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DTSTAMP:20240626T180034Z
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DTSTART;TZID=America/Los_Angeles:20240625T110000
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UID:dac_DAC 2024_sess103_RESEARCH1788@linklings.com
SUMMARY:Deep Harmonic Finesse: Signal Separation in Wearable Systems with 
 Limited Data
DESCRIPTION:Research Manuscript\n\nMahya Saffarpour, Weitai Qian, Kourosh 
 Vali, Begum Kasap, Herman Hedriana, and Soheil Ghiasi (University of Calif
 ornia, Davis)\n\nWe present a method, referred to as Deep Harmonic Finesse
  (DHF), for separation of non-stationary quasi-periodic signals when limit
 ed data is available. The problem frequently arises in wearable systems in
  which, a combination of quasi-periodic physiological phenomena give rise 
 to the sensed signal, and excessive data collection is prohibitive. Our ap
 proach utilizes prior knowledge of time-frequency patterns in the signals 
 to mask and in-paint spectrograms. This is achieved through an application
 -inspired deep harmonic neural network coupled with an integrated pattern 
 alignment component. The network's structure embeds the implicit harmonic 
 priors within the time-frequency domain, while the pattern-alignment metho
 d transforms the sensed signal, ensuring a strong alignment with the netwo
 rk. The effectiveness of the algorithm is demonstrated in the context of n
 on-invasive fetal monitoring using both synthesized and in vivo data. When
  applied to the synthesized data, our method exhibits significant improvem
 ents in signal-to-distortion ratio (26% on average) and mean squared error
  (80% on average), compared to the best competing method. When applied to 
 in vivo data captured in pregnant animal studies, our method improves the 
 correlation error between estimated fetal blood oxygen saturation and the 
 ground truth by 80.5% compared to the state of the art.\n\nTopic: AI\n\nKe
 yword: AI/ML Algorithms\n\nSession Chairs: Hongyang Jia (Tsinghua Universi
 ty) and Grace Li Zhang (Technische Universität Darmstadt)
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