scholarly journals Predicting atrial fibrillation episodes with rapid ventricular rates associated with low levels of activity

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhi Li ◽  
Kevin M. Wheelock ◽  
Sangeeta Lathkar-Pradhan ◽  
Hakan Oral ◽  
Daniel J. Clauw ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
High Activity ◽  
Activity Level ◽  
Training Data ◽  
Ventricular Rate ◽  
Just In Time ◽  
Accelerometer Data ◽  
Data Set ◽  
Low Levels ◽  
Low Activity

Abstract Background Rapid and irregular ventricular rates (RVR) are an important consequence of atrial fibrillation (AF). Raw accelerometry data in combination with electrocardiogram (ECG) data have the potential to distinguish inappropriate from appropriate tachycardia in AF. This can allow for the development of a just-in-time intervention for clinical treatments of AF events. The objective of this study is to develop a machine learning algorithm that can distinguish episodes of AF with RVR that are associated with low levels of activity. Methods This study involves 45 patients with persistent or paroxysmal AF. The ECG and accelerometer data were recorded continuously for up to 3 weeks. The prediction of AF episodes with RVR and low activity was achieved using a deterministic probabilistic finite-state automata (DPFA)-based approach. Rapid and irregular ventricular rate (RVR) is defined as having heart rates (HR) greater than 110 beats per minute (BPM) and high activity is defined as greater than 0.75 quantile of the activity level. The AF events were annotated using the FDA-cleared BeatLogic algorithm. Various time intervals prior to the events were used to determine the longest prediction intervals for predicting AF with RVR episodes associated with low levels of activity. Results Among the 961 annotated AF events, 292 met the criterion for RVR episode. There were 176 and 116 episodes with low and high activity levels respectively. Out of the 961 AF episodes, 770 (80.1%) were used in the training data set and the remaining 191 intervals were held out for testing. The model was able to predict AF with RVR and low activity up to 4.5 min before the events. The mean prediction performance gradually decreased as the time to events increased. The overall Area under the ROC Curve (AUC) for the model lies within the range of 0.67–0.78. Conclusion The DPFA algorithm can predict AF with RVR associated with low levels of activity up to 4.5 min before the onset of the event. This would enable the development of just-in-time interventions that could reduce the morbidity and mortality associated with AF and other similar arrhythmias.

Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms

Heart ◽  
2018 ◽  
Vol 104 (23) ◽  
pp. 1921-1928 ◽  
Author(s):  
Ming-Zher Poh ◽  
Yukkee Cheung Poh ◽  
Pak-Hei Chan ◽  
Chun-Ka Wong ◽  
Louise Pun ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Test Data ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Learning System ◽  
Training Data ◽  
Validation Data ◽  
Data Set

ObjectiveTo evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms.MethodsWe trained a deep convolutional neural network (DCNN) to detect AF in 17 s PPG waveforms using a training data set of 149 048 PPG waveforms constructed from several publicly available PPG databases. The DCNN was validated using an independent test data set of 3039 smartphone-acquired PPG waveforms from adults at high risk of AF at a general outpatient clinic against ECG tracings reviewed by two cardiologists. Six established AF detectors based on handcrafted features were evaluated on the same test data set for performance comparison.ResultsIn the validation data set (3039 PPG waveforms) consisting of three sequential PPG waveforms from 1013 participants (mean (SD) age, 68.4 (12.2) years; 46.8% men), the prevalence of AF was 2.8%. The area under the receiver operating characteristic curve (AUC) of the DCNN for AF detection was 0.997 (95% CI 0.996 to 0.999) and was significantly higher than all the other AF detectors (AUC range: 0.924–0.985). The sensitivity of the DCNN was 95.2% (95% CI 88.3% to 98.7%), specificity was 99.0% (95% CI 98.6% to 99.3%), positive predictive value (PPV) was 72.7% (95% CI 65.1% to 79.3%) and negative predictive value (NPV) was 99.9% (95% CI 99.7% to 100%) using a single 17 s PPG waveform. Using the three sequential PPG waveforms in combination (<1 min in total), the sensitivity was 100.0% (95% CI 87.7% to 100%), specificity was 99.6% (95% CI 99.0% to 99.9%), PPV was 87.5% (95% CI 72.5% to 94.9%) and NPV was 100% (95% CI 99.4% to 100%).ConclusionsIn this evaluation of PPG waveforms from adults screened for AF in a real-world primary care setting, the DCNN had high sensitivity, specificity, PPV and NPV for detecting AF, outperforming other state-of-the-art methods based on handcrafted features.

scholarly journals Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample

2011 ◽  
Vol 111 (6) ◽  
pp. 1804-1812 ◽  
Author(s):  
Patty S. Freedson ◽  
Kate Lyden ◽  
Sarah Kozey-Keadle ◽  
John Staudenmayer
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Accelerometer Data ◽  
Activity Type ◽  
Data Set ◽  
University Of Tennessee ◽  
Metabolic Equivalents ◽  
Artificial Neural ◽  
University Of Massachusetts ◽  
Mean Square Errors

Previous work from our laboratory provided a “proof of concept” for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330–1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample ( n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.

The interactive effects of infant activity level and fear on growth trajectories of early childhood behavior problems

2002 ◽  
Vol 14 (1) ◽  
pp. 1-23 ◽  
Author(s):  
CRAIG R. COLDER ◽  
JOSHUA A. MOTT ◽  
ARIELLE S. BERMAN
Keyword(s):  
Early Childhood ◽  
Behavior Problems ◽  
Internalizing Symptoms ◽  
Activity Level ◽  
Interactive Effects ◽  
Growth Trajectories ◽  
Low Levels ◽  
Childhood Behavior Problems ◽  
Specific Symptoms ◽  
Low Activity

The current study examined the interactive effects of infant activity level and fear on growth trajectories of behavior problems in early childhood (age 4 to 8 years) using maternal ratings. The sample was drawn from the National Longitudinal Survey of Youth (NLSY) and included children who were between 1 and 11 months in 1986. Findings suggested that boys characterized by high activity level and low levels of fear in infancy escalated in both externalizing and internalizing symptoms. Also, boys characterized by high fear and low activity level increased in internalizing symptoms and these effects seemed to be specific to depression rather than anxiety. Temperament did not predict escalation in externalizing symptomatology for girls, but low levels of fear predicted increases in internalizing symptoms. There was also evidence for a decline in depression specific symptoms for girls characterized by high fear and low activity in infancy. These findings suggest the importance of examining interactive models of temperament risk and considering gender specific pathways to behavior problems.

scholarly journals A scalable, satellite-transmitted data product for monitoring high-activity events in mobile aquatic animals

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Rachel A. Skubel ◽  
Kenady Wilson ◽  
Yannis P. Papastamatiou ◽  
Hannah J. Verkamp ◽  
James A. Sulikowski ◽  
...  
Keyword(s):  
Time Series ◽  
High Activity ◽  
Activity Level ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Data Logger ◽  
Activity Data ◽  
Aquatic Animals ◽  
Receiver Array ◽  
In The Wild

AbstractA growing number of studies are using accelerometers to examine activity level patterns in aquatic animals. However, given the amount of data generated from accelerometers, most of these studies use loggers that archive acceleration data, thus requiring physical recovery of the loggers or acoustic transmission from within a receiver array to obtain the data. These limitations have restricted the duration of tracking (ranging from hours to days) and/or type of species studied (e.g., relatively sessile species or those returning to predictable areas). To address these logistical challenges, we present and test a satellite-transmitted metric for the remote monitoring of changes in activity, measured via a pop-off satellite archival tag (PSAT) with an integrated accelerometer. Along with depth, temperature, and irradiance for geolocation, the PSAT transmits activity data as a time-series (ATS) with a user-programmable resolution. ATS is a count of high-activity events, relative to overall activity/mobility during a summary period. An algorithm is used to identify the high-activity events from accelerometer data and reports the data as a count per time-series interval. Summary statistics describing the data used to identify high-activity events accompany the activity time-series. In this study, we first tested the ATS activity metric through simulating PSAT output from accelerometer data logger archives, comparing ATS to vectorial dynamic body acceleration. Next, we deployed PSATs with ATS under captive conditions with cobia (Rachycentron canadum). Lastly, we deployed seven pop-off satellite archival tags (PSATs) able to collect and transmit ATS in the wild on adult sandbar sharks (Carcharhinus plumbeus). In the captive trials, we identified both resting and non-resting behavior for species and used logistic regression to compare ATS values with observed activity levels. In captive cobia, ATS was a significant predictor of observed activity levels. For 30-day wild deployments on sandbar sharks, satellites received 57.4–73.2% of the transmitted activity data. Of these ATS datapoints, between 21.9 and 41.2% of records had a concurrent set of temperature, depth, and light measurements. These results suggest that ATS is a practical metric for remotely monitoring and transmitting relative high-activity data in large-bodied aquatic species with variable activity levels, under changing environmental conditions, and across broad spatiotemporal scales.

Temperament and friendship in preschool-aged children

2005 ◽  
Vol 29 (4) ◽  
pp. 336-344 ◽  
Author(s):  
Tracy R. Gleason ◽  
Amy L. Gower ◽  
Lisa M. Hohmann ◽  
Terry C. Gleason
Keyword(s):  
High Activity ◽  
Choice Model ◽  
Activity Level ◽  
High Activity Level ◽  
Low Activity ◽  
Three Components ◽  
Preschool Aged Children

The influence of three components of temperament (activity level, impulsivity, and soothability) on children's friendships was investigated. Children (40 girls, 35 boys) aged 43 to 69 months responded to a sociometric interview and teachers provided temperament ratings. The probability of children choosing particular classmates as friends was evaluated based on the genders and temperaments of the dyad. A logistic choice model revealed that the choice of friends is highly influenced by gender, high impulsivity, and high soothability. Furthermore, the gender of the chooser and the activity level of the friend interacted such that girls chose low activity level friends and boys chose high activity level friends. In addition, the likelihood of a child being chosen as a friend based on gender and temperament was significantly correlated with popularity for girls, but not for boys.

Hemispheric asymmetry in field-aligned and ionospheric horizontal currents from the Swarm satellite measurements

2020 ◽  
Author(s):  
Abiyot Workayehu ◽  
Heikki Vanhamäki ◽  
Anita Aikio
Keyword(s):  
Magnetic Field ◽  
High Activity ◽  
Hemispheric Asymmetry ◽  
Current System ◽  
Activity Level ◽  
Activity Levels ◽  
Hemispheric Differences ◽  
High Activity Level ◽  
Low Activity ◽  
Seasonal Dependence

&lt;p&gt;&lt;span&gt;We present statistical investigation of the high-latitude ionospheric current systems in the Northern hemisphere (NH) and Southern hemisphere (SH) during low (Kp &lt; 2) and high (Kp &amp;#8805; 2) geomagnetic activity levels. Nearly &lt;/span&gt;&lt;span&gt;four &lt;/span&gt;&lt;span&gt;years of vector magnetic field measurements are analyzed from the two parallel flying Swarm A and C satellites using the spherical elementary current system (SECS) method. The ionospheric horizontal and field-aligned currents (FACs) for each auroral oval crossing are calculated. The mean values of FACs, as well as the horizontal curl-free (CF) and divergence-free (DF) currents in 1&lt;sup&gt;o&lt;/sup&gt; magnetic latitude by 1 h magnetic local time grid cells,&amp;#160;are calculated for each hemisphere and activity level. To estimate the NH/SH current ratios for the two activity levels, we remove seasonal bias in the number of samples and in the Kp distribution by bootstrap resampling. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Averaging over all seasons, we found that for the low activity level the currents in the NH are stronger than in the SH by 12 &amp;#177; 4 % for FAC, 9 &amp;#177; 2% for the horizontal CF current and 8 &amp;#177; 2% for the horizontal DF current. During the high activity level, the hemispheric differences are not statistically significant. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;When making the statistical analysis for the four seasons separately, we find a seasonal dependence in the hemispheric asymmetry. During low Kp conditions, both FACs and horizontal currents are larger in the NH than SH with the largest difference observed in winter. In winter, the currents in the NH are larger than the SH by 21 &amp;#177; 5 % &amp;#160;for FAC, 14 &amp;#177; 3% for the horizontal CF current and 10&amp;#177;3% &amp;#160;for the horizontal DF current. During the high activity level, the asymmetry is smaller compared to the low activity level with the largest and smallest hemispheric differences observed in autumn and summer, respectively. In autumn, the currents in the NH are larger than the SH by 8 &amp;#177; 5% &amp;#160;for FAC, 9 &amp;#177; 2% &amp;#160;for the horizontal CF current and 8 &amp;#177; 3% &amp;#160;for the horizontal DF current. Interestingly, during high Kp conditions, the NH/SH ratio of horizontal current is &gt;1 in autumn and &lt;1 in spring. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;The physical mechanism producing the hemispheric asymmetry is not known. One hypothesis is that the local ionospheric conditions, such as magnetic field strength or daily variations in insolation may play a role. We present preliminary results indicating that only a small part of the seasonal dependence in the NH/SH total current ratios can be explained by variations in the background conductances caused by solar irradiance and affected by local hemispheric values of the magnetic field.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

Very Low Levels of Physical Activity in Older Patients During Hospitalization at an Acute Geriatric Ward: A Prospective Cohort Study

2015 ◽  
Vol 23 (4) ◽  
pp. 542-549 ◽  
Author(s):  
Morten Villumsen ◽  
Martin Gronbech Jorgensen ◽  
Jane Andreasen ◽  
Michael Skovdal Rathleff ◽  
Carsten Møller Mølgaard
Keyword(s):  
Physical Activity ◽  
Cohort Study ◽  
Older Patients ◽  
Geriatric Patients ◽  
Functional Decline ◽  
Activity Level ◽  
Accelerometer Data ◽  
Geriatric Ward ◽  
Acute Geriatric Ward ◽  
Low Levels

Lack of activity during hospitalization may contribute to functional decline. The purpose of this study was to investigate (1) the time spent walking during hospitalization by geriatric patients referred to physical and/or occupational therapy and (2) the development in time spent walking during hospitalization. In this observational study, 24-hr accelerometer data (ActivPal) were collected from inclusion to discharge in 124 patients at an acute geriatric ward. The median time spent walking was 7 min per day. During the first quartile of hospitalization, the patients spent 4 (IQR:1;11) min per day walking, increasing to 10 (IQR:1;29) min during the last quartile. Improvement in time spent walking was primarily observed in the group able to perform the Timed Up & Go task at admission. When walking only 7 min per day, patients could be classified as inactive and at risk for functional decline; nonetheless, the physical activity level increased significantly during hospitalization.

Separation of Heparin into Subtractions by DEAE-Cellulose Chromatography

1979 ◽  
Vol 42 (05) ◽  
pp. 1452-1459 ◽  
Author(s):  
Robert H Yue ◽  
Toby Starr ◽  
Menard M Gertler
Keyword(s):  
High Activity ◽  
Uronic Acid ◽  
Deae Cellulose ◽  
Exchange Chromatography ◽  
Net Charge ◽  
Uronic Acid Content ◽  
Successful Separation ◽  
Salt Gradient ◽  
Structure And Activity ◽  
Low Activity

SummaryCommercial porcine heparin can be separated into three distinct subtractions by using DEAE-cellulose chromatography and a stepped salt gradient. Gram quantities of heparin can be fractionated by this technique. All three heparin subtractions can accelerate the inhibition of thrombin by antithrombin III with different efficiency. The specific activities of the high activity heparin, intermediate activity heparin and low activity heparin are 228 units/mg, 142 units/mg and 95 units/mg, respectively. Both the uronic acid content and the quantity of N-SO4 for all three heparin subfractions have been evaluated. The high activity heparin has the lowest uronic acid and N-SO4 content. The successful separation of commercial heparin into three distinct subfractions by means of ion-exchange chromatography suggests that the net charge on these three heparin components will serve as a model system in the elucidation of the structure and activity relationship to the biological function of heparin.

Optimizing Heart Rate and Controlling Symptoms in Atrial Fibrillation

2016 ◽  
Vol 10 (1) ◽  
pp. 26
Author(s):  
Pragnesh Parikh ◽  
◽  
KL Venkatachalam ◽  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Rate Control ◽  
Stroke Risk ◽  
Symptom Control ◽  
Review Article ◽  
Rhythm Control ◽  
Ventricular Rate ◽  
Control Methods ◽  
Novel Approaches

Atrial fibrillation (AF) is the most common arrhythmia noted in clinical practice and its incidence and prevalence are on the rise. The single most important intervention is the evaluation and treatment of stroke risk. Once the risk for stroke has been minimized, controlling the ventricular rate and treating symptoms become relevant. In this review article, we emphasize the importance of confirming and treating the appropriate arrhythmia and correlating symptoms with rhythm changes. Furthermore, we evaluate some of the risk factors for AF that independently result in symptoms, underlining the need to treat these risk factors as part of symptom control. We then discuss existing and novel approaches to rate control in AF and briefly cover rhythm control methods.

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