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The purpose of this research is to expand the understanding of certain properties of electrodes used for electrical bioimpedance measurements. Specifically, this work investigates the acclimation effect of the skin-electrode interface contact impedance. It also attempts to study the relationship between electrode spacing and amplitude of bioimpedance pulsatile signals. It was found that as soon as dry electrodes are placed on the skin, the contact impedance exponentially decreases until it reaches a constant level. The acclimation time, time to reach a constant contact impedance, is dependent of the electrode size and frequency. Increasing the size of the electrode, as well as increasing the frequency, decreases the acclimation time. The acclimation of wet electrodes was also studied, and it was found that changes in contact impedance over time are negligible in comparison to the amount dry electrodes contact impedance change. However, the contact impedance of wet electrodes, instead of decreasing, tends to increase just slightly before reaching a steady state. Electrodes that do not carry current have contact impedance magnitudes similar to those that carried current after 60 minutes. This acclimation effect seems to be driven by the moisture level in the skin-electrode interface. As sweat and moisture build up with time when using dry electrodes, contact impedance decreases; and as the moisture in wet electrodes dries up with time, contact impedance increases. Capturing the small bioimpedance changes due to blood flow in the artery proved to be quite challenging under the circular orientation and with low levels of current injected. Only 5% of all the pulsatile data acquired had high enough quality to have a discernible pulsatile signal present on it. From the analysis of this 5% of data there were not conclusive results with regards the effect of electrode spacing on the pulsatile signal amplitude. However, the placement of the electrodes relative to the artery did seem to play a role on the pulse signal amplitude since the pulse amplitude seemed to peak when the center of the 4 electrodes was close to the artery. Pulsatile signal does not seem to be consistent throughout time; performing the same measurement 50 minutes apart sometimes resulted in the same or very similar measurements and other time the measurements were very different from each other. Despite the inconclusive results, the system for switching and selecting electrodes from an array of multiple electrodes along with the algorithm to determine the quality of the measured pulsatile signal proved to be efficient and serves as a foundation for developing a measurement system that can search and identify the the electrode configuration and placement that results in acquiring high quality signals.

Submitted by PPG Engenharia El?trica (engenharia.pg.eletrica@pucrs.br) on 2018-10-29T13:30:23Z No. of bitstreams: 1 TIAGO BENETTI_DIS.pdf: 5038519 bytes, checksum: 95fa8d1b367b574eee27e772a55a9a49 (MD5) / Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2018-10-30T17:21:55Z (GMT) No. of bitstreams: 1 TIAGO BENETTI_DIS.pdf: 5038519 bytes, checksum: 95fa8d1b367b574eee27e772a55a9a49 (MD5) / Made available in DSpace on 2018-10-30T17:27:25Z (GMT). No. of bitstreams: 1 TIAGO BENETTI_DIS.pdf: 5038519 bytes, checksum: 95fa8d1b367b574eee27e772a55a9a49 (MD5) Previous issue date: 2018-08-31 / Heart rate monitoring using Photoplethysmography (PPG) signals acquired from the individuals pulse has become popular due to emergence of numerous low cost wearable devices. However, monitoring during physical activities has obstacles because of the influence of motion artifacts in PPG signals. The objective of this work is to introduce a new algorithm capable of removing motion artifacts and estimating heart rate from pulse PPG signals. Normalized Least Mean Square (NLMS) and Recursive Least Squares (RLS) algorithms are proposed for an adaptive filtering structure that uses acceleration signals as reference to remove motion artifacts. The algorithm uses the Periodogram of the filtered signals to extract their heart rates, which will be used together with a PPG Signal Quality Index to feed the input of a Kalman Filter. Specific heuristics and the Quality Index collaborate so that the Kalman filter provides a heart rate estimate with high accuracy and robustness to measurement uncertainties. The algorithm was validated from the heart rate obtained from Electrocardiography signals and the proposed method with the RLS algorithm presented the best results with an absolute mean error of 1.54 beats per minute (bpm) and standard deviation of 0.62 bpm, recorded for 12 individuals performing a running activity on a treadmill with varying speeds. The results make the performance of the algorithm comparable and even better than several recently developed methods in this field. In addition, the algorithm presented a low computational cost and suitable to the time interval in which the heart rate estimate is performed. Thus, it is expected that this algorithm will improve the obtaining of heart rate in currently available wearable devices. / O monitoramento da frequ?ncia card?aca utilizando sinais de Fotopletismografia ou PPG (do ingl?s, Photopletismography) adquiridos do pulso de indiv?duos tem se popularizado devido ao surgimento de in?meros dispositivos wearable de baixo custo. No entanto, o monitoramento durante atividades f?sicas tem dificuldades em raz?o da influ?ncia de artefatos de movimento nos sinais de PPG. O objetivo deste trabalho ? introduzir um novo algoritmo capaz de remover artefatos de movimento e estimar a frequ?ncia card?aca de sinais de PPG de pulso. Os algoritmos do M?nimo Quadrado M?dio Normalizado ou NLMS (do ingl?s, Normalized Least Mean Square) e de M?nimos Quadrados Recursivos ou RLS (do ingl?s, Recursive Least Squares) s?o propostos para uma estrutura de filtragem adaptativa que utiliza sinais de acelera??o como refer?ncia para remover os artefatos de movimento. O algoritmo utiliza o Periodograma dos sinais filtrados para extrair suas frequ?ncias card?acas, que ser?o utilizadas juntamente com um ?ndice de Qualidade do Sinal de PPG para alimentar a entrada de um Filtro de Kalman. Heur?sticas espec?ficas e o ?ndice de Qualidade colaboram para que filtro de Kalman forne?a uma estimativa da frequ?ncia card?aca com alta acur?cia e robustez a incertezas de medi??o. O algoritmo foi validado a partir da frequ?ncia card?aca obtida de sinais de Eletrocardiografia e o m?todo proposto com o algoritmo RLS apresentou os melhores resultados com um erro m?dio absoluto de 1,54 batimentos por minuto (bpm) e desvio padr?o de 0,62 bpm, registrados para 12 indiv?duos realizando uma atividade de corrida em uma esteira com velocidades variadas. Os resultados tornam o desempenho do algoritmo compar?vel e at? mesmo melhor que v?rios m?todos desenvolvidos recentemente neste campo. Al?m disso, o algoritmo apresentou um custo computacional baixo e adequado ao intervalo de tempo em que a estimativa da frequ?ncia card?aca ? realizada. Dessa forma, espera-se que este algoritmo melhore a obten??o da frequ?ncia card?aca em dispositivos wearable atualmente dispon?veis.