Premature Atrial and Ventricular Contraction Detection using Photoplethysmographic Data from a Smartwatch

We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincare plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC

Poincaré Plot Method for Physiological Analysis of the Gadget Use Effect on Children Stress Level

Stress in children can affect the way they think, act, and feel. The habit of using gadgets has several advantages and disadvantages, but there has been no in-depth study of the effect of using gadgets on stress levels in children. This study aims to determine the representation of the physiological condition of using gadgets on stress levels in children. A total of 18 electrocardiogram data were extracted with poincaré plot features. This research has found that there is no difference in the level of stress in children between before and after using gadgets in terms of autonomic nervous activity (Sig. > 0.05). However, there is an increase in sympathetic activity that occurs in children even though they have finished using gadgets. Such conditions certainly need to get more attention, especially related to the duration of gadget use and accessible content

The Ava bracelet for collection of fertility and pregnancy data in free-living conditions: An exploratory validity and acceptability study

Objective To evaluate the validity and acceptability of the Ava bracelet for collecting heart rate, sleep, mood, and physical activity data among reproductive-aged women (pregnant and nonpregnant) under free-living conditions. Methods Thirty-three participants wore the Ava bracelet on their non-dominant wrist and reported mood and physical activity in the Ava mobile application for seven nights. Criterion validity was determined by comparing the Ava bracelet heart rate and sleep duration measures to criterion measures from the Polar chest strap and ActiGraph GTX3 + accelerometer. Construct validity was determined by comparing self-report measures and the heart rate variability ratio collected in the Ava mobile application to previously validated measures. Acceptability was evaluated using the modified Acceptability of Health Apps among Adolescents Scale. Results Mean absolute percentage error was 11.4% for heart rate and 8.5% for sleep duration. There was no meaningful difference between the Ava bracelet, ActiGraph, and construct a measure of sleep quality. Compared to construct measures, Ava bracelet heart rate variability had a significant low negative correlation (r:−0.28), mood had a significant low positive correlation (r : 0.39), and physical activity level had a significant low (rlevel of physical activity: 0.56) to moderate positive correlation (rMET−minutes/week: 0.71). The acceptability of the Ava bracelet was high for fertility and low for pregnancy tracking. Conclusion Preliminary evidence suggests the Ava bracelet and mobile application estimates of sleep and heart rate are not equivalent to criterion measures in free-living conditions. Further research is needed to establish its utility for collecting prospective, subjective data throughout periods of preconception and pregnancy

Efectos de la pérdida de datos en las métricas de Variabilidad del Ritmo Cardíaco

La explosión en el mercado de dispositivos wearables ha supuesto una revolución en el ámbito de la monitorización de la salud. Gran parte de la población, incluida la población no paciente, posee dispositivos de pulsera capaces de detectar sus latidos a lo largo de todo el día. Juntocon las ventajas que esto supone, aparecen nuevos retos. Uno de ellos es la estabilidad de la calidad de la señal. Los movimientos constantes de estos dispositivos hacen que se produzcan grandes pérdidas de datos, que pueden ocasionar un deterioro de las mediciones. Esto es especialmente relevante en los dispositivos que analizan la variabilidad de ritmo cardíaco, una técnica que permite inferir información del sistema nervioso autónomo de forma no invasiva a partir del control que éste ejerce sobre el sistema circulatorio. Esta técnica necesita que todos los pulsos sean detectados para funcionar correctamente, por lo que la pérdida de datos supone inevitablemente un deterioro. Este trabajo se centra en investigar cómo se produce esta degradación para diferentes métodos y qué técnicas se pueden utilizar para reducirla. Para ello, se ha desarrollado un método de simulación de pérdida de pulsos que permite analizar los dos tipos de errores que se suelen dar: errores aleatoriamente distribuidos y en ráfagas. A su vez, se propone un nuevo método de rellenado de pulsos como una posibilidad de preprocesado, que obtiene mejores resultados que el método de referencia. Dependiendo de la aplicación y de los requerimientos de los dispositivos, se sugieren los métodos más robustos teniendo en cuenta también su coste y la información que proveen. Los métodos se han probado en una base de datos con 17 sujetos sometidos a una prueba de mesa basculante, que permite provocar cambios en la activación del sistema nervioso autónomo sin involucrar al sistema central o causar actividad en los músculos. Las métricas se han comparado tanto en la degradación de sus valores como en la capacidad para distinguir los cambios provocados por la prueba de mesa basculante.<br /

Effects of ECG Data Length on Heart Rate Variability Among Young Healthy Adults

The relationship between the robustness of HRV derived by linear and nonlinear methods to the required minimum data lengths has yet to be well understood. The normal electrocardiography (ECG) data of 14 healthy volunteers were applied to 34 HRV measures using various data lengths, and compared with the most prolonged (2000 R peaks or 750 s) by using the Mann–Whitney U test, to determine the 0.05 level of significance. We found that SDNN, RMSSD, pNN50, normalized LF, the ratio of LF and HF, and SD1 of the Poincaré plot could be adequately computed by small data size (60–100 R peaks). In addition, parameters of RQA did not show any significant differences among 60 and 750 s. However, longer data length (1000 R peaks) is recommended to calculate most other measures. The DFA and Lyapunov exponent might require an even longer data length to show robust results. Conclusions: Our work suggests the optimal minimum data sizes for different HRV measures which can potentially improve the efficiency and save the time and effort for both patients and medical care providers

A Feature Selection Method for Driver Stress Detection Using Heart Rate Variability and Breathing Rate

Driver stress is a major cause of car accidents and death worldwide. Furthermore, persistent stress is a health problem, contributing to hypertension and other diseases of the cardiovascular system. Stress has a measurable impact on heart and breathing rates and stress levels can be inferred from such measurements. Galvanic skin response is a common test to measure the perspiration caused by both physiological and psychological stress, as well as extreme emotions. In this paper, galvanic skin response is used to estimate the ground truth stress levels. A feature selection technique based on the minimal redundancy-maximal relevance method is then applied to multiple heart rate variability and breathing rate metrics to identify a novel and optimal combination for use in detecting stress. The support vector machine algorithm with a radial basis function kernel was used along with these features to reliably predict stress. The proposed method has achieved a high level of accuracy on the target dataset.Comment: In Proceedings of the 15th International Conference on Machine Vision (ICMV), Rome, Italy, 18-20 November 2022. arXiv admin note: text overlap with arXiv:2206.0322

Photoplethysmography based atrial fibrillation detection: an updated review from July 2019

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 59 pertinent studies. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis

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