Abstract-We propose a new ECG data compression algorithm based on a learned overcomplete dictionary to exploit the correlation between signals in adjacent heart beats. The learned overcomplete dictionary is constructed by K-SVD dictionary learning algorithm, after preprocessing and normalization of length and magnitude. Using the overcomplete dictionary, the proposed algorithm can find sparse estimation, which can represent the ECG signal effectively. Experimental results on MIT-BIH arrhythmia database confirms that our proposed algorithm has high compression ratio while minimizing data distortion. Electrocardiogram (ECG) data often require compression for storage, transfer over networks, and handling, especially given the trend towards miniaturization and wearable healthmonitoring systems. ECG signal compression algorithm introduced in the past several decades can be lossless [1], [2], but lossy ones achieve better compression ratios by allowing small distortions in the parts of data that are not critical to the analysis of the signals. Lossy algorithms can be further categorized into direct signal compression and transformation compression. The former attempts to obtain redundancies on the signals to be compressed and extract meaningful parameters after analysis; the latter analyzes the signals in the transformed domain and retains the coefficients of the particular features in the data [3]-[6]. We propose a new approach to ECG signal compression based on heartbeat and dictionary learning. Our algorithm is divided into two stages. The training stage performs length normalization by interpolation to construct the overcomplete dictionary. During the compression/decompression stage, it calculates the optimal coefficients, which can capture most of the information content of the ECG signals. We compare our results with those of several well-known ECG compression algorithms using the MIT-BIH arrhythmia database [7] . This paper is organized as follows. In Section II, we introduce the background regarding sparse representation and dictionary learning. In Section III, we describe our proposed ECG compression algorithm using overcomplete dictionary constructed by K-SVD. Section IV presents experimental and comparison results using MIT-BIH arrhythmia database.
Lee S et al. REF proposed an ECG data compression algorithm based on K-singular value decomposition (K-SVD).
A new approach to compressing ECG signals with trained overcomplete dictionary
2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH)
2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH)
Computer Science