ECG-derived Respiration Research Articles

Estimation of a respiratory signal from a single-lead ECG using the 4th order central moments

Abstract For a variety of clinical applications like magnetic resonance imaging (MRI) the monitoring of vital signs is a common standard in clinical daily routine. Besides the electrocardiogram (ECG), the respiratory activity is an important vital parameter and might reveal pathological changes. Thoracic movement and the resulting impedance change between ECG electrodes enable the estimation of the respiratory signal from the ECG. This ECG-derived respiration (EDR) can be used to calculate the breathing rate without the need for additional devices or monitoring modules. In this paper a new method is presented to estimate the respiratory signal from a single-lead ECG. The 4th order central moments was used to estimate the EDR signal exploiting the change of the R-wave slopes induced by respiration. This method was compared with two approaches by analyzing the Fantasia database from www.physionet.org. Furthermore, the ECG signals of 24 healthy subjects placed in an 3 T MR-scanner were acquired.

Obstructive sleep apnea detection using spectrum and bispectrum analysis of single-lead ECG signal

A novel method for the automatic diagnosis of obstructive sleep apnea (OSA) from an electrocardiogram (ECG) is presented. This method aims to detect OSA utilizing exclusively ECG recordings during sleep and present a minute-by-minute signal processing technique. In the proposed algorithm, a wide range of features based on heart rate variability (HRV) and ECG-derived respiratory (EDR) signals are considered. The novelty of this study arises from employing bispectral analysis to the HRV and EDR signals in order to illustrate quadratic phase-coupling that can be observed among signal components with different frequencies. From this perspective, in the proposed algorithm, a new feature set based on a higher order spectrum of HRV and EDR signals is introduced and it is utilized to extract information regarding their non-linearity and non-Gaussianity. This feature vector is then fed into the input of a least-square support vector machine classifier. To implement the proposed method, the apnea-ECG database, which contains 70 nocturnal ECG records gathered from volunteer men and women, is used in this work. Results obtained from cross-validating 35 data records show that the normal recordings could be separated from the apneic recordings with an accuracy of 95.57% and a sensitivity and specificity of 98.64% and 92.51%, respectively. In addition, 35 other records were used for a pure independent validation of the proposed method and the obtained accuracy, sensitivity and specificity was 94.12%, 93.46% and 94.79% respectively in OSA episode detection. The performance of our proposed technique is better than in other existing approaches. It can be used as a reliable tool for automatic OSA identification and as a result, it will improve medical services.

Real-time obstructive sleep apnea detection from frequency analysis of EDR and HRV using Lomb Periodogram.

In this study, an effective real-time obstructive sleep apnea (OSA) detection method from frequency analysis of ECG-derived respiratory (EDR) and heart rate variability (HRV) is proposed. Compared to traditional Polysomnography (PSG) which needs several physiological signals measured from patients, the proposed OSA detection method just only use ECG signals to determine the time interval of OSA. In order to be feasible to be implemented in hardware to achieve the real-time detection and portable application, the simplified Lomb Periodogram is utilized to perform the frequency analysis of EDR and HRV in this study. The experimental results of this work indicate that the overall accuracy can be effectively increased with values of Specificity (Sp) of 91%, Sensitivity (Se) of 95.7%, and Accuracy of 93.2% by integrating the EDR and HRV indexes.

EMG-Derived Respiration Signal Using the Fixed Sample Entropy during an Inspiratory Load Protocol.

Extracting clinical information from one single measurement represents a step forward in the assessment of the respiratory muscle function. This attracting idea entails the reduction of the instrumentation and fosters to develop new medical integrated technologies. We present the use of the fixed sample entropy (fSampEn) as a more direct method to non-invasively derive the breathing activity from the diaphragm electromyographic (EMGdi) signal, and thus to extract the respiratory rate, an important vital sign which is cumbersome and time-consuming to be measured by clinicians. fSampEn is a method to evaluate the EMGdi activity that is less sensitive to the cardiac activity (ECG) and its application has proven to be useful to evaluate the load of the respiratory muscles. The behavior of the proposed method was tested in signals from two subjects that performed an inspiratory load protocol, which consists of increments in the inspiratory mouth pressure (P mouth). Two respiratory signals were derived and compared to the P mouth signal: the ECG-derived respiration (EDR) signal from the lead-I configuration, and the EMG-derived respiration (EMGDR) signal by applying the fSampEn method over the EMGdi signal. The similitude and the lag between signals were calculated through the cross-correlation between each derived respiratory signal and the P mouth. The EMGDR signal showed higher correlation and lower lag values (≥ 0.91 and ≤ 0.70 s, respectively) than the EDR signal (≥ 0.83 and ≤ 0.99 s, respectively). Additionally, the respiratory rate was estimated with the P mouth, EDR and EMGDR signals showing very similar values. The results from this preliminary work suggest that the fSampEn method can be used to derive the respiration waveform from the respiratory muscle electrical activity.

Detecting Subclinical Diabetic Cardiac Autonomic Neuropathy by Analyzing Ventricular Repolarization Dynamics.

In this study, a linear parametric modeling technique was applied to model ventricular repolarization (VR) dynamics. Three features were selected from the surface ECG recordings to investigate the changes in VR dynamics in healthy and cardiac autonomic neuropathy (CAN) participants with diabetes including heart rate variability (calculated from RR intervals), repolarization variability (calculated from QT intervals), and respiration [calculated by ECG-derived respiration (EDR)]. Surface ECGs were recorded in a supine resting position from 80 age-matched participants (40 with no cardiac autonomic neuropathy (NCAN) and 40 with CAN). In the CAN group, 25 participants had early/subclinical CAN (ECAN) and 15 participants were identified with definite/clinical CAN (DCAN). Detecting subclinical CAN is crucial for designing an effective treatment plan to prevent further cardiovascular complications. For CAN diagnosis, VR dynamics was analyzed using linear parametric autoregressive bivariate (ARXAR) and trivariate (ARXXAR) models, which were estimated using 250 beats of derived QT, RR, and EDR time series extracted from the first 5 min of the recorded ECG signal. Results showed that the EDR-based models gave a significantly higher fitting value (p < 0.0001) than models without EDR, which indicates that QT-RR dynamics is better explained by respiratory-information-based models. Moreover, the QT-RR-EDR model fitting values gradually decreased from the NCAN group to ECAN and DCAN groups, which indicate a decoupling of QT from RR and the respiration signal with the increase in severity of CAN. In this study, only the EDR-based model significantly distinguished ECAN and DCAN groups from the NCAN group (p < 0.05) with large effect sizes (Cohen's d > 0.75) showing the effectiveness of this modeling technique in detecting subclinical CAN. In conclusion, the EDR-based trivariate QT-RR-EDR model was found to be better in detecting the presence and severity of CAN than the bivariate QT-RR model. This finding also establishes the importance of adding respiratory information for analyzing the gradual deterioration of normal VR dynamics in pathological conditions, such as diabetic CAN.

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

Respiratory rate extraction from single-lead ECG using homomorphic filtering

In this paper a new technique for the extraction of respiratory signal from the single-lead ECG using generalized homomorphic filtering is presented. It is proposed to perform band pass filtering on the cepstrum of the ECG signal to extract the respiratory signal. For this study, transforms used in generalized homomorphic filtering are the discrete Fourier transform (DFT) and the discrete cosine transform (DCT). The performance of the ECG-derived respiration (EDR) signal obtained using the proposed method is compared with the reference respiratory signal in terms of the correlation, magnitude squared coherence coefficients and breath rate accuracy. It is observed from the comparisons that the EDR technique based on generalized homomorphic filtering using DFT performs better than the homomorphic filtering using DCT. The proposed EDR technique is also compared with the two well-known EDR techniques: principal component analysis and R peak amplitude algorithm. It is seen from the results that the proposed EDR technique (RDFT) performs significantly better than the R peak amplitude algorithm, but significant improvements are not observed when compared with the PCA based EDR technique.

Holter ECG-Based Apnea Hypopnea Index to Screen Obstructive Sleep Apnea: A New Proposal and Evaluation of Feasibility

Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder. It has been reported that approximately 40% of patients with moderate or severe OSAS die within the first eight years of disease. In hospitals, OSAS is inspected using polysomnography, which uses a number of sensors. Because of the cumbersome nature of this polysomnography, an initial OSAS screening is usually conducted. In recent years, OSAS screening techniques using Holter electrocardiogram (ECG) have been reported. However, the techniques so far reported cannot perform an OSAS severity assessment. The present study presents a new method to distinguish the obstructive sleep apnea (OSA) and non-OSA epochs at one-second intervals based on the Apnea Hypopnea Index assessment, defined as the duration of continuous apnea. In the proposed method, the time-frequency components of the heart rate variability and three ECG-derived respiration signals calculated by the complex Morlet wavelet transformation are adopted as features. A support vector machine is employed for classification. The proposed method is evaluated using three eight-hour ECG recordings containing OSA episodes from three subjects. As a result, the sensitivity and specificity of classification are found to reach approximately 90%, a level suitable for OSAS screening in clinical settings.

Monitoring respiration rate in sleep-disordered breathing patients using chest belts or ECG

Background: For patients with suspected sleep-disordered breathing respiration rate (RR) is typically measured using a nasal airflow sensor. Additionally, two belts measure the movement of the chest ribcage and upper abdominal walls. At least one channel of ECG is also monitored. Instead of airflow, which could be considered the gold standard, RR may be computed using belts or ECG-derived respiration (EDR) techniques. In this study we compare the performance of such techniques to using airflow. Methods: Evaluation was done on 24 adult polysomnograms from the publicly available PhysioNet/ucddb database. Subjects were randomly selected from patients referred to a sleep lab for possible diagnosis of sleep apnea. Subjects had no known cardiac disease, autonomic dysfunction, and were not on medication known to interfere with heart rate. ECG was automatically analyzed using the Philips ST/AR algorithm to detect normal and exclude abnormal QRS complexes. The EDR waveform was then constructed by measuring the peak to trough amplitude in each normal QRS complex, and interpolating the measurements using cubic splines. A frequency-domain method was developed to calculate RR in a 30-second sliding window with a 5-second step by finding the fundamental frequency of oscillations. This method was applied to all four waveforms: airflow, EDR, ribcage, and abdominal belts. The RR values computed from airflow, used as reference, were compared with RRs from the other waveforms. Results: A total of 119,698 respiration cycles were analyzed. The average difference in RR from airflow was 0.7 ± 1.8 breaths-per-minute (bpm) for abdominal belt, 1.0 ± 2.4 for ribcage belt, and 1.8 ± 2.7 for EDR. Percentage distributions of RR differences are listed below.

Reduction of corrected QT interval during the first week after cardioversion of atrial fibrillation to sinus rhythm during sotalol treatment

Background: For patients with suspected sleep-disordered breathing respiration rate (RR) is typically measured using a nasal airflow sensor. Additionally, two belts measure the movement of the chest ribcage and upper abdominal walls. At least one channel of ECG is also monitored. Instead of airflow, which could be considered the gold standard, RR may be computed using belts or ECG-derived respiration (EDR) techniques. In this study we compare the performance of such techniques to using airflow. Methods: Evaluation was done on 24 adult polysomnograms from the publicly available PhysioNet/ucddb database. Subjects were randomly selected from patients referred to a sleep lab for possible diagnosis of sleep apnea. Subjects had no known cardiac disease, autonomic dysfunction, and were not on medication known to interfere with heart rate. ECG was automatically analyzed using the Philips ST/AR algorithm to detect normal and exclude abnormal QRS complexes. The EDR waveform was then constructed by measuring the peak to trough amplitude in each normal QRS complex, and interpolating the measurements using cubic splines. A frequency-domain method was developed to calculate RR in a 30-second sliding window with a 5-second step by finding the fundamental frequency of oscillations. This method was applied to all four waveforms: airflow, EDR, ribcage, and abdominal belts. The RR values computed from airflow, used as reference, were compared with RRs from the other waveforms. Results: A total of 119,698 respiration cycles were analyzed. The average difference in RR from airflow was 0.7 ± 1.8 breaths-per-minute (bpm) for abdominal belt, 1.0 ± 2.4 for ribcage belt, and 1.8 ± 2.7 for EDR. Percentage distributions of RR differences are listed below.

Effect of ECG-derived respiration (EDR) on modeling ventricular repolarization dynamics in different physiological and psychological conditions.

Ventricular repolarization dynamics is an important predictor of the outcome in cardiovascular diseases. Mathematical modeling of the heart rate variability (RR interval variability) and ventricular repolarization variability (QT interval variability) is one of the popular methods to understand the dynamics of ventricular repolarization. Although ECG derived respiration (EDR) was previously suggested as a surrogate of respiration, but the effect of respiratory movement on ventricular repolarization dynamics was not studied. In this study, the importance of considering the effect of respiration and the validity of using EDR as a surrogate of respiration for linear parametric modeling of ventricular repolarization variability is studied in two cases with different physiological and psychological conditions. In the first case study, we used 20 young and 20 old healthy subjects' ECG and respiration data from Fantasia database at Physionet to analyze a bivariate QT-RR and a trivariate [Formula: see text] model structure to study the aging effect on cardiac repolarization variability. In the second study, we used 16 healthy subjects' data from drivedb (stress detection for automobile drivers) database at Physionet to do the same analysis for different psychological condition (i.e., in stressed and no stress condition). The results of our study showed that model having respiratory information (QT-RR-RESP and QT-RR-EDR) gave significantly better fit value (p < 0.05) than that of found from the QT-RR model. EDR showed statistically similar (p > 0.05) performance as that of respiration as an exogenous model input in describing repolarization variability irrespective of age and different mental conditions. Another finding of our study is that both respiration and EDR-based models can significantly (p < 0.05) differentiate the ventricular repolarization dynamics between healthy subjects of different age groups and with different psychological conditions, whereas models without respiration or EDR cannot distinguish between the groups. These results established the importance of using respiration and the validity of using EDR as a surrogate of respiration in the absence of respiration signal recording in linear parametric modeling of ventricular repolarization variability in healthy subjects.

Electrocardiogram derived respiratory rate from QRS slopes and R-wave angle.

A method for estimating respiratory rate from electrocardiogram (ECG) signals is presented. It is based on QRS slopes and R-wave angle, which reflect respiration-induced beat morphology variations. The 12 standard leads, 3 leads from vectorcardiogram (VCG), and 2 additional non-standard leads derived from VCG loops were analyzed. The following series were studied as ECG derived respiration (EDR) signals: slope between the peak of Q and R waves, slope between the peak of R and S waves, and the R-wave angle. Information from several EDR signals was combined in order to increase the robustness of estimation. Evaluation is performed over two databases containing ECG and respiratory signals simultaneously recorded during two clinical tests with different characteristics: tilt test, representing abrupt cardiovascular changes, and stress test representing a highly non-stationary and noisy environment. A combination of QRS slopes and R-wave angle series derived from VCG leads obtained a respiratory rate estimation relative error of 0.50 ± 4.11% (measuring 99.84% of the time) for tilt test and 0.52 ± 8.99% (measuring 96.09% of the time) for stress test. These results outperform those obtained by other reported methods, both in tilt and stress testing.

Development of three methods for extracting respiration from the surface ECG: A review

BackgroundRespiration rate (RR) is a critical vital sign that can be monitored to detect acute changes in patient condition (e.g., apnea) and potentially provide an early warning of impending life-threatening deterioration. Monitoring respiration signals is also critical for detecting sleep disordered breathing such as sleep apnea. Additionally, analyzing a respiration signal can enhance the quality of medical images by gating image acquisition based on the same phase of the patient's respiratory cycle. Although many methods exist for measuring respiration, in this review we focus on three ECG-derived respiration techniques we developed to obtain respiration from an ECG signal. MethodsThe first step in all three techniques is to analyze the ECG to detect beat locations and classify them. 1) The EDR method is based on analyzing the heart axis shift due to respiration. In our method, one respiration waveform value is calculated for each normal QRS complex by measuring the peak to QRS trough amplitude. Compared to other similar EDR techniques, this method does not need removal of baseline wander from the ECG signal. 2) The RSA method uses instantaneous heart rate variability to derive a respiratory signal. It is based on the observed respiratory sinus arrhythmia governed by baroreflex sensitivity. 3) Our EMGDR method for computing a respiratory waveform uses measurement of electromyogram (EMG) activity created by respiratory effort of the intercostal muscles and diaphragm. The ECG signal is high-pass filtered and processed to reduce ECG components and accentuate the EMG signal before applying RMS and smoothing. ResultsOver the last five years, we have performed six studies using the above methods: 1) In 1907 sleep lab patients with >1.5M 30-second epochs, EDR achieved an apnea detection accuracy of 79%. 2) In 24 adult polysomnograms, use of EDR and chest belts for RR computation was compared to airflow RR; mean RR error was EDR: 1.8±2.7 and belts: 0.8±2.1. 3) During cardiac MRI, a comparison of EMGDR breath locations to the reference abdominal belt signal yielded sensitivity/PPV of 94/95%. 4) Another comparison study for breath detection during MRI yielded sensitivity/PPV pairs of EDR: 99/97, RSA: 79/78, and EMGDR: 89/86%. 5) We tested EMGDR performance in the presence of simulated respiratory disease using CPAP to produce PEEP. For 10 patients, no false breath waveforms were generated with mild PEEP, but they appeared in 2 subjects at high PEEP. 6) A patient monitoring study compared RR computation from EDR to impedance-derived RR, and showed that EDR provides a near equivalent RR measurement with reduced hardware circuitry requirements.

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.

Sleep apnea classification using ECG-signal wavelet-PCA features.

Sleep apnea is often diagnosed using an overnight sleep test called a polysomnography (PSG). Unfortunately, though it is the gold standard of sleep disorder diagnosis, a PSG is time consuming, inconvenient, and expensive. Many researchers have tried to ameliorate this problem by developing other reliable methods, such as using electrocardiography (ECG) as an observed signal source. Respiratory rate interval, ECG-derived respiration, and heart rate variability analysis have been studied recently as a means of detecting apnea events using ECG during normal sleep, but these methods have performance weaknesses. Thus, the aim of this study is to classify the subject into normal- or apnea-subject based on their single-channel ECG measurement in regular sleep. In this proposed study, ECG is decomposed into five levels using wavelet decomposition for the initial processing to determine the detail coefficients (D3-D5) of the signal. Approximately 15 features were extracted from every minute of ECG. Principal component analysis and a support vector machine are used for feature dimension reduction and classification, respectively. According to classification that been done from a data set consisting of thirty-five patients, the proposed minute-to-minute classifier specificity, sensitivity, and subject-based classification accuracy are 95.20%, 92.65%, and 94.3%, respectively. Furthermore, the proposed system can be used as a basis for future development of sleep apnea screening tools.

Implementation and Validation of ECG-Derived Respiration with QRS Characteristics

This study presents a method for reconstructing respiration signals from ECG tracings based on average electrical-cardiac-axis theory with heart rate variability. The algorithm is simplified to use QRS characteristic points and subsequent spectrum analysis. The experiments were conducted on eight healthy patients during rest with spontaneous (voluntarily) and paced breathings. Five QRS characteristic series are constructed, and the corresponding electrocardiography (ECG)-derived respiration (EDR) is compared with measured data. The experiment results show the R-Q-S series-derived respiration exhibited superior correlation with the original respiration from the perspective of breathing frequency.

Association of Â-adrenergic receptor polymorphisms and nocturnal cardiopulmonary coupling in the skara sleep cohort

Electrocardiogram (ECG) based cardiopulmonary coupling (CPC) analysis has been used for assessment of fragmented sleep and sleep disordered breathing. We investigated the relationships between â1- and â2- adrenergic receptor (AR) gene polymorphisms and CPC in a gender-balanced community-dwelling cohort of hypertensive patients and normotensive controls (Skara Sleep Cohort). 280 subjects (137 males, 126 hypertensive patients, age 61.3 ± 6.5 years, BMI 28.5 ± 4.7 kg/m 2 , AHI 24.6 ± 22.1 events/hour) underwent home polysomnography recording. The ECG signal was used to calculate the coupling between heart-rate variability oscillations and ECG-derived respiration. The percentage time spent with high-frequency coupling (HFC, 0.1–0.4 Hz), low-frequency coupling (LFC, 0.01–0.1 Hz) and very low-frequency coupling (VLFC, 0.0039–0.01 Hz) during the sleep period was quantified in subjects with various AR genotypes (â1-[Ser49Gly, Gly389Arg] and â2-[Gly16Arg, Gln27Glu]). The â2-AR Arg16 homozygous group had a higher HFC and lower LFC than the Gly16 group independent of gender, total apnea/hypopnea count and sleep efficiency ( p = 0.026 and 0.011, respectively). The â1-AR Arg389 and Gly389 homozygous groups differed in terms of time spent with HFC and VLFC ( p = 0.067 and 0.003, respectively). There was a significant interaction between â2-AR Arg16Gly and â1-AR Arg389Gly polymorphisms such that â2-AR Arg16 homozygotes had further increased HFC and reduced LFC when â1-AR Gly389 allele was present. The â1-AR Ser49Gly and â2-AR Gln27Glu genotypes did not associate with CPC indices. The â2-AR Arg16Gly polymorphism influences CPC during sleep. Difference in the â-AR genotype may explain variations in cardiac autonomic modulation during sleep. The study was supported by the Swedish Heart and Lung Foundation, the Swedish Society of Medicine and the Göteborg Medical Society.

A new approach to sleep study: does heart tell us a lot?

Introduction It has been proven that ECG-derived respiration signal is highly correlated with the actual respiration waveforms. Cardiopulmonary coupling (CPC) analysis is derived from an estimation of the coupling between the autonomic and respiratory drives, using heart rate and respiratory modulation of QRS amplitude, respectively. This dual information can be extracted from a single channel of ECG, and is highly correlated with the actual respiration waveforms. High frequency coupling (HFC) is the marker of stable sleep, and low-frequency coupling (LFC) is the marker of unstable sleep. Fragmented sleep is characterized by coupled low-frequency behaviors across numerous sleep based physiological stream. There have been an increasing number of papers evaluating CPC or using CPC as a clinical measurement. Materials and methods The literature search was performed via the internet using the PubMed, the Cochrane database and Sleep abstract supplements. The search included papers only in English, published up to June 2013. The key words which were searched in the titles and abstracts were the terms “cardiopulmonary”, “coupling”, “CPC”, “ECG-derived” “Electrocardiogram-based spectrogram” in combination with the name of all types of sleep disorders (e.g. “insomnia”). Results 46 relevant English articles were found, 29 (63%) was cardiopulmonary coupling. 9 (19.6%) articles explain mechanism, 30 (65.2%) articles are studies on SDB, and 7 (15.2%) articles relate to other sleep disorders or comorbidities. The methods of CPC analysis as a measurement of sleep evaluation has been compared with conventional PSG or PTT. While most of the articles are about sleep apnea, sleep quality, detecting central and obstructive, and evaluating effects of PAP therapies, studies have covered sleep quality study in sleep apnea, insomnia, hypertension, chronic heart failure, diabetics, fibromyalgia, as well as healthy subjects. Conclusion Using data derived from ECG can be used as clinical screen or post-treatment follow-up. This review confirmed the association between sleep physiology and sleep spectrums analyzed by cardiopulmonary coupling. As sleep problems are of growing concern, easier access of overnight ECG data can be used broadly when sleep monitor is necessary. With the techniques of cardiopulmonary analysis, a portable monitor for sleep can be effective by collecting enough data for sleep analysis, meanwhile be more convenient and cost-effective. Furthermore, adding actigraphy and/or oximetry will be recommended for clinical applications. Acknowledgements I would like to thank Professor C.K. Peng (Division of Interdisciplinary Medicine & Biotechnology in BIDMC) for his help in organizing a project and mentoring me with the Cardiopulmonary Coupling techniques. I would also like to thank Dr. Robert Thomas (Sleep specialist in BIDMC, affiliated Hospital of Harvard Medical School) for his mentoring me with sleep clinic observation.

Ictal and interictal respiratory changes in temporal lobe and absence epilepsy in childhood

Autonomic dysfunctions occur during but also in between seizures. During seizures, the direct involvement of central autonomic control centers cause specific changes in heart rate and respiration. The pathophysiology of autonomic dysfunctions that are observed in the interictal period is more difficult to explain. These alterations are most likely due to changes in the epileptic network and/or to a lesser extent due to direct interictal spike activity disturbing central autonomic centers. The aim of our study is to investigate whether ictal and interictal respiratory changes do occur in temporal lobe and absence epilepsy in children. We hypothesize that the interictal autonomic changes are due to changes in the neuronal network, by studying epilepsy patients with normal interictal background EEG. Ictal and interictal single-lead ECG signals were extracted from 24h video-EEG recordings in 10 children with refractory temporal lobe seizures, in 10 children with absence seizures with occasional interictal discharges and 10 control subjects. RR interval time series were calculated and respiration parameters were derived from the ECG signal. ECG-derived respiration (EDR) signals were computed and time and frequency domain parameters were extracted to characterize the respiratory function. In the ictal registrations we observed bradypnea in 10 out of the 12 recorded seizures from the temporal lobe. In absence seizures, we observed a variable ictal effect on respiratory rate. In the analysis of the interictal data, the most remarkable finding was the higher power in the low frequency band and lower power in the high frequency band of the EDR signals in patients with absence seizures compared to control subjects, indicating a shift of respiratory rate to the lower frequencies. In conclusion we found a uniform pattern in ictal respiratory changes in temporal lobe seizures, due to direct involvement of central respiratory centers. In absence epilepsy, we found a disturbed respiratory control in between seizures. These changes were not present in the patients with temporal lobe epilepsy. The observed interictal changes in respiration in absence epilepsy are most likely due to epileptogenetic changes in the thalamocortical network, involved in absence epilepsy and could not be explained by interictal spike activity.