Compressed Deep Learning to Classify Arrhythmia in an Embedded Wearable Device.

The importance of an embedded wearable device with automatic detection and alarming cannot be overstated, given that 15-30% of patients with atrial fibrillation are reported to be asymptomatic. These asymptomatic patients do not seek medical care, hence traditional diagnostic tools including Holter are not effective for the further prevention of associated stroke or heart failure. This is likely to be more so in the era of COVID-19, in which patients become more reluctant on hospitalization and checkups. However, little literature is available on this important topic. For this reason, this study developed efficient deep learning with model compression, which is designed to use ECG data and classify arrhythmia in an embedded wearable device. ECG-signal data came from Korea University Anam Hospital in Seoul, Korea, with 28,308 unique patients (15,412 normal and 12,896 arrhythmia). Resnets and Mobilenets with model compression (TensorFlow Lite) were applied and compared for the diagnosis of arrhythmia in an embedded wearable device. The weight size of the compressed model registered a remarkable decrease from 743 MB to 76 KB (1/10000), whereas its performance was almost the same as its original counterpart. Resnet and Mobilenet were similar in terms of accuracy, i.e., Resnet-50 Hz (97.3) vs. Mo-bilenet-50 Hz (97.2), Resnet-100 Hz (98.2) vs. Mobilenet-100 Hz (97.9). Here, 50 Hz/100 Hz denotes the down-sampling rate. However, Resnets took more flash memory and longer inference time than did Mobilenets. In conclusion, Mobilenet would be a more efficient model than Resnet to classify arrhythmia in an embedded wearable device.

When a lack of passion intertwines with thought and action: Neutral feelings about COVID-19 are associated with U.S. presidential candidate attitudes and voting behavior.

Researchers might assume that neutrality does not shape thought and action because it signals that nothing in the environment needs attention, hence a person has little need to alter their behavior. However, feeling neutral about an issue might be consequential. The COVID-19 pandemic was a major issue during the 2020 U.S. presidential election. We examined whether feeling neutral about COVID-19 was associated with attitudes about the top 2 presidential candidates (Trump vs. Biden) and behavior (i.e., whether a person voted and who they voted for). Data were collected at 2 critical time points: Study 1 was conducted immediately after the U.S. presidential election and Study 2 was conducted prior to the second Senate impeachment trial of Trump. Because feeling neutral about COVID-19 might indicate that a person views the issue as unworthy of attention, a perspective more aligned with Trump's approach, we hypothesized that feeling neutral about COVID-19 would be associated with more pro-Trump attitudes and behaviors. Even after accounting for other affects about COVID-19, in both studies, neutrality was associated with more favorable attitudes toward Trump, less favorable attitudes toward Biden, being less likely to vote, and if a person did vote, being more likely to vote for Trump. In Sudy 2, neutrality was associated with less support for impeaching Trump. Overall, in contrast to the view that neutral affect exerts little influence, neutrality can be critically intertwined with thought and action. (PsycInfo Database Record (c) 2022 APA, all rights reserved).