TY - GEN
T1 - Pattern Recognition in Vital Signs Using Spectrograms
AU - Srivatsav Sribhashyam, Sidharth
AU - Sirajus Salekin, Md
AU - Goldgof, Dmitry
AU - Zamzmi, Ghada
AU - Last, Mark
AU - Sun, Yu
N1 - Funding Information:
1Department of Computer Science and Engineering, University of South Florida, Tampa, Florida, United States 2Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Israel *This research is partially supported by University of South Florida Nexus Initiative (UNI) Grant and National Institutes of Health Grant (NIH R21NR018756).
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - frequency modulation
KW - physiological signals
KW - reconstructed signal
KW - spectrograms
KW - Vital signs
UR - http://www.scopus.com/inward/record.url?scp=85124315077&partnerID=8YFLogxK
U2 - 10.1109/SMC52423.2021.9658924
DO - 10.1109/SMC52423.2021.9658924
M3 - Conference contribution
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 1133
EP - 1138
BT - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Y2 - 17 October 2021 through 20 October 2021
ER -