Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Background There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility. Objective In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal. This serves as a proof-of-concept study of the applicability of a machine learning architecture to the derivation of respiratory metrics. Methods A pulse oximeter was mounted to the left index finger of 9 healthy subjects who breathed at controlled respiratory rates. A respiratory band was used to collect a reference signal as a comparison. Results Over a 40-second window, the LSTM model predicted a respiratory waveform through which breathing metrics could be derived with a bias value and 95% CI. Metrics included inspiration time (–0.16 seconds, –1.64 to 1.31 seconds), expiration time (0.09 seconds, –1.35 to 1.53 seconds), respiratory rate (0.12 breaths per minute, –2.13 to 2.37 breaths per minute), interbreath intervals (–0.07 seconds, –1.75 to 1.61 seconds), and the inspiration-expiration ratio (0.09, –0.66 to 0.84). Conclusions A trained LSTM model shows acceptable accuracy for deriving breathing metrics and could be useful for long-term breathing monitoring in health. Its utility in respiratory disease (eg, asthma) warrants further investigation.

Sustainable Smartphone-Based Healthcare Systems: A Systems Engineering Approach to Assess the Efficacy of Respiratory Monitoring Apps

Recent technological developments along with advances in smart healthcare have been rapidly changing the healthcare industry and improving outcomes for patients. To ensure reliable smartphone-based healthcare interfaces with high levels of efficacy, a system dynamics model with sustainability indicators is proposed. The focus of this paper is smartphone-based breathing monitoring systems that could possibly use breathing sounds as the data acquisition input. This can especially be useful for the self-testing procedure of the ongoing global COVID-19 crisis in which the lungs are attacked and breathing is affected. The method of investigation is based on a systems engineering approach using system dynamics modeling. In this paper, first, a causal model for a smartphone-based respiratory function monitoring is introduced. Then, a systems thinking approach is applied to propose a system dynamics model of the smartphone-based respiratory function monitoring system. The system dynamics model investigates the level of efficacy and sustainability of the system by studying the behavior of various factors of the system including patient wellbeing and care, cost, convenience, user friendliness, in addition to other embedded software and hardware breathing monitoring system design and performance metrics (e.g., accuracy, real-time response, etc.). The sustainability level is also studied through introducing various indicators that directly relate to the three pillars of sustainability. Various scenarios have been applied and tested on the proposed model. The results depict the dynamics of the model for the efficacy and sustainability of smartphone-based breathing monitoring systems. The proposed ideas provide a clear insight to envision sustainable and effective smartphone-based healthcare monitoring systems.

P1281CLINICAL CHARACTERISTICS OF OBSTRUCTIVE SLEEP APNEA HYPOPNEA SYNDROME IN MAINTENANCE HEMODIALYSIS PATIENTS

Background and Aims To explore the clinical characteristics of obstructive sleep apnea hypopnea syndrome (OSAHS) in maintenance hemodialysis patients. Method 42 patients with maintenance hemodialysis in our hemodialysis center from April 2019 to December 2019 were enrolled for night-time sleep breathing monitoring. The patients were divided into the OSAHS group(n=25) and the non-OSAHS group(n=17) defined by AHI. The OSAHS group was further divided into a mild group (n=14) and a moderate-severe group (n=11). The clinical datas, sleep breathing monitoring datas, and echocardiographic parameters of the OSAHS group and the non-OSAHS group, the mild OSAHS group, and the moderate-severe OSAHS group were compared. Results 1. There were no differences in gender, age, dialysis age, spKt / V, BMI, TBW, ECW, ICW, E / I ratio, Ca, P, HB, and diastolic blood pressure between the the OSAHS group and the non-OSAHS group. 2. Incidence of left ventricular concentric hypertrophy (84.0% VS 47.1%, P = 0.011), left ventricular wall thickness (12.0 VS 10.2, P = 0.029) and systolic blood pressure (147.0 ± 9.6 VS 139.4 ± 13.8, P = 0.041) ) in OSAHS group was significantly higher than the non-OSAHS group; 3: There was no statistical difference in serum carbon dioxide binding capacity (CO2CP) between the two groups (23.1 ± 2.7 VS 23.8 ± 2.6, P = 0.392), but the daily intake of sodium bicarbonate in the OSAHS group was higher than that of the non-OSAHS group (2.5 ± 1.0 VS 1.5 ± 0.5, P = 0.004); the CO2CP of the moderate-severe OSAHS group was lower than that of the mild group (21.7 ± 2.2 VS 24.2 ± 2.7, P = 0.019 ), the left ventricular wall thickness was significantly higher than the mild group (11.4 ± 1.4 VS 12.6 ± 1.1, P = 0.025). Conclusion Clinicians should pay attention to the early diagnosis and prevention of OSAHS in maintenance hemodialysis patients. Because the dialysis population is different from the general population of OSAHS patients, the serum CO2CP of this population is negatively correlated with the degree of OSAHS, and clinicians need to pay particular attention to the amount of sodium bicarbonate to correct acidosis.

Human Respiratory Monitoring Based on Schottky Resistance Humidity Sensors

Two types of Schottky structure sensors (silicon nanowire (SiNW)/ZnO/reduced graphene oxide (rGO) and SiNW/TiO2/rGO) were designed, their humidity resistance characteristics were studied, and the sensors were applied to detect sleep apnea through breath humidity monitoring. The results show that the resistance of the sensors exhibited significant changes with increasing humidity, the response times of the two sensors within the relative humidity range of 23–97% were 49 s and 67 s, and the recovery times were 24 s and 43 s, respectively. Meanwhile, continuous breathing monitoring results indicate that the sensitivity of the sensors remained basically unchanged during 10 min of normal breathing and simulated apnea. The response of the sensor is still good after 30 days of use. We believe that the Schottky structure composite sensor is a very promising technology for human breathing monitoring.