Search Results (4)

Heart-related ailments have become a significant cause of death around the globe in recent years. Due to lifestyle changes, people of almost all age brackets face these issues. Preventing and treating heart-related issues require the electrocardiogram (ECG) monitoring of patients. The study of patients’ ECG signals helps doctors identify abnormal heart rhythm patterns by which screening problems like arrhythmia (irregular heart rhythm), myocardial infarction (heart attacks), and myocarditis (heart inflammation) is possible. The need for 24 h heart rate monitoring has led to the development of wearable devices, and the constant monitoring of ECG data leads to the generation of a large amount of data since wearable systems are resource-constrained regarding energy, memory, size, and computing capabilities. The optimization of biomedical monitoring systems is required to increase their efficiency. This paper presents an ECG compression system to reduce the amount of data generated, which reduces the energy consumption in the transceiver, which is a significant part of the overall energy consumed. The proposed system uses hybrid Golomb–Rice coding for data compression, a lossless data compression technique. The data compression is performed on the MIT BIH arrhythmia database; the achieved compression ratio of the compression system is 2.75 and 3.14 for average and maximum values, which, compared to the raw ECG samples, requires less transmission cost in terms of power consumed. Full article

In this paper, we propose a lossless electrocardiogram (ECG) compression method using a prediction error-based adaptive linear prediction technique. This method combines the adaptive linear prediction, which minimizes the prediction error in the ECG signal prediction, and the modified Golomb–Rice coding, which encodes the prediction error to the binary code as the compressed data. We used the PTB Diagnostic ECG database, the European ST-T database, and the MIT-BIH Arrhythmia database for the evaluation and achieved the average compression ratios for single-lead ECG signals of 3.16, 3.75, and 3.52, respectively, despite different signal acquisition setup in each database. As the prediction order is very crucial for this particular problem, we also investigate the validity of the popular linear prediction coefficients that are generally used in ECG compression by determining the prediction coefficients from the three databases using the autocorrelation method. The findings are in agreement with the previous works in that the second-order linear prediction is suitable for the ECG compression application. Full article

Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 μm CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity. Full article

In this paper, a new low complexity and lossless image compression system for capsule endoscopy (CE) is presented. The compressor consists of a low-cost YEF color space converter and variable-length predictive with a combination of Golomb-Rice and unary encoding. All these components have been heavily optimized for low-power and low-cost and lossless in nature. As a result, the entire compression system does not incur any loss of image information. Unlike transform based algorithms, the compressor can be interfaced with commercial image sensors which send pixel data in raster-scan fashion that eliminates the need of having large buffer memory. The compression algorithm is capable to work with white light imaging (WLI) and narrow band imaging (NBI) with average compression ratio of 78% and 84% respectively. Finally, a complete capsule endoscopy system is developed on a single, low-power, 65-nm field programmable gate arrays (FPGA) chip. The prototype is developed using circular PCBs having a diameter of 16 mm. Several in-vivo and ex-vivo trials using pig's intestine have been conducted using the prototype to validate the performance of the proposed lossless compression algorithm. The results show that, compared with all other existing works, the proposed algorithm offers a solution to wireless capsule endoscopy with lossless and yet acceptable level of compression. Full article