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 architecture of the proposed ESP was implemented using 65-nm CMOS process. It occupied 0.112-mm 2 area and consumed 2.78-µW power at an operating frequency of 10 kHz and from an operating voltage of 1 V. It is worth mentioning that the proposed ESP is the first ASIC implementation of an ECG-based processor that is used for the prediction of ventricular arrhythmia up to 3 h before the onset.
The T and P waves of electrocardiogram signals are excellent indicators in the analysis and interpretation of cardiac arrhythmia. As such, the need to address and develop an accurate delineation technique for the detection of these waves is necessary. In this paper, we present a novel robust and adaptive T and P wave delineation method for real-time analysis and nonstandard ECG morphologies. The proposed method is based on ECG signal filtering, value estimation of different fiducial points, applying backward and forward search windows as well as adaptive thresholds. Simulations and evaluations prove the accuracy of the proposed technique in comparison to those proposed techniques in the literature. The mean error for the T peak, T offset, P peak and P offset values are found to be 9.8, 2.3, 7.3 and 3.5 milliseconds, respectively, based on the Physionet QT database, rendering our algorithm as an excellent candidate for ECG signal analysis.
This paper presents an electrocardiogram (ECG) processor on chip for full ECG feature extraction and cardiac autonomic neuropathy (CAN) classification. Full ECG extraction is performed using absolute value curve length transform (A-CLT) for $\text{QRS}_{\text{peak}}$ detection and using low-pass differentiation for other ECG features such as $\text{QRS}_{\text{on}}$, $\text{QRS}_{\text{off}}$, Pwave, and Twave. The proposed QRS detector attained a sensitivity of 99.37% and predictivity of 99.38%. The extracted $\text{QRS}_{\text{peak}}$ to $\text{QRS}_{\text{peak}}$ intervals (RR intervals) along with QT intervals enable CAN severity detection, which is a cardiac arrhythmia usually seen in diabetic patients leading to increased risk of sudden cardiac death. This paper presents the first hardware real-time implementation of CAN severity detector that is based on RR variability and QT variability analysis. RR variability metrics are based on mean RR interval and root mean square of standard differences of the RR intervals. The proposed architecture was implemented in 65-nm technology and consumed 75 nW only at 0.6 V, when operating at 250 Hz. Ultralow power dissipation of the system enables it to be integrated into wearable healthcare devices.
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