Spectral Heart Rate Variability analysis using the heart timing signal for the screening of the Sleep Apnea–Hypopnea Syndrome

The final publication is available http://dx.doi.org/10.1016/j.compbiomed.2016.01.023[Abstract] Some approaches have been published in the past using Heart Rate Variability (HRV) spectral features for the screening of Sleep Apnea–Hypopnea Syndrome (SAHS) patients. However there is a big variability among these methods regarding the selection of the source signal and the specific spectral components relevant to the analysis. In this study we investigate the use of the Heart Timing (HT) as the source signal in comparison to the classical approaches of Heart Rate (HR) and Heart Period (HP). This signal has the theoretical advantage of being optimal under the Integral Pulse Frequency Modulation (IPFM) model assumption. Only spectral bands defined as standard for the study of HRV are considered, and for each method the so-called LF/HF and VLFn features are derived. A comparative statistical analysis between the different resulting methods is performed, and subject classification is investigated by means of ROC analysis and a Naïve-Bayes classifier. The standard Apnea-ECG database is used for validation purposes. Our results show statistical differences between SAHS patients and controls for all the derived features. In the subject classification task the best performance in the testing set was obtained using the LF/HF ratio derived from the HR signal (Area under ROC curve=0.88). Only slight differences are obtained due to the effect of changing the source signal. The impact of using the HT signal in this domain is therefore limited, and has not shown relevant differences with respect to the use of the classical approaches of HR or HP.Xunta de Galicia; CN2011/007Ministerio de Economía y Competitividad; TIN2013-40686-PXunta de Galicia; CN2012/21

Blood Pressure Non-Dipping and Obstructive Sleep Apnea Syndrome: A Meta-Analysis

AIM: We examined the reduced blood pressure (BP) nocturnal fall in patients with obstructive sleep apnea (OSA) by a meta-analysis including studies that provided data on prevalence rates of non-dipping (ND) pattern during 24-h ambulatory blood pressure monitoring (ABPM). DESIGN: The PubMed, OVID-MEDLINE, and Cochrane CENTRAL literature databases were searched for appropriate articles without temporal restriction up to April 2019 through focused and sensitive search methods. Studies were identified by crossing the search terms as follows: "obstructive sleep apnea", "sleep quality", "non dipping", "reduced nocturnal BP fall", "circadian BP variation", "night-time BP", and "ambulatory blood pressure monitoring". RESULTS: Meta-analysis included 1562 patients with OSA from different clinical settings and 957 non-OSA controls from 14 studies. ND pattern prevalence in patients with OSA widely varied among studies (36.0-90.0%). This was also the case for non-OSA controls (33.0% to 69.0%). Overall, the ND pattern, assessed as an event rate in the pooled OSA population, was 59.1% (confidence interval (CI): 52.0-65.0%). Meta-analysis of the seven studies comparing the prevalence of ND pattern in participants with OSA and controls showed that OSA entails a significantly increased risk of ND (Odds ratio (OR) = 1.47, CI: 1.07-1.89, p < 0.01). After the exclusion of patients with mild OSA, OR increased to 1.67 (CI: 1.21-2.28, p < 0.001). CONCLUSIONS: The present meta-analysis, extending previous information on the relationship between OSA and impaired BP dipping, based on single studies, suggests that this condition increases by approximately 1.5 times the likelihood of ND, which is a pattern associated with a greater cardiovascular risk than normal BP dipping

Sleep apnoea and cardiac surgery: Screening, prevalence and postoperative outcomes

Introduction: An excess of postoperative complications have been reported in patients with Obstructive Sleep Apnoea (OSA) following surgical procedures, however, studies reporting outcomes in patients with OSA following cardiac surgery are sparse and of limited quality. The cause of worse surgical outcomes in the OSA population is unknown but deleterious effects of opiates/opioids, common pain relieve medication following surgery have previously been proposed. There is a move towards pre-operative screening for OSA prior to surgery but the best screening methodology has not yet been established and more importantly the effect of treatment, in particular Continuous Positive Airway Pressure (CPAP), on surgical outcomes in patients with OSA is unknown. Aim: This thesis examined the prevalence of sleep apnoea and its association with postoperative outcomes in patients undergoing major cardiac surgery. It also explored the usefulness of the STOP-Bang questionnaire, as a screening tool for OSA prior to cardiac surgery. In addition, current evidence regarding the effects of opiates/opioids and sedatives on patients with OSA was investigated and summarised in the Systematic Cochrane Review. The effect of morphine on severity of sleep apnoea in patients with moderate OSA was examined in a separate study. Methods: The prevalence and association of sleep apnoea with postoperative outcomes in patients undergoing cardiac surgery and the usefulness of the STOP-Bang questionnaire in identifying patients at risk of OSA prior surgery was examined in a prospective, observational cohort study. The Systematic Cochrane review included randomised controlled trials examining the effects of opioids and sedatives, compared to placebo on severity of OSA in patients with established diagnosis of OSA. The effect of intravenous morphine sulphate on the severity of sleep apnoea was examined in a prospective, paired design trial which recruited patients with moderate OSA. Results: A high prevalence of sleep apnoea (47%) and a significant association between its severity and postoperative complications was found in 122 participants undergoing major cardiac surgery. The most significant risk factor for complications was found to be oxygen desaturations during the night reflecting the severity of sleep apnoea (OR=1.1 for each unit increase in oxygen desaturation index (ODI), 95% CI 1.02-1.17; p=0.014). It was found that the STOP-Bang scores between 0-2 would with high confidence exclude patients with at least moderate sleep apnoea prior surgery. The best diagnostic performance for diagnosis of at least moderate sleep apnoea was found at higher STOP-Bang scores of ≥6 which could identify those patients who might benefit from a sleep study before cardiac surgery. A systematic Cochrane review found that opiates/opioids, sedatives and hypnotics have no deleterious effect on the severity of OSA but most of the studies included in the review were of short duration, small size and with indiscernible methodological quality. The results of the Systematic Cochrane Review informed the development of my study, studying the effect of opiate, morphine sulphate, on patients with moderate OSA. This showed no change in Apnoea/Hypopnoea Index (AHI) where median difference (MD) was -12.95, IQR 9.45, p=0.173 but showed significant improvement in sleep apnoea indices including: obstructive apnoea index (MD -2.7, IQR 7.37, p=0.03), central apnoea index (MD – 0.35, IQR 0.83, p=0.04 ).However there was a fall in median nocturnal oxygen saturation. Conclusion: This thesis reports high prevalence of sleep apnoea which was also found to be a risk factor for postoperative complications in patients undergoing major cardiac surgery. In this population, STOP-Bang score ≥6 could identify patients in need of a sleep study to identify those who may be at increased risk of postoperative complications. To date there is no strong evidence supporting deleterious effects of opioids/opiates on patients with OSA but larger studies are needed to clarify its effect.Funded by the Royal Papworth Hospital, department of the Respiratory Support and Sleep Centr

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning

[EN] Obstructive sleep apnea (OSA) is a respiratory disorder highly correlated with severe cardiovascular diseases that has unleashed the interest of hundreds of experts aiming to overcome the elevated requirements of polysomnography, the gold standard for its detection. In this regard, a variety of algorithms based on heart rate variability (HRV) features and machine learning (ML) classifiers have been recently proposed for epoch-wise OSA detection from the surface electrocardiogram signal. Many researchers have employed freely available databases to assess their methods in a reproducible way, but most were purely tested with cross-validation approaches and even some using solely a single database for training and testing procedures. Hence, although promising values of diagnostic accuracy have been reported by some of these methods, they are suspected to be overestimated and the present work aims to analyze the actual generalization ability of several epoch-wise OSA detectors obtained through a common ML pipeline and typical HRV features. Precisely, the performance of the generated OSA detectors has been compared on two validation approaches, i.e., the widely used epoch-wise, k-fold cross-validation and the highly recommended external validation, both considering different combinations of well-known public databases. Regardless of the used ML classifiers and the selected HRV-based features, the external validation results have been 20 to 40% lower than those obtained with cross-validation in terms of accuracy, sensitivity, and specificity. Consequently, these results suggest that ML-based OSA detectors trained with public databases are still not sufficiently general to be employed in clinical practice, as well as that larger, more representative public datasets and the use of external validation are mandatory to improve the generalization ability and to obtain reliable assessment of the true predictive power of these algorithms, respectively.This research has received financial support from public grants PID2021-00X128525-IV0 and PID2021-123804OB-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund, SBPLY/17/180501/000411 and SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana. Moreover, Daniele Padovano holds a predoctoral scholarship 2022-PRED-20642, which is cofinanced by the operating program of European Social Fund (ESF) 2014-2020 of Castilla-La Mancha.Padovano, D.; Martínez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2022). On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning. IEEE Access. 10:92710-92725. https://doi.org/10.1109/ACCESS.2022.320191192710927251

Sleep-disordered breathing in patients with implanted cardiac devices: validation of the ApneaScanTM algorithm and implications for prognosis

Aims Sleep-disordered breathing (SDB) is common in heart failure (HF) and frequently undiagnosed. The ApneaScanTM algorithm, available on certain ICD and CRT devices, uses changes in transthoracic impedance with breathing to quantify SDB. This research tests 3 hypotheses: 1) The ApneaScanTM algorithm can accurately detect moderate-to-severe SDB in patients with HF 2) There is minimal night-to-night variability in the ApneaScanTM-determined severity of SDB 3) Those with moderate-to-severe SDB, assessed by ApneaScanTM, have a higher rate of adverse cardiovascular events than those without. Methods Patients with EF≤40% and ICD or CRT devices incorporating ApneaScanTM were recruited. For hypothesis 1, 54 subjects underwent a successful sleep polygraphy study and simultaneous download of ApneaScanTM data. 22 subjects (44%) had undiagnosed moderate-to-severe SDB. The area under the ROC curve was 0.84 for the diagnosis of moderate-to-severe SDB. The optimal ApneaScan cut-off was 30.5/hour (sensitivity 95%, specificity 69%, positive predictive value 68%, negative predictive value 95%). For hypothesis 2, ApneaScanTM data over 28- and 92-nights in 35 patients was reviewed. There was minimal variability in SDB and no significant difference between durations. For hypothesis 3, 72 patients were followed up at a median of 532 (IQR 386-736) days.Mean event-free survival was 660±344 days (95% CI 535-785 days) in the insignificant SDB group and 854±413 days (95% CI 730-978 days) in the significant SDB group (p=0.25 by log rank test). Conclusions ApneaScanTM, with an optimal cut-off of 30.5 events/hour, is a sensitive means of screening for SDB in patients with HF with a high negative predictive value. Readings above 30.5/hour require further investigation with a sleep study. Night-to-night variability in SDB is minimal and repeat sleep studies should be reserved for those with ‘borderline’ AHI. In this cohort, the presence of SDB was not associated with adverse cardiovascular outcomes. Recruitment is on-going to test this further.Open Acces

Sudden Cardiac Death in Dialysis: Arrhythmic Mechanisms and the Value of Non-invasive Electrophysiology

Sudden Cardiac Death (SCD) is the leading cause of cardiovascular death in dialysis patients. This review discusses potential underlying arrhythmic mechanisms of SCD in the dialysis population. It examines recent evidence from studies using implantable loop recorders and from electrophysiological studies in experimental animal models of chronic kidney disease. The review summarizes advances in the field of non-invasive electrophysiology for risk prediction in dialysis patients focusing on the predictive value of the QRS-T angle and of the assessments of autonomic imbalance by means of heart rate variability analysis. Future research directions in non-invasive electrophysiology are identified to advance the understanding of the arrhythmic mechanisms. A suggestion is made of incorporation of non-invasive electrophysiology procedures into clinical practice.Key Concepts:– Large prospective studies in dialysis patients with continuous ECG monitoring are required to clarify the underlying arrhythmic mechanisms of SCD in dialysis patients.– Obstructive sleep apnoea may be associated with brady-arrhythmias in dialysis patients. Studies are needed to elucidate the burden and impact of sleeping disorders on arrhythmic complications in dialysis patients.– The QRS-T angle has the potential to be used as a descriptor of uremic cardiomyopathy.– The QRS-T angle can be calculated from routine collected surface ECGs. Multicenter collaboration is required to establish best methodological approach and normal values.– Heart Rate Variability provides indirect assessment of cardiac modulation that may be relevant for cardiac risk prediction in dialysis patients. Short-term recordings with autonomic provocations are likely to overcome the limitations of out of hospital 24-h recordings and should be prospectively assessed

심박변이도 분석을 활용하여 폐쇄성수면무호흡증 환자의 치료 유효성 평가에 관한 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의학과, 2020. 8. 김정훈.Introduction: The aim of this study is (Chapter 1) to evaluate the effects of mandibular advancement device (MAD) on nocturnal heart rate variability (HRV) in obstructive sleep apnea (OSA) and (Chapter 2) to compare the treatment efficacy between sleep surgery and MAD using HRV. Material and methods: (Chapter 1) We retrospectively reviewed anthropometric data, questionnaire results, and HRV parameters (evaluated using time- and frequency-domain methods) of 58 adult patients with OSA treated via MAD therapy. (Chapter 2) Subjects treated for OSA with sleep surgery or MAD (n = 30/group) were matched for sex, body mass index (BMI), and baseline apnea–hypopnea index (AHI). The efficacy of these treatments according to HRV time- and frequency-domain parameters were compared between pre-treatment and 3-months post-treatment. Results: (Chapter 1) The average normal-to-normal (NN) interval, standard deviation of the NN interval, low-frequency power in normalized units (LFnu), and high-frequency power in normalized units (HFnu) showed significant changes with MAD therapy. Based on the criteria for success (decrease in the apnea-hypopnea index by >50% and value <20/h), 34 and 24 patients were classified into the response and nonresponse groups, respectively. No differences in baseline characteristics were detected between groups, except for higher body mass index and lower minimal oxygen saturation in the nonresponse group. A subgroup analysis indicated that the average NN interval and HFnu significantly increased, and that Total power (TP), very low frequency, low frequency (LF), low frequency/high frequency and LFnu significantly decreased compared to baseline in the response group; however, no HRV changes were found in the nonresponse group. After adjusting for age, sex, and BMI, the response group showed significant changes from baseline in TP and LF compared to the nonresponse group. (Chapter 2) In time-domain HRV analysis, average NN intervals increased significantly in the surgery (942.2 ± 140.8 to 994.6 ± 143.1, P = 0.008) and MAD (901.1 ± 131.7 to 953.7 ± 123.1, P = 0.002) groups. LF decreased significantly in the surgery group (P = 0.012). The LF/HF ratio decreased in both groups (2.9 ± 1.8 to 2.3 ± 1.7, P = 0.017, vs 3.0 ± 1.8 to 2.4 ± 1.4, P = 0.025). HFnu increased significantly in both groups (31.0 ± 13.2 to 36.8 ± 13.7, P = 0.009, vs. 29.1 ± 10.7 to 33.7 ± 12.5, P = 0.024), in contrast to LFnu. However, no HRV parameter changes differed significantly between the groups after adjusting for age, BMI, and AHI. Conclusion: (Chapter 1) HRV may be useful for determining the efficacy of MAD therapy in OSA. (Chapter 2) Sleep surgery and MAD are equally effective treatments for OSA according to cardiac autonomic activity.서론: 본 연구의 목표는 (1장) 심박변이도 분석을 통하여 하악구강전진 장치의 효능을 평가해 보는 것과 (2장) 심박변이도 분석을 활용하여 수면수술과 하악전진장치의 효능에 관하여 비교 분석하는 것이다. 대상 및 연구 방법: (1장) 폐쇄성수면무호흡증의 치료를 위해 하악전진장치를 사용한 총 58명의 환자의 치료 이전과 치료 3개월 후의 심박변이도 분석 및 설문조사의 결과를 비교하였다. (2장) 폐쇄성수면무호흡증의 치료를 위하여 수면수술을 시행 받은 환자와 하악전진장치를 사용한 환자의 성별, 나이, 수면무호흡-저호흡 지수를 대응시켜 각각 30명의 환자를 선별하였고 선별 환자의 심박변이도 변화를 측정하여 치료 방법 간의 우월성에 관한 평가를 시행하였다. 결과: (1장) 심박변이도 분석 항목 중 NN interval, SDNN, LFnu, HFnu가 치료 전에 비해 하악전진장치의 사용 후 유의미하게 변하였다. 더 자세한 분석을 위하여 환자들을 치료의 성공 및 실패 기준(수면무호흡-저호흡 지수가 치료 후 치료 이전 대비 50% 이상 감소하고 동시에 절대치가 20 이하로 감소)에 따라 치료 반응군 및 비반응군으로 분류하여 각 군의 심박변이도의 변화를 측정하였다. 반응군에서 NN inverval, HFnu는 유의하게 증가하였고, TP, VLF, LF, LF/HF 비율, LFnu는 유의하게 감소한 반면 비반응군에서는 HRV의 유의한 변화가 전혀 관찰되지 않았다. 나이와 성별 및 BMI에 HRV의 변화를 보정한 결과에도 TP, LF가 반응군에서 비반응군에 비해 유의한 변화를 보였다. (2장) 심박변이도의 시간 변이 분석 상 NN interval 평균값의 변화가 수면수술군(942.2 ± 140.8 to 994.6 ± 143.1, P = 0.008)과 하악전진장치군(901.1 ± 131.7 to 953.7 ± 123.1, P = 0.002)에서 모두 유의하게 관찰되었다. LF/HF 비율도 두 군 모두 유의하게 감소(2.9 ± 1.8 to 2.3 ± 1.7, P = 0.017, vs 3.0 ± 1.8 to 2.4 ± 1.4, P = 0.025)하였고 HFnu는 LFnu의 감소와 반비례하여 유의한 증가(31.0 ± 13.2 to 36.8 ± 13.7, P = 0.009, vs. 29.1 ± 10.7 to 33.7 ± 12.5, P = 0.024) 양상을 두 군 모두에서 확인할 수 있었다. 하지만 심박변이도의 변화를 나이, BMI 및 수면무호흡-저호흡 지수에 보정하여 변화를 비교해 본 결과 심박변이도의 변화에는 군간의 우열이 관찰되지 않았다. 결론: (1장) 하악전진장치를 이용한 폐쇄성수면무호흡증의 치료는 심박변이도의 변화를 가져오며 이러한 변화는 치료 효과를 예측할 수 있는 지표로 사용될 가능성이 있다. (2장) 수면수술과 하악전진장치는 심장자율신경활성도의 측면에서 동등한 효능을 가지고 있는 치료이다.5. Chapter 1 9 5-1. Introduction 10 5-2. Material and Methods 12 5-3. Results 16 5-4. Discussion 19 6. Chapter 2 24 6-1. Introduction 25 6-2. Material and Methods 27 6-3. Results 31 6-4. Discussion 33 7. References 38 8. Abstract in Korean 64Docto