Joint Time-Frequency Pseudo Anomalies for Multimodal Electrocardiogram Quality Assessment in Healthcare Service Computing
Huang, Xunhua Xi, Liang Fan, Haoyi Zhang, Fengbin Yu, Xu Liu, Lei Dong, Mianxiong Guizani, Mohsen
Huang, Xunhua
Xi, Liang
Fan, Haoyi
Zhang, Fengbin
Yu, Xu
Liu, Lei
Dong, Mianxiong
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Electrocardiogram(ECG) signal analysis is crucial in healthcare service computing. Ensuring accurate assessment of ECG signal quality is vital to prevent wastage of transmission bandwidth and ineffective analysis caused by noise. This enables the efficient utilization of service resources. However, existing ECG signal quality assessment(SQA) methods primarily focus on single-modal learning, overlooking the interrelation of ECG in a multimodal feature space and failing to effectively exploit available information for pattern mining. In this paper, we model the SQA for ECG as an anomaly detection problem and propose a multimodal unsupervised SQA method. It jointly explores the boundaries between high-quality ECG and noise in both the time and frequency domains by introducing time-frequency pseudo anomalies. Specifically, we first simulate real ECG noise from the time-domain using a combination of a series of noises and convert it to the frequency-domain to form time-frequency pseudo-anomalies. Next, we map the time-frequency pseudo anomalies onto hyperspheres and jointly refine the hyperspheres learned only from high-quality ECG samples in both feature spaces. Finally, the noise score is defined as the distance from the joint time-frequency features to the center of the hypersphere. Multiple experiments on various real-world ECG datasets validate the superior performance of our proposed method.
Citation
X. Huang et al., "Joint Time-Frequency Pseudo Anomalies for Multimodal Electrocardiogram Quality Assessment in Healthcare Service Computing," in IEEE Transactions on Services Computing, doi: 10.1109/TSC.2025.3583116
Source
IEEE Transactions on Services Computing
Conference
Keywords
ECG, Healthcare Service Computing, Quality Assessment, Anomaly Detection, Time-Frequency
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Source
Publisher
IEEE