A Novel Single Lead to 12-Lead ECG Reconstruction Methodology Using Convolutional Neural Networks and LSTM

Gundlapalle, Vishnuvardhan and Acharyya, Amit (2022) A Novel Single Lead to 12-Lead ECG Reconstruction Methodology Using Convolutional Neural Networks and LSTM. In: 13th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2022, 1 March 2022 through 4 March 2022, Santiago.

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Abstract

The Electrocardiogram (ECG) is a useful diagnostic tool to diagnose cardiovascular diseases (CVD). Standard 12-Lead ECG setup is most commonly used by doctors for the diagnosis. But the promising type of wearable ECG device uses minimal wire setup on the body to increase patients' comfort resulting in fewer recorded leads, mainly single lead. There is a need to reconstruct the remaining leads from these less recorded leads. Accounting for this, we are proposing a novel Single Lead to 12-Lead ECG reconstruction methodology using convolution neural networks (CNN) and long short term memory (LSTM). In the proposed methodology, lead-II is taken as the basis lead to reconstruct the remaining independent leads (I, V1, V2, V3, V4, V5, and V6). Seven individual models corresponding to the above mentioned seven independent leads have been trained, where each model takes lead-II as input and gives I/V1/V2/V3/V4/V5/V6 as output. Leads III, aVR, aVL, and aVF are reconstructed using a standard approach using original lead II and reconstructed lead I signals, without the need for deep learning models. The proposed methodology was evaluated on myocardial infarction data from PTBDB using R2 statistics, correlation coefficient, and regression coefficient. The mean values averaged across all the 11 leads of the stated performance metrics obtained were 93.62%, 0.973, and 0.959, respectively. © 2022 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Acharyya, Amithttp://orcid.org/0000-0002-5636-0676
Item Type: Conference or Workshop Item (Paper)
Additional Information: ACKNOWLEDGMENT This work is partly supported by Sense Health Technologies Private Limited and the Department of Science & Technology (DST) under the Internet of Things (IoT) Research of Interdisciplinary Cyber Physical Systems (ICPS) Program, GOI, New Delhi, with the Project entitled “IOT Based Holistic Prevention and Prediction of CVD (i-PREACT)”.
Uncontrolled Keywords: CNN; CVD; ECG; LSTM; Reconstruction
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 08 Oct 2022 07:35
Last Modified: 08 Oct 2022 07:35
URI: http://raiith.iith.ac.in/id/eprint/10855
Publisher URL: http://doi.org/10.1109/LASCAS53948.2022.9789045
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