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Advances in ECG Sensing and Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 18645

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Prof. Dr. Piotr Augustyniak

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Department of Biocybernetics and Biomedical Engineering, AGH University of Krakow, Al. Adama Mickiewicza 30, 30-059 Kraków, Poland
Interests: ECG processing; medical instrumentation and algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid progress of modern sensing and communication technologies and application of artificial intelligence to data processing and interpretation recently contributed to several new trends beyond traditional frontiers of cardiology. Fast response of human circulatory system to physical load and emotions opens new ways to monitor individuals in a wide range of scenarios. To this point fast, simple, easy-to-use, portable, and cost-effective circulatory sensing devices are particularly sought after to accompany regular people in their everyday life.

Dr. Piotr Augustyniak
Guest Editor

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Keywords

Cardiovascular measurements in assisted living
Remote sensing of blood pulse
Pregnancy wellbeing / Non-invasive Fetal recordings
Implantable cardiovascular sensors
Wearable ECG sensors for fitness, emotions, emergency and rehabilitation
Distributed cardiac sensing and sensor networks
Ultra long-term monitoring technologies
Artificial Intelligence and Deep Learning in ECG interpretation

Published Papers (5 papers)

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Research

25 pages, 6655 KiB

Open AccessArticle

Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation

by Irena Jekova and Vessela Krasteva

Sensors 2021, 21(12), 4105; https://doi.org/10.3390/s21124105 - 15 Jun 2021

Cited by 32 | Viewed by 4991

Abstract

High performance of the shock advisory analysis of the electrocardiogram (ECG) during cardiopulmonary resuscitation (CPR) in out-of-hospital cardiac arrest (OHCA) is important for better management of the resuscitation protocol. It should provide fewer interruptions of chest compressions (CC) for non-shockable organized rhythms (OR) and Asystole, or prompt CC stopping for early treatment of shockable ventricular fibrillation (VF). Major disturbing factors are strong CC artifacts corrupting raw ECG, which we aimed to analyze with optimized end-to-end convolutional neural network (CNN) without pre-filtering or additional sensors. The hyperparameter random search of 1500 CNN models with 2–7 convolutional layers, 5–50 filters and 5–100 kernel sizes was done on large databases from independent OHCA interventions for training (3001 samples) and validation (2528 samples). The best model, named CNN3-CC-ECG network with three convolutional layers (filters@kernels: 5@5,25@20,50@20) presented Sensitivity Se(VF) = 89%(268/301), Specificity Sp(OR) = 91.7%(1504/1640), Sp(Asystole) = 91.1%(3325/3650) on an independent test OHCA database. CNN3-CC-ECG’s ability to effectively extract features from raw ECG signals during CPR was comprehensively demonstrated, and the dependency on the CPR corruption level in ECG was tested. We denoted a significant drop of Se(VF) = 74.2% and Sp(OR) = 84.6% in very strong CPR artifacts with a signal-to-noise ratio of SNR < −9 dB, p < 0.05. Otherwise, for strong, moderate and weak CC artifacts (SNR > −9 dB, −6 dB, −3 dB), we observed insignificant performance differences: Se(VF) = 92.5–96.3%, Sp(OR) = 93.4–95.5%, Sp(Asystole) = 92.6–94.0%, p > 0.05. Performance stability with respect to CC rate was validated. Generalizable application of the optimized computationally efficient CNN model was justified by an independent OHCA database, which to our knowledge is the largest test dataset with real-life cardiac arrest rhythms during CPR. Full article

(This article belongs to the Special Issue Advances in ECG Sensing and Monitoring)

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21 pages, 11706 KiB

Open AccessArticle

Novel Stable Capacitive Electrocardiogram Measurement System

by Chi-Chun Chen, Shu-Yu Lin and Wen-Ying Chang

Sensors 2021, 21(11), 3668; https://doi.org/10.3390/s21113668 - 25 May 2021

Cited by 7 | Viewed by 3924

Abstract

This study presents a noncontact electrocardiogram (ECG) measurement system to replace conventional ECG electrode pads during ECG measurement. The proposed noncontact electrode design comprises a surface guard ring, the optimal input resistance, a ground guard ring, and an optimal voltage divider feedback. The surface and ground guard rings are used to reduce environmental noise. The optimal input resistor mitigates distortion caused by the input bias current, and the optimal voltage divider feedback increases the gain. Simulated gain analysis was subsequently performed to determine the most suitable parameters for the design, and the system was combined with a capacitive driven right leg circuit to reduce common-mode interference. The present study simulated actual environments in which interference is present in capacitive ECG signal measurement. Both in the case of environmental interference and motion artifact interference, relative to capacitive ECG electrodes, the proposed electrodes measured ECG signals with greater stability. In terms of R–R intervals, the measured ECG signals exhibited a 98.6% similarity to ECGs measured using contact ECG systems. The proposed noncontact ECG measurement system based on capacitive sensing is applicable for use in everyday life. Full article

(This article belongs to the Special Issue Advances in ECG Sensing and Monitoring)

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35 pages, 3180 KiB

Open AccessArticle

Diagnostic Interpretation of Non-Uniformly Sampled Electrocardiogram

by Piotr Augustyniak

Sensors 2021, 21(9), 2969; https://doi.org/10.3390/s21092969 - 23 Apr 2021

Cited by 1 | Viewed by 2948

Abstract

We present a set of three fundamental methods for electrocardiogram (ECG) diagnostic interpretation adapted to process non-uniformly sampled signal. The growing volume of ECGs recorded daily all over the world (roughly estimated to be 600 TB) and the expectance of long persistence of these data (on the order of 40 years) motivated us to challenge the feasibility of medical-grade diagnostics directly based on arbitrary non-uniform (i.e., storage-efficient) ECG representation. We used a refined time-independent QRS detection method based on a moving shape matching technique. We applied a graph data representation to quantify the similarity of asynchronously sampled heartbeats. Finally, we applied a correlation-based non-uniform to time-scale transform to get a multiresolution ECG representation on a regular dyadic grid and to find precise P, QRS and T wave delimitation points. The whole processing chain was implemented and tested with MIT-BIH Database (probably the most referenced cardiac database) and CSE Multilead Database (used for conformance testing of medical instruments) signals arbitrarily sampled accordingly to a perceptual model (set for variable sampling frequency of 100–500 Hz, compression ratio 3.1). The QRS detection shows an accuracy of 99.93% with false detection ratio of only 0.18%. The classification shows an accuracy of 99.27% for 14 most frequent MIT-BIH beat types and 99.37% according to AAMI beat labels. The wave delineation shows cumulative (i.e., sampling model and non-uniform processing) errors of: 9.7 ms for P wave duration, 3.4 ms for QRS, 6.7 ms for P-Q segment and 17.7 ms for Q-T segment, all the values being acceptable for medical-grade interpretive software. Full article

(This article belongs to the Special Issue Advances in ECG Sensing and Monitoring)

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Graphical abstract

13 pages, 1473 KiB

Open AccessArticle

Nonlinear T-Wave Time Warping-Based Sensing Model for Non-Invasive Personalised Blood Potassium Monitoring in Hemodialysis Patients: A Pilot Study

by Flavio Palmieri, Pedro Gomis, José Esteban Ruiz, Dina Ferreira, Alba Martín-Yebra, Esther Pueyo, Juan Pablo Martínez, Julia Ramírez and Pablo Laguna

Sensors 2021, 21(8), 2710; https://doi.org/10.3390/s21082710 - 12 Apr 2021

Cited by 3 | Viewed by 2593

Abstract

Background: End-stage renal disease patients undergoing hemodialysis (ESRD-HD) therapy are highly susceptible to malignant ventricular arrhythmias caused by undetected potassium concentration ([

K^{+}

]) variations (

Δ [K^{+}]

) out of normal ranges. Therefore, a reliable method for continuous, [...] Read more.

Background: End-stage renal disease patients undergoing hemodialysis (ESRD-HD) therapy are highly susceptible to malignant ventricular arrhythmias caused by undetected potassium concentration ([

K^{+}

]) variations (

Δ [K^{+}]

) out of normal ranges. Therefore, a reliable method for continuous, noninvasive monitoring of [

K^{+}

] is crucial. The morphology of the T-wave in the electrocardiogram (ECG) reflects

Δ [K^{+}]

and two time-warping-based T-wave morphological parameters,

d_{w}

and its heart-rate corrected version

d_{w, c}

, have been shown to reliably track

Δ [K^{+}]

from the ECG. The aim of this study is to derive polynomial models relating

d_{w}

and

d_{w, c}

with

Δ [K^{+}]

, and to test their ability to reliably sense and quantify

Δ [K^{+}]

values. Methods: 48-hour Holter ECGs and [

K^{+}

] values from six blood samples were collected from 29 ESRD-HD patients. For every patient,

d_{w}

and

d_{w, c}

were computed, and linear, quadratic, and cubic fitting models were derived from them. Then, Spearman’s (

ρ

) and Pearson’s (r) correlation coefficients, and the estimation error (

e_{d}

) between

Δ [K^{+}]

and the corresponding model-estimated values (

\hat{Δ} [K^{+}]

) were calculated. Results and Discussions: Nonlinear models were the most suitable for

Δ [K^{+}]

estimation, rendering higher Pearson’s correlation (median 0.77

\leq r \leq

0.92) and smaller estimation error (median 0.20

\leq e_{d} \leq

0.43) than the linear model (median 0.76

\leq r \leq

0.86 and 0.30

\leq e_{d} \leq

0.40), even if similar Spearman’s

ρ

were found across models (median 0.77

\leq ρ \leq

0.83). Conclusion: Results support the use of nonlinear T-wave-based models as

Δ [K^{+}]

sensors in ESRD-HD patients. Full article

(This article belongs to the Special Issue Advances in ECG Sensing and Monitoring)

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23 pages, 770 KiB

Open AccessArticle

Artifact Correction in Short-Term HRV during Strenuous Physical Exercise

by Aleksandra Królak, Tomasz Wiktorski, Magnus Friestad Bjørkavoll-Bergseth and Stein Ørn

Sensors 2020, 20(21), 6372; https://doi.org/10.3390/s20216372 - 8 Nov 2020

Cited by 10 | Viewed by 3029

Abstract

Heart rate variability (HRV) analysis can be a useful tool to detect underlying heart or even general health problems. Currently, such analysis is usually performed in controlled or semi-controlled conditions. Since many of the typical HRV measures are sensitive to data quality, manual artifact correction is common in literature, both as an exclusive method or in addition to various filters. With proliferation of Personal Monitoring Devices with continuous HRV analysis an opportunity opens for HRV analysis in a new setting. However, current artifact correction approaches have several limitations that hamper the analysis of real-life HRV data. To address this issue we propose an algorithm for automated artifact correction that has a minimal impact on HRV measures, but can handle more artifacts than existing solutions. We verify this algorithm based on two datasets. One collected during a recreational bicycle race and another one in a laboratory, both using a PMD in form of a GPS watch. Data include direct measurement of electrical myocardial signals using chest straps and direct measurements of power using a crank sensor (in case of race dataset), both paired with the watch. Early results suggest that the algorithm can correct more artifacts than existing solutions without a need for manual support or parameter tuning. At the same time, the error introduced to HRV measures for peak correction and shorter gaps is similar to the best existing solution (Kubios-inspired threshold-based cubic interpolation) and better than commonly used median filter. For longer gaps, cubic interpolation can in some cases result in lower error in HRV measures, but the shape of the curve it generates matches ground truth worse than our algorithm. It might suggest that further development of the proposed algorithm may also improve these results. Full article

(This article belongs to the Special Issue Advances in ECG Sensing and Monitoring)

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