access icon free Efficient fuzzy-controlled and hybrid entropy coding strategy lossless ECG encoder VLSI design for wireless body sensor networks

An efficient VLSI design of a lossless electrocardiogram (ECG) encoder is proposed for wireless body sensor networks. To save wireless transmission power, a novel lossless encoding algorithm had been created for ECG signal compression. The proposed algorithm consists of a novel adaptive predictor based on fuzzy decision control, and a novel hybrid entropy encoder including both a two-stage Huffman and a Golomb-Rice coding. The VLSI architecture contains only 2.71 K gate counts and its core area is 33 929 μm2 synthesized by a 0.18 μm CMOS process. Moreover, this design can be operated at 100 MHz processing rate by consuming only 30 μW. It achieves an average compression rate of 2.56 for the MIT-BIH arrhythmia database. Compared with previous low-complexity and high-performance lossless ECG encoder studies, this design has a higher compression rate, lower power consumption and lower hardware cost than other VLSI designs.

Inspec keywords: biomedical transducers; integrated circuit design; radio transmitters; Huffman codes; VLSI; biomedical electronics; prediction theory; electrocardiography; CMOS integrated circuits; fuzzy control; entropy codes; medical control systems; body sensor networks

Other keywords: power 30 muW; Golomb-Rice coding; fuzzy decision control; two-stage Huffman coding; adaptive predictor; hybrid entropy encoder; size 0.18 mum; hybrid entropy coding strategy; CMOS process; power consumption; wireless transmission power; lossless ECG encoder VLSI design; frequency 100 MHz; MIT-BIH arrhythmia database; electrocardiogram; ECG signal compression; wireless body sensor network; fuzzy-controlled coding strategy

Subjects: Semiconductor integrated circuit design, layout, modelling and testing; Biological and medical control systems; Transducers and sensing devices; Wireless sensor networks; Bioelectric signals; Fuzzy control; Sensing devices and transducers; Signal processing and detection; Codes; CMOS integrated circuits; Biomedical communication

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