A congestive heart failure detection system via multi-input deep learning networks
Journal
2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
Date Issued
2019
Author(s)
Abstract
In this study, a detection system of congestive heart failure (CHF) based on multiple input neural network was proposed. In previous research, the majority of studies focused on 24-hour electrocardiogram (ECG) data analysis for classification. To provide a convenient and rapid screen process, we proposed to offer a short- term analysis with the data size of 7-minute segment of ECG signal. The proposed detection system consisted of four steps: data pre- processing, model- establishment, multi-input configuration, and deep learning model classification. We proposed RR intervals instead of raw ECG data for the model input to reduce computation complexity. Also, by feeding in RR interval signal in both time and frequency domain, we can leverage the model performance by the known study results from HRV analysis to obtain the significant features more easily. The recognition accuracy between CHF and conctrol groups of proposed detection system is up to 93.76% for training set, and 86.74% for testing set.
SDGs
Publisher
Institute of Electrical and Electronics Engineers Inc.
Type
conference paper