ABSTRACT
BACKGROUND: Sleep disorders lead to many adverse complications and chronic diseases. Sleep disorder-related healthcare costs are tens of billions of dollars worldwide. Sleep monitoring solutions have thus been the focus of research and industrial interest. However, the problem of limited bandwidth and battery consumption hinders the accuracy and practical use of sleep monitoring aids. INTRODUCTION: The aim of this study is to propose Near-Lossless Wireless Real-time Efficient electroencephalogram Transmission Solution (N-WRETS) solution that solves the issue of limited bandwidth and battery consumption, thereby supporting platforms dedicated to sleep disorder monitoring. MATERIALS AND METHODS: Electroencephalography (EEG) data materials were obtained from the Physionet PhysioBank database. The CAP Sleep Database was used. C programming was used for development. RESULTS: To evaluate transmission efficiency, the compression ratio (CR) was compared to prior studies. The N-WRETS CR of 11.34 exceeded other reported values. DISCUSSION: Compared to prior related research, N-WRETS showed the highest compression performance for EEG, but showed the lowest stability, which was a trade-off for its high efficiency. This article opens a possibility for future research to improve the performance of EEG compression algorithms according to sleep disease type. N-WRETS is also near-lossless, which is fit for priceless EEG data that contain important information on the patient's health. The proposed solution also supported wireless real-time transmission, which was another distinctive characteristic compared to related studies. CONCLUSIONS: N-WRETS may provide a platform in which sleep disorder patients may be properly monitored in real time. The system could overcome the problems of limited bandwidth and battery consumption.
Subject(s)
Electroencephalography/methods , Remote Sensing Technology/methods , Sleep Wake Disorders/diagnosis , Algorithms , Data Compression , Humans , Signal Processing, Computer-Assisted , Sleep Wake Disorders/pathologyABSTRACT
Recent medical information systems are striving towards real-time monitoring models to care patients anytime and anywhere through ECG signals. However, there are several limitations such as data distortion and limited bandwidth in wireless communications. In order to overcome such limitations, this research focuses on compression. Few researches have been made to develop a specialized compression algorithm for ECG data transmission in real-time monitoring wireless network. Not only that, recent researches' algorithm is not appropriate for ECG signals. Therefore this paper presents a more developed algorithm EDLZW for efficient ECG data transmission. Results actually showed that the EDLZW compression ratio was 8.66, which was a performance that was 4 times better than any other recent compression method widely used today.
