Detection of Obstructive Sleep Apnea from ECG Signal using SVM based Grid Search

Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient and new detection techniques are now being developed to overcome these difficulties. Heart rate variability has proven to be related to sleep apnea episodes and thus the features from the ECG signal can be used in the detection of sleep apnea. The proposed detection technique uses Support Vector Machines using Grid search algorithm and the classifier is trained using features based on heart rate variability derived from the ECG signal. The developed system is tested using the dataset and the results show that this classification system can recognize the disorder with an accuracy rate of 89%. Further, the use of the grid search algorithm has made this system a reliable and an accurate means for the classification of sleep apnea and can serve as a basis for the future development of its screening

Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection

A wise feature selection from minute-to-minute Electrocardiogram (ECG) signal is a challenging task for many reasons, but mostly because of the promise of the accurate detection of clinical disorders, such as the sleep apnea. In this study, the ECG signal was modeled in order to obtain the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR). Selected features techniques were used for benchmark with different classifiers such as Artificial Neural Networks (ANN) and Support Vector Machine(SVM), among others. The results evidence that the best accuracy was 82.12%, with a sensitivity and specificity of 88.41% and 72.29%, respectively. In addition, experiments revealed that a wise feature selection may improve the system accuracy. Therefore, the proposed model revealed to be reliable and simpler alternative to classical solutions for the sleep apnea detection, for example the ones based on the Polysomnography.info:eu-repo/semantics/publishedVersio

Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors

Obstructive Sleep Apnea (OSA) is one of the main under-diagnosed sleep disorder. It is an aggravating factor for several serious cardiovascular diseases, including stroke. There is, however, a lack of medical devices for long-term ambulatory monitoring of OSA since current systems are rather bulky, expensive, intrusive, and cannot be used for long-term monitoring in ambulatory settings. In this paper, we propose a wearable, accurate, and energy efficient system for monitoring obstructive sleep apnea on a long-term basis. As an embedded system for Internet of Things (IoT), it reduces the gap between home health-care and professional supervision. Our approach is based on monitoring the patient using a single-channel electrocardiogram (ECG) signal. We develop an efficient time-domain analysis to meet the stringent resources constraints of embedded systems to compute the sleep apnea score. Our system, for a publicly available database (PhysioNet Apnea-ECG), has a classification accuracy of up to 88.2% for our new online and patient-specific analysis, which takes the distinct profile of each patient into account. While accurate, our approach is also energy efficient and can achieve a battery lifetime of 46 days for continuous screening of OSA

Vauvojen unen luokittelu patja-sensorilla ja EKG:lla

Infants spend the majority of their time asleep. Although extensive studies have been carried out, the role of sleep for infant cognitive, psychomotor, temperament and developmental outcomes is not clear. The current contradictory results may be due to the limited precision when monitoring infant sleep for prolonged periods of time, from weeks to even months. Sleep-wake cycle can be assessed with sleep questionnaires and actigraphy, but they cannot separate sleep stages. The gold standard for sleep state annotation is polysomnography (PSG), which consist of several signal modalities such as electroencephalogram, electrooculogram, electrocardiogram (ECG), electromyogram, respiration sensor and pulse oximetry. A sleep clinician manually assigns sleep stages for 30 sec epochs based on the visual observation of these signals. Because method is obtrusive and laborious it is not suitable for monitoring long periods. There is, therefore, a need for an automatic and unobtrusive sleep staging approach. In this work, a set of classifiers for infant sleep staging was created and evaluated. The cardiorespiratory and gross body movement signals were used as an input. The different classifiers aim to distinguish between two or more different sleep states. The classifiers were built on a clinical sleep polysomnography data set of 48 infants with ages ranging from 1 week to 18 weeks old (a median of 5 weeks). Respiration and gross body movements were observed using an electromechanical film bed mattress sensor manufactured by Emfit Ltd. ECG of the PSG setup was used for extracting cardiac activity. Signals were preprocessed to remove artefacts and an extensive set of features (N=81) were extracted on which the classifiers were trained. The NREM3 vs other states classifier provided the most accurate results. The median accuracy was 0.822 (IQR: 0.724-0.914). This is comparable to previously published studies on other sleep classifiers, as well as to the level of clinical interrater agreement. Classification methods were confounded by the lack of muscle atonia and amount of gross body movements in REM sleep. The proposed method could be readily applied for home monitoring, as well as for monitoring in neonatal intensive care units.Vauvat nukkuvat suurimman osan vuorokaudesta. Vaikkakin laajasti on tutkittu unen vaikutusta lapsen kognitioon, psykomotoriikkaan, temperamenttiin ja kehitykseen, selkeää kuvaa ja yhtenäistä konsensusta tiedeyhteisössä ei ole saavutettu. Yksi syy tähän on että ei ole olemassa menetelmää, joka soveltuisi jatkuva-aikaiseen ja pitkäkestoiseen unitilan monitorointiin. Vauvojen uni-valve- sykliä voidaan selvittää vanhemmille suunnatuilla kyselyillä ja aktigrafialla, mutta näillä ei voi havaita unitilojen rakennetta. Kliinisenä standardina unitilojen seurannassa on polysomnografia, jossa samanaikaisesti mitataan mm. potilaan elektroenkelografiaa, elektro-okulografiaa, elektrokardiografiaa, electromyografiaa, hengitysinduktiivisesta pletysmografiaa, happisaturaatiota ja hengitysvirtauksia. Kliinikko suorittaa univaiheluokittelun signaaleista näkyvien, vaiheille tyypillisten, hahmojen perusteella. Työläyden ja häiritsevän mittausasetelman takia menetelmä ei sovellu pitkäaikaiseen seurantaan. On tarvetta kehittää tarkoitukseen sopivia automaattisia ja huomaamattomia unenseurantamenetelmiä. Tässä työssä kehitettiin ja testattiin sydämen syke-, hengitys ja liikeanalyysiin perustuvia koneluokittimia vauvojen unitilojen havainnointiin. Luokittimet opetettiin kliinisessa polysomnografiassa kerätyllä datalla 48 vauvasta, joiden ikä vaihteli 1. viikosta 18. viikkoon (mediaani 5 viikkoa). Vauvojen hengitystä ja liikkeitä seurattiin Emfit Oy:n valmistamalla elektromekaaniseen filmiin pohjatuvalla patja-sensorilla. Lisäksi ECG:lla seurattiin sydäntä ja opetuksessa käytettiin lääkärin suorittamaa PSG-pohjaista luokitusta. Esikäsittelyn jälkeen signaaleista laskettiin suuri joukko piirrevektoreita (N=81), joihin luokittelu perustuu. NREM3-univaiheen tunnistus onnistui parhaiten 0.822 mediaani-tarkkuudella ja [0.724,0.914] kvartaaleilla. Tulos on yhtenevä kirjallisuudessa esitettyjen arvojen kanssa ja vastaa kliinikkojen välistä toistettavuutta. Muilla luokittimilla univaiheet sekoituivat keskenään, mikä on oletattavasti selitettävissä aikuisista poikeavalla REM-unen aikaisella lihasjäykkyydellä ja kehon liikkeillä. Työ osoittaa, että menetelmällä voi seurata vauvojen uniluokkien oskillaatiota. Järjestelmää voisi käyttää kotiseurannassa tai vastasyntyneiden teholla unenvalvontaan

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice

Pulse Rate Variability for the Assessment of Cardiovascular Changes

Pulse rate variability (PRV) describes the way pulse rate changes through time and is measured from pulsatile signals such as the photoplethysmogram (PPG). It has been proposed as a surrogate for heart rate variability (HRV). Nonetheless, the relationship between these variables is not entirely clear, probably due to both physiological and technical aspects involved in the extraction of PRV. Moreover, the effects of cardiovascular changes on PRV have not been elucidated. In this thesis, four studies were performed to (1) determine the best combination of some technical aspects for the extraction of PRV from PPG signals; (2) evaluate the relationship between PRV and HRV under different cardiovascular conditions; and (3) explore the effects of cardiovascular changes on PRV. First, PRV extraction gave lower errors when (1) signals were acquired for at least 120 s with a 256 Hz sampling rate and filtered with lower low cut-off frequencies and elliptic, equiripple or Parks-McClellan filter; (2) cardiac cycles were determined using the D2max algorithm and the a fiducial points; and (3) the Fast Fourier Transform was applied to obtain frequency spectra. Secondly, the relationship between HRV and PRV was found to be affected by cold exposure and changes in blood pressure, while PRV was found to be different at different body sites. Finally, PRV was affected by haemodynamic changes, such as target flow, stroke rate and blood pressure, both in an in-vitro model and in-vivo data. Additionally, PRV was found to be a potential tool for the estimation of blood pressure, with errors as low as 1:54 ± 0:17 mmHg, 1:07 ± 0:06 mmHg and 1:22 ± 0:09 mmHg for the estimation of systolic, diastolic and mean arterial pressure. Although more studies are needed to fully understand PRV and its clinical potential, PRV should not be regarded as the same as HRV, and it could be consider as a potential valuable biomarker for cardiovascular health

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines

Procesado y transmisión de señales biomédicas para el diagnostico de trastornos y enfermedades del sueño

El objetivo de esta Tesis Doctoral es la propuesta y estudio de viabilidad de un sistema de uso portátil-domiciliario y de los procedimientos validados asociados, para el análisis y caracterización de diversas señales biomédicas, de las que se extraen parámetros fundamentales para las más novedosas técnicas de diagnóstico. Se trata de reducir el número de sensores y señales relevantes a efectos de diagnosis, respecto de los empleados en la PolisomNografla (PSG), actual estándar de facto para la diagnosis del SAHS. En particular, el estudio se centra en la validación de los resultados proporcionados por en el empleo de un sensor de aceleración, aplicado a la altura de la traquea, en el hueco supraesternal, a un conjunto de pacientes internados en una Unidad del Sueño Hospitalaria, bajo sospecha de padecer SAHS. El análisis sistemático comparativo de sensores multifunción, aplicados a trastornos cardiorrespiratorios, frente a las técnicas convencionales, es un aspecto poco abordado en la literatura. Por ello, en este estudio se propone, mediante técnicas de procesado digital de la señal, extraer las variables cardiorrespiratorias útiles para el diagnóstico de los diferentes tipos de fenómenos respiratorios anonnales durante el sueño o en posición de decúbito. A partir de la segregación, partiendo de una única componente, de la información respiratoria, cardiaca y vinculada al ronquido, se obtienen los parámetros relativos al ritmo cardiaco y su variabilidad, la actividad simpática y parasimpática, el ritmo respiratorio, y la actividad roncadora, y se comparan los resultados obtenidos con los calculados a partir de los sensores cardiorrespiratorios clásicos (termistor, galgas extensiométricas, electrocardiograma y micrófono). Con las técnicas aplicadas se demuestra experimentalmente, que es posible realizar una aproximación eficaz a la valoración de los parámetros anteriores, empleando para ello un sistema de bajo coste y uso sencillo, en sustitución parcial de la electrocardiografla (se obtiene una variante de la fonocardiografla), la valoración respiratoria mediante termistor o cánula, y el micrófono. Además se propone el empleo de técnicas adicionales para la identificación de eventos apneicos o para la discriminación de pacientes. Finalmente, se ha desarrollado un sistema prototipo, para el registro inalámbrico y el análisis de los datos proporcionados por un sensor de aceleración de fácil auto-aplicación. Se persigue la reducción de costes y el aumento de la productividad, índices fundamentales de la futura sanidad electrónica

Symbolic Dynamics Analysis: a new methodology for foetal heart rate variability analysis

Cardiotocography (CTG) is a widespread foetal diagnostic methods. However, it lacks of objectivity and reproducibility since its dependence on observer's expertise. To overcome these limitations, more objective methods for CTG interpretation have been proposed. In particular, many developed techniques aim to assess the foetal heart rate variability (FHRV). Among them, some methodologies from nonlinear systems theory have been applied to the study of FHRV. All the techniques have proved to be helpful in specific cases. Nevertheless, none of them is more reliable than the others. Therefore, an in-depth study is necessary. The aim of this work is to deepen the FHRV analysis through the Symbolic Dynamics Analysis (SDA), a nonlinear technique already successfully employed for HRV analysis. Thanks to its simplicity of interpretation, it could be a useful tool for clinicians. We performed a literature study involving about 200 references on HRV and FHRV analysis; approximately 100 works were focused on non-linear techniques. Then, in order to compare linear and non-linear methods, we carried out a multiparametric study. 580 antepartum recordings of healthy fetuses were examined. Signals were processed using an updated software for CTG analysis and a new developed software for generating simulated CTG traces. Finally, statistical tests and regression analyses were carried out for estimating relationships among extracted indexes and other clinical information. Results confirm that none of the employed techniques is more reliable than the others. Moreover, in agreement with the literature, each analysis should take into account two relevant parameters, the foetal status and the week of gestation. Regarding the SDA, results show its promising capabilities in FHRV analysis. It allows recognizing foetal status, gestation week and global variability of FHR signals, even better than other methods. Nevertheless, further studies, which should involve even pathological cases, are necessary to establish its reliability.La Cardiotocografia (CTG) è una diffusa tecnica di diagnostica fetale. Nonostante ciò, la sua interpretazione soffre di forte variabilità intra- e inter- osservatore. Per superare tali limiti, sono stati proposti più oggettivi metodi di analisi. Particolare attenzione è stata rivolta alla variabilità della frequenza cardiaca fetale (FHRV). Nel presente lavoro abbiamo suddiviso le tecniche di analisi della FHRV in tradizionali, o lineari, e meno convenzionali, o non-lineari. Tutte si sono rivelate efficaci in casi specifici ma nessuna si è dimostrata più utile delle altre. Pertanto, abbiamo ritenuto necessario effettuare un’indagine più dettagliata. In particolare, scopo della tesi è stato approfondire una specifica metodologia non-lineare, la Symbolic Dynamics Analysis (SDA), data la sua notevole semplicità di interpretazione che la renderebbe un potenziale strumento di ausilio all’attività clinica. Sono stati esaminati all’incirca 200 riferimenti bibliografici sull’analisi di HRV e FHRV; di questi, circa 100 articoli specificamente incentrati sulle tecniche non-lineari. E’ stata condotta un’analisi multiparametrica su 580 tracciati CTG di feti sani per confrontare le metodologie adottate. Sono stati realizzati due software, uno per l’analisi dei segnali CTG reali e l’altro per la generazione di tracciati CTG simulati. Infine, sono state effettuate analisi statistiche e di regressione per esaminare le correlazioni tra indici calcolati e parametri di interesse clinico. I risultati dimostrano che nessuno degli indici calcolati risulta più vantaggioso rispetto agli altri. Inoltre, in accordo con la letteratura, lo stato del feto e le settimane di gestazione sono parametri di riferimento da tenere sempre in considerazione per ogni analisi effettuata. Riguardo la SDA, essa risulta utile all’analisi della FHRV, permettendo di distinguere – meglio o al pari di altre tecniche – lo stato del feto, la settimana di gestazione e la variabilità complessiva del segnale. Tuttavia, sono necessari ulteriori studi, che includano anche casi di feti patologici, per confermare queste evidenze