique to enhance spectrogram analysis of vital signs, improving pattern recognition in low-frequency, low-variability signals for medical prediction and classification tasks.

Contribution

The study proposes a new frequency modulation method to adapt spectrograms for vital signs, enabling better pattern detection compared to traditional approaches.

Findings

Achieved over 91% accuracy in prediction tasks.

Demonstrated improved pattern recognition in vital signs.

Validated across four diverse medical datasets.

Abstract

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% in prediction and classification tasks respectively.