Key result
Second-order delta arithmetic coding achieves ~3.2-fold lossless ECG compression without introducing QRS timing error.
Why the study?
Portable ECG recorders have limited memory yet require storing recordings without any loss of diagnostic information, motivating the development of a lossless compression method simple enough for low-cost embedded devices.
Does a lossless ECG compression method combining second-order delta encoding and arithmetic coding improve compression ratio while preserving bit-exact reconstruction and heartbeat detection accuracy on a low-cost embedded ECG system?
Does a lossless ECG compression method combining second-order delta encoding and arithmetic coding improve compression ratio while preserving bit-exact reconstruction and heartbeat detection accuracy on a low-cost embedded ECG system?
Combining second-order delta encoding with arithmetic coding on a low-cost Raspberry Pi embedded system achieves bit-exact lossless ECG compression that roughly doubles device recording capacity without degrading QRS detection accuracy.
Portable electrocardiogram (ECG) recorders have limited memory, yet their recordings must be stored without any loss of diagnostic information. This study aims to develop and validate a lossless ECG compression method simple enough for a low-cost embedded device. The main contribution is a compression method that combines second-order delta encoding with arithmetic coding, together with a mathematical rule that predicts when second-order delta encoding compresses better than first-order delta encoding. The method was implemented on a Raspberry Pi Zero 2 W with an AD8232 ECG sensor and an ADS1115 analog-to-digital converter, and it was compared with Huffman, Lempel-Ziv-Welch, and run-length coding, with and without delta encoding. The evaluation used hardware recordings from a single volunteer and 687 records from three public databases (MIT-BIH Arrhythmia, European ST-T, and PTB Diagnostic). All 8,244 tested combinations were reconstructed exactly, bit for bit. The proposed method achieved a mean compression ratio of 3.248 on the MIT-BIH database, whereas first-order delta encoding performed better on the European ST-T (2.969) and PTB (4.964) databases, as predicted by the derived rule. On hardware recordings, the compression ratio was about 2.0, and a standing posture raised 50 Hz power-line noise and lowered the compression ratio by up to 23%. Heartbeat detection on 745 beats showed 100% sensitivity and 100% positive predictivity, with no significant timing difference caused by 8-bit quantization (p = 0.361). In conclusion, the proposed method can roughly double the recording time of a memory-limited ECG device without additional hardware, which makes it a practical option for long-term wearable heart monitoring. Future work will extend the validation to multiple subjects and multi-lead recordings.
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Wicaksana et al. (2026) studied Electrocardiogram (ECG) monitoring and lossless signal compression (n=1). Second-order delta encoding combined with Arithmetic Coding (8-bit quantized ECG) vs. First-order delta encoding, no delta preprocessing, Huffman coding, LZW, RLE, and unquantized original ECG signal was evaluated on Beat-level QRS detection timing error (ms) between 8-bit quantized signal and original pre-quantization signal across 745 matched beats (MD 0.045 ms, 95% CI -0.051 to 0.140, p=0.361). Second-order delta encoding with arithmetic coding achieved bit-exact lossless ECG compression (CR 3.248 on MIT-BIH; ~2.0 on hardware) with 100% QRS sensitivity and no timing error (p=0.361).
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