A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model
作者：
Highlights：
• A model named SCDDS for early diagnosis of Cardiovascular Disease has been proposed.
• Proposed model is in the form of wearable devices embedded with electrodes and IoT sensors.
• Technologies like IoT, AI, Smartphones, and Cloud are integrated to realize the concept of SCDDS.
• An ensemble of ConvNet and ConvNet-LSTM model is used to detect atrial fibrillation heartbeats.
• Model has achieved an overall classification accuracy of 98% for the test set's heartbeat data.
摘要
•A model named SCDDS for early diagnosis of Cardiovascular Disease has been proposed.•Proposed model is in the form of wearable devices embedded with electrodes and IoT sensors.•Technologies like IoT, AI, Smartphones, and Cloud are integrated to realize the concept of SCDDS.•An ensemble of ConvNet and ConvNet-LSTM model is used to detect atrial fibrillation heartbeats.•Model has achieved an overall classification accuracy of 98% for the test set's heartbeat data.
论文关键词：Arrhythmia,Cardiovascular diseases,Convolution Neural Network architecture,Electrocardiogram,Internet of Things,Long Short-Term Memory Networks
论文评审过程：Received 3 November 2021, Revised 11 September 2022, Accepted 26 September 2022, Available online 29 September 2022, Version of Record 30 September 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118933
