Cardiopulmonary coupling analysis using smart wearables and mobile computing

Cardiopulmonary coupling (CPC) analysis links heart and respiration rates to assess sleep-related parameters. Typically, the CPC is measured using multi-lead electrocardiography (ECG) and ECG-derived respiration (EDR). Novel textile shirts with embedded ECG sensors offer convenient and continuously monitored sleep at home. We investigate the feasibility of a shirt with textile sensors (Pro- Kit, Hexoskin, Quebec, Canada) for CPC analysis by mobile computing. ECG data is continuously transmitted from the shirt to a smartphone via Bluetooth Low Energy (BLE). We customize a CPC algorithm and use twelve whole-night recordings from four volunteers to perform qualitative and quantitative analysis. We compare EDR with respiratory inductive plethysmography (RIP). In average, EDR and RIP differ 17.22%. After one night, the batteries are reduced to approx. 70% (shirt) and 90% (smartphone). The run time for CPC processing is approx. 3 min. Hence, smart wearables in combination with mobile computing show technical feasibility for CPC analysis. Eventually, this could yield a useful solution for sleep analysis of non-expert users in a private environment.

Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15–20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

472 Sleep Quality in Pregnancy: An Analysis of Cardiopulmonary Coupling in the nuMoM2b Cohort

Introduction The impact of pregnancy on sleep quality and sleep-breathing is of interest due to concerns of an impact on maternal, intra-uterine and neonatal health. The Sleep Disordered Breathing (SDB) sub-study of the Nulliparous Pregnancy Outcomes Study (NuMoM2b) provided a large cohort of single gestational women who underwent home sleep apnea testing (HSAT) to evaluate for SDB. To evaluate changes in sleep during pregnancy, we utilized publicly available data from http://www.sleepdata.org for cardiopulmonary coupling (CPC) analysis to evaluate SDB, as well as sleep duration and quality. No outcomes data is currently available. Methods Standardized Level 3 HSAT was performed after visit 1 (V1), (6–15 weeks’ gestation) and visit 3 (V3), (22 -31 weeks’ gestation) on 3702 women from the NuMoM2b cohort. CPC-analysis was performed using clinically validated algorithms based on CPC-method using ECG and oxygen saturation data (SpO2) as the input signals. SleepImage Apnea Hypopnea Index (sAHI) was calculated to evaluate for SDB (FDA; 182618). Additional calculations to determine sleep latency, sleep duration, wake after sleep onset, and sleep quality (SQI) were performed. The SQI-metric incorporates measures of sleep stability and instability and is then presented on a scale of 0 – 100 where a higher number is desirable. Results 3,261 & 2,511 participants had data at visits 1 & 3, respectively. A total of 3,012 and 2,332 individuals had ECG data of sufficient quality. The mean age of the analyzed cohort was 27 years old. SDB events were overall low, but significantly increased across visits, sAHI [(1.6 ± 2.5)/hour (V1) vs (2.9 ± 4.1) (V3)], p< 0.001. There was a statistically significant increase in sleep latency [7.4 ± 12.7 (V1) vs 18.7 ± 27.8 (V3)], p< 0.001 and reduction in total sleep time [401.2 ± 85.6 (V1) vs 348.5 ± 79.3 (V3)], p< 0.001. Most notably, there was a >10% reduction in the SQI, indicative of increased unstable, fragmented sleep as pregnancy progressed [72.1 ± 13.8 (V1), 60.5 ± 16.2 (V3)], p < 0.001. Conclusion Using objective measures based on CPC analysis from HSAT derived signals, sleep disordered breathing, sleep duration and sleep quality are all adversely impacted as gestation progresses. Support (if any):

Weaning from Mechanical Ventilation: On the Improvement of the Prediction of Patients' Readiness with Cardiopulmonary Coupling Indices

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation (MV) at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15-20% of predictive failure rate. This work explores both Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) estimates as complementary information for readiness prediction. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to measure CPC, including Orthogonal Subspace Projections, Dynamic Mutual Information and Time-Frequency Coherence. 22 patients undergoing SBT in pressure support ventilation are analysed in the 24 hours previous to the SBT. 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. However, significant statistical differences are found for the novel CPC parameters, throughout the whole recordings, comparing the values of the two groups. In addition, the night prior to SBT is the moment where differences are higher, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced RSA. Therefore, results suggest that the traditional measures could be used in combination with these novel CPC biomarkers to help clinicians better predict if patients are ready to be weaned.