Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria.

Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings. Graphical Abstract Clinical Noise Severity Classification based on Machine Learning techniques towards Long-Term ECG Monitoring.

On Power Line Positioning Systems.

Power line infrastructure is available almost everywhere. Positioning systems aim to estimate where a device or target is. Consequently, there may be an opportunity to use power lines for positioning purposes. This survey article reports the different efforts, working principles, and possibilities for implementing positioning systems relying on power line infrastructure for power line positioning systems (PLPS). Since Power Line Communication (PLC) systems of different characteristics have been deployed to provide communication services using the existing mains, we also address how PLC systems may be employed to build positioning systems. Although some efforts exist, PLPS are still prospective and thus open to research and development, and we try to indicate the possible directions and potential applications for PLPS.

Rate-Distortion Performance and Incremental Transmission Scheme of Compressive Sensed Measurements in Wireless Sensor Networks.

We consider a Wireless Sensor Network (WSN) monitoring environmental data. Compressive Sensing (CS) is explored to reduce the number of coefficients to transmit and consequently save the energy of sensor nodes. Each sensor node collects N samples of environmental data, these are CS coded to transmit M < N values to a sink node. The M CS coefficients are uniformly quantized and entropy coded. We investigate the rate-distortion performance of this approach even under CS coefficient losses. The results show the robustness of the CS coding framework against packet loss. We devise a simple strategy to successively approximate/quantize CS coefficients, allowing for an efficient incremental transmission of CS coded data. Tests show that the proposed successive approximation scheme provides rate allocation adaptivity and flexibility with a minimum rate-distortion performance penalty.

Consistent quality control in ECG compression by means of direct metrics.

The aim of electrocardiogram (ECG) compression is to reduce the amount of data as much as possible while preserving the significant information for diagnosis. Objective metrics that are derived directly from the signal are suitable for controlling the quality of the compressed ECGs in practical applications. Many approaches have employed figures of merit based on the percentage root mean square difference (PRD) for this purpose. The benefits and drawbacks of the PRD measures, along with other metrics for quality assessment in ECG compression, are analysed in this work. We propose the use of the root mean square error (RMSE) for quality control because it provides a clearer and more stable idea about how much the retrieved ECG waveform, which is the reference signal for establishing diagnosis, separates from the original. For this reason, the RMSE is applied here as the target metric in a thresholding algorithm that relies on the retained energy. A state of the art compressor based on this approach, and its PRD-based counterpart, are implemented to test the actual capabilities of the proposed technique. Both compression schemes are employed in several experiments with the whole MIT-BIH Arrhythmia Database to assess both global and local signal distortion. The results show that, using the RMSE for quality control, the distortion of the reconstructed signal is better controlled without reducing the compression ratio.