In this paper, we propose a novel signal quality-aware Internet of Things (IoT)-enabled electrocardiogram (ECG) telemetry system for continuous cardiac health monitoring applications. The proposed quality-aware ECG monitoring system consists of three modules: 1) ECG signal sensing module; 2) automated signal quality assessment (SQA) module; and 3) signal-quality aware (SQAw) ECG analysis and transmission module. The main objectives of this paper are: design and development of a light-weight ECG SQA method for automatically classifying the acquired ECG signal into acceptable or unacceptable class and real-time implementation of proposed IoT-enabled ECG monitoring framework using ECG sensors, Arduino, Android phone, Bluetooth, and cloud server. The proposed framework is tested and validated using the ECG signals taken from the MIT-BIH arrhythmia and Physionet challenge databases and the real-time recorded ECG signals under different physical activities. Experimental results show that the proposed SQA method achieves promising results in identifying the unacceptable quality of ECG signals and outperforms existing methods based on the morphological and RR interval features and machine learning approaches. This paper further shows that the transmission of acceptable quality of ECG signals can significantly improve the battery lifetime of IoT-enabled devices. The proposed quality-aware IoT paradigm has great potential for assessing clinical acceptability of ECG signals in improvement of accuracy and reliability of unsupervised diagnosis system.

Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring

A completely automated algorithm to detect poor-quality electrocardiograms (ECGs) is described. The algorithm is based on both novel and previously published signal quality metrics, originally designed for intensive care monitoring. The algorithms have been adapted for use on short (5-10 s) single- and multi-lead ECGs. The metrics quantify spectral energy distribution, higher order moments and inter-channel and inter-algorithm agreement. Seven metrics were calculated for each channel (84 features in all) and presented to either a multi-layer perceptron artificial neural network or a support vector machine (SVM) for training on a multiple-annotator labelled and adjudicated training dataset. A single-lead version of the algorithm was also developed in a similar manner. Data were drawn from the PhysioNet Challenge 2011 dataset where binary labels were available, on 1500 12-lead ECGs indicating whether the entire recording was acceptable or unacceptable for clinical interpretation. We re-annotated all the leads in both the training set (1000 labelled ECGs) and test dataset (500 12-lead ECGs where labels were not publicly available) using two independent annotators, and a third for adjudication of differences. We found that low-quality data accounted for only 16% of the ECG leads. To balance the classes (between high and low quality), we created extra noisy data samples by adding noise from PhysioNet's noise stress test database to some of the clean 12-lead ECGs. No data were shared between training and test sets. A classification accuracy of 98% on the training data and 97% on the test data were achieved. Upon inspection, incorrectly classified data were found to be borderline cases which could be classified either way. If these cases were more consistently labelled, we expect our approach to achieve an accuracy closer to 100%.

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Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms

A machine learning approach to multi-level ECG signal quality classification

Electrocardiogram (ECG) signal quality assessment (SQA) plays a vital role in significantly improving the diagnostic accuracy and reliability of unsupervised ECG analysis systems. In practice, the ECG signal is often corrupted with different kinds of noises and artifacts. Therefore, numerous SQA methods were presented based on the ECG signal and/or noise features and the machine learning classifiers and/or heuristic decision rules. This paper presents an overview of current state-of-the-art SQA methods and highlights the practical limitations of the existing SQA methods. Based upon past and our studies, it is noticed that a lightweight ECG noise analysis framework is highly demanded for real-time detection, localization, and classification of single and combined ECG noises within the context of wearable ECG monitoring devices which are often resource constrained.

A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment

Recently, development of wearable and Internet of Things (IoT) technologies enables the real-time and continuous individual electrocardiogram (ECG) monitoring. In this paper, we develop a novel IoT-based wearable 12-lead ECG SmartVest system for early detection of cardiovascular diseases, which consists of four typical IoT components: 1) sensing layer using textile dry ECG electrode; 2) network layer utilizing Bluetooth, WiFi, etc.; 3) cloud saving and calculation platform and server; and 4) application layer for signal analysis and decision making. We focus on addressing the challenge of real-time signal quality assessment (SQA) and lightweight QRS detection for wearable ECG application. First, a combination method of multiple signal quality indices and machine learning is proposed for classifying 10-s single-channel ECG segments as acceptable and unacceptable. Then a lightweight QRS detector is developed for accurate location of QRS complexes. The results show that the proposed SQA method can efficiently deal with tradeoff between accepting good (97.9%) and rejecting poor (96.4%) quality ECGs, ensuring that only a low percentage of recorded ECGs are discarded. The proposed lightweight QRS detector achieves a ${F_{1}}$ score higher than 99.5% for processing clean ECGs. Meanwhile, it reports significantly higher ${F_{1}}$ scores than two existing QRS detectors for processing noisy ECGs. In addition, it also has a fine computation efficiency. This paper demonstrates that the developed IoT-driven ECG SmartVest system can be applied for widely monitoring the population during daily life and has a promising application future.

Signal Quality Assessment and Lightweight QRS Detection for Wearable ECG SmartVest System

ECG, measuring body-surface electrical waves generated in the heart, is the golden standard for diagnosis of various cardiovascular diseases. The 2011 Physionet challenge visions mobile phones that can be used to collect and analyse ECG records. Such devices are particularly useful in underdeveloped regions, which have a large population size but lack adequate primary care capacity. Signals collected using mobile phones can be sent via mobile network to experienced doctors for further diagnosis. In response to Physionet 2011 challenge, we explore various time series techniques for their potentials in evaluating quality of an ECG, including time domain analysis, frequency domain analysis, joint time-frequency analysis, self correlation, cross correlation, and entropy analysis. Two algorithms are developed based on these techniques. The first algorithm consists of multi-stage tests. A record that passes all tests is regarded as of acceptable quality. In the second algorithm, results from various analyses are assembled into a matrix, which measures the regularity of the ECG. The quality of the ECG is then measured by the spectrum radius of the Matrix of Regularity. Since spectrum radius is continuous, the results can lead to continuous grades of ECGs. The algorithms are tested using training data from Physionet. Influences of various parameters are examined.

http://cinc.mit.edu/archives/2011/pdf/0369.pdf

Computer algorithms for evaluating the quality of ECGs in real time

The 12-lead electrocardiography (ECG) is the gold standard for diagnosis of abnormalities of the heart. However, the ECG is susceptible to artifacts, which may lead to wrong diagnosis and thus mistreatment. It is a clinical challenge of great significance differentiating ECG artifacts from patterns of diseases. We propose a computational framework, called the matrix of regularity, to evaluate the quality of ECGs. The matrix of regularity is a novel mechanism to fuse results from multiple tests of signal quality. Moreover, this method can produce a continuous grade, which can more accurately represent the quality of an ECG. When tested on a dataset from the Computing in Cardiology/PhysioNet Challenge 2011, the algorithm achieves up to 95% accuracy. The area under the receiver operating characteristic curve is 0.97. The developed framework and computer program have the potential to improve the quality of ECGs collected using conventional and portable devices.

Matrix of regularity for improving the quality of ECGs

Introduction: This review focuses on the role of the fixed-dose combination (FDC) drug valsartan/hydrochlorothiazide (HCTZ) in the treatment of hypertension. Effective blood pressure control often is not achieved with monotherapy and, instead, requires combinations of drugs with different mechanisms of action to produce additive or synergistic effects. Areas covered: FDC valsartan/HCTZ enhances not only efficacy for blood pressure control but also provides beneficial effects on target organs beyond that expected from arterial pressure reduction alone. Data describe key clinical trial experiences with the FDC, with particular attention to efficacy and tolerability. Literature searches of these various topics were conducted in January 2011. There is evidence of potential benefits with this combination associated with left ventricular hypertrophy, left ventricular dysfunction and renal disease. The FDC is an effective treatment for patients with hypertension and is superior to monotherapy than either drug al...

Valsartan plus hydrochlorothiazide: a review of its use since its introduction

Sudden cardiac arrest (SCA) due to fatal cardiac arrhythmias such as ventricular fibrillation is the leading cause of death in the United States, killing 350,000 Americans each year. Thus, it is of great importance to investigate the mechanisms that can suppress abnormal heart rhythms. In this work, we study the effects of drugs such as channel blockers through mathematical modeling of cardiac electrophysiological phenomena. In particular, we carry out multi-level simulations to study how channel blockers affect arrhythmias at cellular, fiber, and tissue levels. Numerical simulations show that the drugs have different effects at different scales (cellular versus fiber or tissue). Moreover, the drugs may appear to be arrhythmic in one model but antiarrhythmic in another. These observations indicate that analysis and simulation based on multiple scales and multiple models are crucial to fully understand the properties of drugs in treating arrhythmias.

Influence of channel blockers on cardiac alternans

The human cardiovascular system exhibits complicated dynamics. Various signal analysis techniques have been utilized to study electrocardiogram (ECG) rate and morphology for their potentials in diagnosis and prediction of cardiovascular diseases. Conventional methods of linear signal theory, including power spectral analysis and time-domain analysis, have been widely adopted to analyze heart rhythm disorders due to cardiomyopathy and fibrillation. However, the success of these techniques is often hampered by the nonstationary and nonlinear character of the heart's activities. To overcome these limitations, many techniques based on nonlinear dynamics and chaos theory have been developed. In this work, we investigate various existing techniques for their potential application in the diagnosis of rhythm disorders. This comparison of different methodologies can be used as a guideline in selecting proper ECG signal analysis techniques.

Jujhar Bains

Papers

Computer algorithms for evaluating the quality of ECGs in real time

Matrix of regularity for improving the quality of ECGs

Valsartan plus hydrochlorothiazide: a review of its use since its introduction

Influence of channel blockers on cardiac alternans

A review of diagnosis methods for heart rhythm disorders