BCG Signal Processing

Bakalářská práce se zabývá návrhem a vývojem softwarového nástroje pro komplexní zpracování a následnou analýzu balistokardiografického signálu (BKG). Software byl vyvinut v interaktivním programové prostředí a skriptovacím programovacím jazyce Matlab v podobě grafické uživatelské rozhraní (GUI). Zpracováním signálu je myšlena primárně jeho filtrace a úprava pro následnou analýzu. Na základě literární rešerše byly implementovány lineární frekvenčně selektivní filtry a filtr využívající vlnkovou transformaci. Aplikace dále umožňuje frekvenční analýzu a úpravu signálu pro výpočet tepové frekvence. Jednotlivé použité metody jsou v práci testovány na syntetických i reálných datech. V poslední části jsou vybrané metody srovnání na základě objektivního hodnocení v podobě odstupu signálu od šumu (SNR).Bachelor thesis deals with design and development of software tool for complex processing and analysis of balistocardiographic signal (BCG). The software was developed in an interactive programming environment and the Matlab scripting programming language in the form of a graphical user interface (GUI). Signal processing means primarily its filtration and treatment for subsequent analysis. On the basis of literary research, linear frequency selective filters and a wavelet transform filter were implemented. The application also allows frequency analysis and signal processing to calculate pulse rate. The individual methods used are tested on synthetic and real data. In the last part, selected methods of comparison are based on objective evaluation in the form of signal-to-noise ratio (SNR).450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Estimación del ritmo cardíaco mediante análisis de secuencias de vídeo

La estimación y el seguimiento de la frecuencia cardíaca han sido esenciales en los campos de la medicina y la ingeniería biomédica a lo largo de los años. En la actualidad, existe un gran interés por extraerla sin contacto, por lo que la estimación de la frecuencia cardíaca mediante el uso de la cámara digital es un área de investigación en rápido crecimiento debido a su bajo costo y su naturaleza no invasiva. El objetivo de este trabajo es diseñar y desarrollar un algoritmo que nos permita detectar el ritmo cardíaco de un individuo mediante el análisis de secuencias de vídeo de su cara. Se ha diseñado e implementado una nueva aproximación, basada en un algoritmo base fundamentado en el trabajo previo en el que nos apoyamos. En primer lugar se realiza la detección facial mediante el algoritmo de Viola-Jones para localizar el área de la imagen que contiene la cara. A continuación, se extrae el movimiento de la cabeza usando el método de Lucas-Kanade y se aísla el movimiento correspondiente al pulso mediante un filtrado. Posteriormente se realiza el análisis de componentes principales (PCA) para seleccionar la componente que mejor se corresponda con los latidos del corazón en función de su espectro de frecuencia temporal. Finalmente, se analiza el movimiento proyectado en esta componente y se estima el ritmo cardíaco como la frecuencia (en bpm) con mayor potencia. Los experimentos muestran que, si los vídeos están grabados bajo unas buenas condiciones, podemos detectar el ritmo cardiaco de una persona obteniendo unas muy bajas tasas de error.The estimation and monitoring of heart rate have been essential in the fields of medicine and biomedical engineering over the years. At present, there is a great interest to extract it without contact, so the estimation of the heart rate by using the digital camera is a research area in rapid growth due to its low cost and non-invasive nature. The objective of this work is to design and develop an algorithm that allows us to detect the heart rhythm of an individual by analyzing video sequences of his face. A new approach has been designed and implemented, starting from a base algorithm based on previous work in which we rely. First, facial detection is performed using the Viola-Jones algorithm to locate the area of the image that contains the face. Then the motion of the head is extracted using the Lucas-Kanade method and the motion corresponding to the pulse is isolated by filtering. Afterwards, the main components analysis (PCA) is performed to select the component that best corresponds to the heartbeat according to its temporal frequency spectrum. Finally, the motion projected to this component is analyzed and the heart rate is estimated as the frequency (in bpm) with greater power. The experiments show that, if the videos are recorded under good conditions, we can detect the heart rate of a person getting low error ratesUniversidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació

High-Performance Accelerometer Based On Asymmetric Gapped Cantilevers For Physiological Acoustic Sensing

Continuous or mobile monitoring of physiological sounds is expected to play important role in the emerging mobile healthcare field. Because of the miniature size, low cost, and easy installation, accelerometer is an excellent choice for continuous physiological acoustic signal monitoring. However, in order to capture the detailed information in the physiological signals for clinical diagnostic purpose, there are more demanding requirements on the sensitivity/noise performance of accelerometers. In this thesis, a unique piezoelectric accelerometer based on the asymmetric gapped cantilever which exhibits significantly improved sensitivity is extensively studied. A meso-scale prototype is developed for capturing the high quality cardio and respiratory sounds on healthy people as well as on heart failure patients. A cascaded gapped cantilever based accelerometer is also explored for low frequency vibration sensing applications such as ballistocardiogram monitoring. Finally, to address the power issues of wireless sensors such as wireless wearable health monitors, a wide band vibration energy harvester based on a folded gapped cantilever is developed and demonstrated on a ceiling air condition unit

VOLUNTARY CONTROL OF BREATHING ACCORDING TO THE BREATHING PATTERN DURING LISTENING TO MUSIC AND NON-CONTACT MEASUREMENT OF HEART RATE AND RESPIRATION

We investigated if listening to songs changes breathing pattern which changes autonomic responses such as heart rate (HR) and heart rate variability (HRV) or change in breathing pattern is a byproduct of listening to songs or change in breathing pattern as well as listening to songs causes changes in autonomic responses. Seven subjects (4 males and 3 females) participated in a pilot study where they listened to two types of songs and used a custom developed biofeedback program to control their breathing pattern to match the one recorded during listening to the songs. Coherencies between EEG, breathing pattern and RR intervals (RRI) were calculated to study the interaction with neural responses. Trends in HRV varied only during listening to songs, suggesting that autonomic response was affected by listening to songs irrespective of control of breathing. Effective coherence during songs while spontaneously breathing was more than during silence and during control of breathing. These results, although preliminary, suggest that listening to songs as well as change in breathing patterns changes the autonomic response but the effect of listening to songs may surpass the effect of changes in breathing. We explored feasibility of using non-contact measurements of HR and breathing rate (BR) by using recently developed Facemesh and other methods for tracking regions of interests from videos of faces of subjects. Performance was better for BR than HR, and over currently used methods. However, refinement of the approach would be needed to get the precision required for detecting subtle changes