ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition

Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography

Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and nasal flow sensors, and more detailed physiology, evaluations via a nasal mask, pneumotachograph, and airway pressure sensors. However, these measurement approaches can be invasive and time-consuming to perform and analyze. This work compares the performance of a non-invasive wearable stretchable morphic sensor, which does not require direct skin contact, embedded in a t-shirt worn by 32 volunteer participants (26 males, 6 females) with sleep-disordered breathing who performed a detailed, overnight in-laboratory sleep study. Direct comparison of computed respiratory parameters from morphic sensors versus traditional polysomnography had approximately 95% (95 ± 0.7) accuracy. These findings confirm that novel wearable morphic sensors provide a viable alternative to non-invasively and simultaneously capture respiratory rate and chest and abdominal motions.

Improved ECG-Derived Respiration Using Empirical Wavelet Transform and Kernel Principal Component Analysis

Many methods have been developed to derive respiration signals from electrocardiograms (ECGs). However, traditional methods have two main issues: (1) focusing on certain specific morphological characteristics and (2) not considering the nonlinear relationship between ECGs and respiration. In this paper, an improved ECG-derived respiration (EDR) based on empirical wavelet transform (EWT) and kernel principal component analysis (KPCA) is proposed. To tackle the first problem, EWT is introduced to decompose the ECG signal to extract the low-frequency part. To tackle the second issue, KPCA and preimaging are introduced to capture the nonlinear relationship between ECGs and respiration. The parameter selection of the radial basis function kernel in KPCA is also improved, ensuring accuracy and a reduction in computational cost. The correlation coefficient and amplitude square coherence coefficient are used as metrics to carry out quantitative and qualitative comparisons with three traditional EDR algorithms. The results show that the proposed method performs better than the traditional EDR algorithms in obtaining single-lead-EDR signals.

The predictive value of Holter monitoring in the risk of obstructive sleep apnea

Background

Patients with obstructive sleep apnea (OSA) often present with cardiovascular symptoms. Holter monitors were reported to predict sleep apnea, though were rarely used in everyday clinical practice. In this study, by comparing Holter monitoring to polysomnography (PSG), we try to find out an operable way for clinicians to use Holter to predict OSA risk.

Methods

Patients (n=63) suspected of OSA underwent Holter monitoring with concurrent PSG at a sleep center. Respiration and heart rate variability (HRV) indices were calculated from the Holter and compared with PSG indices.

Results

The sensitivity of the Holter-derived respiratory waveform for OSA was 90.0%, and the specificity was 82.6%. The time domain indices including standard deviation of all NN intervals during 24 hours, mean of standard deviation of the averages of NN intervals in all 5-minute segments, square root of the mean squared differences of successive NN intervals, percentage of beat-to-beat NN interval differences that were more than 50 milliseconds, and the frequency domain index of high frequency decreased in participants with OSA and correlated with the PSG derived indices including apnea-hypopnea index (AHI), oxygen reduction index (ODI) and nadir SaO₂. Logistic regression showed that standard deviation of all NN intervals during 24 hours and gender could predict the risk of OSA (P<0.001), with a sensitivity for diagnosing moderate to severe OSA of 87.5% and could accurately distinguish the risk of OSA in 77.8% of patients. Males with standard deviation of all NN intervals during 24 hours ≤177 ms or females with standard deviation of all NN intervals during 24 hours ≤80.9 ms were considered to be at high risk for OSA.

Conclusions

Commercial and common parameters from Holter monitoring could predict the risk of OSA with high sensitivity. Therefore, the risk of OSA may be assessed using the Holter examination to improve the diagnosis and treatment rate of OSA.