ECG-derived Respiration Methods Research Articles

Three methods for determining respiratory waves from ECG (Part I)

In the diagnosis of many disease entities directly or indirectly related to disorders of respiratory parameters and heart disease, an important support would be to estimate the temporal changes in these parameters (most often respiratory wave (RW) and respiratory rate (RR)) on the basis the results of measurements of other physiological parameters of the patient. Such a possibility exists during ECG examination. The paper presents three methods for estimating RWand RR using ECG signal processing. The three procedures developed are shown: using Savitzky–Golay filtering (S-G), the ECG-Derived Respiration method (EDR) and the Respiratory Sinus Arrhythmia Analysis method (RSA). It must be clearly stated that the proposed methods are not designed to fully diagnose the patient’s respiratory function, but they can be applied to detect some conditions that are difficult to diagnose when performing an ECG, such as sleep-disordered breathing. The obtained results of the analysis were compared with those obtained from a dedicated measurement system developed by the authors. The second part of the paper will show the results of preliminary clinical verification of the developed analysis methods, taking into account the physiological parameters of the patient.

A Novel ECG-Derived Respiration Method Combining Frequency-Domain Feature and Interacting Multiple Model Smoother.

ECG-derived respiration (EDR) is a low-cost and productive means for capturing respiratory activity. In particular, as the primary procedure in some cardiorespiratory-related studies, the quality of EDR is decisive for the performance of subsequent analyses. In this paper, we proposed a novel EDR method based on the feature derived from the first moment (mean frequency) of the power spectrum (FMS). After obtaining the EDR signal from the feature, we introduced the Interacting Multiple Model (IMM) smoother to enhance the similarity of the EDR signal to the reference respiration. The assessment of the approach consisted of two steps: 1) the performance of extracted feature was verified against R-peak misalignment and noise. 2) the enhancement of IMM smoother to EDR waveforms was evaluated based on waveform correlation and respiratory rate estimation. All the assessments were conducted under the Fantasia database and Drivers database. The FMS improved robustness against R peak offsets compared to most established feature-based EDR algorithms, but a slight 5% improvement of waveform correlation against RR interval-based feature under accurate R peaks. The IMM smoother performed similarly with the Kalman filter in the static database but realized the enhancement of some extent of the EDR waveform in the ambulatory database. The proposed method investigated frequency domain mapping of ECG morphological changes caused by respiratory modulation and explained the EDR signal as a non-stationary time series, which provided a direction of better fitting the natural respiration process and enhancing the EDR waveform.

Electrocardiogram Derived Respiratory Rate Using a Wearable Armband

A method for deriving respiratory rate from an armband, which records three-channel electrocardiogram (ECG) using three pairs of dry (no hydrogel) electrodes, is presented. The armband device is especially convenient for long-term (months-years) monitoring because it does not use obstructive leads nor hydrogels/adhesives, which cause skin irritation even after few days. An ECG-derived respiration (EDR) based on respiration-related modulation of QRS slopes and R-wave angle approach was used. Moreover, we modified the EDR algorithm to lower the computational cost. Respiratory rates were estimated with the armband-ECG and the reference plethysmography-based respiration signals from 15 subjects who underwent breathing experiment consisting of five stages of controlled breathing (at 0.1, 0.2, 0.3, 0.4, and 0.5Hz) and one stage of spontaneous breathing. The respiratory rates from the armband obtained a relative error with respect to the reference (respiratory rate estimated from the plethysmography-based respiration signal) that was not higher than 2.26% in median nor interquartile range (IQR) for all stages of fixed and spontaneous breathing, and not higher than 3.57% in median nor IQR in the case when the low computational cost algorithm was applied. These results demonstrate that respiration-related modulation of the ECG morphology are also present in the armband ECG device. Furthermore, these results suggest that respiration-related modulation can be exploited by the EDR method based on QRS slopes and R-wave angles to obtain respiratory rate, which may have a wide range of applications including monitoring patients with chronic respiratory diseases, epileptic seizures detection, stress assessment, and sleep studies, among others.

Sleep Apnea Detection from Single-Lead ECG Using Features Based on ECG-Derived Respiration (EDR) Signals

Background and objectiveOne of the important applications of non-invasive respiration monitoring using ECG signal is the detection of obstructive sleep apnea (OSA). ECG-derived respiratory (EDR) signals, contribute to useful information about apnea occurrence. In this paper, two EDR extraction methods are proposed, and their application in automatic OSA detection using single-lead ECG is investigated. MethodsEDR signals are extracted based on new respiration-related features in ECG beats morphology, such as ECG variance (EDRVar) and phase space reconstruction area (EDRPSR). After evaluating the EDRs by comparing them to a reference respiratory signal, they are used in an automatic OSA detection application. Fantasia and Apnea-ECG database from PhysioNet are used for EDRs assessments and OSA detection, respectively. The final performance of our OSA detection is tested on an independent test data which is also compared with results of other techniques in the literature. ResultsThe extracted EDRs, EDRVar and EDRPSR show correlations of 72% and 70% with reference respiration, which outperform the other state-of-the-art EDR methods. After feature extraction from EDRs and RR intervals series, the combination of RR and EDRPSR feature sets achieved 100% accuracy in subject-based apnea detection on independent test data, and also minute-based apnea detection is done with accuracy, sensitivity and specificity of 90.9%, 89.6% and 91.8%, which is better than other automatic algorithms in the literature. ConclusionsOur OSA detection system using EDRs features yields better independent test results compared with other state-of-the-art automatic apnea detection methods. The results indicate that ECG-based OSA detection system can classify OSA events with high accuracy and suggest a promising, non-invasive and efficient method for apnea detection.

New algorithm for the estimation of the electrocardiogram derived respiration signal

Recently, ECG-derived respiration methods (EDR) have become a widely used tool in respiratory monitoring. In this paper, four new methods are presented for EDR signal estimation. The first two methods we have developed are based on ECG interval and QRS energy measurement. A wavelet transform method, based on the Mexican-Hat wavelet is also used in the present study. The final EDR signal method we have proposed is obtained by summing the first three estimated ECG-derived respiration signals. Correlation and magnitude squared coherence coefficients are used to evaluate the performance of the proposed EDR techniques. Compared to other recent works cited in the literature, the proposed algorithms allow us to achieve high estimation performances.

New algorithm for the estimation of the electrocardiogram derived respiration signal

Recently, ECG-derived respiration methods (EDR) have become a widely used tool in respiratory monitoring. In this paper, four new methods are presented for EDR signal estimation. The first two methods we have developed are based on ECG interval and QRS energy measurement. A wavelet transform method, based on the Mexican-Hat wavelet is also used in the present study. The final EDR signal method we have proposed is obtained by summing the first three estimated ECG-derived respiration signals. Correlation and magnitude squared coherence coefficients are used to evaluate the performance of the proposed EDR techniques. Compared to other recent works cited in the literature, the proposed algorithms allow us to achieve high estimation performances.

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

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.

ECG-derived respiration methods: Adapted ICA and PCA

Respiration is an important signal in early diagnostics, prediction, and treatment of several diseases. Moreover, a growing trend toward ambulatory measurements outside laboratory environments encourages developing indirect measurement methods such as ECG derived respiration (EDR). Recently, decomposition techniques like principal component analysis (PCA), and its nonlinear version, kernel PCA (KPCA), have been used to derive a surrogate respiration signal from single-channel ECG. In this paper, we propose an adapted independent component analysis (AICA) algorithm to obtain EDR signal, and extend the normal linear PCA technique based on the best principal component (PC) selection (APCA, adapted PCA) to improve its performance further. We also demonstrate that the usage of smoothing spline resampling and bandpass-filtering improve the performance of all EDR methods. Compared with other recent EDR methods using correlation coefficient and magnitude squared coherence, the proposed AICA and APCA yield a statistically significant improvement with correlations 0.84, 0.82, 0.76 and coherences 0.90, 0.91, 0.85 between reference respiration and AICA, APCA and KPCA, respectively.

ECG-derived respiration: A promising tool for sleep-disordered breathing diagnosis in chronic heart failure patients

Theprevalenceofsleep-disorderedbreathing(SDB)inchronicheartfailure (CHF) patients exceeds 60%, including both central sleep apnea(CSA)andobstructivesleepapnea(OSA)[1].Asobservedinthegeneralpopulation, SDB in CHF patients is associated with a poor prognosis [2]and a higher risk of nocturnal arrhythmia [3]. Continuous positiveairway pressure or adaptive servo-ventilation treatments might im-provebothLVEFandmortalityandreducetheriskofarrhythmicevents,but this remains debatable [4]. Nevertheless, at least 75% of severe SDBcasesremainundiagnosedanduntreatedduetotheabsenceofdaytimesymptoms, waiting lists in sleep laboratories, and diagnostic costs. Thehigh SDB prevalence in patients referred for cardiologic assessmentcalls for the development of automated screening tools based on ECG-derived parameters for use by cardiologists in routine practice.MethodsbasedonheartratevariabilityanalysisallowdetectingSDBpatients whopresentsinusal rhythm[5] butare not suitable for cardiacfailureordysautonomicpopulations,whofrequentlyexhibitprematureatrial or ventricular beat, are frequently implanted with permanentpacemakers, or present recurrent atrial fibrillation, bundle branchblocks, or flat heart rate variability [6]. ECG-derived respiration (EDR),a method based on the reconstruction of thoracic signals by analysingchangesinECGmorphologythatareinducedbyrespiratorymovements[7], is a promisingalternative,but has not yet been evaluatedincardiacpopulations [8].Werecruited105patientswithLVEFb45%andNYHAclass≥2fromroutine medical follow-ups of stable CHF patients (Saint-Etienne andGrenoble University Hospitals) to test an automated EDR method withreference to visually scored nocturnal in-home ventilatory polygraphy.This study (clinical trial NCT02116686) complies with the DeclarationofHelsinkiandwasapprovedbythelocalethicscommittee.Allpatientsgave their written informed consent to participate.Standard nocturnal in-home ventilatory polygraphy was performedusinganEmbladevice(Embla®,Broomfield,USA)andscoredaccordingto AASM recommendations using RemLogic® software. Based onpreviously published methods [7,9,10] and developed with Matlab®(Mathworks,Natick,USA),anEDRmethodwasusedasfollows:thoracicmovements derived from ECG signals were analysed in order toreconstruct respiratory movements. The two mechanisms involved,changesintheelectricalheartaxisandchangesintransthoracicimped-ance,areeasilyobservedintheQRSheightorarea,allowingreconstruc-tion of the thoracic signal, which is then used to score apnea andhypopnea events [7]. Analyses were performed on the portion of thepolygraphic ECG signal recorded when the light was turned off. TheECG baseline was removed, each R-peak was detected, and R-peakoutliers were excluded. The EDR signal was then calculated as thesuccessive surfaces of each QRS complex under the R-peak. An SDBevent was then scored when the algorithm detected either a 50%decrease in thepeak-to-peak EDR amplitudefor 10 s [9] or an invertedU-shape of the EDR trace corresponding to a baseline drift due torespiratory effort during obstruction [10].ECG results were subjected to automated analysis only, and poly-graph recordings were scored manually and blinded to EDR results. Uni-variate regression analysis for continuous variables and Bland–Altmanplot were used to assess the relationship between the polygraphicapnea/hypopnea index (AHI

ECG-derived respirations based on phase-space reconstruction of single-lead ECG: validations over various physical activities based on parallel recordings of ECG, respiration, and body accelerations.

ECG derived respiration (EDR) provides a comfortable measurement of respiration and is recently applied to sleep studies. Nevertheless, the mechanic disturbances due to postural changes or other physical activity during long-term recording is less investigated. In the present study, ECG, impedance-based respiration, and body accelerations were parallel recorded during a series of scheduled postures in 8 healthy subjects and during 24 hours in one subject. In addition, a novel EDR method based on phase space reconstruction of single-lead ECG is also proposed. The area under major portrait radius (MPR) curve is employed to quantify the deviation of phase-space loop which is related to respiration. Coherence analysis between the EDR and the impedance-based respiration demonstrated that the MPR-based EDR had better performance than using R peak or QRS area as the EDR feature in the scheduled postures.

Electrocardiogram-derived respiration in screening of sleep-disordered breathing

Methods for assessment of sleep-disordered breathing (SDB), including sleep apnea, range from a simple questionnaire to complex multichannel polysomnography. Inexpensive and efficient electrocardiogram (ECG)–based solutions could potentially fill the gap and provide a new SDB screening tool. In addition to the heart rate variability (HRV)–based SDB screening method that we reported a year ago, we have developed a novel method based on ECG-derived respiration (EDR). This method derives the respiratory waveform by (a) measuring peak-to-trough QRS amplitude in a single-channel ECG, (b) removing outlier introduced by noise and artifacts, (c) interpolating the derived values, and (d) filtering values within the respiration rates of 5 and 25 cycles per minute. Each 30 seconds of the respiratory waveform is then classified as normal, SDB, or indeterminate epoch. The previously reported HRV-based method, applied at the same time, is based on power spectrum of heart rate over a sliding 6-minute time window to classify the middle 30-second epoch. We then combined the EDR- and HRV-based techniques to optimize the classification of each epoch. The combined method further improved the accuracy of SDB screening in an independent test database with annotated SDB epochs. The development database was from PhysioNet (n = 25 polysomnograms). The test database was from Sleep Health Centers in Boston (n = 1907 polysomnogram) where the SDB epochs (n = 1 538 222 epochs) were scored using American Academy of Sleep Medicine criteria. The first test was to classify every epoch in the evaluation data set. The combined EDR and HRV method classified 78% of the epochs as either normal or SDB and 22% as indeterminate, with a total accuracy of 88% for scored epochs (not indeterminate). The second test was to evaluate the SDB status for each patient. The algorithm correctly classified 71% of patients with either moderate-to-severe SDB or mild-to-no SDB. We believe that the ECG-based methods provide an efficient and inexpensive tool for SDB screening in both home and hospital settings and make SDB screening feasible in large populations.