A machine learning-enabled intelligent application for public health and safety

Yong, Z; Xiaoming, Z; Alshehri, MD

A machine learning-enabled intelligent application for public health and safety

Yong, Z Xiaoming, Z Alshehri, MD

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2023, 35, (20), pp. 14551-14564
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Yong, Z
dc.contributor.author	Xiaoming, Z
dc.contributor.author	Alshehri, MD
dc.date.accessioned	2024-01-23T04:04:12Z
dc.date.available	2024-01-23T04:04:12Z
dc.date.issued	2023
dc.identifier.citation	Neural Computing and Applications, 2023, 35, (20), pp. 14551-14564
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/174869
dc.description.abstract	This article uses a machine learning approach to analyze and detect burst signals in a smart healthcare system to protect and safeguard the public health. We consider the time-series electrocardiogram (ECG) waveforms for the detection of burst signals. For this purpose, we propose an intelligent differential correlation burst detection (DCBD) approach by establishing a mathematical model and deriving the analytical expressions to detect false alarm rate and missed detection rate in ECG signals. DCBD feeds the burst signals of a large-scale ECG waveform into various filters for noise removal, which are then passed through data augmentation to achieve high specificity and sensitivity. These waveforms are then segmented for feature extraction and machine learning (ML) classification. Finally, burst-free ECG waveforms are broadcast to a database server, where ML algorithms are used to detect the presence of any abnormal activities. Furthermore, the ECG signal is classified to a set of heart diseases using the well-known LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) models. Our proposed approach highlights that the probability of false alarm rate is similar to that caused by pure noise within the ECG waveforms. Our evaluation, using numerical experiments, suggests that the accuracy of the LSTM based ECG signal classification could be approximately 11.7% and 12.8% improved, subsequently, using the proposed burst detection method.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-021-06301-2
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4611 Machine learning
dc.title	A machine learning-enabled intelligent application for public health and safety
dc.type	Journal Article
utslib.citation.volume	35
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2024-01-23T04:04:11Z
pubs.issue	20
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	20

Abstract:

This article uses a machine learning approach to analyze and detect burst signals in a smart healthcare system to protect and safeguard the public health. We consider the time-series electrocardiogram (ECG) waveforms for the detection of burst signals. For this purpose, we propose an intelligent differential correlation burst detection (DCBD) approach by establishing a mathematical model and deriving the analytical expressions to detect false alarm rate and missed detection rate in ECG signals. DCBD feeds the burst signals of a large-scale ECG waveform into various filters for noise removal, which are then passed through data augmentation to achieve high specificity and sensitivity. These waveforms are then segmented for feature extraction and machine learning (ML) classification. Finally, burst-free ECG waveforms are broadcast to a database server, where ML algorithms are used to detect the presence of any abnormal activities. Furthermore, the ECG signal is classified to a set of heart diseases using the well-known LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) models. Our proposed approach highlights that the probability of false alarm rate is similar to that caused by pure noise within the ECG waveforms. Our evaluation, using numerical experiments, suggests that the accuracy of the LSTM based ECG signal classification could be approximately 11.7% and 12.8% improved, subsequently, using the proposed burst detection method.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/174869