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.

Recent advancements in the measurement of pathogenic airborne viruses.

Air-transmissible pathogenic viruses, such as influenza viruses and coronaviruses, are some of the most fatal strains and spread rapidly by air, necessitating quick and stable measurements from sample air volumes to prevent further spread of diseases and to take appropriate steps rapidly. Measurements of airborne viruses generally require their collection into liquids or onto solid surfaces, with subsequent hydrosolization and then analysis using the growth method, nucleic-acid-based techniques, or immunoassays. Measurements can also be performed in real time without sampling, where species-specific determination is generally disabled. In this review, we introduce some recent advancements in the measurement of pathogenic airborne viruses. Air sampling and measurement technologies for viral aerosols are reviewed, with special focus on the effects of air sampling on damage to the sampled viruses and their measurements. Measurement of pathogenic airborne viruses is an interdisciplinary research area that requires understanding of both aerosol technology and biotechnology to effectively address the issues. Hence, this review is expected to provide some useful guidelines regarding appropriate air sampling and virus detection methods for particular applications.