Low-Power ECG-Based Processor for Predicting Ventricular Arrhythmia


This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial points. Those techniques are robust to any variations in the ECG signal with high sensitivity and precision. Two databases of the heart signal recordings from the MIT PhysioNet and the American Heart Association were used as a validation set to evaluate the performance of the processor. Based on application-specified integrated circuit (ASIC) simulation results, the overall classification accuracy was found to be 86% on the out-of-sample validation data with 3-s window size.            The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2.

Enhancement of the project:

Existing System:

Recently, due to the remarkable advancement in technology, the development of dedicated hardware for accurate ECG analysis and classification in real time has become possible. The main requirements are low-power consumption and low-energy operation in order to have longer battery lifetime along with the small area for wearability. Many attempts succeeded to implement ECG signal processing and classification systems in hardware. Shiu et al. implemented an integrated electrocardiogram signal processor (ESP) for the identification of heart diseases using the 90-nm CMOS technology. The system employed an instrumentation amplifier and a low-pass filter (LPF) to remove the baseline wander and the power line interference form the ECG and employed a time-domain morphological analysis for the feature extraction and classification based on the evaluation of the ST segment. The system was carried out in a field programmable gate array and consumed a total of 40.3-μW power and achieved an accuracy of 96.6%. The main disadvantage of the system is that it uses fixed search window with predefined size to locate S and T fiducial points, which is not suitable for real-time scenarios.

The system was fabricated on the 0.18-μm CMOS technology and executed different functions for the three stages of preprocessing, feature extraction, and classification. The algorithm behind these functions was based on the quad level vector. Moreover, the functions were all pipelined to increase hardware utilization and reduce power consumption. Besides, the system employed clock gating techniques to enable and disable each processing unit individually according to the need and it applied voltage scaling up to 0.7 V. The ECG processor consumed 6 μW at 1.8 V and 1.26 μW at 0.7 V, which is much better than the system due to the low-power techniques it employed. One recent system for ECG classification was presented and comprised of three chips. The first chip contained the body-end circuits that were the high-pass sigma delta modulator-based bio-signal processor and the ON–OFF keying transmitter. The second chip, the receiving end, had the receiver and the digital signal processing (DSP) unit. The last chip was the classifier. Discrete wavelet transform was adopted by the DSP unit for the ECG feature extraction and classification. The chip was fabricated on the 0.18-μm CMOS technology and consumed a total power of 5.967 μW at 1.2 V for the DSP unit only. The accuracy of the beat detection and the ECG classification was 99.44% and 97.25%, respectively.

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