An arrhythmia detector and heart rate estimator for overnight polysomnography studies

Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions

OBJECTIVE

Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed.

METHODS

The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing.

RESULTS

The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device.

CONCLUSION

The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions.

SIGNIFICANCE

The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.

Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing

In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA) based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG) signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan) 40 nm standard cell library. The integrated circuit (IC) synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz.

Signal quality estimation with multichannel adaptive filtering in intensive care settings

A signal quality estimate of a physiological waveform can be an important initial step for automated processing of real-world data. This paper presents a new generic point-by-point signal quality index (SQI) based on adaptive multichannel prediction that does not rely on ad hoc morphological feature extraction from the target waveform. An application of this new SQI to photoplethysmograms (PPG), arterial blood pressure (ABP) measurements, and ECG showed that the SQI is monotonically related to signal-to-noise ratio (simulated by adding white Gaussian noise) and to subjective human quality assessment of 1361 multichannel waveform epochs. A receiver-operating-characteristic (ROC) curve analysis, with the human "bad" quality label as positive and the "good" quality label as negative, yielded areas under the ROC curve of 0.86 (PPG), 0.82 (ABP), and 0.68 (ECG).

Employment of a Healthgrid for evaluation and development of polysomnographic biosignal processing methods

Longterm biosignal recordings, such as overnight sleep recordings (so-called polysomnographies, PSG), often vary in signal quality and signal shape. Movement artifacts occur frequently. This may impede successful application of automated processing algorithms designed for well-defined short-term recordings. To test existing algorithms on suitability for PSG analysis, and develop robust analysis tools, an environment that offers efficient application of biosignal methods on comprehensive and representative reference data is required. In this article, a Grid based biosignal processing platform is presented, that provides a large set of clinical PSG reference data collected within the SIESTA project. To date, different processing and evaluation methods with focus on polysomnographic electrocardiogram (PSG-ECG) based analysis are implemented. First results for heart rate analysis of PSG-ECG are given, including the introduction of a performance quality measure for non-annotated PSG-ECG. Different publicly available heart beat detection algorithms have been tested. As an example, the wqrs algorithm, provided by the PhysioNet shows a sensitivity of over 99% and a positive predictive value of over 94% on the PhysioNet's PSG reference data. Processed on the SIESTA data, it only detects around 60% of the heart beats, resulting in a low average performance quality of 0.28. Evaluation of further algorithms has led to the development of an improved, robust algorithm with a high average performance quality of 0.98.

Ictal asystole: a benign condition?

Ictal asystole (IA) has been implicated as a preventable cause of sudden unexplained death in epilepsy presumably provoked by a direct autonomic effect of the electrical stimulus on the heart. An electronic database search of patients with IA was performed comparing heart rate (HR) characteristics to a group of patients with vasovagal asystole. IA was seen in eight patients, all with temporal lobe epilepsy. No statistical difference was found in duration of asystole, bradycardia, and baseline HR characteristics except of a higher HR acceleration postasystole in the controls. None of the six patients with IA who underwent pacemaker implantation had recurrence of asystolic events during mean follow-up of 5 years. This study in a small group of patients suggests that the epileptic activation leading to IA is possibly mediated through a transient increase in vagal tone and not by a direct autonomic effect on the heart.

A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising

In this paper, a simple moving average-based computing method for real-time QRS detection is proposed. In addition, for signal preprocessing our detection algorithm also incorporates a wavelet-based denoising procedure to effectively reduce the noise level for electrocardiogram (ECG) data. The overall computational structure of the proposed algorithm allows the QRS detection to be performed and implemented in real-time with high time- and memory-efficiency. Algorithm performance was evaluated against the MIT-BIH Arrhythmia Database. The numerical results indicated that the novel algorithm finally achieved about 99.5% of the detection rate for the standard database, and also, it could function reliably even under the condition of poor signal quality in the measured ECG data.