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Bioelectrical Signal Processing In Cardiac And Neurological Applications

Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence, strong artifact components can temporarily impair the clinical measurements from the LTM recordings. Traditionally, the noise presence has been dealt with as a problem of non-desirable component removal by means of several quantitative signal metrics such as the signal-to-noise ratio (SNR), but current systems do not provide any information about the true impact of noise on the ECG clinical evaluation. As a first step towards an alternative to classical approaches, this work assesses the ECG quality under the assumption that an ECG has good quality when it is clinically interpretable. Therefore, our hypotheses are that it is possible (a) to create a clinical severity score for the effect of the noise on the ECG, (b) to characterize its consistency in terms of its temporal and statistical distribution, and (c) to use it for signal quality evaluation in LTM scenarios. For this purpose, a database of external event recorder (EER) signals is assembled and labeled from a clinical point of view for its use as the gold standard of noise severity categorization. These devices are assumed to capture those signal segments more prone to be corrupted with noise during long-term periods. Then, the ECG noise is characterized through the comparison of these clinical severity criteria with conventional quantitative metrics taken from traditional noise-removal approaches, and noise maps are proposed as a novel representation tool to achieve this comparison. Our results showed that neither of the benchmarked quantitative noise measurement criteria represent an accurate enough estimation of the clinical severity of the noise. A case study of long-term ECG is reported, showing the statistical and temporal correspondences and properties with respect to EER signals used to create the gold standard for clinical noise. The proposed noise maps, together with the statistical consistency of the characterization of the noise clinical severity, paves the way towards forthcoming systems providing us with noise maps of the noise clinical severity, allowing the user to process different ECG segments with different techniques and in terms of different measured clinical parameters.

https://www.mdpi.com/1424-8220/17/11/2448/pdf

Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring.

In wearable devices, power consumption is a serious issue since wearable devices must maintain the power-on state at any time. In healthcare system, a variety of signal processing operations occupy...

Design of Low-Power SoC for Wearable Healthcare Device

Ventricular arrhythmias such as ventricular tachycardia (VT) and ventricular fibrillation (VF) are the main life-threatening arrhythmias which have to be detected accurately by designing automated system. In this work, we propose a novel method based on ensemble empirical mode decomposition to decompose the ECG signal and classified with decision tree classifier and support vector machine (SVM) for discriminating the VT/VF conditions using informative ranked features. Total fifty-seven records of ECG signals from Creighton University Ventricular Tachyarrhythmia Database (CUDB) and the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB) database of PhysioNet were taken for evaluation. We obtained the sensitivity of 97.74%, specificity of 99% and accuracy of 98.69% in C4.5 classifier, whereas the accuracy of 90.52% was achieved with SVM classifier. These results indicate that the C4.5 algorithm is a superior approach for identification of cardiac arrhythmia class. The proposed system is an effective method that may be used to assist in decision support system in clinical practice for accurate recognition of ventricular arrhythmias.

Classification of ventricular arrhythmias using empirical mode decomposition and machine learning algorithms

Artefacts which are present in electrocardiogram (ECG) recordings distort detection of life-threatening arrhythmias such as ventricular tachycardia and ventricular fibrillation. The method examines single ECG lead and exploits time domain signal parameters for real-time detection of severe cardiac arrhythmias. The method is dedicated to implementation in mobile ECG telemetry systems, which are designed by using low-power microcontrollers, operating more than a week on a single battery charge. The method has been validated on publicly available databases and the results are presented. We verified our method on ECG signals obtained without pre-selection meaning that the noisy intervals were not omitted from signal analysis.

VT/VF Detection Method Based On ECG Signal Quality Assessment

Recent development in the area of wireless communications enabled advances in healthcare technology. Now, medical personnel are able to simultaneously observe electrocardiogram signals of large number of remote patients. The electrocardiogram signals are acquired by the patients themselves at their homes or other places far away from hospitals, and are transmitted almost instantly through modern communication systems. Unfortunately, the quality of the acquired signals is highly susceptible to artefacts. Here we outline a new algorithm dedicated to electrocardiograph telemetry systems which evaluates the quality of signals measured in unsupervised environments, rises the certainty of the produced diagnoses, and accelerates protective actions when necessary.

/pdf/qrs-complex-detection-based-ecg-signal-artefact-1tusxsmmbx.pdf

Qrs complex detection based ecg signal artefact discrimination

Microcontrollers represent unavoidable parts of state-of-the-art system-on-chips (SoCs) and they are widely embedded as IP blocks. This paper describes design steps and the application of available low-power techniques, to the design of a microcontroller IP core with 8051 instruction set, based on a prescribed standard cell libraries. Choice of the technology node and the cell library supplier is a design challenge that was considered and conclusions reached. The necessary steps of microcontroller design flow are presented which enable power reduction at several abstraction levels. An optimal microcontroller was designed to be embedded in various SoCs. The goal was to get energy-efficient microcontroller operation in applications which don't require intensive data processing. The impact of technology scaling on microcontroller energy efficiency is considered by comparison of the results obtained from implementations in three standard cell technologies. Moreover, power dissipation models are created which allow for microcontroller's power estimation in low throughput sensors networks applications.

Standard Cell-Based Low Power Embedded Controller Design

The paper considers the architecture and low power design aspects of the digital signal processing block embedded into a three-phase integrated power meter IC. Utilized power reduction techniques were focused on the optimization of control logic block. The operations that control unit performs are described together with power optimization results.

/pdf/the-decomposition-of-dsp-s-control-logic-block-for-power-296jtr7uvi.pdf

The Decomposition of DSP’s Control Logic Block for Power Reduction

Digital predistortion (DPD), based on complex-valued memory polynomials (MP), is established as an efficient method for power amplifier (PA) linearization. The DPD facilitates compliance of the tel...

Borisav Jovanovic

Papers

Qrs complex detection based ecg signal artefact discrimination

VT/VF Detection Method Based On ECG Signal Quality Assessment

Standard Cell-Based Low Power Embedded Controller Design

The Decomposition of DSP’s Control Logic Block for Power Reduction

PA Linearization by Digital Predistortion and Peak-to-Average Power Ratio Reduction in Software Defined Radios