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

Continuous monitoring of vital parameters for clinically valid assessment of human health status

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2019The lack of devices suitable for acquiring accurate and reliable measures of patients' physiolog-ical signals in a remote and continuous manner together with the advances in data acquisition technol-ogies during the last decades, have led to the emergence of wearable devices for healthcare. Wearable devices enable remote, continuous and long-term health monitoring in unattended setting. In this con-text, the Swiss Federal Laboratories for Material Science and Technology (Empa) developed a wearable system for long-term electrocardiogram measurements, referred to as textile belt. It consists of a chest strap with two embroidered textile electrodes. The validity of Empa’s system for electrocardiogram monitoring has been proven in a clinical setting. This work aimed to assess the validity of the textile belt for electrocardiogram monitoring in a home setting and to supplement the existing system with sensors for respiratory monitoring. Another objective was to evaluate the suitability of the same weara-ble, as a multi-sensor system, for activity monitoring. A study involving 12 patients (10 males and 2 females, interquartile range for age of 48–59 years and for body mass indexes of 28.0–35.5 kg.m-2) with suspected sleep apnoea was carried out. Overnight electrocardiogram was measured in a total of 28 nights. The quality of recorded signals was assessed using signal-to-noise ratio, artefacts detection and Poincaré plots. Study data were compared to data from the same subjects, acquired in the clinical setting. For respiratory monitoring, optical fibre-based sensors of different geometries were integrated into the textile belt. Signal processing algorithms for breathing rate and tidal volume estimation based on respiratory signals acquired by the sensors were developed. Pilot studies were conducted to compare the different approaches for respiratory monitoring. The quality of respiratory signals was determined based on signal segments “sinusoidality”, evaluated through the calculation of the cross-correlation between signal segments and segment-specific reference waves. A method for accelerometry-based lying position recognition was proposed, and the proof of concept of activity intensity classification through the combination of subjects’ inertial acceleration, heart rate and breathing rate data, was presented. Finally, a study with three participants (1 male and 2 females, aged 21 ± 2 years, body mass index of 20.3 ± 1.5 kg.m-2) was conducted to assess the validity of the textile belt for respiratory and activity monitoring. Electrocardiogram signals acquired by the textile belt in the home setting were found to have better quality than the data acquired by the same device in the clinical setting. Although a higher artefact percentage was found for the textile belt, signal-to-noise ratio of electrocardiogram signals recorded by the textile belt in the home setting was similar to that of signals acquired by the gel electrodes in the clinical setting. A good agreement was found between the RR-intervals derived from signals recorded in home and clinical settings. Besides, for artefact percentages greater than 3%, visual assessment of Poincaré plots proved to be effective for the determination of the primary source of artefacts (noise or ectopic beats). Acceleration data allowed posture recognition (i.e. lying or standing/sitting, lying position) with an accuracy of 91% and positive predictive value of 80%. Lastly, preliminary results of physical activity intensity classification yielded high accuracy, showing the potential of the proposed method. The textile belt proved to be appropriate for long-term, remote and continuous monitoring of subjects’ physical and physiological parameters. It can monitor not only electrocardiogram, but also breathing rate, body posture and physical activity intensity, having the potential to be used as tool for disease prediction and diagnose support.Contexto: A falta de dispositivos adequados para a monitorização de sinais fisiológicos de um modo remoto e contínuo, juntamente com avanços tecnológicos na área de aquisição de dados nas últimas décadas, levaram ao surgimento de wearable devices, i.e. dispositivos vestíveis, no sector da saúde. Wearable devices possibilitam a monitorização do estado de saúde, de uma forma remota, contínua e de longa duração. Quando feito em ambiente domiciliar, este tipo de monitorização (i.e. contínua, remota e de longa duração) tem várias vantagens: diminui a pressão posta sobre o sistema de saúde, reduz despesas associadas ao internamento e acelera a resposta a emergências, permitindo deteção precoce e prevenção de condições crónicas. Neste contexto, a Empa, Laboratórios Federais Suíços de Ciência e Tecnologia de Materiais, desenvolveu um sistema vestível para a monitorização de eletrocardiograma de longa duração. Este sistema consiste num cinto peitoral com dois elétrodos têxteis integrados. Os elétrodos têxteis são feitos de fio de polietileno tereftalato revestido com prata e uma ultrafina camada de titânio no topo. De modo a garantir a aquisição de sinais de alta qualidade, o cinto tem nele integrado um reservatório de água que liberta vapor de água para humidificar os elétrodos. Este reservatório per-mite a monitorização contínua de eletrocardiograma por 5 a 10 dias, sem necessitar de recarga. A vali-dade do cinto para a monitorização de eletrocardiograma em ambiente clínico já foi provada. Objetivo: Este trabalho teve por objetivo avaliar a validade do cinto para a monitorização de eletrocar-diograma em ambiente domiciliar e complementar o sistema existente com sensores para monitorização respiratória. Um outro objetivo foi analisar a adequação do cinto, como um sistema multisensor, para monitorização da atividade física. Métodos: Um estudo com 12 pacientes com suspeita de apneia do sono (10 homens e 2 mulheres, am-plitude interquartil de 48–59 anos para a idade e de 28.0–35.5 kg.m-2 para o índice de massa corporal) foi conduzido para avaliar a qualidade do sinal de eletrocardiograma medido em ambiente domiciliar. O sinal de eletrocardiograma dos pacientes foi monitorizado continuamente, num total de 28 noites. A qualidade dos sinais adquiridos foi analisada através do cálculo da razão sinal-ruído; da deteção de ar-tefactos, i.e., intervalos RR com um valor inviável de um ponto de vista fisiológico; e de gráficos de Poincaré, um método de análise não linear da distribuição dos intervalos RR registados. Os dados ad-quiridos neste estudo foram comparados com dados dos mesmos pacientes, adquiridos em ambiente hospitalar. Para a monitorização respiratória, sensores feitos de fibra óptica foram integrados no cinto. Al-gorítmicos para a estimar a frequência respiratória e o volume corrente dos sujeitos tendo por base o sinal medido pelas fibras ópticas foram desenvolvidos neste trabalho. As diferentes abordagens foram comparadas através de estudos piloto. Diferentes métodos para avaliação da qualidade do sinal adquirido foram sugeridos. Um método de reconhecimento da postura corporal através do cálculo de ângulos de orientação com base na aceleração medida foi proposto. A prova de conceito da determinação da intensidade da atividade física pela combinação de informações relativas á aceleração inercial e frequências cardíaca e respiratória dos sujeitos, é também apresentada neste trabalho. Um estudo foi conduzido para avaliar a validade do cinto para monitorização da respiração e da atividade física. O estudo contou com 10 parti-cipantes, dos quais 3 vestiram o cinto para monitorização da respiração (1 homem e 2 mulheres, idade 21 ± 2 anos, índice de massa corporal 20.3 ± 1.5 kg.m-2). Resultados: O estudo feito com pacientes com suspeita de apneia do sono revelou que os sinais eletro-cardiográficos adquiridos pelo cinto em ambiente domiciliar foram de melhor qualidade que os sinais adquiridos pelo mesmo dispositivo em ambiente hospitalar. Uma percentagem de artefacto de 2.87% ±4.14% foi observada para os dados adquiridos pelos elétrodos comummente usados em ambiente hospi-talar, 7.49% ± 10.76% para os dados adquiridos pelo cinto em ambiente domiciliar e 9.66% ± 14.65% para os dados adquiridos pelo cinto em ambiente hospitalar. Embora tenham tido uma maior percenta-gem de artefacto, a razão sinal-ruído dos sinais eletrocardiográficos adquiridos pelo cinto em ambiente domiciliar foi semelhante á dos sinais adquiridos pelos elétrodos de gel em ambiente hospitalar. Resul-tados sugerem uma boa concordância entre os intervalos RR calculados com base nos eletrocardiogra-mas registados em ambientes hospitalar e domiciliar. Além disso, para sinais com percentagem de arte-facto superior a 3%, a avaliação visual dos gráficos de Poincaré provou ser um bom método para a determinação da fonte primária de artefactos (batimentos irregulares ou ruído). A monitorização da aceleração dos sujeitos permitiu o reconhecimento da postura corporal (isto é, deitado ou sentado/em pé) com uma exatidão de 91% e valor preditivo positivo de 80%. Por fim, a classificação da intensidade da atividade física baseado na aceleração inercial e frequências cardíaca e respiratória revelou elevada exatidão, mostrando o potencial desta técnica. Conclusão: O cinto desenvolvido pela Empa provou ser apropriado para monitorização de longa-dura-ção de variáveis físicas e fisiológicos, de uma forma remota e contínua. O cinto permite não só monito-rizar eletrocardiograma, mas também frequência respiratória, postura corporal e intensidade da atividade física. Outros estudos devem ser conduzidos para corroborar os resultados e conclusões deste trabalho. Outros sensores poderão ser integrados no cinto de modo a possibilitar a monitorização de outras vari-áveis fisiológicas de relevância clínica. Este sistema tem o potencial de ser usado como uma ferramenta para predição de doenças e apoio ao diagnóstico

Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning

Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an overnight sleep study in a specialized sleep clinic. This setup is expensive and the number of beds and staff are limited, leading to a long waiting time. To enable more patients to be tested, and repeated monitoring for diagnosed patients, portable sleep monitoring devices are being developed. These devices automatically detect sleep apnea events in one or more respiration-related signals. There are multiple methods to measure respiration, with varying levels of signal quality and comfort for the patient. In this study, the potential of using the bio-impedance (bioZ) of the chest as a respiratory surrogate is analyzed. A novel portable device is presented, combined with a two-phase Long Short-Term Memory (LSTM) deep learning algorithm for automated event detection. The setup is benchmarked using simultaneous recordings of the device and the traditional polysomnography in 25 patients. The results demonstrate that using only the bioZ, an area under the precision-recall curve of 46.9% can be achieved, which is on par with automatic scoring using a polysomnography respiration channel. The sensitivity, specificity and accuracy are 58.4%, 76.2% and 72.8% respectively. This confirms the potential of using the bioZ device and deep learning algorithm for automatically detecting sleep respiration events during the night, in a portable and comfortable setup

A review of ECG-based diagnosis support systems for obstructive sleep apnea

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy

A comparative study of ECG-derived respiration in ambulatory monitoring using the single-lead ECG

Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems

A review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Producción CientíficaHeart rate variability (HRV) provides useful information about heart dynamics both under healthy and pathological conditions. Entropy measures have shown their utility to characterize these dynamics. In this paper, we assess the ability of spectral entropy (SE) and multiscale entropy (MsE) to characterize the sleep apnoea-hypopnea syndrome (SAHS) in HRV recordings from 188 subjects. Additionally, we evaluate eventual differences in these analyses depending on the gender. We found that the SE computed from the very low frequency band and the low frequency band showed ability to characterize SAHS regardless the gender; and that MsE features may be able to distinguish gender specificities. SE and MsE showed complementarity to detect SAHS, since several features from both analyses were automatically selected by the forward-selection backward-elimination algorithm. Finally, SAHS was modelled through logistic regression (LR) by using optimum sets of selected features. Modelling SAHS by genders reached significant higher performance than doing it in a jointly way. The highest diagnostic ability was reached by modelling SAHS in women. The LR classifier achieved 85.2% accuracy (Acc) and 0.951 area under the ROC curve (AROC). LR for men reached 77.6% Acc and 0.895 AROC, whereas LR for the whole set reached 72.3% Acc and 0.885 AROC. Our results show the usefulness of the SE and MsE analyses of HRV to detect SAHS, as well as suggest that, when using HRV, SAHS may be more accurately modelled if data are separated by gender.Ministerio de Economía, Industria y Competitividad (TEC2011-22987)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA059U13

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients’ needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed

The effect of respiratory event type and duration on heart rate variability in suspected obstructive sleep apnea patients

Abstract. Obstructive sleep apnea (OSA) patients have often reduced long-term heart rate variability (HRV) which is a known risk factor for several cardiovascular diseases such as hypertension and stroke. Albeit OSA being actively studied, it has remained uncharacterized how the duration and type of respiratory events affect the heart rate (HR), i.e. RR intervals, and ultra-short-term HRV during and immediately after the individual respiratory events. This study aimed to investigate whether the changes in ultra-short-term HRV and HR are modulated by the duration and type of the individual respiratory events and whether these changes are sex-specific. It was hypothesized that longer respiratory events cause higher ultra-short-term HRV and greater differences between RR intervals during and after the respiratory event. Moreover, it was hypothesized that the higher HRV and greater differences in HR are associated with apneas and men stronger than hypopneas and women. Electrocardiograms (ECG) of 862 suspected OSA patients were collected during clinical polysomnography (PSG) at the Princess Alexandra Hospital (Brisbane, Australia) and they were analyzed retrospectively. Ultra-short-term HRV was studied with time-domain parameters determined from the ECG segments measured during (in-event) and 15 seconds after (post-event) the respiratory event. The respiratory events of all subjects were divided into groups based on the sex, the type of the respiratory events (apneas and hypopneas), and the duration of the respiratory events (10–20 s, 20–30 s, over 30 s). A clear bradycardia-tachycardia rhythm associated with respiratory events was observed. The ultra-short-term HRV and the difference between in- and post-event RR intervals increased with increasing respiratory event duration. However, the difference between in- and post-event HRV parameter values decreased with increasing duration of the respiratory events. Furthermore, higher ultra-short-term HRV and a greater decrease in RR interval were observed in apneas and men. Based on the results, the duration and type of the respiratory events modulate the HR and ultra-short-term HRV during and after the respiratory events, and these phenomena appear to be sex-specific. Therefore, considering the characteristics of respiratory events and ultra-short-term HRV could be useful in OSA diagnostics when estimating the OSA-related cardiac consequences. A scientific article based on the results of this thesis, Hietakoste et al. Longer apneas and hypopneas are associated with greater ultra-short-term HRV in OSA, has been submitted to a peer-reviewed scientific journal.Tiivistelmä. Uniapneapotilailla havaitaan usein matalaa pitkän aikavälin sykevälivaihtelua, jonka tiedetään myös olevan riskitekijä useille sydän- ja verisuonisairauksille. Ei kuitenkaan tiedetä, miten uniapneaan liittyvät erimittaiset hengityskatkot tai niiden tyyppi vaikuttavat yksittäisten hengityskatkojen aikaiseen ja jälkeiseen ultralyhyeen sykevälivaihteluun ja sydämen lyöntien väliseen kestoon, ts. RR-intervalleihin. Tässä tutkimuksessa tavoitteena oli tutkia ultralyhyen sykevälivaihtelun ja RR-intervallien sukupuolisidonnaisia muutoksia eri mittaisten apneoiden ja hypopneoiden aikana ja jälkeen. Hypoteesina oli, että pidemmät hengityskatkot aiheuttavat suurempia muutoksia hengityskatkojen aikaisen ja jälkeisen keskimääräisen RR-intervallien kestojen välille ja siten korkeampaa ultralyhyttä sykevälivaihtelua. Oletettiin myös, että apneat aiheuttavat suurempia muutoksia kuin hypopneat ja havaitut muutokset ovat suurempia miehillä kuin naisilla. Potilasaineisto koostui 862 uniapneasta epäillyn potilaan sydänsähkökäyristä (EKG), jotka oli mitattu Prinsessa Alexandran sairaalassa (Brisbane, Australia) osana kliinistä unipolygrafiaa. Ultralyhyen sykevälivaihtelun määrittämiseen käytettiin keskimääräistä RR-intervallien kestoa ja aikatason sykevälivaihteluparametreja, jotka määritettiin hengityskatkojen aikaisista ja jälkeisistä (15 s hengityskatkon jälkeen) EKG-segmenteistä. Tutkittavat hengityskatkot jaettiin ryhmiin niiden tyypin (apneat ja hypopneat) ja keston (10–20 s, 20–30 s ja yli 30 s) perusteella. Lisäksi miesten ja naisten hengityskatkoja tutkittiin erikseen. Tutkimuksessa havaittiin, että hengityskatkojen aikaisten ja jälkeisten RR-intervallien ero sekä ultralyhyt sykevälivaihtelu kasvoivat hengityskatkojen keston kasvaessa riippumatta sukupuolesta tai hengityskatkojen tyypistä. Havaittiin myös, että ero hengityskatkojen aikaisten ja jälkeisten sykevälivaihteluparametrien arvojen välillä pieneni hengityskatkojen pidentyessä riippumatta sukupuolesta tai hengityskatkojen tyypistä. Apneat kuitenkin aiheuttivat suuremman muutoksen kuin hypopneat, ja muutokset olivat suurempia miehillä. Tulosten perusteella hengityskatkojen tyyppi ja kesto vaikuttavat ultralyhyeen sykevälivaihteluun ja RR-intervalleihin. Ultralyhyen sykevälivaihtelun ja hengityskatkojen ominaisuuksien huomioonottaminen uniapnean diagnostiikassa voisi olla hyödyllistä arvioitaessa taudin vakavuutta ja sydänterveyteen liittyviä riskejä. Tämän tutkimuksen tuloksista on kirjoitettu tieteellinen artikkeli Hietakoste ym. Longer apneas and hypopneas are associated with greater ultra-short-term HRV in OSA, joka on lähetetty vertaisarvioitavaksi alan kansainväliseen tieteelliseen julkaisusarjaan

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.Peer Reviewe