A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device

This paper presents a novel approach, Adaptive Spectrum Noise Cancellation (ASNC), for motion artifacts removal in Photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared to the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) beats·min-1 and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) beats·min-1 and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) beats·min-1 and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our Verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate

Photopletysmography based on Green Light

Práce se zabývá realizací zařízení pro měření pulsu pomocí senzoru umístěného na hlavě a na prstu horní končetiny s využitím pletysmografického fotoelektrického reflexního senzoru. Senzor je realizován s využitím zelené LED diody s vlnovou délkou 527 nm a fotodiody. Je snímána pulsní křivka, která je dále zpracovávána a vyhodnocována. Zobrazení signálu v reálném čase probíhá v přenosném zařízení LabQuest2. Analýza signálu offline probíhá v softwaru Matlab, do kterého je vkládán textový soubor s daty ze zařízení LabQuest2. Analýza signálu obsahuje detekci vrcholů pulsní křivky za účelem výpočtu tepové frekvence a následného zobrazení tepové křivky. Proto je vytvořeno GUI zobrazující jak pulsní křivku s detekovanými vrcholy, tak tepovou křivku. V rámci měření bylo zařízení otestováno na 11 probandech.This thesis deals with the realization of pulse measurment device using a sensor, that is located on the head and on the finger of an upper limb with the use of plethysmographic photoelectric reflexive sensor. The sensor is realized by the usage of green LED diode with a wave length of 527 nm and a photodiode. The recorded pulse wave is then processed and evaluated. A real-time projection of the signal is monitored in a LabQuest2 portable device. Offline analysis of the signal runs in Matlab software, into which the text file with a data of the LabQuest2 is put in. The signal analysis mainly contains of peak detection of the pulse wave and the follow-up heartbeat curve display. Therefore, a GUI, showing both of the curves, pulse curve and heartbeat curve, is created. The device is tested on 11 probands for the purposes of the measurement.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Multi-wavelength SPAD photoplethysmography for cardio-respiratory monitoring

There is a growing interest in photoplethysmography (PPG) for the continuous monitoring of cardio-respiratory signals by portable instrumentation aimed at the early diagnosis of cardiovascular diseases. In this context, it is conceivable that PPG sensors working at different wavelengths simultaneously can optimize the identification of apneas and the quantification of the associated heart-rate changes or other parameters that depend on the PPG shape (e.g., systematic vascular resistance and pressure), when evaluating the severity of breathing disorders during sleep and in general for health monitoring. Therefore, the objective of this work is to present a novel pulse oximeter that provides synchronous data logging related to three light wavelengths (green, red, and infrared) in transmission mode to optimize both heart rate measurements and a reliable and continuous assessment of oxygen saturation. The transmission mode is considered more robust over motion artifacts than reflection mode, but current pulse oximeters cannot employ green light in transmission mode due to the high absorbance of body tissues at this wavelength. For this reason, our device is based on a Single-Photon Avalanche Diode (SPAD) with very short deadtime (less than 1 ns) to have, at the same time, the single photon sensitivity and high-count rate that allows acquiring all the wavelengths of interest on the same site and in transmission mode. Previous studies have shown that SPAD cameras can be used for measuring the heart rate through remote PPG, but oxygen saturation and heart-rate measures through contact SPAD-based PPG sensors have never been addressed so far. The results of the preliminary validation on six healthy volunteers reflect the expected physiological phenomena, providing rms errors in the Inter Beat Interval estimation smaller than 70 ms (with green light) and a maximum error in the oxygen saturation smaller than 1% during the apneas. Our prototype demonstrates the reliability of SPAD-based devices for continuous long-term monitoring of cardio-respiratory variables as an alternative to photodiodes especially when minimal area and optical power are required

Heart Rate Estimation During Physical Exercise Using Wrist-Type Ppg Sensors

Accurate heart rate monitoring during intense physical exercise is a challenging problem due to the high levels of motion artifacts (MA) in photoplethysmography (PPG) sensors. PPG is a non-invasive optical sensor that is being used in wearable devices to measure blood flow changes using the property of light reflection and absorption, allowing the extraction of vital signals such as the heart rate (HR). However, the sensor is susceptible to MA which increases during physical activity. This occurs since the frequency range of movement and HR overlaps, difficulting correct HR estimation. For this reason, MA removal has remained an active topic under research. Several approaches have been developed in the recent past and among these, a Kalman filter (KF) based approach showed promising results for an accurate estimation and tracking using PPG sensors. However, this previous tracker was demonstrated for a particular dataset, with manually tuned parameters. Moreover, such trackers do not account for the correct method for fusing data. Such a custom approach might not perform accurately in practical scenarios, where the amount of MA and the heart rate variability (HRV) depend on numerous, unpredictable factors. Thus, an approach to automatically tune the KF based on the Expectation-Maximization (EM) algorithm, with a measurement fusion approach is developed. The applicability of such a method is demonstrated using an open-source PPG database, as well as a developed synthetic generation tool that models PPG and accelerometer (ACC) signals during predetermined physical activities

Multi-wavelength SPAD photoplethysmography for cardio-respiratory monitoring

There is a growing interest in photoplethysmography (PPG) for the continuous monitoring of cardio-respiratory signals by portable instrumentation aimed at the early diagnosis of cardiovascular diseases. In this context, it is conceivable that PPG sensors working at different wavelengths simultaneously can optimize the identification of apneas and the quantification of the associated heart-rate changes or other parameters that depend on the PPG shape (e.g., systematic vascular resistance and pressure), when evaluating the severity of breathing disorders during sleep and in general for health monitoring. Therefore, the objective of this work is to present a novel pulse oximeter that provides synchronous data logging related to three light wavelengths (green, red, and infrared) in transmission mode to optimize both heart rate measurements and a reliable and continuous assessment of oxygen saturation. The transmission mode is considered more robust over motion artifacts than reflection mode, but current pulse oximeters cannot employ green light in transmission mode due to the high absorbance of body tissues at this wavelength. For this reason, our device is based on a Single-Photon Avalanche Diode (SPAD) with very short deadtime (less than 1 ns) to have, at the same time, the single photon sensitivity and high-count rate that allows acquiring all the wavelengths of interest on the same site and in transmission mode. Previous studies have shown that SPAD cameras can be used for measuring the heart rate through remote PPG, but oxygen saturation and heart-rate measures through contact SPAD-based PPG sensors have never been addressed so far. The results of the preliminary validation on six healthy volunteers reflect the expected physiological phenomena, providing rms errors in the Inter Beat Interval estimation smaller than 70 ms (with green light) and a maximum error in the oxygen saturation smaller than 1% during the apneas. Our prototype demonstrates the reliability of SPAD-based devices for continuous long-term monitoring of cardio-respiratory variables as an alternative to photodiodes especially when minimal area and optical power are required

Harmonic Sum-based Method for Heart Rate Estimation using PPG Signals Affected with Motion Artifacts

Wearable photoplethysmography has recently become a common technology in heart rate (HR) monitoring. General observation is that the motion artifacts change the statistics of the acquired PPG signal. Consequently, estimation of HR from such a corrupted PPG signal is challenging. However, if an accelerometer is also used to acquire the acceleration signal simultaneously, it can provide helpful information that can be used to reduce the motion artifacts in the PPG signal. By dint of repetitive movements of the subjects hands while running, the accelerometer signal is found to be quasi-periodic. Over short-time intervals, it can be modeled by a finite harmonic sum (HSUM). Using the HSUM model, we obtain an estimate of the instantaneous fundamental frequency of the accelerometer signal. Since the PPG signal is a composite of the heart rate information (that is also quasi-periodic) and the motion artifact, we fit a joint HSUM model to the PPG signal. One of the harmonic sums corresponds to the heart-beat component in PPG and the other models the motion artifact. However, the fundamental frequency of the motion artifact has already been determined from the accelerometer signal. Subsequently, the HR is estimated from the joint HSUM model. The mean absolute error in HR estimates was 0.7359 beats per minute (BPM) with a standard deviation of 0.8328 BPM for 2015 IEEE Signal Processing cup data. The ground-truth HR was obtained from the simultaneously acquired ECG for validating the accuracy of the proposed method. The proposed method is compared with four methods that were recently developed and evaluated on the same dataset

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Sleep detection with photoplethysmography for wearable-based health monitoring

Remote health monitoring has gained increasing attention in the recent years. Detecting sleep patterns provides users with insights on their personal health issues, and can help in the diagnosis of various sleep disorders. Conventional methods are focused on the acceleration data, or are not suitable for continuous monitoring, like the polysomnography. Wearable devices enable a way to continuously measure photoplethysmography signal. Photoplethysmography signal contains information on multiple physiological systems, and can be used to detect sleep patterns. Sleep detection using wearable-based photoplethysmography signal offers a convenient and easy way to monitor health. In this thesis, a photoplethysmography-based sleep detection method for wearable-based health monitoring is described. This technique aims to separate wakefulness and asleep states with adequate accuracy. To examine the importance of good quality data in sleep detection, the quality of the signal is assessed. The proposed method uses statistical and heart rate based features extracted from the photoplethysmography signal. Using the most relevant features, various supervised learning algorithms are trained, compared and evaluated. These algorithms are logistic regression, decision tree, random forest, support vector machine, k-nearest neighbors, and Naive Bayes. The best performance is obtained by the random forest classifier. The method received an overall accuracy of 81 percent. It was able to detect the sleep periods with 86 percent accuracy and the awake periods with 74 percent accuracy. Motion artifacts occurring during the awake time caused distortion to the signal. Features related to the shape of the signal improved the accuracy of sleep detection, since signal distortion was associated with the awake time. It is concluded that photoplethysmography signal provides a good alternative for wearable-based sleep detection. Future studies with more comprehensive sleep level analysis could be conducted to provide valuable information on the quality of sleep.Viime vuosina etänä tapahtuva terveyden seuranta on saanut yhä enemmän huomiota. Unen tunnistaminen antaa käyttäjille tietoa heidän henkilökohtaisista terveysongelmistaan ja voi auttaa erilaisten unihäiriöiden diagnosoinnissa. Tavanomaiset menetelmät käyttävät kiihtyvyyteen perustuvaa dataa, tai eivät ole soveltuvia jatkuvaan seurantaan, kuten polysomnografia. Puettavan teknologian avulla fotopletysmografiasignaalin jatkuva mittaus on mahdollista. Fotopletysmografiasignaali sisältää tietoa useista fysiologisista järjestelmistä ja sitä voidaan käyttää unen tunnistamiseen. Puettavan teknologian avulla mitatun fotopletysmografiasignaalin käyttö unen tunnistuksessa tarjoaa kätevän ja helpon tavan seurata terveyttä. Tässä diplomityössä kuvataan fotopletysmografiaan perustuva unenhavaitsemismenetelmä, joka soveltuu puettavaa teknologiaa hyödyntävään terveyden seurantaan. Tekniikalla pyritään erottamaan hereillä olo ja uni riittävän tarkasti. Signaalin laatu arvioidaan, jotta voidaan tutkia datan laadun tärkeys unen tunnistuksessa. Kehitetty menetelmä käyttää tilastollisia ja sykkeeseen perustuvia ominaisuuksia, jotka on erotettu fotopletysmografiasignaalista. Tärkeimpiä ominaisuuksia käyttämällä erilaisia valvottuja oppimisalgoritmeja koulutetaan, vertaillaan ja arvioidaan. Käytetyt algoritmit ovat logistinen regressio, päätöspuu, satunnainen metsä, tukivektorikone, k-lähimmät naapurit ja Naive Bayes. Paras tulos saadaan käyttämällä satunnainen metsä -algoritmia. Menetelmällä saavutetaan 81 prosentin kokonaistarkkuus. Uni pystytään tunnistamaan 86 prosentin tarkkuudella ja hereillä olo 74 prosentin tarkkuudella. Hereillä ollessa liikkeestä johtuvat häiriöt aiheuttavat vääristymää signaaliin. Signaalin muotoon liittyvät ominaisuudet paransivat unentunnistuksen tarkkuutta, koska signaalin vääristyminen yhdistettiin hereilläoloaikaan. Tutkimuksen tuloksista voidaan tehdä johtopäätös, että fotopletysmografiasignaali tarjoaa hyvän vaihtoehdon puettavaa teknologiaa hyödyntävään unen tunnistamiseen. Tulevaisuudessa unen eri vaiheita voitaisiin tutkia kattavammin, jolloin saataisiin arvokasta tietoa unen laadusta

Optical heart rate measurement with Arduino MKR1000

Abstract. The primary goal of this thesis is to introduce the reader to the fundamentals of optical methods for cardiovascular monitoring. Opposed to traditional methods that rely on measuring the electrical activity of the heart with electrodes, optical methods utilize light and are thus considered indirect methods. The thesis begins with an introduction to photoplethysmography, a commonly used technique based on illuminating the skin and measuring the intensity of the reflected or transmitted light. In addition to presenting the operating principle of this method, some of the key issues and use cases are discussed; in particular, methods for estimating heart rate will be presented in more detail, including an example algorithm. Additionally, the core principles of the Arduino ecosystem and the WebSocket protocol will be considered. In the latter part of this thesis, an implementation of an Internet of Things -capable optical heart rate meter based on the Arduino MKR1000 will be presented. Finally, the drawbacks and benefits of both photoplethysmography and the implemented system will be discussed in brief.Optinen sykemittaus Arduino MKR1000 -laitteella. Tiivistelmä. Tämän kandidaatintyön tavoitteena on esitellä lukijalle sydän- ja verisuonijärjestelmän toiminnan mittaamiseen käytettävien optisten menetelmien pääperiaatteita. Toisin kuin perinteiset menetelmät, jotka perustuvat sydämen sähköisen toiminnan mittaamiseen elektrodien avulla, optiset menetelmät hyödyntävät valoa ja ovat siten epäsuoria menetelmiä. Työssä käsitellään yleisesti käytössä olevaa fotopletysmografiaa, joka perustuu ihon valaisemiseen ja heijastuneen tai läpäisseen valon intensiteetin mittaamiseen. Fotopletysmografian toimintaperiaatteen esittelemisen lisäksi käsitellään joitakin tärkeimpiä käyttökohteita ja menetelmän haasteita. Tarkemmin käsitellään fotopletysmografiaan perustuvan sykemittauksen toimintaperiaate ja esitellään esimerkkialgoritmi. Tämän jälkeen käsitellään pääpiirteittäin Arduino-ekosysteemiä ja WebSocket-protokollaa. Työn jälkimmäisessä osassa esitellään esineiden Internet -käyttöön soveltuva toteutus optisesta sykemittarista. Toteutukseen käytetään Arduino MKR1000 -kehitysalustaa ja hyödynnetään WebSocket-protokollaa. Lopuksi pohditaan lyhyesti sekä fotopletysmografian että toteutetun sykemittarin ansioita, haasteita ja jatkokehitysmahdollisuuksia