Novel methods of faster cardiovascular diagnosis in wireless telecardiology
IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
ECG signal compression and classification algorithm with quad level vector for ECG holter system
IEEE Transactions on Information Technology in Biomedicine
On-node processing of ECG signals
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Clinical assessment of wireless ECG transmission in real-time cardiac telemonitoring
IEEE Transactions on Information Technology in Biomedicine
A clustering based system for instant detection of cardiac abnormalities from compressed ECG
Expert Systems with Applications: An International Journal
A contextual based double watermarking of PET images by patient ID and ECG signal
Computer Methods and Programs in Biomedicine
Electrocardiogram Signal Compression Using Beta Wavelets
Journal of Mathematical Modelling and Algorithms
Cardioids-based faster authentication and diagnosis of remote cardiovascular patients
Security and Communication Networks
Beta wavelet based ECG signal compression using lossless encoding with modified thresholding
Computers and Electrical Engineering
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The delay performance of compression algorithms is particularly important when time-critical data transmission is required. In this paper, we propose a wavelet-based electrocardiogram (ECG) compression algorithm with a low delay property for instantaneous, continuous ECG transmission suitable for telecardiology applications over a wireless network. The proposed algorithm reduces the frame size as much as possible to achieve a low delay, while maintaining reconstructed signal quality. To attain both low delay and high quality, it employs waveform partitioning, adaptive frame size adjustment, wavelet compression, flexible bit allocation, and header compression. The performances of the proposed algorithm in terms of reconstructed signal quality, processing delay, and error resilience were evaluated using the Massachusetts Institute of Technology University and Beth Israel Hospital (MIT-BIH) and Creighton University Ventricular Tachyarrhythmia (CU) databases and a code division multiple access-based simulation model with mobile channel noise