PMID- 34568878
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231107
VI  - 2021
DP  - 2021 Jul
TI  - An Adaptively Parameterized Algorithm Estimating Respiratory Rate from a Passive 
      Wearable RFID Smart Garment.
PG  - 774-784
LID - 10.1109/COMPSAC51774.2021.00110 [doi]
AB  - Currently, wired respiratory rate sensors tether patients to a location and can 
      potentially obscure their body from medical staff. In addition, current wired 
      respiratory rate sensors are either inaccurate or invasive. Spurred by these 
      deficiencies, we have developed the Bellyband, a less invasive smart garment 
      sensor, which uses wireless, passive Radio Frequency Identification (RFID) to 
      detect bio-signals. Though the Bellyband solves many physical problems, it 
      creates a signal processing challenge, due to its noisy, quantized signal. Here, 
      we present an algorithm by which to estimate respiratory rate from the Bellyband. 
      The algorithm uses an adaptively parameterized Savitzky-Golay (SG) filter to 
      smooth the signal. The adaptive parameterization enables the algorithm to be 
      effective on a wide range of respiratory frequencies, even when the frequencies 
      change sharply. Further, the algorithm is three times faster and three times more 
      accurate than the current Bellyband respiratory rate detection algorithm and is 
      able to run in real time. Using an off-the-shelf respiratory monitor and 
      metronome-synchronized breathing, we gathered 25 sets of data and tested the 
      algorithm against these trials. The algorithm's respiratory rate estimates 
      diverged from ground truth by an average Root Mean Square Error (RMSE) of 4.1 
      breaths per minute (BPM) over all 25 trials. Further, preliminary results suggest 
      that the algorithm could be made as or more accurate than widely used algorithms 
      that detect the respiratory rate of non-ventilated patients using data from an 
      Electrocardiogram (ECG) or Impedance Plethysmography (IP).
FAU - Ross, Robert
AU  - Ross R
AUID- ORCID: 0000-0002-0726-4741
AD  - Drexel University, College of Engineering, Philadelphia, PA USA.
FAU - Mongan, William M
AU  - Mongan WM
AUID- ORCID: 0000-0002-4122-5156
AD  - Drexel University, College of Engineering, Philadelphia, PA USA.
FAU - O'Neill, Patrick
AU  - O'Neill P
AD  - Drexel University, College of Computing and Informatics, Philadelphia, PA USA.
FAU - Rasheed, Ilhaan
AU  - Rasheed I
AD  - Drexel University, College of Engineering, Philadelphia, PA USA.
FAU - Fontecchio, Adam
AU  - Fontecchio A
AD  - Drexel University, College of Engineering, Philadelphia, PA USA.
FAU - Dion, Genevieve
AU  - Dion G
AD  - Drexel University, College of Media Arts and Design, Philadelphia, PA USA.
FAU - Dandekar, Kapil R
AU  - Dandekar KR
AUID- ORCID: 0000-0003-1936-2514
AD  - Drexel University, College of Engineering, Philadelphia, PA USA.
LA  - eng
GR  - R01 EB029364/EB/NIBIB NIH HHS/United States
GR  - U01 EB023035/EB/NIBIB NIH HHS/United States
PT  - Journal Article
DEP - 20210909
PL  - United States
TA  - Proc COMPSAC
JT  - Proceedings : Annual International Computer Software and Applications Conference. 
      COMPSAC
JID - 101613261
PMC - PMC8463037
MID - NIHMS1701078
OTO - NOTNLM
OT  - biomedical signal processing
OT  - filtering algorithms
OT  - parameter estimation
OT  - wearable sensors
EDAT- 2021/09/28 06:00
MHDA- 2021/09/28 06:01
PMCR- 2021/09/24
CRDT- 2021/09/27 06:25
PHST- 2021/09/27 06:25 [entrez]
PHST- 2021/09/28 06:00 [pubmed]
PHST- 2021/09/28 06:01 [medline]
PHST- 2021/09/24 00:00 [pmc-release]
AID - 10.1109/COMPSAC51774.2021.00110 [doi]
PST - ppublish
SO  - Proc COMPSAC. 2021 Jul;2021:774-784. doi: 10.1109/COMPSAC51774.2021.00110. Epub 
      2021 Sep 9.