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Article

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

1
Department of Management, Finance and Technology, University LUM Jean Monnet, 70010 Casamassima, Italy
2
UVARP Azienda Sanitaria Locale, 70132 Bari, Italy
3
Department of Computer Science, University of Torino, 10124 Torino, Italy
4
Department of Computer Science, University of Bari, 70125 Bari, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Yudong Zhang
Received: 27 November 2020 / Revised: 2 February 2021 / Accepted: 2 February 2021 / Published: 5 February 2021
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis)
The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings. View Full-Text
Keywords: ECG signal detection; portable monitoring devices; 1D-convolutional neural network; deep learning ECG signal detection; portable monitoring devices; 1D-convolutional neural network; deep learning
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MDPI and ACS Style

Nannavecchia, A.; Girardi, F.; Fina, P.R.; Scalera, M.; Dimauro, G. Personal Heart Health Monitoring Based on 1D Convolutional Neural Network. J. Imaging 2021, 7, 26. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020026

AMA Style

Nannavecchia A, Girardi F, Fina PR, Scalera M, Dimauro G. Personal Heart Health Monitoring Based on 1D Convolutional Neural Network. Journal of Imaging. 2021; 7(2):26. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020026

Chicago/Turabian Style

Nannavecchia, Antonella; Girardi, Francesco; Fina, Pio R.; Scalera, Michele; Dimauro, Giovanni. 2021. "Personal Heart Health Monitoring Based on 1D Convolutional Neural Network" J. Imaging 7, no. 2: 26. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020026

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