Clinical evaluation of stretchable and wearable inkjet-printed strain gauge sensor for respiratory rate monitoring at different body postures

Respiratory rate (RR) is a vital sign with continuous, convenient, and accurate measurement which is difficult and still under investigation. The present study investigates and evaluates a stretchable and wearable inkjet-printed strain gauge sensor (IJP) to estimate the RR continuously by detecting the respiratory volume change in the chest area. As the volume change could cause different strain changes at different body postures, this study aims to investigate the accuracy of the IJP RR sensor at selected postures. The evaluation was performed twice on 15 healthy male subjects (mean ± SD of age: 24 ± 1.22 years). The RR was simultaneously measured in breaths per minute (BPM) by the IJP RR sensor and a reference RR sensor (e-Health nasal thermal sensor) at each of the five body postures namely standing, sitting at 90°, Flower’s position at 45°, supine, and right lateral recumbent. There was no significant difference in measured RR between IJP and reference sensors, between two trials, or between different body postures (all p \u3e 0.05). Body posture did not have any significant effect on the difference of RR measurements between IJP and the reference sensors (difference \u3c 0.01 BPM for each measurement in both trials). The IJP sensor could accurately measure the RR at different body postures, which makes it a promising, simple, and user-friendly option for clinical and daily uses

Deep transfer learning for improving single-EEG arousal detection

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model (F1=0.682 and F1=0.694, respectively), and was significantly better than a comparable single-channel model. Our results are promising for researchers working with small databases who wish to use deep learning models pre-trained on larger databases.Comment: Accepted for presentation at EMBC202

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 182, July 1978

This bibliography lists 165 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1978