Design and evaluation of a handheld impedance plethysmograph for measuring heart rate variability

Heart rate variability (HRV) analysis from 10s ECGs has been shown to be reliable. However, the short examination time warrants a user-friendly system that can be used forad-hoc examinations without normal preparation, unlike ECG. A handheld device has been developed that can measure ultra-short HRV from impedance plethysmographic recordings of the pulse wave in distal superficial arteries. The prototype device was made user-friendly through a compact, pen-like design and the use of integrated metal electrodes that were especially designed for dry operation. The main signal processing was performed by a digital signal processor, where the discrete heart beats were detected using a correlation algorithm that could adapt to individual pulse wave shapes to account for biological variation. The novel device was evaluated in 20 mainly young volunteers, using 10 s time-correlated ECG recordings as the reference method. Agreement between the two methods in measuring heart rate and root mean square of successive differences in the heart beat interval (RMSSD) was analysed using correlation coefficients (Pearson's R²), mean differences with 95% confidence intervals and 95% limits of agreement, and Bland-Altman plots. The correlation between the two methods was R²=1.00 and R²=0.99 when heart rate and RMSSD were measured, respectively. The Bland-Altman plots showed suitable agreement between the novel device and standard 10 s ECGs, which was substantiated by 95% limits of agreement of the difference of ± 0.1 beats min⁻¹ and ∼ ± 10 ms for heart rate and RMSSD, respectively. Therefore the evaluation showed no significant systematic error of the novel device compared with ECG.     

Sleep apnea in heart failure increases heart rate variability and sympathetic dominance

AIMS: Sleep disordered breathing (SDB) is common in heart failure and ventilation is known to influence heart rate. Our aims were to assess the influence of SDB on heart rate variability (HRV) and to determine whether central sleep apnea (CSA) and obstructive sleep apnea (OSA) produced different patterns of HRV. METHODS AND RESULTS: Overnight polysomnography was performed in 21 patients with heart failure and SDB. Two 10-minute segments each of SDB and stable breathing from each patient were visually identified and ECG signal exported for HRV analysis. SDB increased total power (TP) with very low frequency (VLF) power accounting for the greatest increase (1.89+/-0.54 vs 2.96+/-0.46 ms2, P <0.001); LF/HF ratio increased during SDB (1.2+/-1.0 vs 2.7+/-2.1, P <0.001). Compared to OSA, CSA was associated with lower absolute LF (2.10+/-0.47 vs 2.52+/-0.55 ms2, P = 0.049) and HF power (1.69+/-0.41 vs 2.34+/-0.58 ms2, P = 0.004), increased VLF% (78.9%+/-13.4% vs 60.9%+/-19.2%, P = 0.008), decreased HF% (6.9%+/-7.8% vs 16.0%+/-11.7%, P = 0.046) with a trend to higher LF/HF ratio. CONCLUSIONS: SDB increases HRV in the setting of increased sympathetic dominance. HRV in CSA and OSA have unique HRV patterns which are likely to reflect the different pathophysiological mechanisms involved.     

Sleep‐disordered breathing in children is associated with impairment of sleep stage‐specific shift of cardiac autonomic modulation

We examined the effects of sleep stages and sleep‐disordered breathing (SDB) on autonomic modulation in 700 children. Apnea hypopnea index (AHI) during one 9 h night‐time polysomnography was used to define SDB. Sleep stage‐specific autonomic modulation was measured by heart rate variability (HRV) analysis of the first available 5 min RR intervals from each sleep stage. The mean [standard deviation (SD)] age was 112 (21) months (49% male and 25% non‐Caucasian). The average AHI was 0.79 (SD = 1.03) h^?1, while 73.0%, 25.8% and 1.2% of children had AHI <1 (no SDB), 1–5 (mild SDB) and ≥5 (moderate SDB), respectively. In the no SDB group, the high frequency (HF) and root mean square SD (RMSSD) increased significantly from wake to Stage 2 and slow wave sleep (SWS), and then decreased dramatically when shifting into rapid eye movement (REM) sleep. In the moderate SDB group, the pattern of HRV shift was similar to that of no SDB. However, the decreases in HF and RMSSD from SWS to REM were more pronounced in moderate SDB children [between‐group differences in HF (?24% in moderate SDB versus ?10% in no SDB) and RMSSD (?27% versus ?12%) were significant (P < 0.05)]. The REM stage HF is significantly lower in the moderate SDB group compared to the no SDB group [mean (standard error): 4.49 (0.43) versus 5.80 (0.05) ms², respectively, P < 0.05]. Conclusions are that autonomic modulation shifts significantly towards higher parasympathetic modulation from wake to non‐rapid eye movement sleep, and reverses to a less parasympathetic modulation during REM sleep. However, the autonomic modulation is impaired among children with moderate SDB in the directions of more reduction in parasympathetic modulation from SWS to REM sleep and significantly weaker parasympathetic modulation in REM sleep, which may lead to higher arrhythmia vulnerability, especially during REM sleep.     

Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method

STUDY OBJECTIVES: Complex sleep apnea is defined as sleep disordered breathing secondary to simultaneous upper airway obstruction and respiratory control dysfunction. The objective of this study was to assess the utility of an electrocardiogram (ECG)-based cardiopulmonary coupling technique to distinguish obstructive from central or complex sleep apnea. DESIGN: Analysis of archived polysomnographic datasets. SETTING: A laboratory for computational signal analysis. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The PhysioNet Sleep Apnea Database, consisting of 70 polysomnograms including single-lead ECG signals of approximately 8 hours duration, was used to train an ECG-based measure of autonomic and respiratory interactions (cardiopulmonary coupling) to detect periods of apnea and hypopnea, based on the presence of elevated low-frequency coupling (e-LFC). In the PhysioNet BIDMC Congestive Heart Failure Database (ECGs of 15 subjects), a pattern of "narrow spectral band" e-LFC was especially common. The algorithm was then applied to the Sleep Heart Health Study-I dataset, to select the 15 records with the highest amounts of broad and narrow spectral band e-LFC. The latter spectral characteristic seemed to detect not only periods of central apnea, but also obstructive hypopneas with a periodic breathing pattern. Applying the algorithm to 77 sleep laboratory split-night studies showed that the presence of narrow band e-LFC predicted an increased sensitivity to induction of central apneas by positive airway pressure. CONCLUSIONS: ECG-based spectral analysis allows automated, operator-independent characterization of probable interactions between respiratory dyscontrol and upper airway anatomical obstruction. The clinical utility of spectrographic phenotyping, especially in predicting failure of positive airway pressure therapy, remains to be more thoroughly tested.     

Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders

Sleep apnea is one of the most common sleep disorders. Here, patients suffer from multiple breathing pauses longer than 10 s during the night which are referred to as apneas. The standard method for the diagnosis of sleep apnea is the attended cardiorespiratory polysomnography (PSG). However, this method is expensive and the extensive recording equipment can have a significant impact on sleep quality falsifying the results. To overcome these problems, a comfortable and novel system for sleep monitoring based on the recording of tracheal sounds and movement data is developed. For apnea detection, a unique signal processing method utilizing both signals is introduced. Additionally, an algorithm for extracting the heart rate from body sounds is developed. For validation, ten subjects underwent a full-night PSG testing, using the developed sleep monitor in concurrence. Considering polysomnography as gold standard the developed instrumentation reached a sensitivity of 92.8% and a specificity of 99.7% for apnea detection. Heart rate measured with the proposed method was strongly correlated with heart rate derived from conventional ECG (r ² = 0.8164). No significant signal losses are reported during the study. In conclusion, we demonstrate a novel approach to reliably and noninvasively detect both apneas and heart rate during sleep.     

Algorithm for the classification of multi-modulating signals on the electrocardiogram

This article discusses the algorithm to measure electrocardiogram (ECG) and respiration simultaneously and to have the diagnostic potentiality for sleep apnoea from ECG recordings. The algorithm is composed by the combination with the three particular scale transform of a(j)(t), u(j)(t), o(j)(a(j)) and the statistical Fourier transform (SFT). Time and magnitude scale transforms of a(j)(t), u(j)(t) change the source into the periodic signal and tau(j) = o(j)(a(j)) confines its harmonics into a few instantaneous components at tau(j) being a common instant on two scales between t and tau(j). As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals by inverse transform. The algorithm is expected to get the partial ventilation and the heart rate variability from scale transforms among a(j)(t), a(j+1)(t) and u(j+1)(t) joining with each modulation. The algorithm has a high potentiality of the clinical checkup for the diagnosis of sleep apnoea from ECG recordings.     

Die diagnostische Genauigkeit der Polygraphie bei Patienten mit Herzinsuffizienz

Background and objectives

The prevalence of sleep-disordered breathing (SDB) in heart failure is high. Ambulatory polygraphy (PG) is increasingly being used as a diagnostic tool especially in the area of cardiology. To date, conclusive data about the accuracy of polygraphy in patients with heart failure is not available. The aim of the study was to compare the apnea-hypopnea index (AHI) of polysomnography (PSG) and PG as the basis for therapeutic decisions and to elucidate possible contrasts and their influence on the clinical decision.

Methods

Patients with symptomatic heart failure and systolic left ventricular dysfunction (ejection fraction ≤45%) were examined by PSG. The AHI based on total sleep time (TST) as well as on time in bed (TIB) was compared. The SDB was subdivided according to severity: clinically not relevant 0–5/h, mild >5–15/h, moderate >15–30/h and severe >30/h.

Results

Sleep efficiency based on 203 patients was 60.4% (±18.8); 136 (69%) showed SDB. In exact comparison of the results based on TIB and TST, 156 (79%) agreements and 41 (21%) disagreements were found.

Conclusion

Cardiorespiratory PG must be considered critically in patients with heart failure since the majority of these patients sleep very poorly. The diminished sleep efficiency and thereby increased mismatch between TIB and TST significantly influences the AHI. In some cases therapeutic decisions would have been influenced markedly by this fact. From our point of view, PSG is still mandatory as a diagnostic instrument especially in patients with heart failure.     

Screening for left ventricular systolic dysfunction using GP-reported ECGs

BACKGROUND: Diagnostic echocardiography has poor access for patients with suspected heart failure. Pre-echocardiography screening with electrocardiograms (ECGs) is recommended as a means of targeting this scarce resource. There are data to support this policy when ECGs are interpreted by cardiologists but not by GPs. AIM: To assess the value of GP-reported ECGs as a pre-echocardiography screening test for left ventricular systolic dysfunction (LVSD). DESIGN OF STUDY: Cross-sectional study of GPs' ECG reporting skills. SETTING: General practice, NHS in Scotland. METHOD: A randomly selected, stratified sample of 123 Scottish GPs reviewed 180 ECGs (100 abnormal, 50 normal and 30 duplicate) from 150 patients with suspected heart failure. Forty-one patients had LVSD on echocardiography. GPs were required to categorise ECGs as normal or abnormal. RESULTS: Mean sensitivity was 0.94 (95% CI = 0.92 to 0.95). Mean specificity 0.58 (95% CI = 0.56 to 0.60). Mean positive predictive value (PPV) was 0.47 (95% CI = 0.46 to 0.48). Mean negative predictive value (NPV) was 0.96 (95% CI = 0.95 to 0.97). Mean likelihood ratio was 2.39 (95% CI = 2.28 to 2.50). Seventy of 123 (57%) GPs achieved sensitivity of 0.9 and specificity of 0.5 for the detection of LVSD. CONCLUSION: Most Scottish GPs have the skills to perform pre-echocardiography screening ECGs in patients with suspected LVSD. However, differences in ECG reporting performance between individual GPs will result in widely varying referral rates for echocardiography and differences in the detection rate of LVSD. The implications of these findings need to be considered when heart failure diagnostic services are being developed.     

Real-time multichannel abdominal fetal ECG monitor using Digital Signal CoProcessor

A real-time multichannel fetal ECG monitor based on a personal computer (PC) and a MOTOROLA DSP56001 Digital Signal CoProcessor (DSP) is introduced. The DSP board is plugged into the PC, which functions as a HOST computer. An analog 8 Leads Interface and Analog to Digital circuits module is connected to the DSP through a synchronous, opticalisolated communication channel.

The fetal ECG detection is based on a cross-correlation technique. An averaged maternal ECG waveform is generated using a cross-correlation alignment procedure and a user-defined template. The fetal ECG signals present in the maternal waveform is suppressed during the averaging procedure, since both are uncorrelated. The average maternal ECG waveform is then subtracted from the abdominal real time signals, and maternal-free fetal ECGs signals are obtained, including fetal QRS complexes that coincide with maternal ones. Using the abdominal ECGs signals after subtraction, an averaged fetal waveform is generated. The maternal and the fetal heart rate are calculated during the process.

The algorithm described above can be performed in real time on up to eight abdominal ECG traces by the DSP, and the desired results are passed to the HOST PC, to be stored and displayed. Electrodes positioning procedures for detecting the fetal QRS complexes with the best signal to noise ratio are not needed. Using the multichannel system, the user can select the best channel for fetal QRS detection, and accurate results for the heart rate signal are obtained. Averaged fetal waveforms are obtained from all the leads.     

Method for detection of respiratory cycle-related EEG changes in sleep-disordered breathing

STUDY OBJECTIVES: In sleep-disordered breathing (SDB), visual or computerized analysis of electroencephalogram (EEG) signals shows that disruption of sleep architecture occurs in association with apneas and hypopneas. We developed a new signal analysis algorithm to investigate whether brief changes in cortical activity can also occur with individual respiratory cycles. DESIGN: Retrospective. SETTING: University sleep laboratory. PARTICIPANTS: A 6 year-old boy with SDB. INTERVENTION: Polysomnography before and after clinically indicated adenotonsillectomy. MEASUREMENTS: For the first 3 hours of nocturnal sleep, a computer algorithm divided nonapneic respiratory cycles into 4 segments and, for each, computed mean EEG powers within delta, theta, alpha, sigma, and beta frequency ranges. Differences between segment-specific EEG powers were tested by analysis of variance. Respiratory cycle-related EEG changes (RCREC) were quantified. RESULTS: Preoperative RCREC were statistically significant in delta (P < .0001), theta (P < .001), and sigma (P < .0001) but not alpha or beta (P > .01) ranges. One year after the operation, RCREC in all ranges showed statistical significance (P < .01), but delta, theta, and sigma RCREC had decreased, whereas alpha and beta RCREC had increased. Preoperative RCREC also were demonstrated in a sequence of 101 breaths that contained no apneas or hypopneas (P < .0001). Several tested variations in the signal-analysis approach, including analysis of the entire nocturnal polysomnogram, did not meaningfully improve the significance of RCREC. CONCLUSIONS: In this child with SDB, the EEG varied with respiratory cycles to a quantifiable extent that changed after adenotonsillectomy. We speculate that RCREC may reflect brief but extremely numerous microarousals.     

Variability of QT interval duration in obstructive sleep apnea: an indicator of disease severity

STUDY OBJECTIVE: To determine OSA-related changes in variability of QT interval duration and in heart rate variability (HRV), and to evaluate the relationship of these parameters to disease severity. DESIGN: Retrospective analysis of diagnostic sleep records. SETTINGS: Clinical sleep laboratory in a hospital setting. PATIENTS: Twenty patients (12 males and 8 females) without significant comorbidities who were undergoing polysomnography were studied. MEASUREMENTS AND RESULTS: Standard heart rate variability measures and QT variability (Berger algorithm) were computed over consecutive 5-minute ECG epochs throughout the night. The effect of sleep stage and the relationship between these parameters and the severity of OSA as determined by the respiratory disturbance index (RDI) were explored. Further, a linear regression model of QT variability was developed. Severity of OSA (RDI) was 49 +/- 28 (range from 17-107) events/ hr. QT variability was the only ECG measure significantly correlated with RDI (both log-transformed; r = 0.6, P = 0.006). Further, QT variability was correlated with the minimum oxygen saturation (r = -0.55, P = 0.01). Sleep stage showed a significant effect on HRV, but not on QT variability. In the regression model, RDI was the strongest predictor of QT variability (R2 increase 38%), followed by high and low frequency power of HRV (R2 increase 10% each). CONCLUSION: Obstructive sleep apnea is associated with changes in QT interval variability during sleep. The variance of beat-to-beat QT intervals correlates more strongly with the severity of OSA (as determined by RDI) than standard measures of heart rate variability, and is correlated with blood oxygenation, but not sleep stage.     

Quantification of the fetal electrocardiogram using averaging technique

A signal analysis procedure is described for obtaining time intervals parameters of the fetal electrocardiogram as recorded from the maternal abdomen. Applying averaging to the fetal electrocardiogram quantification of the PR interval, QRS duration and QT interval were measured. This technique which includes the subtraction of an averaged maternal ECG waveform using cross-correlation function and fast Fourier transform algorithm, enables the detection of all the fetal QRS complexes in spite of their coincidence with the maternal ECGs. Results that were obtained from 21 pregnant women at the gestational age of 32-41 weeks and an example of a recording with fetal premature ventricular contractions are presented. This method shows an important improvement with respect to detection of fetal heart rate and detection of arrhythmia disturbances in the fetal ECG. The averaging procedure can be used to evaluate long-lived alterations in the fetal ECG.     

Hippocampus volume and subjective sleepiness in older people with sleep‐disordered breathing: a preliminary report

Sleep‐disordered breathing (SDB) is associated with excessive daytime sleepiness (EDS) and explained by sleep fragmentation and hypoxaemia, both contributing to brain morphology abnormalities. Recent data on middle‐aged SDB patients suggest a link between hippocampus volume (HV) and EDS. We tested this hypothesis in a group of SDB older subjects. A total of 232 healthy participants aged 75 ± 0.9 years were examined. Subjective EDS was assessed by the Epworth Sleep Questionnaire (ESS), with a mean score of 5.6 ± 3.5. Volumetric segmentation of the right (RHV) and left HV (LHV) were measured using FreeSurfer software. All subjects underwent extensive cognitive testing to exclude neurological disease, as well as ambulatory polygraphy to assess SDB status. Sleepy subjects showed a lower HV. In a correlation analysis, RHV (r = ?0.162, P = 0.01) and LHV (r = ?170, P = 0.05) were correlated negatively with ESS and not associated with respiratory data. Multiple regression analysis did not reveal any effect of age, gender, SDB severity and hypoxia. ESS was the only factor possibly explaining the lower RHV (P = ?0.03) and LHV (P = ?0.04). In older people with SDB, the subjective EDS was associated with lower HV. This morphological finding should be considered on the pathogenesis of sleepiness in SDB patients. Clinical trial registration: NCT 00759304 and NCT 00766584.     

Impact of sleep‐disordered breathing in patients with acute myocardial infarction: a retrospective analysis

Sleep‐disordered breathing (SDB) is associated with an increased risk of cardiovascular events. Previous studies showed that severe SDB has a negative impact on myocardial salvage and progression of left ventricular dysfunction after acute myocardial infarction (AMI). This study investigated the frequency of SDB and the effects of SDB on left ventricular function after AMI. This retrospective study enrolled all patients with AMI who had undergone cardiorespiratory polygraphy for SDB diagnosis. The apnea–hypopnea index was used as a standard metric of SDB severity. SDB was classified as mild (apnea–hypopnea index >5 to <15 per h), moderate (≥15 to <30 per h) or severe (apnea–hypopnea index ≥30 per h). According to the majority of events, SDB was classified as predominant obstructive sleep apnea, central sleep apnea or mixed sleep apnea (mixed SDB). A total of 223 patients with AMI (112 with ST elevation and 111 without ST elevation; 63.2 ± 11.2 years, 82% male, left ventricular ejection fraction 49 ± 12%) were enrolled. SDB was present in 85.6%, and was moderate‐to‐severe in 63.2%; 40.8% had obstructive sleep apnea, 41.7% had central sleep apnea and 3.1% had mixed SDB. Left ventricular ejection fraction was lower in patients with AMI with severe SDB (45 ± 14%) versus those without SDB (57 ± 7%; P < 0.005). In addition, lower left ventricular ejection fraction (≤45%) was associated with increased frequency (apnea–hypopnea index ≥5 per h in 96%) and severity (apnea–hypopnea index ≥30 per h in 48%) of SDB in general and a higher percentage of central sleep apnea (57%) in particular. SDB is highly frequent in patients with AMI. SDB severity appeared to be linked to impaired left ventricular function, especially in patients with central sleep apnea.     

The comparison of different feed forward neural network architectures for ECG signal diagnosis

The electrocardiograms (ECGs) record the electrical activity of the heart and are used to diagnose many heart disorders. This paper proposes a two-stage feed forward neural network for ECG signal classification. The research is aimed at the design of an intelligent ECG diagnosis tool that can recognise heart abnormalities while reducing the complexity, cost, and response time of the system. A number of neural network architectures are designed and compared for their ability to classify six different heart conditions. Two network architectures based on one stage and two stage feed forward neural networks are chosen for this investigation. The training and testing ECG signals are obtained from MIT-BIH database. The network inputs are comprised of 12 ECG features and 13 compressed components of each heart beat signal. The performance of the different modules as well as the efficiency of the whole system is presented. Among different architectures, a proposed multi-stage network named NET_BST possesses the highest recognition rate of around 93%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems.     

Cross-correlation of EEG frequency bands and heart rate variability for sleep apnoea classification

Sleep apnoea is a sleep breathing disorder which causes changes in cardiac and neuronal activity and discontinuities in sleep pattern when observed via electrocardiogram (ECG) and electroencephalogram (EEG). Using both statistical analysis and Gaussian discriminative modelling approaches, this paper presents a pilot study of assessing the cross-correlation between EEG frequency bands and heart rate variability (HRV) in normal and sleep apnoea clinical patients. For the study we used EEG (delta, theta, alpha, sigma and beta) and HRV (LF_nu, HF_nu and LF/HF) features from the spectral analysis. The statistical analysis in different sleep stages highlighted that in sleep apnoea patients, the EEG delta, sigma and beta bands exhibited a strong correlation with HRV features. Then the correlation between EEG frequency bands and HRV features were examined for sleep apnoea classification using univariate and multivariate Gaussian models (UGs and MGs). The MG outperformed the UG in the classification. When EEG and HRV features were combined and modelled with MG, we achieved 64% correct classification accuracy, which is 2 or 8% improvement with respect to using only EEG or ECG features. When delta and acceleration coefficients of the EEG features were incorporated, then the overall accuracy improved to 71%.     

Reduced NREM sleep instability in children with sleep disordered breathing

STUDY OBJECTIVES: To evaluate NREM sleep instability, as measured by the cyclic alternating pattern (CAP), in a cohort of children with mild sleep disordered breathing (SDB) or frank obstructive sleep apnea (OSA) and normal controls. DESIGN: Prospective study. SETTINGS: Sleep laboratory in academic center. PARTICIPANTS: Twenty-two patients (13 boys; mean age 6.5 +/- 2.4 years; 10 with mild SDB and 12 with OSA) and 15 normal children matched for age underwent overnight polysomnographic recordings in a standard laboratory setting. Sleep was visually scored for sleep macrostructure and CAP in a blinded fashion using standard criteria. Markovian analysis was also performed. MEASUREMENTS AND RESULTS: Participants with OSA had reduced total CAP rates than normal controls and mild SDB patients. Children with mild SDB or OSA had a lower number of A1, lower A1 percentage, and lower A1 index than controls. Children with OSA also showed longer intervals between consecutive A phases and a decrease in entropy in the Markovian analysis. CONCLUSIONS: The lower CAP rate and its reduced entropy in children with mild SDB or OSA seem to indicate the presence of subtle sleep alterations that are not clearly identifiable with other approaches and might provide more robust correlates of neurocognitive and behavioral dysfunction in snoring children.     

Sleep and neuropsychological performance in HIV+ subjects on efavirenz-based therapy and response to switch in therapy

The antiretroviral drug efavirenz (EFV) has been linked to disordered sleep and cognitive abnormalities. We examined sleep and cognitive function and subsequent changes following switch to an alternative integrase inhibitor-based regimen. Thirty-two HIV-infected individuals on EFV, emtricitabine, and tenofovir (EFV/FTC/TDF) without traditional risk factors for obstructive sleep apnea (OSA) were randomized 2:1 to switch to elvitegravir/cobicistat/emtricitabine/tenofovir (EVG/COBI/FTC/TDF) or to continue EFV/FTC/TDF therapy for 12 weeks. Overnight polysomnography and standardized sleep and neuropsychological assessments were performed at baseline and at 12 weeks. No significant differences in change over 12 weeks were noted between the two arms in any sleep or neuropsychological test parameter. At entry, however, the rate of sleep disordered breathing (SDB) was substantially higher in study subjects compared to published age-matched norms and resulted in a high assessed OSA rate of 59.4%. Respiratory Disturbance Index (RDI), a measure of SDB, correlated with age- and education-adjusted global neuropsychological Z-score (NPZ) (r?=??0.35, p?=?0.05). Sleep Maintenance Efficiency, Wake after Sleep Onset, REM Sleep and RDI correlated with domain-specific NPZ for learning and memory (all p-values ≤ 0.05). Among HIV-infected individuals on EFV-based therapy and without traditional risk factors for OSA, sleep and neuropsychological abnormalities do not readily reverse after discontinuation of EFV. High baseline rates of SDB and abnormalities in sleep architecture exist in this population correlating with neuropsychological impairment. The role of HIV immuno-virologic or lifestyle factors as contributing etiologies should be explored. OSA may be an under-recognized etiology for cognitive dysfunction during chronic HIV.     

Online Digital Filter and QRS Detector Applicable in Low Resource ECG Monitoring Systems

The present work describes fast computation methods for real-time digital filtration and QRS detection, both applicable in autonomous personal ECG systems for long-term monitoring. Since such devices work under considerable artifacts of intensive body and electrode movements, the input filtering should provide high-quality ECG signals supporting the accurate ECG interpretation. In this respect, we propose a combined high-pass and power-line interference rejection filter, introducing the simple principle of averaging of samples with a predefined distance between them. In our implementation (sampling frequency of 250 Hz), we applied averaging over 17 samples distanced by 10 samples (Filter10x17), thus realizing a comb filter with a zero at 50 Hz and high-pass cut-off at 1.1 Hz. Filter10x17 affords very fast filtering procedure at the price of minimal computing resources. Another benefit concerns the small ECG distortions introduced by the filter, providing its powerful application in the preprocessing module of diagnostic systems analyzing the ECG morphology. Filter10x17 does not attenuate the QRS amplitude, or introduce significant ST-segment elevation/depression. The filter output produces a constant error, leading to uniform shifting of the entire P-QRS-T segment toward about 5% of the R-peak amplitude. Tests with standardized ECG signals proved that Filter10x17 is capable to remove very strong baseline wanderings, and to fully suppress 50 Hz interferences. By changing the number of the averaged samples and the distance between them, a filter design with different cut-off and zero frequency could be easily achieved. The real-time QRS detector is designed with simplified computations over single channel, low-resolution ECGs. It relies on simple evaluations of amplitudes and slopes, including history of their mean values estimated over the preceding beats, smart adjustable thresholds, as well as linear logical rules for identification of the R-peaks in real-time. The performance of the QRS detector was tested with internationally recognized ECG databases (AHA, MIT-BIH, European ST-T database), showing mean sensitivity of 99.65% and positive predictive value of 99.57%. The performance of the presented QRS detector can be highly rated, comparable and even better than other published real-time QRS detectors. Examples representing some typical unfavorable conditions in real ECGs, illustrate the common operation of Filter10x17 and the QRS detector.     

Sleep Disordered Breathing,Daytime Symptoms,and Functional Performance in Stable Heart Failure

Study Objectives:

To evaluate characteristics of sleep disordered breathing (SDB); clinical and demographic correlates of SDB; and the extent to which SDB explains functional performance and symptoms in stable heart failure patients receiving care in structured HF disease management programs.

Design:

Cross-sectional, observational study.

Setting:

Structured heart failure disease management programs.

Participants:

170 stable chronic heart failure patients (mean age = 60.3 ± 16.8 years; n = 60 [35%] female; n = 50 [29%] African American; left ventricular ejection fraction mean = 32 ± 14.6).

Interventions:

N/A

Measurements and Results:

Full polysomnography was obtained for one night on participants in their homes. Participants completed the 6-minute walk, 3 days of actigraphy, MOS-SF 36, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, Multi-Dimensional Assessment of Fatigue Scale, and the Centers for the Epidemiological Studies of Depression Scale. Fifty-one percent had significant SDB; Sixteen (9%) of the total sample had central sleep apnea. Severe SDB was associated with a 4-fold increase in the likelihood of poor self-reported physical function (OR = 4.15, 95%CI = 1.19–14.57) and CSA was associated with low levels of daytime mobility (OR = 4.09, 95%CI = 1.23–13.62) after controlling for clinical and demographic variables. There were no statistically significant relationships between SDB and daytime symptoms or self-reported sleep, despite poorer objective sleep quality in patients with SDB.

Conclusions:

Severe SDB is associated with poor physical function in patients with stable HF but not with daytime symptoms or self-reported sleep, despite poorer objective sleep quality in patients with SDB.

Citation:

Redeker NS; Muench U; Zucker MJ; Walsleben J; Gilbert M; Freudenberger R; Chen M; Campbell D; Blank L; Berkowitz R; Adams L; Rapoport DM. Sleep disordered breathing, daytime symptoms, and functional performance in stable heart failure. SLEEP 2010;33(4):551-560.