Key result
A multi-input deep learning network using 7-minute ECG RR intervals achieved a recognition accuracy of 86.74% on the testing set for detecting congestive heart failure.
Why the study?
Most prior studies focused on 24-hour ECG data analysis for CHF classification, creating a need for a convenient, rapid screening process using short-term ECG signals.
Does a multi-input deep learning network using 7-minute ECG segments accurately detect congestive heart failure?
Does a multi-input deep learning network using 7-minute ECG segments accurately detect congestive heart failure?
A deep learning model using short 7-minute ECG segments can detect congestive heart failure with high accuracy, potentially offering a rapid screening tool.
May aid ECG-based CHF screening; leaves open prospective validation before practice change.
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.
No takes yet. Share an insight, caveat, or question.
Huang et al. (2019) studied Congestive heart failure. Multi-input deep learning network using 7-minute ECG RR intervals vs. Control group was evaluated on Recognition accuracy between CHF and control groups. A multi-input deep learning network using 7-minute ECG RR intervals achieved a recognition accuracy of 86.74% on the testing set for detecting congestive heart failure.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: