Detection of Beat-to-Beat Intervals from Wrist Photoplethysmography in Patients with Sinus Rhythm and Atrial Fibrillation after Surgery

Wrist photoplethysmography (PPG) allows unobtrusive monitoring of the heart rate (HR). PPG is affected by the capillary blood perfusion and the pumping function of the heart, which generally deteriorate with age and due to presence of cardiac arrhythmia. The performance of wrist PPG in monitoring beat-to-beat HR in older patients with arrhythmia has not been reported earlier. We monitored PPG from wrist in 18 patients recovering from surgery in the post anesthesia care unit, and evaluated the inter-beat interval (IBI) detection accuracy against ECG based R-to-R intervals (RRI). Nine subjects had sinus rhythm (SR, 68.0y$\pm$10.2y, 6 males) and nine subjects had atrial fibrillation (AF, 71.3y$\pm$7.8y, 4 males) during the recording. For the SR group, 99.44% of the beats were correctly identified, 2.39% extra beats were detected, and the mean absolute error (MAE) was 7.34 ms. For the AF group, 97.49% of the heartbeats were correctly identified, 2.26% extra beats were detected, and the MAE was 14.31 ms. IBI from the PPG were hence in close agreement with the ECG reference in both groups. The results suggest that wrist PPG provides a comfortable alternative to ECG and can be used for long-term monitoring and screening of AF episodes.Comment: Submitted to the 2018 IEEE International Conference on Biomedical and Health Informatic

Optimization of multi-wavelength Photoplethysmographic for wearable heart rate acquisition

Photoplethysmographic is an optical measure technique for heart rate monitoring on the surface of the skin. PPG based wearable heart rate monitor has become popular in consumer targeted market. This thesis work is based on the PulseOn product development and the final implementation will be integrated into the PulseOn OHRM sensor product. Choice of the wavelength of PPG is a trade-off between power consumption and accuracy considering the activity type, skin color and skin perfusion. The subject of this thesis is implementing a channel selection algorithm, which is green and IR channel, on a commercially available PulseOn wrist band to optimize the power consumption and accuracy of the measurement. The channel selection algorithm is first implemented and evaluated in Matlab simulation and then implemented in C code. Performance of the channel selection algorithm on the device is evaluated considering various factors, including skin color, tightness of the wristband. The results show that channel selection algorithm can not only reduce the power consumption but also help to handle the measurement on different measurement conditions

Evaluation of Wearable Optical Heart Rate Monitoring Sensors

Heart rate monitoring provides valuable information about an individual’s physiological condition. The information obtained from heart rate monitoring can be used for a wide range of purposes such as clinical diagnostics, assessment of the efficiency of training for sports and fitness, or of sleep quality and stress levels in wellbeing applications. Other useful parameters for describing a person’s fitness, such as maximal oxygen uptake and energy expenditure, can also be estimated using heart rate measurement. The traditional ‘gold standard’ for heart rate monitoring is the electrocardiograph, but nowadays there are a number of alternative methods too. Of these, optical sensors provide a relatively simple, lowcost and unobtrusive technology for monitoring heart rate and they are widely accepted by users. There are many factors affecting the measurement of optical signals that have an effect on the accuracy of heart rate estimation. However, there is a lack of standardized and unified methodology for comparing the accuracy of optical heart rate sensors to the ‘gold standard’ methods of measuring heart rate. The widespread use of optical sensors for different purposes has led to a pressing need for a common objective methodology for the evaluation of how accurate these sensors are. This thesis presents a methodology for the objective evaluation of optical heart-rate sensors. The methodology is applied in evaluation studies of four commercially available optical sensors. These evaluations were carried out during both controlled and non-controlled sporting and daily life activities. In addition, evaluation of beat detection accuracy was carried out in non-controlled sleep conditions. The accuracy of wrist-worn optical heart-rate sensors in estimating of maximal oxygen uptake during submaximal exercise and energy expenditure during maximal exercise using heart rate as input parameter were also evaluated. The accuracy of a semi-continuous heart rate estimation algorithm designed to reduce power consumption for long-term monitoring was also evaluated in various conditions. The main findings show that optical heart-rate sensors may be highly accurate during rhythmic sports activities, such as jogging, running, and cycling, including ramp-up running during maximal exercise testing. During non-rhythmic activities, such as intermittent hand movements, the sensors’ accuracy depends on where they are worn. During sleep and motionless conditions, the optical heart-rate sensors’ estimates for beat detection and inter-beat interval showed less than one percent inaccuracy against the values obtained using standard measurement techniques. The sensors were also sufficiently accurate at measuring the interbeat intervals to be used for calculating the heart rate variability parameters. The estimation accuracy of the fitness parameters derived from measured heart rate can be described as follows. An assessment of the maximal oxygen uptake estimation during a sub-maximal outdoor exercise had a precision close to a sport laboratory measurement. The energy expenditure estimation during a maximal exercise was more accurate during higher intensity of exercise above aerobic threshold but the accuracy decreased at lower intensity of exercise below the aerobic threshold, in comparison with the standardized reference measurement. The semi-continuous algorithm was nearly as accurate as continuous heart-rate detection, and there was a significant reduction in the power consumption of the optical chain components up to eighty percent. The results obtained from these studies show that, under certain conditions, optical sensors may be similarly accurate in measuring heart rate as the ‘gold standard’ methods and they can be relied on to monitor heart rate for various purposes during sport, everyday activities, or sleep

Evaluation of a wrist-worn photoplethysmography monitor for heart rate variability estimation in patients recovering from laparoscopic colon resection

To evaluate the accuracy of heart rate variability (HRV) parameters obtained with a wrist-worn photoplethysmography (PPG) monitor in patients recovering from minimally invasive colon resection to investigate whether PPG has potential in postoperative patient monitoring. 31 patients were monitored for three days or until discharge or reoperation using a wrist-worn PPG monitor (PulseOn, Finland) with a Holter monitor (Faros 360, Bittium Biosignals, Finland) as a reference measurement device. Beat-to-beat intervals (BBI) and HRV information collected by PPG were compared with RR intervals (RRI) and HRV obtained from the ECG reference after removing artefacts and ectopic beats. The beat-to-beat mean error (ME) and mean absolute error (MAE) of good quality heartbeat intervals obtained by wrist PPG were estimated as - 1.34 ms and 10.4 ms respectively. A significant variation in the accuracy of the HRV parameters was found. In the time domain, SDNN (9.11%), TRI (11.4%) and TINN (11.1%) were estimated with low relative MAE, while RMSSD (34.3%), pNN50 (139%) and NN50 (188%) had higher errors. The logarithmic parameters in the frequency domain (VLF Log, LF Log and HF Log) exhibited the lowest relative error, and for non-linear parameters, SD2 (7.5%), DFA alpha 1 (8.25%) and DFA alpha 2 (4.71%) were calculated much more accurately than SD1 (34.3%). The wrist PPG shows some potential for use in a clinical setting. The accuracy of several HRV parameters analyzed post hoc was found sufficient to be used in further studies concerning postoperative recovery of patients undergoing laparoscopic colon resection, although there were large errors in many common HRV parameters such as RMSSD, pNN50 and NN50, rendering them unusable. ClinicalTrials.gov Identifier: NCT04996511, August 9, 2021, retrospectively registeredPeer reviewe

Tunteiden Havaitseminen Arkielämässä Koneoppimisen ja Puettavien Laitteiden Avulla

Tavoitteet. Tämän tutkimuksen tavoitteena on arvioida tunteiden havaitsemisen mahdollisuutta arkielämässä puettavien laitteiden ja koneoppimismallien avulla. Tunnetiloilla on tärkeä rooli päätöksenteossa, havaitsemisessa ja käyttäytymisessä, mikä tekee objektiivisesta tunnetilojen havaitsemisesta arvokkaan tavoitteen, sekä mahdollisten sovellusten että tunnetiloja koskevan ymmärryksen syventämisen kannalta. Tunnetiloihin usein liittyy mitattavissa olevia fysiologisia ja käyttäymisen muutoksia, mikä mahdollistaa koneoppimismallien kouluttamisen muutoksia aiheuttaneen tunnetilan havaitsemiseksi. Suurin osa tunteiden havaitsemiseen liittyvästä tutkimuksesta on toteutettu laboratorio-olosuhteissa käyttämällä tunteita herättäviä ärsykkeitä tai tehtäviä, mikä herättää kysymyksen siitä että yleistyvätkö näissä olosuhteissa saadut tulokset arkielämään. Vaikka puettavien laitteiden ja kännykkäkyselyiden kehittyminen on helpottanut aiheen tutkimista arkielämässä, tutkimusta tässä ympäristössä on vielä niukasti. Tässä tutkimuksessa itseraportoituja tunnetiloja ennustetaan koneoppimismallien avulla arkielämässä havaittavissa olevien tunnetilojen selvittämiseksi. Lisäksi tutkimuksessa käytetään mallintulkintamenetelmiä mallien hyödyntämien yhteyksien tunnistamiseksi. Metodit. Aineisto tätä tutkielmaa varten on peräisin tutkimuksesta joka suoritettiin osana Helsingin Yliopiston ja VTT:n Sisu at Work projektia, missä 82:ta tietotyöläistä neljästä suomalaisesta organisaatiosta tutkittiin kolmen viikon ajan. Osallistujilla oli jakson aikana käytettävissään mittalaitteet jotka mittasivat fotoplethysmografiaa (PPG), ihon sähkönjohtavuutta (EDA) ja kiihtyvyysanturi (ACC) signaaleita, lisäksi heille esitettiin kysymyksiä koetuista tunnetiloista kolmesti päivässä puhelinsovelluksen avulla. Signaalinkäsittelymenetelmiä sovellettiin signaaleissa esiintyvien liikeartefaktien ja muiden ongelmien korjaamiseksi. Sykettä (HR) ja sykevälinvaihtelua (HRV) kuvaavia piirteitä irroitettiin PPG signaalista, fysiologista aktivaatiota kuvaavia piirteitä EDA signaalista, sekä liikettä kuvaavia piirteitä ACC signaalista. Seuraavaksi koneoppimismalleja koulutettiin ennustamaan raportoituja tunnetiloja irroitetujen piirteiden avulla. Mallien suoriutumista vertailtiin suhteessa odotusarvoihin havaittavissa olevien tunnetilojen määrittämiseksi. Lisäksi permutaatiotärkeyttä sekä Shapley additive explanations (SHAP) arvoja hyödynnettiin malleille tärkeiden yhteyksien selvittämiseksi. Tulokset ja johtopäätökset. Mallit tunnetiloille virkeä, keskittynyt ja innostunut paransivat suoriutumistaan yli odotusarvon, joista mallit tunnetilalle virkeä paransivat suoriutumista tilastollisesti merkitsevästi. Permutaatiotärkeys korosti liike- ja HRV-piirteiden merkitystä, kun SHAP arvojen tarkastelu nosti esiin matalan liikkeen, matalan EDA:n, sekä korkean HRV:n merkityksen mallien ennusteille. Nämä tulokset ovat lupaavia korkean aktivaation positiivisten tunnetilojen havaitsemiselle arkielämässä, sekä nostavat esiin mahdollisia yhteyksiä jatkotutkimusta varten.Objectives. This study aims to evaluate feasibility of affect detection in daily life using wearable devices and machine learning models. Affective states play an important role in decision making, perception and behaviour, making objective detection of affective states a desirable goal both for potential applications and as a way to gain insight into affective phenomena. Affective states have been found to have measurable physiological and behavioral changes, which allows training of machine learning models for detecting the underlying affects. Majority of affect detection studies have been conducted in laboratory conditions using affect elicitation stimuli or tasks, raising the question whether results from these studies will generalize to daily life. Although development of wearable devices and mobile surveys have facilitated evaluation in the context of daily life, research here remains sparse. In this study, self-reported affective states are predicted using machine learning models to identify which affective states can be detected in daily life. Additionally, model interpretation methods will be used to identify which relationships the models found important for their predictions. Methods. Data for this thesis came from a study conducted as a part of Sisu at Work project between University of Helsinki and VTT, where 82 knowledge workers from four Finnish organizations were studied for a period of three weeks. During this period, the participants were queried by mobile surveys about their affective states thrice a day, while they also used wearable devices to record photoplethysmography (PPG), electrodermal activity (EDA) and accelerometry (ACC) signals. A signal processing pipeline was implemented to deal with movement artefacts and other issues with the data. Features describing heart rate (HR) and heart rate variation (HRV) were extraced from PPG, physiological activation from EDA and movement from ACC signals. Models were then fitted to predict the reported affective states using the extracted features. Model performance was compared against a baseline to identify which affects could be reliably detected, while permutation importance and Shapley additive explanations (SHAP) values were used to identify important relationships established by the models. Results and conclusions. Models for affective state vigor showed improvements over baseline with statistical significance, while improvements were also noted for affects focused and enthusiastic. Permutation importance highlighted the significance of movement and HRV features, while examination of SHAP values indicated that low movement, low EDA and high HRV impacted model predictions the most. These results indicate potential for detecting high activation affective states in daily life and propose potential relationships for future research

Respiratory and cardiac monitoring at night using a wrist wearable optical system

Sleep monitoring provides valuable insights into the general health of an individual and helps in the diagnostic of sleep-derived illnesses. Polysomnography, is considered the gold standard for such task. However, it is very unwieldy and therefore not suitable for long-term analysis. Here, we present a non-intrusive wearable system that, by using photoplethysmography, it can estimate beat-to-beat intervals, pulse rate, and breathing rate reliably during the night. The performance of the proposed approach was evaluated empirically in the Department of Psychology at the University of Fribourg. Each participant was wearing two smart-bracelets from Ava as well as a complete polysomnographic setup as reference. The resulting mean absolute errors are 17.4 ms (MAPE 1.8%) for the beat-to-beat intervals, 0.13 beats-per-minute (MAPE 0.20%) for the pulse rate, and 0.9 breaths-per-minute (MAPE 6.7%) for the breath rate.Comment: Submitted to the 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC

Emfit ja PulseOn-antureiden sykemittauksen analysointi ja vertailu

Tässä kandidaatin työssä tutkittiin Emfit QS-unianturin sekä PulseOn Medical Trackerin sykkeenmittaustarkkuutta. Työn tavoitteena oli verrata mittalaitteiden mittaustarkkuuksissa. Vertailussa käytettiin 14 verisuonikirurgiselta potilaalta leikkauksen jälkeen noin vuorokauden pituisissa mittauksissa kerättyä dataa. Työtä taustoitetaan esittelemällä tutkimuksessa käytettyjen antureiden toimintaperiaatteet sekä tarkastellaan mittalaitteiden hyviä ja huonoja puolia. Lisäksi työssä kerrotaan EKG-signaalista laskettavasta sykemittauksesta, jota käytettiin työssä referenssinä. Työ nojautui vahvasti Matlabin avulla tehtyyn signaalinkäsittelyyn sekä virheenlaskentaan. Vertailukelpoisten tulosten saamiseksi kehitettiin menetelmä, jolla signaalit saatiin synkronoitua. Laitteiden tarkkuuksia verrattiin laskemalla niiden tuottamien sykesignaalien keskineliövirheen neliöjuuri (RMSE) sekä absoluuttinen keskivirhe (MAE) suhteessa EKG-signaalista laskettuun referenssisykesignaaliin. Tulokseksi saatiin, että PulseOnin ilmoittama syke oli tilastollisesti merkittävästi tarkempi kuin Emfitin sykelukema. MAE oli Emfitille 3,1 bpm (iskua minuutissa) ja PulseOnille 1,7 bpm. Virhe ei ole kummallakaan mittarilla kovin suuri. Siksi käyttötarkoituksesta riippuen mittalaitteen valinnassa kannattaa keskittyä enemmän niihin ominaisuuksiin, joita kyseisessä käyttötarkoituksessa tarvitaan

Wrist-worn device combining PPG and ECG can be reliably used for atrial fibrillation detection in an outpatient setting

Aims: The aim was to validate the performance of a monitoring system consisting of a wrist-worn device and a data management cloud service intended to be used by medical professionals in detecting atrial fibrillation (AF). Methods: Thirty adult patients diagnosed with AF alone or AF with concomitant flutter were recruited. Continuous photoplethysmogram (PPG) and intermittent 30 s Lead I electrocardiogram (ECG) recordings were collected over 48 h. The ECG was measured four times a day at prescheduled times, when notified due to irregular rhythm detected by PPG, and when self-initiated based on symptoms. Three-channel Holter ECG was used as the reference. Results: The subjects recorded a total of 1,415 h of continuous PPG data and 3.8 h of intermittent ECG data over the study period. The PPG data were analyzed by the system’s algorithm in 5-min segments. The segments containing adequate amounts, at least ~30 s, of adequate quality PPG data for rhythm assessment algorithm, were included. After rejecting 46% of the 5-min segments, the remaining data were compared with annotated Holter ECG yielding AF detection sensitivity and specificity of 95.6 and 99.2%, respectively. The ECG analysis algorithm labeled 10% of the 30-s ECG records as inadequate quality and these were excluded from the analysis. The ECG AF detection sensitivity and specificity were 97.7 and 89.8%, respectively. The usability of the system was found to be good by both the study subjects and the participating cardiologists. Conclusion: The system comprising of a wrist device and a data management service was validated to be suitable for use in patient monitoring and in the detection of AF in an ambulatory setting. Clinical Trial Registration: ClinicalTrials.gov/, NCT05008601.publishedVersionPeer reviewe

Atrial Fibrillation Detection from Photoplethysmography Data Using Artificial Neural Networks

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia- especially in elderly and hypertensive patients, leading to increased risk of heart failure and stroke. Therefore, early screening and diagnosis can reduce the AF impact. The development of photoplethysmography (PPG) technology has enabled comfortable and unobtrusive physiological monitoring of heart rate with a wrist-worn device. It is important to examine the possibility of using PPG signal to diagnose AF in real-world situations. There are several recent studies classifying cardiac arrhythmias with artificial neural networks (ANN) based on RR intervals derived from ECG, but no one has evaluated ANN approach for wrist PPG data. The aim of this MSc thesis is to present an ANN-based classifier to detect AF episodes from PPG data. The used classifier is multilayer perceptron (MLP) that utilizes backpropagation for learning. This classifier is able to distinguish between AF and non-AF rhythms. The input feature of the ANN is based on the information obtained from an interbeat interval (IBI) sequence of 30 consecutive PPG pulses. The PPG dataset was acquired with PulseOn (PO) wearable optical heart rate monitoring device and the recordings were performed in the post-anesthesia care unit of Tampere University Hospital. The study was approved by the local ethical committee. The guidelines of the Declaration of Helsinki were followed. In total 30 patients with multiple comorbidities were monitored during routine postoperative treatment. 15 subjects had sinus rhythm (SR) and 15 had AF during the recording. The average duration of each recording was 1.5 hours. The monitoring included standard ECG as a reference and a wrist-worn PPG monitor with green and infrared light sources. As IBIs extracted from the PPG signals are highly sensitive to motion artefacts, IBI reliability was automatically evaluated using PPG waveform and acceleration signals before AF detection. Based on the achieved results, the ANN algorithm demonstrated excellent performance at recognizing AF from SR, using wrist PPG data

Optisen sykemittarin suorituskyvyn arviointi

Older technologies, which might have been the golden standard in the industry for years, are rapidly becoming available to a wider audience as manufacturing methods become easier and cheaper. Companies are able to provide every consumer the same devices which have been the privilege of only the professional field. This has also been the case with fitness wearables, of which one subclass is the optical heart rate sensors. The goal of this thesis was to evaluate the performance of one such device, namely the PulseOn wrist device. The device utilizes photoplethysmography (PPG) in acquiring the heart rate signal. PPG has been used in clinical settings for oxygen saturation level determination, but the technology can also provide other figures from the cardiovascular system, such as heart rate. The measurement method is based on the detection of light, which is emitted into the skin and then interacts with the tissue. The composition of the blood vessels changes in synch with the beating of the heart, and so does the intensity of the detected light. The PulseOn device was tested in controlled laboratory conditions with 20 subjects. The measurement protocol included periods of rest and activities of varying intensities. A reference measurement was made simultaneously with a Polar heart rate belt, and also two other devices were used to record data for later assessments. The results were analysed in MATLAB, and values for heart rate reading reliability and measurement errors were calculated. For example, the correlation of the PulseOn device against the Polar belt was found to be approximately 96 %, the amount of readings that were within 10 % of the values given by the heart rate belt was 90.4 %, and the average value of the absolute errors between the two devices was 4.76 beats per minute. Even though the PulseOn device was still in its development phase at the time of the measurements, it showed satisfactory results, and that it could be used in the heart rate measurements of everyday fitness activities