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.

Background Camera-based photoplethysmography (cbPPG) is a measurement technique which enables remote vital sign monitoring by using cameras. To obtain valid plethysmograms, proper regions of interest (ROIs) have to be selected in the video data. Most automated selection methods rely on specific spatial or temporal features limiting a broader application. In this work, we present a new method which overcomes those drawbacks and, therefore, allows cbPPG to be applied in an intraoperative environment. Methods We recorded 41 patients during surgery using an RGB and a near-infrared (NIR) camera. A Bayesian skin classifier was employed to detect suitable regions, and a level set segmentation approach to define and track ROIs based on spatial homogeneity. Results The results show stable and homogeneously illuminated ROIs. We further evaluated their quality with regards to extracted cbPPG signals. The green channel provided the best results where heart rates could be correctly estimated in 95.6% of cases. The NIR channel yielded the highest contribution in compensating false estimations. Conclusions The proposed method proved that cbPPG is applicable in intraoperative environments. It can be easily transferred to other settings regardless of which body site is considered.

Background Camera-based photoplethysmography (cbPPG) is a measurement technique which enables remote vital sign monitoring by using cameras. To obtain valid plethysmograms, proper regions of interest (ROIs) have to be selected in the video data. Most automated selection methods rely on specific spatial or temporal features limiting a broader application. In this work, we present a new method which overcomes those drawbacks and, therefore, allows cbPPG to be applied in an intraoperative environment. Methods We recorded 41 patients during surgery using an RGB and a near-infrared (NIR) camera. A Bayesian skin classifier was employed to detect suitable regions, and a level set segmentation approach to define and track ROIs based on spatial homogeneity. Results The results show stable and homogeneously illuminated ROIs. We further evaluated their quality with regards to extracted cbPPG signals. The green channel provided the best results where heart rates could be correctly estimated in 95.6% of cases. The NIR channel yielded the highest contribution in compensating false estimations. Conclusions The proposed method proved that cbPPG is applicable in intraoperative environments. It can be easily transferred to other settings regardless of which body site is considered.

Blood pressure is one of the basic indicators of the health state of the cardiovascular system. High blood pressure is the main risk factor of ischemic heart disease, atherosclerosis and stroke. Therefore, it is important to monitor long-term changes in blood pressure and respond in time to these changes. Blood pressure meters are not standard household equipment, while a well-equipped smartphone is. Smartphones contain a large number of sensors capable of measuring biomedical signals. This thesis focuses on creating an application capable of determining blood pressure using data obtained from these sensors.

The master thesis deals with the issue of gaining the respiratory rate from ECG and PPG signals, which are not only in clinical practice widely used measurable signals. The theoretical part of the work outlines the issue of obtaining a breath curve from these signals. The practical part of the work is focused on the implementation of five selected methods and their final evaluation and comparison.

No description available.

A prospective, non-randomized observational study involving forty-nine patients undergoing coronary artery bypass surgery (CABG) with a unilateral harvesting of the internal thoracic artery (ITA) was carried out at the Department of Cardiac Surgery, Herzzentrum Dresden University hospital. Using a commercially available industrial-grade RGB camera and normal indoor lighting, the chest wall of the patients was scanned before surgery and in three follow-up measurements. The primary aim of this thesis was to show whether iPPG is sensitive enough to detect global signal changes after a major surgery – CABG in this case – and local signal changes due to the removal of the ITA, the main supply vessel of the chest wall. As a secondary aim, the thesis looked at subgroups of data to show if differences in signal existed between the colour channels of the RGB camera, subdivisions of the thorax and the surgical technique used as well as to show if demographic factors had an impact on signal strength. With mathematical programs developed by the Technical University Dresden, the scanned optical data was transformed into signal to noise ratios (SNR) used in imaging photoplethysmographic (iPPG) studies. The signal data was analysed in R and, based on a stepwise deletion, a multivariable mixed effects model was constructed. Adjusted versions of this model were used for the analysis of the subgroups of the data. Analysis of the data showed a significant decrease of iPPG signal strength after the CABG surgery with a steeper decrease and an attenuated recovery on the side of the ITA harvesting. Even though the signal variations were relatively small, using the models in this thesis, the differences were reliably detected by iPPG. The analysis of the data from the subdivisions of the chest and from patients’ groups determined by the surgical technique showed a caudo-cranial signal gradient on the ITA side twenty-four hours after the surgery and a stronger signal in the Pedicled group within twenty-four hours after the surgery. The latter calculations, however, were based on a possibly biased sample and should be verified using a controlled sample in prospective randomised study designs. Demographic factors showed no significant correlation with iPPG signal strength. iPPG was able to detect relatively small signal variations that could be associated with changes of cutaneous perfusion after major surgery. Future development could lead to non-invasive monitoring devices in the clinical practice of post-surgery care.:1. Introduction 1 1.1. Coronary Artery Bypass Grafting (CABG) 1 1.1.1. Historical Overview 1 1.1.2. Coronary Grafts 3 1.1.2.1. Pedicled vs. Skeletonised Grafts 4 1.2. Plethysmography 5 1.2.1. Air-Displacement Plethysmography (APG) 5 1.2.2. Strain Gauge Plethysmography (SGP) 6 1.2.3. Impedance Plethysmography (IPG) 6 1.2.4. Photoplethysmography (PPG) 7 1.2.5. Imaging Photoplethysmography (iPPG) 8 1.3. Hypothesis and Aim of the Thesis 11 2. Methods 13 2.1. Study Setting and Patients 13 2.2. Camera and Technical Setup 14 2.3. Recording Area and Regions of Interest 15 2.4. Signal Processing 16 2.5. Statistical Analysis 17 3. Results 19 3.1. Descriptive Properties of the Data 19 3.2. Signal Strength in the Three Colour Channels 20 3.3. Choosing a Multilevel Model 21 3.4. The Effect of the Major Surgery on the Signal Strength in the Three Colour Channels 22 3.5. The Effect of the Unilateral Resection of the Internal Thoracic Artery 25 3.6. Results from the Model Fitted to the Data 27 3.7. The Effect of Cofactors 28 3.8. Data from the Subdivisions of the Chest 29 3.9. The Effect of the Surgical Technique 31 4. Discussion 34 4.1. Signal Strength in the Red, Green and Blue Colour Channels 34 4.2. Signal from the Entire Chest Area 36 4.3. Signal from the Subdivisions of the Chest 37 4.4. The Influence of the Surgical Technique on Signal Strength 38 5. Conclusion 39 6. Abstract 41 7. Zusammenfassung 42 8. References 44 9. Appendix 60 10. Acknowledgements 82 11. Resume 83 Anlage 184 Anlage 2 85 / Eine prospektive, nicht randomisierte Studie mit neunundvierzig Patienten geplant für eine koronare Bypassoperation (CABG) mit einseitiger Präparation der Arteria thoracica interna (ITA) wurde im Herzzentrum Dresden, Universitätsklinikum durchgeführt. In einer präoperativen und in drei postoperativen Messungen wurde die Brustwand bei den untersuchten Patienten unter normaler Innenbeleuchtung mit Hilfe einer handelsüblichen, industriellen RGB Kamera untersucht. Das primäre Ziel der Arbeit war zu zeigen, ob iPPG als Messmethode genug Sensitivität besitzt um globale Signal-Veränderungen nach einem großen Eingriff – die CABG in diesem Fall – und lokale Signaländerung nach der Abnahme der ITA, die Hauptversorgungsarterie der Brustwand, zu erkennen. Als sekundäres Ziel der Arbeit war zu eruieren, ob iPPG Signaldifferenzen zwischen den Farbkanälen der RGB Kamera, den Brustwandaufteilungen und den Arten der ITA Präparation sowie nach den demographischen Faktoren detektieren konnte. Die gemessenen Daten wurden unter Verwendung von Eigentumsprogrammen der Technischen Universität Dresden in den, bei plethysmographischen Studien genutzten, Signal zu Geräusch Quotienten (SNR - signal to noise ratios) umgewandelt. Die gewonnenen Signaldaten wurden in R verarbeitet und durch Verwendung der Methode schrittweise Löschung wurde ein multivariables gemischte Effekte Modell erstellt. Angepasste Versionen dieses Modells wurden für die Analyse von Patientensubgruppen verwendet. Die Datenanalyse ergab eine signifikante Abschwächung des Signals nach der CABG, wobei die Thorax-Seite mit der ITA Präparation zeigte, im Vergleich mit der anderen Thorax-Seite, eine stärkere Abnahme und eine gedämpfte Rückbildung der Signalstärke. Obwohl die detektierte Signaländerungen relativ klein waren, sie konnten durch die entwickelten Modelle mittels iPPG zuverlässig detektiert werden. Die weitere Analyse der Daten aus den Brustwandaufteilungen und von Patientensubgruppen definiert nach Präparationsart der ITA zeigte auf der ITA Seite eine caudo-craniale Zunahme der Signalstärke ab vierundzwanzig Stunden und ein stärkeres Signal in der pedikulierten Präparationsgruppe bis vierundzwanzig Stunden nach der Operation. Allerdings, diese letztere Berechnungen wurden auf einem möglicherweise unausgewogenen Muster durchgeführt und sollten dementsprechend auf kontrollierten Mustern in prospektiven randomisierten Studien verifiziert werden. Die demographischen Faktoren hatten keiner signifikanten Korrelation mit der iPPG Signalstärke. Die iPPG war geeignet kleine Signaländerungen assoziiert mit den erwarteten Änderungen der dermalen Perfusion bei einem großen chirurgischen Eingriff zu detektieren. Weitere Entwicklung der Technologie kann die Anwendung dieses nicht-invasive Monitoringsverfahren in der klinischen postoperativen Patientenversorgung ermöglichen.:1. Introduction 1 1.1. Coronary Artery Bypass Grafting (CABG) 1 1.1.1. Historical Overview 1 1.1.2. Coronary Grafts 3 1.1.2.1. Pedicled vs. Skeletonised Grafts 4 1.2. Plethysmography 5 1.2.1. Air-Displacement Plethysmography (APG) 5 1.2.2. Strain Gauge Plethysmography (SGP) 6 1.2.3. Impedance Plethysmography (IPG) 6 1.2.4. Photoplethysmography (PPG) 7 1.2.5. Imaging Photoplethysmography (iPPG) 8 1.3. Hypothesis and Aim of the Thesis 11 2. Methods 13 2.1. Study Setting and Patients 13 2.2. Camera and Technical Setup 14 2.3. Recording Area and Regions of Interest 15 2.4. Signal Processing 16 2.5. Statistical Analysis 17 3. Results 19 3.1. Descriptive Properties of the Data 19 3.2. Signal Strength in the Three Colour Channels 20 3.3. Choosing a Multilevel Model 21 3.4. The Effect of the Major Surgery on the Signal Strength in the Three Colour Channels 22 3.5. The Effect of the Unilateral Resection of the Internal Thoracic Artery 25 3.6. Results from the Model Fitted to the Data 27 3.7. The Effect of Cofactors 28 3.8. Data from the Subdivisions of the Chest 29 3.9. The Effect of the Surgical Technique 31 4. Discussion 34 4.1. Signal Strength in the Red, Green and Blue Colour Channels 34 4.2. Signal from the Entire Chest Area 36 4.3. Signal from the Subdivisions of the Chest 37 4.4. The Influence of the Surgical Technique on Signal Strength 38 5. Conclusion 39 6. Abstract 41 7. Zusammenfassung 42 8. References 44 9. Appendix 60 10. Acknowledgements 82 11. Resume 83 Anlage 184 Anlage 2 85

Trötthet i trafiken är ett stort problem i samhället. Det är särskilt farligt att trött framföra tunga lastbilar i trafiken eftersom dessa fordon är stora och har ofta livsavgörande roller vid inblandning i trafikolyckor. För att angripa problemet har det i denna rapport studerats kring vilket sömnstadie som är lämpligast att vakna under, i syfte att vakna pigg och alert samt vilka typer av tekniker och metoder som är lämpliga för att portabelt kunna detektera Rapid-Eye-Movement. Tidigare arbeten och studier har gjorts som påvisar att uppväckning i REM-sömn är optimalt för att känna sig alert. De valda metoderna är baserade på varianter av väletablerade tekniker som används för identifiering av sömnsteg. Elektrookulografi används för att mäta ögonrörelser med hjälp av fyra elektroder som är placerade på huvudet. Kroppsrörelser upptäcks genom en accelerometer som fästs på armen. Pulsmätningar görs och används för att räkna ut pulsvariansen under sömnen. Målet är att skapa en prototyp som ska känna av när användaren är i REM-sömn och sedan väcka användaren. Detta arbete är uppdelat i två inbyggda system som görs mellan två olika examensarbeten. Resultatet blev tre sensorer som fungerar individuellt. På grund av tidsbrist och en längre felsökning blev prototypen inte färdigställd. Innan sensorerna kan tillämpas i en produkt krävs det att ytterligare tester genomförs under monitorering av en sömnspecialist. / Tiredness in traffic is a major problem in society. It is especially dangerous to drive heavy trucks when tired because these vehicles are large and often have vital roles when involved in traffic accidents. To address the problem, this degree project has studied which sleep stage is most appropriate to wake up during, in order to wake up sharp and alert, and what types of techniques and methods are suitable for portable detection of Rapid-Eye-Movement. Previous work and studies have been done which indicates that awakening during REM sleep is optimal for feeling alert. The chosen methods are based on variants of well-established techniques that are used to identify sleep stages. Electrooculography is used to measure eye movements using four electrodes placed on the head. Body movements are detected by an accelerometer attached to the arm. Pulse measurements are made and used to calculate the pulse variation during sleep. The goal is to create a prototype which will know when the user is in REM sleep and then wake the user up. This work is divided into two embedded systems that are made between two different degree projects. The result was three sensors that worked individually. Due to lack of time and a longer troubleshooting, the prototype was not completed. Before the sensors can be used in a product, additional tests are required under the supervision of a sleep specialist.