2021/08/05 by Sidharth Srivatsav Sribhashyam, Md Sirajus Salekin, Sribhashyam, Sidharth Srivatsav +9
Computer Science · Engineering · #Audio and Speech Processing (eess.AS) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Signal Processing (eess.SP) #Sound (cs.SD) #cs.CV #cs.SD #eess.AS #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2108.03168
Accepted in the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2021)
arxiv created 2021/09/02 · arxiv updated 2021/09/06
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.