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

Apneic Events Detection Using Different Features of Airflow Signals

Apneic-event based sleep disorders are very common and affect greatly the daily life of people. However, diagnosis of these disorders by detecting apneic events are very difficult. Studies show that analyzes of airflow signals are effective in diagnosis of apneic-event based sleep disorders. According to these studies, diagnosis can be performed by detecting the apneic episodes of the airflow signals. This work deals with detection of apneic episodes on airflow signals belonging to Apnea-ECG (Electrocardiogram) and MIT (Massachusetts Institute of Technology) BIH (Bastons’s Beth Isreal Hospital) databases. In order to accomplish this task, three representative feature sets namely classic feature set, amplitude feature set and descriptive model feature set were created. The performance of these feature sets were evaluated individually and in combination with the aid of the random forest classifier to detect apneic episodes. Moreover, effective features were selected by OneR Attribute Eval Feature Selection Algorithm to obtain higher performance. Selected 28 features for Apnea-ECG database and 31 features for MITBIH database from 54 features were applied to classifier to compare achievements. As a result, the highest classification accuracies were obtained with the usage of effective features as 96.21% for Apnea-ECG database and 92.23% for MIT-BIH database. Kappa values are also quite good (91.80 and 81.96%) and support the classification accuracies for both databases, too. The results of the study are quite promising for determining apneic events on a minute-by-minute basis

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

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

Photoplethysmographic Waveform Versus Heart Rate Variability to Identify Low Stress States. Attention Test

Our long-term goal is the development of an automatic identifier of attentional states. In order to accomplish it, we should firstly be able to identify different states based on physiological signals. So, the first aim of this work is to identify the most appropriate features, to detect a subject high performance state. For that, a database of electrocardiographic (ECG) and photopletysmographic (PPG) signals is recorded in two unequivocally defined states (rest and attention task) from up to 50 subjects as a sample of the population. Time and frequency parameters of heart/pulse rate variability have been computed from the ECG/PPG signals respectively. Additionally, the respiratory rate has been estimated from both signals and also six morphological parameters from PPG. In total, twenty six features are obtained for each subject. They provide information about the autonomic nervous system and the physiological response of the subject to an attention demand task. Results show an increase of sympathetic activation when the subjects perform the attention test. The amplitude and width of the PPG pulse were more sensitive that the classical sympathetic markers (normalised power in LF and LF/HF ratio) for identifying this attentional state. State classification accuracy reaches a mean of 89 $\pm$ 2%, a maximum of 93% and a minimum of 85%, in the hundred classifications made by only selecting four parameters extracted from the PPG signal (pulse amplitude, pulse width, pulse downward slope and mean pulse rate). These results suggest that attentional states could be identified by PPG

Adquisición y validación de la señal del sensor OH1 de Polar para el análisis de la variabilidad cardíaca de pulso

El estudio del impacto que el sistema nervioso autónomo produce sobre la actividad del corazón es una clara necesidad, ya no solo en situaciones normales si no en situaciones de riesgo como es el buceo. Esta influencia se observa a través de la variabilidad cardiaca y, por lo tanto, de las señales fotopletismográficas (PPG) y electrocardiográficas (ECG). Para el registro de estas señales se suelen utilizar dispositivos con mucho aparataje y que suponen una distracción y molestia en los sujetos de estudio. Por todo ello, en este trabajo se propone la utilización del dispositivo comercial Polar OH1, que es sencillo, ligero y de reducido tamaño. Este dispositivo permite registrar la señal PPG de manera no invasiva en un dispositivo externo a través de su comunicación Bluetooth Low Energy (BLE). El dispositivo Polar OH1 presenta baja frecuencia de muestreo, 134Hz, y en estudios anteriores demuestran que a esta frecuencia aún se puede medir la variabilidad cardiaca. Teniendo en cuenta las características favorables que aconsejan su uso, en este trabajo se ha realizado el diseño de un sistema de registro basado en el SoC (System on a Chip) ESP32 que permite la comunicación BLE con el dispositivo Polar y que almacena en una tarjeta de memoria microSD la señal PPG. Además, debido a la necesidad de que el dispositivo final sea estanco, se propone la utilización del ESP32 como punto de acceso wifi para la extracción de los ficheros almacenados. Por otra parte, este sistema es capaz de registrar señales de varios dispositivos Polar OH1 para así poder registrar varios sensores o sujetos simultáneamente. Este dispositivo externo, además, registra los valores de presión barométrica y temperatura, que también afectan a la respuesta del sistema nervioso autónomo. A partir del sistema de registro construido se ha realizado un estudio de validación de las señales captadas de una muestra de 22 personas a las que se les coloca el nuevo dispositivo en estado de reposo y de estrés seguidamente, comparándolas con un dispositivo ya utilizado con esta misma finalidad, Nautilus. En el análisis realizado posteriormente, se han validado los parámetros estándar de estudio de la variabilidad cardiaca, y se puede observar una clara significancia en el error cometido en la estimación de los parámetros temporales y frecuenciales de la variabilidad cardiaca. Esto puede ser debido al alto nivel de ruido de la señal sumado a su baja frecuencia de muestreo. Por tanto, se concluye que el dispositivo Polar OH1 no es válido para la medición de la variabilidad cardiaca ni sirve para la identificar cambios significativos entre un estado de estrés y uno de reposo.<br /

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

Autonomic nervous system response analysis in patients suffering from sleep apnea syndrome

Sleep apnea syndrome (SAS) is an underdiagnosed disease affecting up to 5% of the general population. It is a multifactorial disease characterized by repeated episodes of breathing pauses (apnea) or significant reduction in respiratory amplitude (hypopnea) during the patient sleep. that may cause sleep fragmentation along with acute cardio-respiratory alterations associated with the development of hypertension and cardiovascular diseases in the long-term. In previous works, it has been proposed a novel monitoring and therapeutic system for SAS, based on non-invasive adaptive kinaesthetic. This study presents preliminary results of the patient’s physiological response in terms of pulse photoplethysmography (PPG) amplitude and pulse transit time (PTT). It has been proposed a chain of signal processing that has allowed to characterize the pulse photoplethysmography in order to estimate an autonomic response to respiratory events when the therapy is active or not. Although beneficial results have been observed on 20% of treated patients, these preliminary results do not allow us to determine in a global way the potential effect of the therapy in terms of peripheral vasoconstriction control. New studies are currently under way to analyse other autonomic indicators in this population

Burmuineko infragorri hurbileko espektroskopia seinaleetan oinarritutako bihotz-maiztasuna eta arnasketa-maiztasuna neurtzeko algoritmoak

Proiektu honen muina, burmuineko oxigenazio maila neurtzen duen NIRS (Near Infrared Spectroscopy) teknika ez-inbaditzailearen egokitasuna frogatzea da, pazientearen bihotz eta arnasketa maiztasunak estimatzerako orduan. Larrialdi egoeretan, pazientearen oxigenazioaren monitorizazioa beharrezkoa denean, gaur eguneko teknika erabiliena pultsu oximetria da, oxigenazio maila atzamarrean neurtzen duena PPG (Photoplethysmogram) seinalearen bitartez. Teknika hau ordea, ez da guztiz eraginkorra pazienteak egoera hemodinamiko konprometitua duenean, izan ere, sistema kardiobaskularrak biziraupenerako ezinbestekoak diren organoei ematen die lehentasuna, hau da, burmuin eta bihotzari, eta pultsu oximetriak ez du funtsezkoak diren bi organo horietan oxigenazio maila neurtzen. Azken urteotan, PPG sistemaren gabeziak osatzeko eta beraz, bai garuneko eta baita bihotzeko oxigenazio maila egokia dela egiaztatzeko, NIRS teknika medikuntza esparru askotan aplikatzen ari da, kirurgia kardiobasularraren monitorizazioan esaterako. NIRS teknikaren bidez burmuineko oxigenazioaz gain pazientearen beste hainbat parametro fisiologikoren neurketa posible izango balitz, arnasketa eta bihotz maiztasunak adibidez, aurrerapauso handia izango litzateke bihotz-biriketako berpiztearen arloan. NIRS seinalea bizi-konstante hauek estimatzeko seinale baliogarria dela egiaztatzea du helburu proiektu honek. Hori lortzeko, hainbat azpi-helburu bete beharko dira. Hasteko, pazientearen seinale fisiologikoak biltegiratzen dituen datu baseko bularreko inpedantzia eta EKG seinaleen markaketa gauzatuko da, ondoren hainbat algoritmotan aplikatuak izateko. Diseinatuko diren algoritmoak hiru zatitan banandu daitezke. Alde batetik, oxyhemoglobina seinalearen pikoen detekzioa gauzatzen duen algoritmoa garatuko da. Bestetik, hortik abiatuz eta beste hainba