This work presents a low-complexity lossless electrocardiogram (ECG) compression ASIC for wireless sensors. Three linear predictors aiming for different signal characteristics are provided for prediction based on a history table that records of the optimum predictors for recent samples. And unlike traditional methods using a unified encoder, the prediction error is encoded by a hybrid Golomb encoder combining Exp-Golomb and Golomb-Rice and can adaptively configure the encoding scheme according to the predictor selection. The novel adaptive prediction and encoding scheme contributes to a compression rate of 2.77 for the MIT-BIH Arrhythmia database. Implemented in 40nm CMOS process, the design takes a small gate count of 1.82K with 37.6nW power consumption under 0.9V supply voltage.

Arrhythmia classification based on electrocardiogram (ECG) is crucial in automatic cardiovascular disease diagnosis. The classification methods used in the current practice largely depend on hand-crafted manual features. However, extracting hand-crafted manual features may introduce significant computational complexity, especially in the transform domains. In this study, an accurate method for patient-specific ECG beat classification is proposed, which adopts morphological features and timing information. As to the morphological features of heartbeat, an attention-based two-level 1-D CNN is incorporated in the proposed method to extract different grained features automatically by focusing on various parts of a heartbeat. As to the timing information, the difference between previous and post RR intervels is computed as a dynamic feature. Both the extracted morphological features and the interval difference are used by multi-layer perceptron (MLP) for classifing ECG signals. In addition, to reduce memory storage of ECG data and denoise to some extent, an adaptive heartbeat normalization technique is adopted which includes amplitude unification, resolution modification, and signal difference. Based on the MIT-BIH arrhythmia database, the proposed classification method achieved sensitivity Sen=93.4% and positive predictivity Ppr=94.9% in ventricular ectopic beat (VEB) detection, sensitivity Sen=86.3% and positive predictivity Ppr=80.0% in supraventricular ectopic beat (SVEB) detection, and overall accuracy OA=97.8% under 6-bit ECG signal resolution. Compared with the state-of-the-art automatic ECG classification methods, these results show that the proposed method acquires comparable accuracy of heartbeat classification though ECG signals are represented by lower resolution.