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1. Fusion Learning on Multiple-Tag RFID Measurements for Respiratory Rate Monitoring

   https://doi.org/10.1109/BIBE50027.2020.00082  

   Hansen, Stephen ; Schwartz, Daniel ; Stover, Jesse ; Tajin, Md Abu ; Mongan, William M. ; Dandekar, Kapil R. ( October 2020 , 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE))

   null (Ed.)

   Future advances in the medical Internet of Things (IoT) will require sensors that are unobtrusive and passively powered. With the use of wireless, wearable, and passive knitted smart garment sensors, we monitor infant respiratory activity. We improve the utility of multi-tag Radio Frequency Identification (RFID) measurements via fusion learning across various features from multiple tags to determine the magnitude and temporal information of the artifacts. In this paper, we develop an algorithm that classifies and separates respiratory activity via a Regime Hidden Markov Model compounded with higher-order features of Minkowski and Mahalanobis distances. Our algorithm improves respiratory rate detection by increasing the Signal to Noise Ratio (SNR) on average from 17.12 dB to 34.74 dB. The effectiveness of our algorithm in increasing SNR shows that higher-order features can improve signal strength detection in RFID systems. Our algorithm can be extended to include more feature sources and can be used in a variety of machine learning algorithms for respiratory data classification, and other applications. Further work on the algorithm will include accurate parameterization of the algorithm's window size. 

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2. An Adaptively Parameterized Algorithm Estimating Respiratory Rate from a Passive Wearable RFID Smart Garment

   https://doi.org/10.1109/COMPSAC51774.2021.00110  

   Ross, Robert ; Mongan, William M. ; O'Neill, Patrick ; Rasheed, Ilhaan ; Fontecchio, Adam ; Dion, Genevieve ; Dandekar, Kapil R. ( July 2021 , 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC))

   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). 

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3. Extracting Individual Respiratory Signatures from Combined Multi-Subject Mixtures with Varied Breathing Pattern Using Independent Component Analysis with the JADE Algorithm

   https://doi.org/10.1109/APMC47863.2020.9331715  

   Islam, Shekh M. ; Lubecke, Victor M. ( December 2020 , 2020 IEEE Asia-Pacific Microwave Conference (APMC))

   null (Ed.)

   Concurrent respiration monitoring of multiple subjects remains a challenge in microwave Doppler radar-based non-contact physiological sensing technology. Prior research using Independent component analysis with the JADE algorithm has been limited to the separation of respiratory signatures for normal breathing patterns. This paper investigates the feasibility of using the ICA-JADE algorithm with a 24-GHz phase comparison monopulse radar transceiver for separating respiratory signatures from combined mixtures of varied breathing patterns. Normal, fast, and slow breathing pattern variations likely to occur due to physiological activity, and emotional stress were used as a basis for assessing separation robustness. Experimental results showed efficacy for recognition of three different breathing patterns, and isolation of respiratory signatures with an accuracy of100% for normal breathing, 92% for slow breathing, and 83.78% for fast breathing using ICA-JADE. Breathing pattern variations were observed to affect the signal-to-noise ratio through multiple mechanisms, decreasing with an increase in the number of breathing cycles and associated motion artifacts. Additionally, for removing motion artifacts of fast breathing pattern empirical mode decomposition (EMD) is employed, and for slow breathing pattern, increasing the breathing cycles helps to achieve an accuracy of 89.2% and 94.5% respectively. 

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4. Validation of a new impedance cardiography analysis algorithm for clinical classification of stress states

   https://doi.org/10.1111/psyp.14013  

   Sheikh, Shafa‐at Ali ; Gurel, Nil Z. ; Gupta, Shishir ; Chukwu, Ikenna V. ; Levantsevych, Oleksiy ; Alkhalaf, Mhmtjamil ; Soudan, Majd ; Abdulbaki, Rami ; Haffar, Ammer ; Clifford, Gari D. ; et al ( February 2022 , Psychophysiology)

   Pre‐ejection period (PEP) is an index of sympathetic nervous system activity that can be computed from electrocardiogram (ECG) and impedance cardiogram (ICG) signals, but sensitive to speech/motion artifact. We sought to validate an ICG noise removal method, three‐stage ensemble‐average algorithm (TEA), in data acquired from a clinical trial comparing active versus sham non‐invasive vagal nerve stimulation (tcVNS) after standardized speech stress. We first compared TEA's performance versus the standard conventional ensemble‐average algorithm (CEA) approach to classify noisy ICG segments. We then analyzed ECG and ICG data to measure PEP and compared group‐level differences in stress states with each approach. We evaluated 45 individuals, of whom 23 had post‐traumatic stress disorder (PTSD). We found that the TEA approach identified artifact‐corrupted beats with intraclass correlation coefficient > 0.99 compared to expert adjudication. TEA also resulted in higher group‐level differences in PEP between stress states than CEA. PEP values were lower in the speech stress (vs. baseline rest) group using both techniques, but the differences were greater using TEA (12.1 ms) than CEA (8.0 ms). PEP differences in groups divided by PTSD status and tcVNS (active vs. sham) were also greater when using the TEA versus CEA method, although the magnitude of the differences was lower. In conclusion, TEA helps to accurately identify noisy ICG beats during speaking stress, and this increased accuracy improves sensitivity to group‐level differences in stress states compared to CEA, suggesting greater clinical utility.

    

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5. Volumetric mapping of the functional neuroanatomy of the respiratory network in the perfused brainstem preparation of rats

   https://doi.org/10.1113/JP279605  

   Dhingra, Rishi R. ; Dick, Thomas E. ; Furuya, Werner I. ; Galán, Roberto F. ; Dutschmann, Mathias ( April 2020 , The Journal of Physiology)

   The functional neuroanatomy of the mammalian respiratory network is far from being understood since experimental tools that measure neural activity across this brainstem‐wide circuit are lacking.

   Here, we use silicon multi‐electrode arrays to record respiratory local field potentials (rLFPs) from 196–364 electrode sites within 8–10 mm³of brainstem tissue in single arterially perfused brainstem preparations with respect to the ongoing respiratory motor pattern of inspiration (I), post‐inspiration (PI) and late‐expiration (E2).

   rLFPs peaked specifically at the three respiratory phase transitions, E2–I, I–PI and PI–E2.

   We show, for the first time, that only the I–PI transition engages a brainstem‐wide network, and that rLFPs during the PI–E2 transition identify a hitherto unknown role for the dorsal respiratory group.

   Volumetric mapping of pontomedullary rLFPs in single preparations could become a reliable tool for assessing the functional neuroanatomy of the respiratory network in health and disease.

   While it is widely accepted that inspiratory rhythm generation depends on the pre‐Bötzinger complex, the functional neuroanatomy of the neural circuits that generate expiration is debated. We hypothesized that the compartmental organization of the brainstem respiratory network is sufficient to generate macroscopic local field potentials (LFPs), and if so, respiratory (r) LFPs could be used to map the functional neuroanatomy of the respiratory network. We developed an approach using silicon multi‐electrode arrays to record spontaneous LFPs from hundreds of electrode sites in a volume of brainstem tissue while monitoring the respiratory motor pattern on phrenic and vagal nerves in the perfused brainstem preparation. Our results revealed the expression of rLFPs across the pontomedullary brainstem. rLFPs occurred specifically at the three transitions between respiratory phases: (1) from late expiration (E2) to inspiration (I), (2) from I to post‐inspiration (PI), and (3) from PI to E2. Thus, respiratory network activity was maximal at respiratory phase transitions. Spatially, the E2–I, and PI–E2 transitions were anatomically localized to the ventral and dorsal respiratory groups, respectively. In contrast, our data show, for the first time, that the generation of controlled expiration during the post‐inspiratory phase engages a distributed neuronal population within ventral, dorsal and pontine network compartments. A group‐wise independent component analysis demonstrated that all preparations exhibited rLFPs with a similar temporal structure and thus share a similar functional neuroanatomy. Thus, volumetric mapping of rLFPs could allow for the physiological assessment of global respiratory network organization in health and disease.

    

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