The effect of obstructive sleep apnea on QRS complex morphology

Sleep apnea and sudden death in the non-cardiac population: A systematic review

INTRODUCTION

Obstructive sleep apnea (OSA) is one of the main risk factors for cardiovascular diseases and is associated with both morbidity and mortality. OSA has also been linked to arrhythmias and sudden death.

OBJECTIVE

To assess whether OSA increases the risk of sudden death in the non-cardiac population.

METHODS

This is a systematic review of the literature. The descriptors "sudden death" and "sleep apnea" and "tachyarrhythmias" and "sleep apnea" were searched in the PubMed/Medline and SciELO databases.

RESULTS

Thirteen articles that addressed the relationship between OSA and the development of tachyarrhythmias and/or sudden death with prevalence data, electrocardiographic findings, and a relationship with other comorbidities were selected. The airway obstruction observed in OSA triggers several systemic repercussions, e.g., changes in intrathoracic pressure, intermittent hypoxia, activation of the sympathetic nervous system and chemoreceptors, and release of catecholamines. These mechanisms would be implicated in the appearance of arrhythmogenic factors, which could result in sudden death.

CONCLUSION

There was a cause-effect relationship between OSA and cardiac arrhythmias. In view of the pathophysiology of OSA and its arrhythmogenic role, studies have shown a higher risk of sudden death in individuals who previously had heart disease. On the other hand, there is little evidence about the occurrence of sudden death in individuals with OSA and no heart disease, and OSA is not a risk factor for sudden death in this population.

Analysis of QRS complex morphology in children and adolescents with obstructive sleep apnea syndrome

Obstructive sleep apnea syndrome (OSAS) leads to many cardiovascular, neurologic, metabolic, and neurocognitive consequences. Conduction deficits, deviations in electrical axis, and changes in QRS morphology reflect the impairments in cardiac muscle activity and underlie the cardiovascular complications of OSAS. Here we aimed to determine the relationship between OSAS and changes in the cardiac conduction system in children and adolescents. During the 6-month duration of the study, all children having the diagnosis of OSAS in Sleep and Disorders Unit following a full-night polysomnography (PSG) were consecutively evaluated. ECGs were performed and analyzed in the Division of Pediatric Cardiology, Department of Pediatrics. The maximum spatial vector size (QRSmax), QRS electrical axis (EA), left and right ventricular hypertrophy, and the presence of fragmented QRS (fQRS) or prolonged R or S wave were examined in detail. A total of 17 boys with OSAS and 13 healthy boys participated in the study. The mean QRSmax and the QRSmax on V5 derivative were significantly lower in the patient group compared to those in the control group (p = 0.011 and p = 0.017, respectively). EA was similar between the two groups. While none of the patients with OSAS nor the control group had left ventricular hypertrophy, only one boy with OSAS had right ventricular hypertrophy according to ECG-derived analysis. The percentage of fQRS or notched R or S waves was significantly higher in patients with OSAS compared to healthy controls (p = 0.035), especially in children below the age of 5 years (p = 0.036).  Conclusion: This study demonstrated that male children and adolescents with OSAS have a combination of QRS complex changes characterized by low QRS voltages, and increased frequency of fragmented QRS. These findings reflect that the electrical remodeling and structural remodeling of the myocardium are considerably affected by OSAS in children and adolescents, leading to ventricular changes and intraventricular conduction problems. What is Known: • Pediatric obstructive sleep apnea syndrome (OSAS) characterized by recurrent intermittent hypoxemia, hypercapnia, and sleep fragmentation results in sympathetic nervous system activation, increased inflammation, and hypoxic endothelial dysfunction. When left untreated, OSAS leads to many cardiovascular, neurologic, metabolic and neurocognitive consequences, and also to sudden infant death syndrome in young children, probably due to the involvement of the cardiac conduction system. What is New: • This study demonstrated that mean QRSmax was significantly lower in male children and adolescents with OSAS, reflecting the structural and electrical remodeling of the myocardium, and one boy with OSAS had RVH according to ECG-derived analysis. The percentage of fQRS or notched R or S waves was much higher in boys with OSAS, especially in children below the age of five years. These finding showed that myocardium was considerably affected to impair the intraventricular conduction in younger children with OSAS.

A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network

Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during sleep and is associated with many other medical conditions, including hypertension, coronary heart disease, and depression. Clinically, the standard for diagnosing OSA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the availability of OSA diagnosis in public health sectors. Therefore, electrocardiogram (ECG)-based methods for OSA detection have been proposed to automate the polysomnography procedure and reduce its discomfort. So far, most of the proposed approaches rely on feature engineering, which calls for advanced expert knowledge and experience. This paper proposes a novel fused-image-based technique that detects OSA using only a single-lead ECG signal. In the proposed approach, a convolutional neural network extracts features automatically from images created with one-minute ECG segments. The proposed network comprises 37 layers, including four residual blocks, a dense layer, a dropout layer, and a soft-max layer. In this study, three time-frequency representations, namely the scalogram, the spectrogram, and the Wigner-Ville distribution, were used to investigate the effectiveness of the fused-image-based approach. We found that blending scalogram and spectrogram images further improved the system's discriminative characteristics. Seventy ECG recordings from the PhysioNet Apnea-ECG database were used to train and evaluate the proposed model using 10-fold cross validation. The results of this study demonstrated that the proposed classifier can perform OSA detection with an average accuracy, recall, and specificity of 92.4%, 92.3%, and 92.6%, respectively, for the fused spectral images.

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG

BACKGROUND

This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal.

METHODS

A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer. An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects.

RESULTS

F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%.

CONCLUSION

The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.

Ventricular Arrhythmias in Patients with Obstructive Sleep Apnea

Obstructive Sleep Apnea (OSA) is a prevalent condition thought to increase in the future. Being mostly undiagnosed, the most serious complications are cardiovascular diseases, among which are arrhythmias. Controversy remains as to whether OSA is a primary etiologic factor for ventricular arrhythmias, because of the high incidence of cardiovascular comorbidities in OSA patients. Howev-er, there is mostly a strong evidence of a relation between OSA and ventricular arrhythmias. A few mechanisms have been proposed to be responsible for this association and some electrocardiographic changes have also been demonstrated to be more frequent in OSA patients. Treatment of OSA with Continuous Positive Airway Pressure (CPAP) has the potential to reduce arrhythmias and confer a mortality benefit.

Screening for Obstructive Sleep Apnea in Commercial Drivers Using EKG-Derived Respiratory Power Index

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is common in commercial motor vehicle operators (CMVOs); however, polysomnography (PSG), the gold-standard diagnostic test, is expensive and inconvenient for screening. OSA is associated with changes in heart rate and voltage on electrocardiography (EKG). We evaluated the utility of EKG parameters in identifying CMVOs at greater risk for sleepiness-related crashes (apnea-hypopnea index [AHI] ≥ 30 events/h).

METHODS

In this prospective study of CMVOs, we performed EKGs with concurrent PSG, and calculated the respiratory power index (RPI) on EKG, a surrogate for AHI calculated from PSG. We evaluated the utility of two-stage predictive models using simple clinical measures (age, body mass index [BMI], neck circumference, Epworth Sleepiness Scale score, and the Multi-Variable Apnea Prediction [MVAP] score) in the first stage, followed by RPI in a subset as the second-stage. We assessed area under the receiver operating characteristic curve (AUC), sensitivity, and negative posttest probability (NPTP) for this two-stage approach and for RPI alone.

RESULTS

The best-performing model used the MVAP, which combines BMI, age, and sex with three OSA symptoms, in the first stage, followed by RPI in the second. The model yielded an estimated (95% confidence interval) AUC of 0.883 (0.767-0.924), sensitivity of 0.917 (0.706-0.962), and NPTP of 0.034 (0.015-0.133). Predictive characteristics were similar using a model with only BMI as the first-stage screen.

CONCLUSIONS

A two-stage model that combines BMI or the MVAP score in the first stage, with EKG in the second, had robust discriminatory power to identify severe OSA in CMVOs.

A different effect of obesity on ECG in premenopausal and postmenopausal women

Both obesity and menopause are significant cardiovascular risk factors. In postmenopausal women the protective effect of estrogens is reduced and menopause is frequently associated with occurrence of other significant cardiovascular factors including obesity. This study was focused on evaluating the effect of obesity on the QRS complex in pre- and postmenopausal women. We present results of analysis of 199 electrocardiograms of pre- and postmenopausal women analyzed in relation to the body mass index within normal limits (BMI 20 to 24.9 kg/m²) and obesity (BMI > 30 kg/m²), respectively. Obesity in premenopausal women and menopause significantly affected both the electrical axis (EA) and maximum QRS spatial vector magnitude (QRSmax). The highest QRSmax and electrical axis values were observed in premenopausal lean women, and they were significantly higher as than in the premenopausal obese women, postmenopausal lean and obese women (QRSmax: 1.66 ± 0.4 mV, 1.17 ± 0.35 mV, 1.4 ± 0.46 mV, and 1.35 ± 0.39 mV, resp.). (EA: 56.4 ± 18.0°, 38.22 ± 18.38°, 45.82 ± 18.63°, and 36.75 ± 17.51°). The differences between obese premenopausal women, lean and obese postmenopausal women were not statistically significant. These differences were reflected in 12-lead ECG amplitude. The presence of additional cardiovascular risk factors did not affect the ECG parameters. Obesity significantly affected QRS complex in premenopausal women. This effect was comparable with the effect of menopause. Because all QRS complex changes were within normal limits, these results suggest that ECG evaluation in women should go beyond traditional diagnostic categories and consider the relationship between ECG changes and two cardiovascular risk factors - obesity and menopause.

Electrocardiographic Markers of Sudden Cardiac Death (Including Left Ventricular Hypertrophy)

Although the electrocardiograph (ECG) was invented more than 100 years ago, it remains the most commonly used test in clinical medicine. It is easy to perform, relatively cheap, and results are readily available. Interpretation, however, needs expertise and knowledge. New data, phenomenon, and syndromes are continually discovered by the ECG. It is important to differentiate between normal and abnormal ECGs first and then try to correlate the findings with clinical pathologies. Furthermore, the ECG is an integral part of the screening model for a variety of conditions such as channelopathies, athletes, preoperative risk profile, and remains the cardiologist's best friend.

The 4th Report of the Working Group on ECG diagnosis of Left Ventricular Hypertrophy

The 4th Report provides a brief review of publications focused on the electrocardiographic diagnosis of left ventricular hypertrophy published during the period of 2010 to 2016 by the members of the Working Group on ECG diagnosis of Left Ventricular Hypertrophy. The Working Group recommended that ECG research and clinical attention be redirected from the estimation of LVM to the identification of electrical remodeling, to better understanding the sequence of events connecting electrical remodeling to outcomes. The need for a re-definition of terms and for a new paradigm is also stressed.