# The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals
## 1 Introduction
This often results in a system architecture whereby processing is not carried out on the device itself, rather, storage of the data for later processing, or wireless transmission of data to a centrally-located server for centralized processing, is required. Minimizing the data storage and/or wireless transmission costs is essential for reducing the total system energy consumption and to extend the battery life of these devices; this is a paramount design challenge in such systems. Data compression can significantly reduce storage and transmission requirements, thus positively impacting battery life, device size and portability. Lossless compression algorithms do not involve any loss in signal information and the original signal waveform can be recovered exactly; lossy compression, on the other hand, does involve loss of signal fidelity, however, much greater compression performance can be achieved with lossy compression algorithms. Most previous research on the compression of bioelectric signals has focused on maintaining signal fidelity with the aim of carrying out visual inspection on the signal, with comparatively less research focusing on the actual diagnostic content of the compressed signal as a performance criterion. However, in diagnostic or other detection applications, it may not be necessary to exactly reproduce the original signal morphology; what is of most importance is that the _diagnostic information_ in the signal is retained. Therefore, the primary goal of effective lossy compression is maximizing the reduction in data storage and wireless transmission costs, whilst retaining the most important information in the signal. It is also important that the compression process is efficiently performed to ensure it does not significantly add to the overall power consumption of the system, for example, previous work in [21] showed the potential benefit of signal compression in wearable healthcare applications, in terms of reduced wireless transmission power requirements, even with an additional energy cost due to the compression algorithm. Compression algorithms reduce the data size by removing redundancies present in the data and have been widely used in audio, video, and speech applications. Most high-performance ECG compression methods are not suitable for wireless biosensors because of their computational complexity, which means battery life may be compromised [22]; furthermore, where fidelity must be preserved (depending on the clinical requirements), lossless compression methods are often applied, which reduces the achievable compression ratio and hence the potential energy savings [22]. Suitable lossy compression methods usually involve a trade-off between compression rate and energy consumption [23]. Among the most effective biomedical data compression approaches [24; 25; 26; 27; 28] are those classified as transform-domain methods, and methods based on other sparse signals representations such as compressed sensing. Wavelet transform approaches have attracted particular attention due to their excellent compression performance. The use of transform-domain methods results in a representation of the signal in which the important information can be more effectively identified and represented in a compact manner, thus permitting more effective compression (less important information can be more readily discarded). In [26] a wavelet-based compression scheme with explicit control of wavelet coefficient quantization was proposed using signal distortion as a performance metric. Compression ratios (the ratio of the original bitrate to the compressed bitrate) of up to 20:1 were obtained with little signal distortion, when tested on signal samples from the MIT-BIH arrhythmia database. The compression approach used in this paper is Set Partitioning in Hierarchical Trees (SPIHT) [29]. SPIHT operates by encoding the output of the Discrete Wavelet Transform (DWT) into a binary stream for storage and wireless transmission, and has been widely used for biomedical signal compression including with ECG.