There is an ever-growing demand for measuring respiratory variables during a variety of applications, including monitoring in clinical and occupational settings, and during sporting activities and exercise. Special attention is devoted to the monitoring of respiratory rate because it is a vital sign, which responds to a variety of stressors. There are different methods for measuring respiratory rate, which can be classed as contact-based or contactless. The present paper provides an overview of the currently available contact-based methods for measuring respiratory rate. For these methods, the sensing element (or part of the instrument containing it) is attached to the subject’s body. Methods based upon the recording of respiratory airflow, sounds, air temperature, air humidity, air components, chest wall movements, and modulation of the cardiac activity are presented. Working principles, metrological characteristics, and applications in the respiratory monitoring field are presented to explore potential development and applicability for each method.

Contact-Based Methods for Measuring Respiratory Rate

Automatic sleep stage classification with cardiorespiratory signals has attracted increasing attention In contrast to the traditional manual scoring based on polysomnography, these signals can be measured using advanced unobtrusive techniques that are currently available, promising the application for personal and continuous home sleep monitoring This paper describes a methodology for classifying wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) light and deep sleep on a 30 s epoch basis A total of 142 features were extracted from electrocardiogram and thoracic respiratory effort measured with respiratory inductance plethysmography To improve the quality of these features, subject-specific Z-score normalization and spline smoothing were used to reduce between-subject and within-subject variability A modified sequential forward selection feature selector procedure was applied, yielding 80 features while preventing the introduction of bias in the estimation of cross-validation performance PSG data from 48 healthy adults were used to validate our methods Using a linear discriminant classifier and a ten-fold cross-validation, we achieved a Cohen's kappa coefficient of 049 and an accuracy of 69% in the classification of wake, REM, light, and deep sleep These values increased to kappa = 056 and accuracy = 80% when the classification problem was reduced to three classes, wake, REM sleep, and NREM sleep

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Sleep stage classification with ECG and respiratory effort.

Objective: Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. Methods: Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. Result: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. Conclusion: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. Significance: The proposed method can contribute to preventing accidents caused by drowsy driving.

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Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG

Sleep stages classification based on heart rate variability and random forest

A review of automated sleep stage scoring based on physiological signals for the new millennia.

Continuous blood pressure measuring device

An ECG-derived respiration (EDR) algorithm based on principal component analysis (PCA) is presented and applied to derive the respiratory signals from single-lead ECG. The respiratory-induced variabilities of ECG features, P-peak amplitude, Q-peak amplitude, R-peak amplitude, S-peak amplitude, T-peak amplitude and RR-interval, are fused by PCA to yield a better surrogate respiratory signal than other methods. The method is evaluated on data from the MIT-BIH polysomnographic database and validated against a “gold standard” respiratory obtained from simultaneously recorded respiration data. The performance of fusion algorithm is assessed by comparing the EDR signals to a reference respiratory signal, using the quantitative evaluation indexes that include true positive (TP), false positive (FP), false negative (FN), sensitivity (SE) and positive predictivity (PP). The statistically difference is significant among the PCA data fusion method and the EDR methods based on the RR intervals and the RS amplitudes, showing that PCA data fusion algorithm outperforms the others in the extraction of respiratory signals from single-lead ECGs.

A principal component analysis based data fusion method for ECG-derived respiration from single-lead ECG

The potential application of multiscale entropy (MSE) to analyze the heart rate variability (HRV) during different sleep stages, i.e. wake/Rapid Eye Movement (REM) /light sleep/deep sleep, was investigated. The RR sequences of 5 min from four sleep stages were analyzed by the measurements of MSE under three scales (MSE1, MSE2 and MSE3). The results demonstrated that the complexity of heart rate (HR) in non-REM(NREM) is significantly higher (p<;0.01) than that in wake and REM, and the complexity becomes higher with sleep deepening (p<;0.01). MSE3 corresponding to r=0.25*STD reveal the most powerful entropy measurement for classifying different sleep stages.

Multiscale entropy based analysis of HRV during sleep

An ECG-derived respiration (EDR) algorithm based on signal reconstruction and filtering is presented and applied to derive the respiratory signals from single-lead ECG. The ECG features, R-peak amplitude, S-peak amplitude, and R-peak position are used to reconstruct the signal by cubic spline interpolation. The EDR signal is obtained by applying a Kaiser filter to the reconstructed signal at last. The method is evaluated on data from the MIT-BIH polysomnographic database and validated against a “gold-standard” respiratory obtained from simultaneously recorded respiration data. Correlation coefficient (C) and magnitude-squared coherence coefficient (MSC) are used to assess the performance of the methods. The statistical difference is significant among the method presented in this study and the EDR methods based on wavelet and empirical mode decomposition (EMD), proving that the algorithm introduced in this article outperforms the others in the extraction of respiratory signals from single-lead ECGs.

Meng Xiao

Papers

Sleep stages classification based on heart rate variability and random forest

Continuous blood pressure measuring device

A principal component analysis based data fusion method for ECG-derived respiration from single-lead ECG

Multiscale entropy based analysis of HRV during sleep

An ECG-Derived Respiration Method Based on Signal Reconstruction of R, S Amplitudes and Filtering