Heart-rate tuned comb filters for processing photoplethysmogram (PPG) signals in pulse oximetry.

Calculation of peripheral capillary oxygen saturation [Formula: see text] levels in humans is often made with a pulse oximeter, using photoplethysmography (PPG) waveforms. However, measurements of PPG waveforms are susceptible to motion noise due to subject and sensor movements. In this study, we compare two [Formula: see text]-level calculation techniques, and measure the effect of pre-filtering by a heart-rate tuned comb peak filter on their performance. These techniques are: (1) "Red over Infrared," calculating the ratios of AC and DC components of the red and infrared PPG signals,[Formula: see text], followed by the use of a calibration curve to determine the [Formula: see text] level Webster (in: Design of pulse oximeters, CRC Press, Boca Raton, 1997); and (2) a motion-resistant algorithm which uses the Discrete Saturation Transform (DST) (Goldman in J Clin Monit Comput 16:475-83, 2000). The DST algorithm isolates individual "saturation components" in the optical pathway, which allows separation of components corresponding to the [Formula: see text] level from components corresponding to noise and interference, including motion artifacts. The comparison we provide here (employing the two techniques with and without pre-filtering) addresses two aspects: (1) accuracy of the [Formula: see text] calculations; and (2) computational complexity. We used both synthetic data and experimental data collected from human subjects. The human subjects were tested at rest and while exercising; while exercising, their measurements were subject to the impacts of motion. Our main conclusion is that if an uninterrupted high-quality heart rate measurement is available, then the "Red over Infrared" approach preceded by a heart-rate tuned comb filter provides the preferred trade-off between [Formula: see text]-level accuracy and computational complexity. A modest improvement in [Formula: see text] estimate accuracy at very low SNR environments may be achieved by switching to the pre-filtered DST-based algorithm (up to 6% improvement in [Formula: see text] level accuracy at -10 dB over unfiltered DST algorithm and the filtered "Red over Infrared" approach). However, this improvement comes at a significant computational cost.

Correlation of Cognitive Scores and the Onset of Hypoxia.

INTRODUCTION: The negative effects of hypoxia on human cognitive function have been well documented. In this study we assess the correlation of performance in the SynWin cognitive Multi-Task Battery (MTB) and the onset of hypoxia and describe the use of cognitive assessment scores for real-time hypoxia detection.METHODS: We performed a correlation analysis between MTB scores (Arithmetic, Memory, Audio Monitoring, Video Monitoring tasks) and blood oxygen saturation levels to discover if the scores are good candidates to detect hypoxia. Since this analysis showed positive correlation, we proceeded to develop a parallel decision fusion system that uses these cognitive scores for real-time hypoxia detection using the Neyman-Pearson criterion.RESULTS: We demonstrate that MTB scores have considerable hypoxia detection potential and can be used (if measurable passively) in a real-time detection framework. Analysis of receiver operating characteristic (ROC) curves established a hierarchy of importance of the various MTB modules. The Arithmetic task module had the most significant contribution toward correct hypoxia detection (improvement of ∼13.5% and ∼13.9% in detection accuracy under global false alarms of 0.1 and 0.05, respectively), followed by the Memory and Audio Monitoring modules. Fusion of multiple cognitive assessment scores resulted in significantly higher detection accuracy (>86%) than using any one of the scores by itself.DISCUSSION: When available, cognitive assessment scores can be a useful tool for real-time hypoxia detection. Since these MTB tests also assess neuropsychological functioning, study of distributed detection systems based on MTB scores could help in designing tests that are more useful for detecting hypoxic symptoms.Rajasekar A, Acharya S, Shender BS, Rorres C, Hrebien L, Kam M. Correlation of cognitive scores and the onset of hypoxia. Aerosp Med Hum Perform. 2019; 90(5):429-439.

Ensemble Learning Approach via Kalman Filtering for a Passive Wearable Respiratory Monitor.

OBJECTIVE: Utilizing passive radio frequency identification (RFID) tags embedded in knitted smart-garment devices, we wirelessly detect the respiratory state of a subject using an ensemble-based learning approach over an augmented Kalman-filtered time series of RF properties. METHODS: We propose a novel approach for noise modeling using a "reference tag," a second RFID tag worn on the body in a location not subject to perturbations due to respiratory motions that are detected via the primary RFID tag. The reference tag enables modeling of noise artifacts yielding significant improvement in detection accuracy. The noise is modeled using autoregressive moving average (ARMA) processes and filtered using state-augmented Kalman filters. The filtered measurements are passed through multiple classification algorithms (naive Bayes, logistic regression, decision trees) and a new similarity classifier that generates binary decisions based on current measurements and past decisions. RESULTS: Our findings demonstrate that state-augmented Kalman filters for noise modeling improves classification accuracy drastically by over 7.7% over the standard filter performance. Furthermore, the fusion framework used to combine local classifier decisions was able to predict the presence or absence of respiratory activity with over 86% accuracy. CONCLUSION: The work presented here strongly indicates the usefulness of processing passive RFID tag measurements for remote respiration activity monitoring. The proposed fusion framework is a robust and versatile scheme that once deployed can achieve high detection accuracy with minimal human intervention. SIGNIFICANCE: The proposed system can be useful in remote noninvasive breathing state monitoring and sleep apnea detection.

Real-Time Hypoxia Prediction Using Decision Fusion.

Humans who operate in high altitudes for prolonged durations often suffer from hypoxia. The commencement of physiological and cognitive changes due to the onset of hypoxia may not be immediately apparent to the exposed individual. These changes can go unrecognized for minutes and even hours and may lead to serious performance degradation or complete incapacitation. A dynamic system capable of monitoring and detecting decreased physiologic states due to the onset of hypoxia has the potential to prevent adverse outcomes. In this study, we develop a real-time hypoxia monitoring system based on a parallel M -ary decision fusion architecture. Blood oxygen saturation levels and altitude readings are the inputs and estimates of the level of hypoxia are the outputs. We develop new temporal evolution models for blood oxygen saturation and functional impairment with respect to varying altitude. The proposed models enable accurate tracking of various hypoxia levels based on the duration of stay of the subject at an altitude. Using a Bayesian decision-making formulation, the system generates global estimates of the degree of hypoxia. The detection system is tested against synthetic and real datasets to demonstrate applicability and accuracy.