Unconstrained video monitoring of breathing behavior and application to diagnosis of sleep apnea

This paper presents a new real-time automated infrared video monitoring technique for detection of breathing anomalies, and its application in the diagnosis of obstructive sleep apnea. We introduce a novel motion model to detect subtle, cyclical breathing signals from video, a new 3-D unsupervised self-adaptive breathing template to learn individuals' normal breathing patterns online, and a robust action classification method to recognize abnormal breathing activities and limb movements. This technique avoids imposing positional constraints on the patient, allowing patients to sleep on their back or side, with or without facing the camera, fully or partially occluded by the bed clothes. Moreover, shallow and abdominal breathing patterns do not adversely affect the performance of the method, and it is insensitive to environmental settings such as infrared lighting levels and camera view angles. The experimental results show that the technique achieves high accuracy (94% for the clinical data) in recognizing apnea episodes and body movements and is robust to various occlusion levels, body poses, body movements (i.e., minor head movement, limb movement, body rotation, and slight torso movement), and breathing behavior (e.g., shallow versus heavy breathing, mouth breathing, chest breathing, and abdominal breathing). © 2013 IEEE

Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications

Contactless recording of sleep apnea and periodic leg movements by nocturnal 3-D-video and subsequent visual perceptive computing

Contactless measurements during the night by a 3-D-camera are less time-consuming in comparison to polysomnography because they do not require sophisticated wiring. However, it is not clear what might be the diagnostic benefit and accuracy of this technology. We investigated 59 persons simultaneously by polysomnography and 3-D-camera and visual perceptive computing (19 patients with restless legs syndrome (RLS), 21 patients with obstructive sleep apnea (OSA), and 19 healthy volunteers). There was a significant correlation between the apnea hypopnea index (AHI) measured by polysomnography and respiratory events measured with the 3-D-camera in OSA patients (r = 0.823; p < 0.001). The receiver operating characteristic curve yielded a sensitivity of 90% for OSA with a specificity of 71.4%. In RLS patients 72.8% of leg movements confirmed by polysomnography could be detected by 3-D-video and a significant moderate correlation was found between PLM measured by polysomnography and by the 3-D-camera (RLS: r = 0.654; p = 0.004). In total, 95.4% of the sleep epochs were correctly classified by the machine learning approach, but only 32.5% of awake epochs. Further studies should investigate, if this technique might be an alternative to home sleep testing in persons with an increased pre-test probability for OSA

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

Thermal imaging developments for respiratory airflow measurement to diagnose apnoea

Sleep-disordered breathing is a sleep disorder that manifests itself as intermittent pauses (apnoeas) in breathing during sleep. The condition disturbs the sleep and can results in a variety of health problems. Its diagnosis is complex and involves multiple sensors attached to the person to measure electroencephalogram (EEG), electrocardiogram (ECG), blood oxygen saturation (pulse oximetry, S

Non-invasive techniques for respiratory information extraction based on pulse photoplethysmogram and electrocardiogram

El objetivo principal de esta tesis es el desarrollo de métodos no invasivos para la extracción de información respiratoria a partir de dos señales biomédicas ampliamente utilizadas en la rutina clínica: el electrocardiograma (ECG) y la señal fotopletismográfica de pulso (PPG). La motivación de este estudio es la conveniencia de monitorizar información respiratoria a partir de dispositivos no invasivos que permita sustituir las técnicas actuales que podrían interferir con la respiración natural y que presentan inconvenientes en algunas aplicaciones como la prueba de esfuerzo y los estudios del sueño. Además, si estos dispositivos no invasivos son los ya utilizados en la rutina clínica, la información respiratoria extraída de ellos representa un valor añadido que permite tener una visión más completa del paciente. DESARROLLO TEÓRICO Esta tesis se divide en 6 capítulos. El Capítulo 1 introduce la problemática, motivaciones y objetivos del estudio. También introduce el origen fisiológico de las señales estudiadas ECG y PPG, y cómo y por qué tienen información autonómica y respiratoria que se puede extraer de ellas. El Capítulo 2 aborda la obtención de información respiratoria a partir del ECG. Se han propuesto varios métodos para la obtención de la respiración a partir del ECG (EDR, del inglés ¿ECG derived respiration?). Su rendimiento se suele ver muy afectado en entornos altamente no estacionarios y ruidosos como la prueba de esfuerzo. No obstante, se han propuesto algunas alternativas, como una basada en el ángulo de rotación del eje eléctrico (obtenido del ECG), que es el que mejor funciona en prueba de esfuerzo según nuestros conocimientos. Este método requiere de tres derivaciones ortogonales y es muy dependiente de cada una de ellas, i.e., el método no es aplicable o su rendimiento se reduce significativamente si hay algún problema en alguna de las derivaciones requeridas. En el Capítulo 2 se propone un método EDR nuevo basado en las pendientes del QRS y el ángulo de la onda R. El Capítulo 3 aborda a obtención de información respiratoria a partir de la señal PPG. Se propone un método nuevo para obtener la tasa respiratoria a partir de la señal PPG. Explota una modulación respiratoria en la variabilidad de anchura de pulso (PWV) relacionada con la velocidad y dispersión de la onda de pulso. El Capítulo 4 aborda la extracción de información respiratoria a partir de señales PPG registradas con smarthpones (SCPPG), mediante la adaptación de los métodos basados en la señal PPG presentados en el Capítulo 3. En el Capítulo 5 se propone un método para el diagnóstico del síndrome de apnea obstructiva del sueño (OSAS) en niños basado únicamente en la señal PPG. El OSAS es una disfunción relacionada con la respiración y el sueño que se diagnostica mediante polisomnografía (PSG). La PSG es el registro nocturno de muchas señales durante el sueño, siendo muy difícil de aplicar en entornos ambulatorios. El método que presenta esta tesis está enfocado a diagnosticar el OSAS en niños utilizando únicamente la señal PPG que permitiría considerar un diagnóstico ambulatorio con sus ventajas económicas y sociales. Finalmente, el Capítulo 6 resume las contribuciones originales y las conclusiones principales de esta tesis, y propone posibles extensiones del trabajo. CONCLUSIÓN El método presentado en el Capítulo 2 para estimar la tasa respiratoria a partir de las pendientes del complejo QRS y el ángulo de la onda R en el ECG demostró ser robusto en entornos altamente no estacionarios y ruidosos y por tanto ser aplicable durante ejercicio incluyendo entrenamiento deportivo. Además, es independiente de un conjunto específico de derivaciones y, por tanto, un problema en alguna de ellas no implica una reducción considerable del rendimiento. El método presentado en el Capítulo 3 para estimar la tasa respiratoria a partir de la PWV extraída de la señal PPG está mucho menos afectada por el tono simpático que otros métodos presentados en la literatura que suelen basarse en la amplitud y/o la tasa de pulso. Esto permite una mayor precisión que otros métodos basados en PPG. Además, se propone un método para combinar información de diferentes señales respiratorias, y se utiliza para estimar la tasa respiratoria a partir de la PWV en combinación con otros métodos basados en la señal PPG, mejorando la precisión de la estimación incluso en comparación con otros métodos en la literatura que requieren el ECG o la presión sanguínea. Los métodos propuestos en el Capítulo 4 para estimar la tasa respiratoria mediante señales SCPPG estimaron de forma precisa la tasa respiratoria en sus rangos espontáneos habituales (0.2-0.4 Hz) e incluso a tasas más altas (hasta 0.5 Hz o 0.6 Hz, dependiendo del dispositivo utilizado). El único requerimiento es que el smartphone tenga un luz tipo flash y una cámara para grabar una yema del dedo sobre ella. La popularidad de los smartphones los convierte en dispositivos de acceso y aceptación r¿apidos. Así, para la población general es potencialmente aceptable un método que funciona en smartphones, pudiendo facilitar la medida de algunas constantes vitales utilizando solo la yema del dedo. El método presentado en el Capítulo 5 para el diagnóstico del OSAS en niños a partir de la PPG obtuvo una precisión suficiente para la clínica, aunque antes de ser aplicado en dicho entorno, el método debería ser validado en una base de datos más grande.The main objective of this thesis is to develop non-invasive methods for respiration information extraction from two biomedical signals which are widely adopted in clinical routine: the electrocardiogram (ECG) and the pulse photoplethysmographic (PPG) signal. This study is motivated by the desirability of monitoring respiratory information from non-invasive devices allowing to substitute the current respiration-monitoring techniques which may interfere with natural breathing and which are unmanageable in some applications such as stress test or sleep studies. Furthermore, if these noninvasive devices are those already used in the clinical routine, the respiratory information obtained from them represents an added value which allows a more complete overview of the patient status. This thesis is divided into 6 chapters. Chapter 1 of this thesis introduces the problematic, motivations and objectives of this study. It also introduces the physiological origin of studied ECG and PPG signals, and why and how they carry autonomic- and respiration-related information which can be extracted from them. Chapter 2 of this thesis addresses the derivation of respiratory information from ECG signal. Several ECG derived respiration (EDR) methods have been presented in literature. Their performance usually decrease considerably in highly non-stationary and noisy environments such as stress test. However, some alternatives aimed to this kind of environments have been presented, such as one based on electrical axis rotation angles (obtained from the ECG), which to the best of our knowledge was the best suited for stress test. This method requires three orthogonal leads, and it is very dependent on each one of those leads, i.e., the performance of the method is significantly decreased if there is any problem at any one of the required leads. A novel EDR method based on QRS slopes and R-wave angle is presented in this thesis. The proposed method demonstrated to be robust in highly non-stationary and noisy environments and so to be applicable to exercise conditions including sports training. Furthermore, it is independent on a specific lead set, and so, a problem at any lead do not imply a significantly reduction of the performance. Chapter 3 addresses the derivation of respiratory information from PPG signals. A novel method for deriving respiratory rate from PPG signal is presented. It exploits respiration-related modulations in pulse width variability (PWV) which is related to pulse wave velocity and dispersion. The proposed method is much less affected by the sympathetic tone than other methods in literature which are usually based on pulses amplitude and/or rate. This leads to highest accuracy than other PPG-based method. Furthermore, a method for combining information from several respiratory signals was developed and used to obtain a respiratory rate estimation from the proposed PWV-based in combination with other known PPG-based methods, improving the accuracy of the estimation and outperforming other methods in literature which involve ECG or BP recording. Chapter 4 addresses the derivation of respiratory information from smartphone- camera-acquired-PPG (SCPPG) signals by adapting the methods for deriving respiratory rate from PPG signal presented in Chapter 3. The proposed method accurately estimates respiratory rate from SCPPG signals at its normal spontaneous ranges (0.2-0.4 Hz) and even at higher rates (up to 0.5 Hz or 0.6 Hz, depending on the used device). The only requirement is that these smartphones and tablets contain a flashlight and a video camera to image a fingertip pressed to it. As smartphones and tablets have become common, they meet the criteria of ready access and acceptance. Hence, a mobile phone/tablet approach has the potential to be widely-accepted by the general population and can facilitate the capability to measure some of the vital signs using only fingertip of the subject. Chapter 5 of this thesis proposes a methodology for obstructive sleep apnea syndrome (OSAS) screening in children just based on PPG signal. OSAS is a sleep-respiration-related dysfunction for which polysomnography (PSG) is the gold standard for diagnosis. PSG consists of overnight recording of many signals during sleep, therefore, it is quite involved and difficult to use in ambulatory scenario. The method presented in this thesis is aimed to diagnose the OSAS in children based just on PPG signal which would allow us to consider an ambulatory diagnosis with both its social and economic advantages. Finally, Chapter 6 summarizes the original contributions and main conclusions of the thesis, and proposes possible extensions of the work