# The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals
## 4 Discussion and Conclusions  
Electrocardiography is a popular non-invasive technique for monitoring human heart electrical activity, and is used in many biomedical engineering applications, including AF detection [41; 42; 43; 44; 45]. Devices based on ECG have shown good performance in AF detection, and in fact the gold standard for atrial fibrillation detection is the 12-lead ECG [46; 47]. Consumer devices are now available which transmit ECG data in addition to heart rate enabling accurate AF detection by a cardiologist through visual inspection, or using automated algorithms [48]. Nevertheless, these devices themselves do not generally have embedded algorithms incorporated for the automated detection of AF, necessitating storage or transmission of ECG data to a central server for processing. Previous studies have proposed the stationary wavelet transform and support vector machine to detect AF [48]. Other detectors are based on parameters which characterize R-R interval irregularity, for example P-wave absence, f-wave presence, and noise level have led to substantial improvements when detecting brief episodes of AF [14]. Research based on the automated detection of AF using R-R variation and density histograms of R-R intervals has also been conducted. The goal of the study in this paper was to examine the effect of ECG signal compression on the automatic detection of atrial fibrillation and in particular, to determine how performance varies as a function of compression ratio. The study used the wavelet transform for signal decomposition, followed by the SPIHT algorithm for ECG signal compression. Shannon entropy estimated from R-R interval statistics was used to detect the presence or absence of AF [49]. An advantage of this method is the detection of AF from single-lead recordings. Another advantage is the small quantity of R-R intervals utilised [50; 51; 52; 53]. The detection performance of the algorithm was reported in terms of sensitivity, specificity and area under the ROC curve. The results indicate that quite high levels of compression (and hence reduced energy for transmission or storage of the compressed signal) can be achieved before there is a noticeable degradation in the performance of AF detection; this is considered to correspond to an AUC value of 0.9 or less. The signal distortion for an AUC of 0.9 is approximately 58%, indicating that a high degree of signal fidelity loss is tolerable while still maintaining good detection performance. Moreover,  
Figure 6: AUC versus PRD, averaged over all records in the test database. the curve of performance versus CR remains comparatively flat (not much degradation) for AUC values greater than 0.9. Early detection of AF in patients is very useful because therapeutic decisions can be facilitated by an accurate estimate risk of AF, particularly in situations where the patient is not in the clinic. Moreover, it could be applied to enhance diagnostic accuracy in paroxysmal AF without the need to store a large amount of data. One limitation of this method is that the strategy of diagnosing atrial arrhythmia using R-R intervals is not suited to detecting atrial flutter and ectopic beats because the ventricular rhythm can be fixed at integer multiples of the atrial rate. This is one of the reasons why the diagnostic performance of the AF detection algorithm is not higher than 0.947 AUC even with uncompressed data. In conclusion, the main benefit of this work is that AF detection accuracy can be preserved with highly compressed signals. This finding mitigates the constraints of limited storage and power budgets present with ambulatory ECG acquisition devices. [PERSON] and [PERSON] developed the theory and performed the computations. [PERSON], [PERSON] and [PERSON] investigated the method and supervised and supported the algorithm development. All authors discussed the results and contributed to the final manuscript. [PERSON] wrote the manuscript with support from [PERSON]. All authors have read and agreed to the published version of the manuscript.