Compressed Segmented Beat Modulation Method using Discrete Cosine Transform

Currently used 24-hour electrocardiogram (ECG) monitors have been shown to skip detecting arrhythmias that may not occur frequently or during standardized ECG test. Hence, online ECG processing and wearable sensing applications have been becoming increasingly popular in the past few years to solve a continuous and long-term ECG monitoring problem. With the increase in the usage of online platforms and wearable devices, there arises a need for increased storage capacity to store and transmit lengthy ECG recordings, offline and over the cloud for continuous monitoring by clinicians. In this work, a discrete cosine transform (DCT) compressed segmented beat modulation method (SBMM) is proposed and its applicability in case of ambulatory ECG monitoring is tested using Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH) ECG Compression Test Database containing Holter tape normal sinus rhythm ECG recordings. The method is evaluated using signal-to-noise (SNR) and compression ratio (CR) considering varying levels of signal energy in the reconstructed ECG signal. For denoising, an average SNR of 4.56 dB was achieved representing an average overall decline of 1.68 dBs (37.9%) as compared to the uncompressed signal processing while 95 % of signal energy is intact and quantized at 6 bits for signal storage (CR=2) compared to the original 12 bits, hence resulting in 50% reduction in storage size.