A Heartbeat Classifier for Continuous Prediction Using a Wearable Device
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ABSTRACT:
SUBMITTER: Pramukantoro E
PROVIDER: S-EPMC9320854 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDetection of arrhythmias is crucial for the treatment of cardiovascular diseases. However, conventional devices do not provide sufficient diagnostic accuracy while patients should suffer from bothersome diagnostic process. We sought to evaluate diagnostic capability and safety of the new adhesive electrocardiogram (ECG) monitoring device in patients who need ECG monitoring during admission.MethodsWe enrolled 10 patients who admitted to Seoul National University Bundang Hospital and required continuous ECG monitoring between October 31, 2019 and December 18, 2019. New adhesive ECG monitoring device and conventional ECG monitoring device were simultaneously applied to the patients and maintained for 48 hours. From each patient, 48 pairs of ECG signal were collected and analyzed by two cardiologists independently. Discrepancy of diagnosis and frequency of noise or signal loss were compared between the two devices.ResultsFrom analyzable ECG data, discrepancy of arrhythmia diagnosis was not observed between the two devices. Noise rate was higher in conventional ECG monitoring device (2.5% vs. 17.3%, P < 0.001) and signal loss was not observed in new adhesive device while there was 9.4% of signal losses in conventional Holter recorder group. The new device was well-tolerated among 48 hours of monitoring period and no adverse event was observed.ConclusionA newer adhesive ECG monitoring device demonstrated similar diagnostic accuracy compared to conventional ECG monitoring device.
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Project description:Wearable digital devices offer potential advantages over traditional methods for the collection of health-related information, including continuous collection of dense data while study subjects are ambulatory or in remote settings. We assessed the utility of collecting continuous actigraphy and cardiac monitoring by deploying two US Food and Drug Administration (FDA) 510(k)-cleared devices in a phase I clinical trial of a novel compound, which included the use of an amphetamine challenge. The Phillips Actiwatch Spectrum Pro (Actiwatch) was used to assess mobility and sleep. The Preventice BodyGuardian (BodyGuardian) was used for monitoring heart rate (HR) and respiratory rate (RR), via single-lead electrocardiogram (ECG) recordings, together with physical activity. We measured data collection rates, compared device readouts with conventional measures, and monitored changes in HR measures during the amphetamine challenge. Completeness of data collection was good for the Actiwatch (96%) and lower for the BodyGuardian (80%). A good correlation was observed between device and in-clinic measures for HR (r = 0.99; P < 0.001), but was poor for RR (r = 0.39; P = 0.004). Manual reviews of selected ECG strips corresponding to HR measures below, within, and above the normal range were consistent with BodyGuardian measurements. The BodyGuardian device detected clear HR responses after amphetamine administration while subjects were physically active, whereas conventional measures collected at predefined timepoints while subjects were resting and supine did not. Wearable digital technology shows promise for monitoring human subjects for physiologic changes and pharmacologic responses, although fit-for-purpose evaluation and validation continues to be important prior to the wider deployment of these devices.
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Project description:The chemical digitalization of sweat using wearable sensing interfaces is an attractive alternative to traditional blood-based protocols in sports. Although sweat lactate has been claimed to be a relevant biomarker in sports, an analytically validated wearable system to prove that has not yet been developed. We present a fully integrated sweat lactate sensing system applicable to in situ perspiration analysis. The device can be conveniently worn in the skin to monitor real-time sweat lactate during sports, such as cycling and kayaking. The novelty of the system is threefold: advanced microfluidics design for sweat collection and analysis, an analytically validated lactate biosensor based on a rational design of an outer diffusion-limiting membrane, and an integrated circuit for signal processing with a custom smartphone application. The sensor covering the range expected for lactate in sweat (1-20 mM), with appropriate sensitivity (-12.5 ± 0.53 nA mM-1), shows an acceptable response time (<90 s), and the influence of changes in pH, temperature, and flow rate are neglectable. Also, the sensor is analytically suitable with regard to reversibility, resilience, and reproducibility. The sensing device is validated through a relatively high number of on-body tests performed with elite athletes cycling and kayaking in controlled environments. Correlation outcomes between sweat lactate and other physiological indicators typically accessible in sports laboratories (blood lactate, perceived exhaustion, heart rate, blood glucose, respiratory quotient) are also presented and discussed in relation to the sport performance monitoring capability of continuous sweat lactate.
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Project description:Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with non-immersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject's arousal and valence perception. The model's accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.
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Project description:This paper introduces two sets of measures as exploratory tools to study physical activity patterns: active-to-sedentary/sedentary-to-active rate function (ASRF/SARF) and active/sedentary rate function (ARF/SRF). These two sets of measures are complementary to each other and can be effectively used together to understand physical activity patterns. The specific features are illustrated by an analysis of wearable device data from National Health and Nutrition Examination Survey (NHANES). A two-level semiparametric regression model for ARF and the associated activity magnitude is developed under a unified framework using the marked point process formulation. The inactive and active states measured by accelerometers are treated as a 0-1 point process, and the activity magnitude measured at each active state is defined as a marked variable. The commonly encountered missing data problem due to device nonwear is referred to as "window censoring," which is handled by a proper estimation approach that adopts techniques from recurrent event data. Large sample properties of the estimator and comparison between two regression models as measurement frequency increases are studied. Simulation and NHANES data analysis results are presented. The statistical inference and analysis results suggest that ASRF/SARF and ARF/SRF provide useful analytical tools to practitioners for future research on wearable device data.
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Project description:Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.
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Project description:BackgroundVariations in body temperature are highly informative during an illness. To date, there are not many adequate studies that have investigated the feasibility of a wearable wrist device for the continuous monitoring of body surface temperatures in humans.ObjectiveThe objective of this study was to validate the performance of HEARThermo, an innovative wearable device, which was developed to continuously monitor the body surface temperature in humans.MethodsWe implemented a multi-method research design in this study, which included 2 validation studies-one in the laboratory and one with human subjects. In validation study I, we evaluated the test-retest reliability of HEARThermo in the laboratory to measure the temperature and to correct the values recorded by each HEARThermo by using linear regression models. We conducted validation study II on human subjects who wore HEARThermo for the measurement of their body surface temperatures. Additionally, we compared the HEARThermo temperature recordings with those recorded by the infrared skin thermometer simultaneously. We used intraclass correlation coefficients (ICCs) and Bland-Altman plots to analyze the criterion validity and agreement between the 2 measurement tools.ResultsA total of 66 participants (age range, 10-77 years) were recruited, and 152,881 completed data were analyzed in this study. The 2 validation studies in the laboratory and on human skin indicated that HEARThermo showed a good test-retest reliability (ICC 0.96-0.98) and adequate criterion validity with the infrared skin thermometer at room temperatures of 20°C-27.9°C (ICC 0.72, P<.001). The corrected measurement bias averaged -0.02°C, which was calibrated using a water bath ranging in temperature from 16°C to 40°C. The values of each HEARThermo improved by the regression models were not significantly different from the temperature of the water bath (P=.19). Bland-Altman plots showed no visualized systematic bias. HEARThermo had a bias of 1.51°C with a 95% limit of agreement between -1.34°C and 4.35°C.ConclusionsThe findings of our study show the validation of HEARThermo for the continuous monitoring of body surface temperatures in humans.
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Project description:Hemiplegia is a symptom that is caused by reduced sensory and motor ability on one side of the body due to stroke-related neural defects. Muscular weakness and abnormal sensation that is induced by hemiplegia usually lead to motor impairments, such as difficulty in controlling the trunk, unstable balance, and poor walking ability. Therefore, most hemiplegia patients show defective and asymmetric gait pattern. The purpose of this study is to distinguish hemiplegic gait by extracting simple characteristics of acceleration signals that are caused by asymmetry during walking using a wearable system. The devised wearable system was equipped with a three-axis accelerometer and a three-axis gyroscope. We selected 165 candidate features without step detection. A random forest algorithm was used for the classification, and the forward search algorithm was also used for optimal feature selection. The developed system and algorithms were verified clinically in 15 normal subjects and 20 hemiplegia patients that were undergoing stroke treatment, and 26 subject's data was used for training, including validation, and nine subject's data used for test. As a result of test set, the accuracy, sensitivity, specificity and positive predictive value were 100.0%, with the two classification attributes of standard deviation of points perpendicular to the axis of line of identity of Poincaré plot of angular velocity around vertical axis and kurtosis of frequency of angular velocity around longitudinal axis.
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