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Compressed Deep Learning to Classify Arrhythmia in an Embedded Wearable Device.
Lee, Kwang-Sig; Park, Hyun-Joon; Kim, Ji Eon; Kim, Hee Jung; Chon, Sangil; Kim, Sangkyu; Jang, Jaesung; Kim, Jin-Kook; Jang, Seongbin; Gil, Yeongjoon; Son, Ho Sung.
  • Lee KS; AI Center, Korea University Anam Hospital, Seoul 02841, Korea.
  • Park HJ; Institute for Health Service Innovation, Korea University College of Medicine, Seoul 02841, Korea.
  • Kim JE; Department of Thoracic and Cardiovascular Surgery, Korea University College of Medicine, Korea University Anam Hospital, Seoul 02841, Korea.
  • Kim HJ; Department of Thoracic and Cardiovascular Surgery, Korea University College of Medicine, Korea University Anam Hospital, Seoul 02841, Korea.
  • Chon S; HUINNO Co., Ltd., Seoul 06011, Korea.
  • Kim S; HUINNO Co., Ltd., Seoul 06011, Korea.
  • Jang J; HUINNO Co., Ltd., Seoul 06011, Korea.
  • Kim JK; HUINNO Co., Ltd., Seoul 06011, Korea.
  • Jang S; HUINNO Co., Ltd., Seoul 06011, Korea.
  • Gil Y; HUINNO Co., Ltd., Seoul 06011, Korea.
  • Son HS; Department of Thoracic and Cardiovascular Surgery, Korea University College of Medicine, Korea University Anam Hospital, Seoul 02841, Korea.
Sensors (Basel) ; 22(5)2022 Feb 24.
Article in English | MEDLINE | ID: covidwho-1715644
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Atrial Fibrillation / Wearable Electronic Devices / Deep Learning / COVID-19 Type of study: Diagnostic study Topics: Long Covid Limits: Humans Language: English Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Atrial Fibrillation / Wearable Electronic Devices / Deep Learning / COVID-19 Type of study: Diagnostic study Topics: Long Covid Limits: Humans Language: English Year: 2022 Document Type: Article