Optimized Time and Frequency Domain Methods for Ecg Signal Compression List of Figures List of Tables 1 Filter Coeecients for F 2 2

Storage and transmission of ElectroCardioGram (ECG) signals involve large amounts of data. Techniques for lossy data compression are useful in reducing the needed storage space or transmission time for such signals. Several dedicated time domain methods exist for this purpose, including the AZTEC, CORTES, Turning Point, and FAN algorithms. This article presents coding results for some modern as well as some novel time and frequency domain data compression algorithms. Key issues in our novel approaches are the way our algorithms are optimized with respect to minimum distortion, within a common ECG time domain compression framework, and the optimized design of lter banks for subband coders taking ECG signal properties into account. The performances of subband coders based on various lter bank types, both optimized and more traditional types, are compared to that of an optimized as well as an established time domain coding algorithm. Our time domain algorithm is based on the idea of extracting signi cant samples from the original signal in a manner that guarantees minimal reconstruction error when using linear interpolation of the retained samples. Thus, it can be used as a best case representative for the whole class of time domain algorithms using the sample reduction technique and linear interpolation. Using a varied signal test set, extensive coding experiments conclude that the frequencydomain method based on a short kernel lter bank tailored to the properties of the ECG signal, and having some other important properties, performs signi cantly better than more traditional transform and subband coders. With respect to the performance of the time domain algorithms, our optimized solution exhibits a performance dramatically superior to the FAN algorithm. Comparing the optimized time domain solution to the subband coders, we observe better performance for the latter class of coders in terms the percentage root mean square di erence (PRD) performance measure. The observations referred to above is con rmed and complemented by visual inspection of the reconstructed signals. 2 List of Figures 1 Arc length in the graph. : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 2 Algorithm for the CCSP problem : : : : : : : : : : : : : : : : : : : : : : : : : 9 3 Structure of encoding system. : : : : : : : : : : : : : : : : : : : : : : : : : : : 10 4 Uniform quantizer with thresholding. : : : : : : : : : : : : : : : : : : : : : : : 12 5 Parallell analysis lter bank. Subsampling by a factor of M is denoted by #M . 14 6 A two-band analysis lter bank employed in a uniform tree-structured lter bank with 8 subbands. Subsampling by a factor of 2 is denoted by # 2. : : : : 15 7 Perfect reconstruction two-band analysis/synthesis IIR lter bank system. : : 16 8 Basic shape of normal heartbeat. : : : : : : : : : : : : : : : : : : : : : : : : : 20 9 Estimated autocorrelation function for normal heartbeat. : : : : : : : : : : : : 21 10 Lowpass unit pulse responses for the ECG-optimized nonunitary lter bank, and the LOT. : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 21 11 Original ECG's (the rst 15 seconds) used in the experiments. mitxxx yyyy denotes record no. xxx starting at time yy.yy. Each record length is in total 10 minutes, corresponding to 216000 samples. : : : : : : : : : : : : : : : : : : 23 12 Coding performance for varying input signals. The lter banks used are: Johnston's F16B [1] (.), IIR lter bank F 2 2 smpl [2] (-.), DCT (-), 32-tap LOT (solid line with circles), and the optimized 32-tap FIR lter bank (solid line). The time domain algorithm are: CCSP (solid line with x-marks), and FAN (solid line with diamonds). : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 25 13 Reconstructed signal segment (taken from mit10