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

Optimal fiducial points for pulse rate variability analysis from forehead and finger PPG signals

Objective: The aim of this work is to evaluate and compare five fiducialpoints for the temporal location of each pulse wave from forehead and fingerphotoplethysmographic pulse waves signals (PPG) to perform pulse rate variability(PRV) analysis as a surrogate of heart rate variability (HRV) analysis. Approach: Forehead and finger PPG signals were recorded during tilt-table testsimultaneously to the ECG. Artifacts were detected and removed and, five fiducialpoints were computed: apex, middle-amplitude and foot points of the PPG signal,apex point of the first derivative signal and, the intersection point of the tangent tothe PPG waveform at the apex of the derivative PPG signal and the tangent to thefoot of the PPG pulse defined as intersecting tangents method. Pulse period (PP)time intervals series were obtained from both PPG signals and compared to the RRintervals obtained from the ECG. Heart and pulse rate variability signals (HRV andPRV) were estimated and, classical time and frequency domain indices were computed. Main Results: The middle-amplitude point of the PPG signal (nM), the apexpoint of the first derivative (n*A), and the tangents intersection point (nT) are themost suitable fiducial points for PRV analysis, which result in the lowest relativeerrors estimated between PRV and HRV indices, higher correlation coefficients and reliability indexes. Statistically significant differences according to the Wilcoxon testbetween PRV and HRV signals were found for the apex and foot fiducial points ofthe PPG, as well as the lowest agreement between RR and PP series according toBland-Altman analysis. Hence, they have been considered less accurate for variabilityanalysis. In addition, the relative errors are significantly lower fornMandn*Afeaturesby using Friedman statistics with Bonferroni multiple-comparison test and, we proposenMas the most accurate fiducial point. Based on our results, forehead PPG seems toprovide more reliable information for a PRV assessment than finger PPG. Significance: The accuracy of the pulse wave detections depends on the morphologyof the PPG. There is therefore a need to widely define the most accurate fiducial pointto perform a PRV analysis under non-stationary conditions based on different PPGsensor locations and signal acquisition techniques

Estimación robusta de la diferencia del tiempo de tránsito del pulso sanguíneo a partir de señales fotopletismográficas

En el presente trabajo se va a estudiar la posibilidad de detectar estrés mental utilizando técnicas no invasivas basadas en la señal fotopletismográfica de pulso (PPG). Para ello se pretende detectar cambios en la velocidad de pulso arterial (PWV), utilizando señales de PPG tomadas en dos puntos distintos del árbol arterial con las que poder medir el tiempo de llegada de pulso arterial a la periferia (PAT) y la diferencia de ese tiempo de llegada entre dos puntos de la periferia distintos (PTTD). Tanto el PAT como el PTTD han sido propuestas en la bibliografía como medidas influenciados por el Tiempo de Tránsito de Pulso (PTT), este último capaz de medir cambios en la dinámica cardiovascular. Sin embargo, el PTTD, al contrario que el PAT, no necesita del electrocardiograma (ECG) para ser obtenido y no está influenciado por el periodo de pre-eyección (PEP) -un intervalo de tiempo en la sístole ventricular que cambia pulso a pulso- el cual genera que el PAT pierda la relación con el PTT, dos factores importantes que aventajan al PTTD frente al PAT. Primero, se estudia de fiabilidad de los puntos fiduciales para la detección de los pulsos de la señal PPG y con ésto comprobar cuál es el método con la mayor precisión. Se demuestra mediante diversos análisis que el mejor punto para detectar los pulsos corresponde al valor de la PPG en el instante de máxima pendiente (valor máximo en la primera derivada). Resulta necesario implementar un detector de artefactos ya que el método de adquisición de la PPG es muy sensible a ellos pudiendo llegar a haber segmentos en los que la señal registrada es absolutamente inutilizable. Posteriormente, se analizan 14 voluntarios sanos sometidos a un protocolo de estrés y se realiza un test estadístico para comprobar la validez del método propuesto. Los resultados muestran que la desviación estándar de la PTTD tiene la capacidad estadística suficiente como para discernir entre estados de estrés y de relajación, para cada uno de los sujetos por separado. Además, se puede ver una tendencia descendente generalizada del descenso de la PTTD en situación de estrés con respecto a relajación. %Sin embargo, resultará necesario repetir el análisis con una muestra de señales mayor ya que se dispone de pocos sujetos en la base de datos utilizada, ya que la calidad de la señal de PPG que se registró en la frente es muy mala y hay muy pocos sujetos con los que se puede computar la PTTD. A modo de conclusión, se ha visto que la PTTD contiene información fisiológica que puede ser interesante para la detección de estrés. A su vez, también es una técnica potencialmente interesante para otros tipos de aplicaciones clínicas tales como la estimación no invasiva de la presión arterial o la evaluación de la rigidez arterial, pero se necesita estudiar la adecuación de ésta en cada escenario en particular. Además, como la PTTD se puede medir a partir de únicamente dos señales PPG, la técnica es idónea para dispositivos wearable y smartphones

Respiratory rate derived from smartphone-camera-acquired pulse photoplethysmographic signals

A method for deriving respiratory rate from smartphone-camera-acquired pulse photoplethysmographic (SCPPG) signal is presented. Our method exploits respiratory information by examining the pulse wave velocity and dispersion from the SCPPG waveform and we term these indices as the pulse width variability (PWV). A method to combine information from several derived respiration signals is also presented and it is used to combine PWV information with other methods such as pulse amplitude variability (PAV), pulse rate variability (PRV), and respiration-induced amplitude and frequency modulations (AM and FM) in SCPPG signals Evaluation is performed on a database containing SCPPG signals recorded from 30 subjects during controlled respiration experiments at rates from 0.2 to 0.6 Hz with an increment of 0.1 Hz, using three different devices: iPhone 4S, iPod 5, and HTC One M8. Results suggest that spontaneous respiratory rates (0.2–0.4 Hz) can be estimated from SCPPG signals by the PWV- and PRVbased methods with low relative error (median of order 0.5% and interquartile range of order 2.5%). The accuracy can be improved by combining PWV and PRV with other methods such as PAV, AM and/or FM methods. Combination of these methods yielded low relative error for normal respiratory rates, and Institute of Physics and Engineering in Medicine maintained good performance at higher rates (0.5–0.6 Hz) when using the iPhone 4S or iPod 5 devices

Pulse rate and transit time analysis to predict hypotension events after spinal anesthesia during programmed cesarean labor

Prophylactic treatment has been proved to reduce hypotension incidence after spinal anesthesia during cesarean labor. However, the use of pharmacological prophylaxis could carry out undesirable side-effects on mother and fetus. Thus, the prediction of hypotension becomes an important challenge. Hypotension events are hypothesized to be related to a malfunctioning of autonomic nervous system (ANS) regulation of blood pressure. In this work, ANS responses to positional changes of 51 pregnant women programmed for a cesarean labor were explored for hypotension prediction. Lateral and supine decubitus, and sitting position were considered while electrocardiographic and pulse photoplethysmographic signals were recorded. Features based on heart rate variability, pulse rate variability (PRV) and pulse transit time (PTT) analysis were used in a logistic regression classifier. The results showed that PRV irregularity changes, assessed by approximate entropy, from supine to lateral decubitus, and standard deviation of PTT in supine decubitus were found as the combination of features that achieved the best classification results sensitivity of 76%, specificity of 70% and accuracy of 72%, being normotensive the positive class. Peripheral regulation and blood pressure changes, measured by PRV and PTT analysis, could help to predict hypotension events reducing prophylactic side-effects in the low-risk population

Statistical and Nonlinear Analysis of Oximetry from Respiratory Polygraphy to Assist in the Diagnosis of Sleep Apnea in Children

Producción CientíficaObstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a sleep related breathing disorder that has important consequences in the health and development of infants and young children. To enhance the early detection of OSAHS, we propose a methodology based on automated analysis of nocturnal blood oxygen saturation (SpO2) from respiratory polygraphy (RP) at home. A database composed of 50 SpO2 recordings was analyzed. Three signal processing stages were carried out: (i) feature extraction, where statistical features and nonlinear measures were computed and combined with conventional oximetric indexes, (ii) feature selection using genetic algorithms (GAs), and (iii) feature classification through logistic regression (LR). Leave-one-out cross-validation (loo-cv) was applied to assess diagnostic performance. The proposed method reached 80.8% sensitivity, 79.2% specificity, 80.0% accuracy and 0.93 area under the ROC curve (AROC), which improved the performance of single conventional indexes. Our results suggest that automated analysis of SpO2 recordings from at-home RP provides essential and complementary information to assist in OSAHS diagnosis in children.Ministerio de Economía y Competitividad (TEC2011-22987)Fundación General CSIC (Proyecto Cero 2011 sobre Envejecimiento)Obra social de la Caixa y CSICJunta de Castilla y León (VA059U13

Impact of the PPG sampling rate in the pulse rate variability indices evaluating several fiducial points in different pulse waveforms

The main aim of this work is to study the effect of the sampling rate of the photoplethysmographic (PPG) signal for pulse rate variability (PRV) analysis in the time and frequency domains, in stationary conditions. Forehead and finger PPG signals were recorded at 1000 Hz during a rest state, with red and infrared wavelengths, simultaneously with the electrocardiogram (ECG). The PPG sampling rate has been reduced by decimation, obtaining signals at 500 Hz, 250 Hz, 125 Hz, 100 Hz, 50 Hz and 25 Hz. Five fiducial points were computed: apex, up-slope, medium, line-medium and medium interpolate point. The medium point is located in the middle of the up-slope of the pulse. The medium interpolate point is a new proposal as fiducial point that consider the abrupt up-slope of the PPG pulse, so it can be recovered by linear interpolation when the sampling rate is reduced. The error performed in the temporal location of the fiducial points was computed. Pulse period time interval series were obtained from all PPG signals and fiducial points, and compared with the RR intervals obtained from the ECG. Heart rate variability and PRV signals were estimated and classical time and frequency domain indices were computed. The results showed that the medium interpolate point of the PPG pulse was the most accurate fiducial point under different PPG morphologies and sensor locations, when sampling rate was reduced. The error in the temporal location points and in the estimation of time and frequency indices was always lower when medium interpolate point was used for all considered sampling rates and for both signals, finger and forehead. The results also showed that the sampling rate of PPG signal can be reduced up to 100 Hz without causing significant changes in the time and frequency indices, when medium interpolate point was used as fiducial point. Therefore, the use of the medium interpolate point is recommended when working at low sampling rates

Respiratory Rate Derived from Pulse Photoplethysmographic Signal by Pulse Decomposition Analysis

A novel technique to derive respiratory rate from pulse photoplethysmographic (PPG) signals is presented. It exploits some morphological features of the PPG pulse that are known to be modulated by respiration: amplitude, slope transit time, and width of the main wave, and time to the first reflected wave. A pulse decomposition analysis technique is proposed to measure these features. This technique allows to decompose the PPG pulse into its main wave and its subsequent reflected waves, improving the robustness against noise and morphological changes that usually occur in long-term recordings. Proposed methods were evaluated with a data base containing PPG and plethysmography-based respiratory signals simultaneously recorded during a paced-breathing experiment. Results suggest that normal ranges of spontaneous respiratory rate (0.1-0.5 Hz) can be accurately estimated (median and interquartile range of relative error less than 5%) from PPG signals by using the studied features

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

Non-Invasive Detection of Mechanical Alternans Utilizing Photoplethysmography

Background and Significance: Mechanical alternans (MA) is a biomarker associated with mortality and life-threatening arrhythmias in heart failure patients. Despite showing prognostic value, its use is limited by the requirement of measuring continuous blood pressure (BP), which is costly and impractical. Objective: To develop and test, for the first time, non-invasive MA surrogates based on photoplethysmography (PPG). Methods: Continuous BP and PPG were recorded during clinical procedures and tests in 35 patients. MA was induced either by ventricular pacing (Group A, N=19) or exercise (Group B, N=16). MA was categorized as sustained or intermittent if MA episodes were observed in at least 20 or between 12 to 20 consecutive beats, respectively. Eight features characterizing pulse morphology were derived from the PPG and MA surrogates were evaluated. Results: Sustained alternans was observed in 9 patients (47%) from Group A, whereas intermittent alternans was observed in 13 patients (68%) from Group A and in 10 patients (63%) from Group B. The PPG-based MA surrogate showing the highest accuracy, V'M, was based on the maximum of the first derivative of the PPG pulse. It detected both sustained and intermittent MA with 100% sensitivity and 100% specificity in Group A and intermittent MA with 100% sensitivity and 83% specificity in Group B. Furthermore, the magnitudes of MA and its PPG-based surrogate were linearly correlated (R 2 =0.83, p<0.001). Conclusion: MA can be accurately identified non-invasively through PPG analysis. This may have important clinical implications for risk stratification and remote monitoring