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Recent Advances in ECG Monitoring

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

Deadline for manuscript submissions: closed (30 December 2020) | Viewed by 62829

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Special Issue Editor

Prof. Dr. Manuel Blanco-Velasco

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Guest Editor

Signal Theory and Communications Department, Alcalá University, Madrid, Spain
Interests: biomedical engineering; ECG signal processing; multirate systems; machine learning

Special Issue Information

Dear Colleagues,

Nowadays, electrocardiogram (ECG) monitoring is drawing great attention because the ECG data—which is noninvasively collected on the human body surface—conveys a multitude of useful information for assessing cardiac disease and patients are able to conduct follow-ups in their own home. ECG monitoring is convenient tool for providing medical assistance to numerous patients at a lower cost. Although ambulatory ECG is widely used to seek anomalies only found in 24-hour-long registers (Holter), the use of automated ECG analysis to assist in medical diagnosis still remains a challenge. Thus, classification of ST morphology for ischemia or fragmented QRS identification as marker of myocardial scar are still open issues; the evaluation of sudden cardiac death risk is also a hot topic, as well as the study of atrial arrhythmias. ECG monitoring has gone beyond one-day observations as interest is currently also focused on monitoring for longer periods of several days. Such monitoring has been found to be useful for the assessment of pathologies and rhythms that are not present in shorter registers. The challenge with these larger data streams is that the treatment of information requires the use of specific methods.

This Special Issue aims to gather articles covering the different stages within any ECG monitoring framework and to offer an overview of the current advances in this area. Contributions related to early stages are welcome, e.g., sensing and collecting data from human body by means of novel sensor technology and electronic devices, or the use of new sampling paradigms such as compressive sensing, including ECG compression. Subsequent preprocessing algorithms to prepare the signal are crucial for the correct functioning at later stages. Important matters to be dealt at this level are noise and artifact reduction, cancelation, and pattern classification; ECG delineation; confident heartbeat extraction; wave identification; and morphology classification. Finally, contributions that support clinical decision making over long-term recordings, like those described in the previous paragraph, are also within the scope of this Special Issue. The recognition of pathology using techniques such as detection, estimation, prediction, and classification by means of probability theory, Bayes inference, or methods relying on machine learning is commonly employed for this purpose.

Prof. Dr. Manuel Blanco-Velasco
Guest Editor

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Keywords

electrocardiogram (ECG)
Holter
long-term monitoring
signal compression
QRS detection
ECG delineation
signal quality
signal processing
machine learning
clinical decision making

Published Papers (14 papers)

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16 pages, 9256 KiB

Open AccessArticle

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier

by Ahmed Faeq Hussein, Shaiful Jahari Hashim, Fakhrul Zaman Rokhani and Wan Azizun Wan Adnan

Sensors 2021, 21(7), 2311; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072311 - 26 Mar 2021

Cited by 12 | Viewed by 7702

Abstract

Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases. Full article

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

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16 pages, 836 KiB

Open AccessArticle

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG

by Jonathan Moeyersons, John Morales, Amalia Villa, Ivan Castro, Dries Testelmans, Bertien Buyse, Chris Van Hoof, Rik Willems, Sabine Van Huffel and Carolina Varon

Sensors 2021, 21(2), 662; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020662 - 19 Jan 2021

Cited by 3 | Viewed by 2418

Abstract

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy. Full article

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

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15 pages, 1529 KiB

Open AccessArticle

Using the Redundant Convolutional Encoder–Decoder to Denoise QRS Complexes in ECG Signals Recorded with an Armband Wearable Device

by Natasa Reljin, Jesus Lazaro, Md Billal Hossain, Yeon Sik Noh, Chae Ho Cho and Ki H. Chon

Sensors 2020, 20(16), 4611; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164611 - 17 Aug 2020

Cited by 12 | Viewed by 2919

Abstract

Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. Denoising was performed using the redundant convolutional encoder–decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70–100% vs. 34–97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7–19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 ± 0.25 dB (mean ± standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm improves the quality of the signal and can potentially be applied to most ECG measurement devices. Full article

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

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20 pages, 2785 KiB

Open AccessArticle

Spectral Analysis and Mutual Information Estimation of Left and Right Intracardiac Electrograms during Ventricular Fibrillation

by Milton Fabricio Pérez-Gutiérrez, Juan José Sánchez-Muñoz, Mayra Erazo-Rodas, Alicia Guerrero-Curieses, Estrella Everss, Aurelio Quesada-Dorador, Ricardo Ruiz-Granell, Alicia Ibáñez-Criado, Alex Bellver-Navarro, José Luis Rojo-Álvarez and Arcadi García-Alberola

Sensors 2020, 20(15), 4162; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154162 - 27 Jul 2020

Cited by 2 | Viewed by 2701

Abstract

Ventricular fibrillation (VF) signals are characterized by highly volatile and erratic electrical impulses, the analysis of which is difficult given the complex behavior of the heart rhythms in the left (LV) and right ventricles (RV), as sometimes shown in intracardiac recorded Electrograms (EGM). However, there are few studies that analyze VF in humans according to the simultaneous behavior of heart signals in the two ventricles. The objective of this work was to perform a spectral and a non-linear analysis of the recordings of 22 patients with Congestive Heart Failure (CHF) and clinical indication for a cardiac resynchronization device, simultaneously obtained in LV and RV during induced VF in patients with a Biventricular Implantable Cardioverter Defibrillator (BICD) Contak Renewal IV^TM (Boston Sci.). The Fourier Transform was used to identify the spectral content of the first six seconds of signals recorded in the RV and LV simultaneously. In addition, measurements that were based on Information Theory were scrutinized, including Entropy and Mutual Information. The results showed that in most patients the spectral envelopes of the EGM sources of RV and LV were complex, different, and with several frequency peaks. In addition, the Dominant Frequency (DF) in the LV was higher than in the RV, while the Organization Index (OI) had the opposite trend. The entropy measurements were more regular in the RV than in the LV, thus supporting the spectral findings. We can conclude that basic stochastic processing techniques should be scrutinized with caution and from basic to elaborated techniques, but they can provide us with useful information on the biosignals from both ventricles during VF. Full article

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

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19 pages, 5254 KiB

Open AccessArticle

Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis

by Ching-Yao Chou, Yo-Woei Pua, Ting-Wei Sun and An-Yeu (Andy) Wu

Sensors 2020, 20(11), 3279; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113279 - 09 Jun 2020

Cited by 10 | Viewed by 2938

Abstract

Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people. Full article

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

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29 pages, 9098 KiB

Open AccessArticle

A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data

by Juan Carlos Carrillo-Alarcón, Luis Alberto Morales-Rosales, Héctor Rodríguez-Rángel, Mariana Lobato-Báez, Antonio Muñoz and Ignacio Algredo-Badillo

Sensors 2020, 20(11), 3139; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113139 - 02 Jun 2020

Cited by 12 | Viewed by 2915

Abstract

The electrocardiogram records the heart’s electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classification models are highly complex, reflecting on the use of computational resources. Moreover, the performance of electrocardiogram classification depends on the approach and parameter estimation to generate the model with high accuracy, sensitivity, and precision. Previous works have proposed hybrid approaches and only a few implemented parameter optimization. Instead, they generally applied an empirical tuning of parameters at a data level or an algorithm level. Hence, a scheme, including metrics of sensitivity in a higher precision and accuracy scale, deserves special attention. In this article, a metaheuristic optimization approach for parameter estimations in arrhythmia classification from unbalanced data is presented. We selected an unbalanced subset of those databases to classify eight types of arrhythmia. It is important to highlight that we combined undersampling based on the clustering method (data level) and feature selection method (algorithmic level) to tackle the unbalanced class problem. To explore parameter estimation and improve the classification for our model, we compared two metaheuristic approaches based on differential evolution and particle swarm optimization. The final results showed an accuracy of 99.95%, a F1 score of 99.88%, a sensitivity of 99.87%, a precision of 99.89%, and a specificity of 99.99%, which are high, even in the presence of unbalanced data. Full article

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

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28 pages, 5764 KiB

Open AccessArticle

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (I): Preprocessing and Bipolar Potentials

by Raúl Caulier-Cisterna, Margarita Sanromán-Junquera, Sergio Muñoz-Romero, Manuel Blanco-Velasco, Rebeca Goya-Esteban, Arcadi García-Alberola and José Luis Rojo-Álvarez

Sensors 2020, 20(11), 3131; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113131 - 01 Jun 2020

Cited by 4 | Viewed by 2707

Abstract

During the last years, Electrocardiographic Imaging (ECGI) has emerged as a powerful and promising clinical tool to support cardiologists. Starting from a plurality of potential measurements on the torso, ECGI yields a noninvasive estimation of their causing potentials on the epicardium. This unprecedented amount of measured cardiac signals needs to be conditioned and adapted to current knowledge and methods in cardiac electrophysiology in order to maximize its support to the clinical practice. In this setting, many cardiac indices are defined in terms of the so-called bipolar electrograms, which correspond with differential potentials between two spatially close potential measurements. Our aim was to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology. For this purpose, we first analyzed the basic stages of conventional cardiac signal processing and scrutinized the implications of the spatial-temporal nature of signals in ECGI scenarios. Specifically, the stages of baseline wander removal, low-pass filtering, and beat segmentation and synchronization were considered. We also aimed to establish a mathematical operator to provide suitable bipolar electrograms from the ECGI-estimated epicardium potentials. Results were obtained on data from an infarction patient and from a healthy subject. First, the low-frequency and high-frequency noises are shown to be non-independently distributed in the ECGI-estimated recordings due to their spatial dimension. Second, bipolar electrograms are better estimated when using the criterion of the maximum-amplitude difference between spatial neighbors, but also a temporal delay in discrete time of about 40 samples has to be included to obtain the usual morphology in clinical bipolar electrograms from catheters. We conclude that spatial-temporal digital signal processing and bipolar electrograms can pave the way towards the usefulness of ECGI recordings in the cardiological clinical practice. The companion paper is devoted to analyzing clinical indices obtained from ECGI epicardial electrograms measuring waveform variability and repolarization tissue properties. Full article

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

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24 pages, 12555 KiB

Open AccessArticle

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-Wave Alternans

by Raúl Caulier-Cisterna, Manuel Blanco-Velasco, Rebeca Goya-Esteban, Sergio Muñoz-Romero, Margarita Sanromán-Junquera, Arcadi García-Alberola and José Luis Rojo-Álvarez

Sensors 2020, 20(11), 3070; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113070 - 29 May 2020

Cited by 2 | Viewed by 2556

Abstract

During the last years, attention and controversy have been present for the first commercially available equipment being used in Electrocardiographic Imaging (ECGI), a new cardiac diagnostic tool which opens up a new field of diagnostic possibilities. Previous knowledge and criteria of cardiologists using intracardiac Electrograms (EGM) should be revisited from the newly available spatial–temporal potentials, and digital signal processing should be readapted to this new data structure. Aiming to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology, we previously presented two results: First, spatial consistency can be observed even for very basic cardiac signal processing stages (such as baseline wander and low-pass filtering); second, useful bipolar EGMs can be obtained by a digital processing operator searching for the maximum amplitude and including a time delay. In addition, this work aims to demonstrate the functionality of ECGI for cardiac electrophysiology from a twofold view, namely, through the analysis of the EGM waveforms, and by studying the ventricular repolarization properties. The former is scrutinized in terms of the clustering properties of the unipolar an bipolar EGM waveforms, in control and myocardial infarction subjects, and the latter is analyzed using the properties of T-wave alternans (TWA) in control and in Long-QT syndrome (LQTS) example subjects. Clustered regions of the EGMs were spatially consistent and congruent with the presence of infarcted tissue in unipolar EGMs, and bipolar EGMs with adequate signal processing operators hold this consistency and yielded a larger, yet moderate, number of spatial–temporal regions. TWA was not present in control compared with an LQTS subject in terms of the estimated alternans amplitude from the unipolar EGMs, however, higher spatial–temporal variation was present in LQTS torso and epicardium measurements, which was consistent through three different methods of alternans estimation. We conclude that spatial–temporal analysis of EGMs in ECGI will pave the way towards enhanced usefulness in the clinical practice, so that atomic signal processing approach should be conveniently revisited to be able to deal with the great amount of information that ECGI conveys for the clinician. Full article

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

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24 pages, 4543 KiB

Open AccessArticle

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

by Vessela Krasteva, Sarah Ménétré, Jean-Philippe Didon and Irena Jekova

Sensors 2020, 20(10), 2875; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102875 - 19 May 2020

Cited by 41 | Viewed by 7258

Abstract

Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust training and optimization on large datasets at high computational cost. So far, limited research and optimization of DNNs in shock advisory systems is found on large ECG arrhythmia databases from out-of-hospital cardiac arrests (OHCA). The objective of this study is to optimize the hyperparameters (HPs) of deep convolutional neural networks (CNN) for detection of shockable (Sh) and nonshockable (NSh) rhythms, and to validate the best HP settings for short and long analysis durations (2–10 s). Large numbers of (Sh + NSh) ECG samples were used for training (720 + 3170) and validation (739 + 5921) from Holters and defibrillators in OHCA. An end-to-end deep CNN architecture was implemented with one-lead raw ECG input layer (5 s, 125 Hz, 2.5 uV/LSB), configurable number of 5 to 23 hidden layers and output layer with diagnostic probability p ∈ [0: Sh,1: NSh]. The hidden layers contain N convolutional blocks × 3 layers (Conv1D (filters = Fi, kernel size = Ki), max-pooling (pool size = 2), dropout (rate = 0.3)), one global max-pooling and one dense layer. Random search optimization of HPs = {N, Fi, Ki}, i = 1, … N in a large grid of N = [1, 2, … 7], Fi = [5;50], Ki = [5;100] was performed. During training, the model with maximal balanced accuracy BAC = (Sensitivity + Specificity)/2 over 400 epochs was stored. The optimization principle is based on finding the common HPs space of a few top-ranked models and prediction of a robust HP setting by their median value. The optimal models for 1–7 CNN layers were trained with different learning rates LR = [10⁻⁵; 10⁻²] and the best model was finally validated on 2–10 s analysis durations. A number of 4216 random search models were trained. The optimal models with more than three convolutional layers did not exhibit substantial differences in performance BAC = (99.31–99.5%). Among them, the best model was found with {N = 5, Fi = {20, 15, 15, 10, 5}, Ki = {10, 10, 10, 10, 10}, 7521 trainable parameters} with maximal validation performance for 5-s analysis (BAC = 99.5%, Se = 99.6%, Sp = 99.4%) and tolerable drop in performance (<2% points) for very short 2-s analysis (BAC = 98.2%, Se = 97.6%, Sp = 98.7%). DNN application in future-generation shock advisory systems can improve the detection performance of Sh and NSh rhythms and can considerably shorten the analysis duration complying with resuscitation guidelines for minimal hands-off pauses. Full article

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

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17 pages, 8667 KiB

Open AccessArticle

Noise-Resistant CECG Using Novel Capacitive Electrodes

by Chi-Chun Chen, Cheng-Wei Chen and Chang-Wei Hsieh

Sensors 2020, 20(9), 2577; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092577 - 01 May 2020

Cited by 4 | Viewed by 3472

Abstract

For years, capacitive electrocardiogram (CECG) has been known to be susceptible to ambient interference. In light of this, a novel capacitive electrode was developed as an effective way to reduce the interference effect. This was done by simply introducing the capacitive elector in series with a 1 pF capacitor, and the 60 Hz common mode noise induced by AC power lines was cancelled using a capacitive right leg (CRL) circuit. The proposed electrode did as expected outperform two counterparts in terms of SNR, and particularly gave an up to 99.8% correlation between RRIs extracted from an ECG and a CECG signal, a figure far beyond 52% and 63% using the two counterparts. This capacitive electrode was originally designed for long-term noncontact monitoring of heart rate, and hopefully can be integrated to portable devices for other medical care services in the near future. Full article

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

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14 pages, 3089 KiB

Open AccessArticle

Optimal Lead Position in Patch-Type Monitoring Sensors for Reconstructing 12-Lead ECG Signals with Universal Transformation Coefficient

by Dongseok Lee, Hyunbin Kwon, Hongji Lee, Chulhun Seo and Kwangsuk Park

Sensors 2020, 20(4), 963; https://0-doi-org.brum.beds.ac.uk/10.3390/s20040963 - 11 Feb 2020

Cited by 10 | Viewed by 4121

Abstract

The aim of this study was to reconstruct a 12-lead electrocardiograph (ECG) with a universal transformation coefficient and find the appropriate electrode position and shape for designing a patch-type ECG sensor. A 35-channel ECG monitoring system was developed, and 14 subjects were recruited for the experiment. A feedforward neural network with one hidden layer was applied to train the transformation coefficient. Three electrode shapes (5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle) were considered for the patch-type ECG sensor. The mean correlation coefficient (CC) and minimum CC methods were applied to evaluate the reconstruction performance. The average CCs between the standard 12-lead ECG and reconstructed 12-lead ECG were 0.860, 0.893, and 0.893 for a 5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle shape. The right-angled triangle showed the highest performance among the considered shapes. The results also suggested that the bottom of the central area of the chest was the most suitable position for attaching the patch-type ECG sensor. Full article

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

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11 pages, 1385 KiB

Open AccessArticle

Single-Lead ECG Recordings Including Einthoven and Wilson Leads by a Smartwatch: A New Era of Patient Directed Early ECG Differential Diagnosis of Cardiac Diseases?

by Alexander Samol, Kristina Bischof, Blerim Luani, Dan Pascut, Marcus Wiemer and Sven Kaese

Sensors 2019, 19(20), 4377; https://0-doi-org.brum.beds.ac.uk/10.3390/s19204377 - 10 Oct 2019

Cited by 64 | Viewed by 9609

Abstract

Background: Smartwatches that are able to record a bipolar ECG and Einthoven leads were recently described. Nevertheless, for detection of ischemia or other cardiac diseases more leads are required, especially Wilson’s chest leads. Objectives: Feasibility study of six single-lead smartwatch (Apple Watch Series 4) ECG recordings including Einthoven (I, II, III) and Wilson-like pseudo-unipolar chest leads (Wr, Wm, Wl). Methods: In 50 healthy subjects (16 males; age: 36 ± 11 years, mean ± SD) without known cardiac disorders, a standard 12-lead ECG and a six single-lead ECG using an Apple Watch Series 4 were performed under resting conditions. Recording of Einthoven I was performed with the watch on the left wrist and the right index finger on the crown, Einthoven II was recorded with the watch on the left lower abdomen and the right index finger on the crown, Einthoven III was recorded with the watch on the left lower abdomen and the left index finger on the crown. Wilson-like chest leads were recorded corresponding to the locations of V1 (Wr), V4 (Wm) and V6 (Wl) in the standard 12-lead ECG. Wr was recorded in the fourth intercostal space right parasternal, Wm was recorded in the fifth intercostal space on the midclavicular line, and Wl was recorded in the fifth intercostal space in left midaxillary line. For all Wilson-like chest lead recordings, the smartwatch was placed on the described three locations on the chest, the right index finger was placed on the crown and the left hand encompassed the right wrist. Both hands and forearms also had contact to the chest. Three experienced cardiologists were independently asked to allocate three bipolar limb smartwatch ECGs to Einthoven I–III leads, and three smartwatch Wilson-like chest ECGs (Wr, Wm, Wl) to V1, V4 and V6 in the standard 12-lead ECG for each subject. Results: All 300 smartwatch ECGs showed a signal quality useable for diagnostics with 281 ECGs of good signal quality (143 limb lead ECGs (95%), 138 chest lead ECGs (92%). Nineteen ECGs had a moderate signal quality (7 limb lead ECGs (5%), 12 chest lead ECGs (8%)). One-hundred percent of all Einthoven and 92% of all Wilson-like smartwatch ECGs were allocated correctly to corresponding leads from 12-lead ECG. Forty-six subjects (92%) were assigned correctly by all cardiologists. Allocation errors were due to similar morphologies and amplitudes in at least two of the three recorded Wilson-like leads. Despite recording with a bipolar smartwatch device, morphology of all six leads was identical to standard 12-lead ECG. In two patients with acute anterior myocardial infarction, all three cardiologists recognized the ST-elevations in Wilson-like leads and assumed an occluded left anterior descending coronary artery correctly. Conclusion: Consecutive recording of six single-lead ECGs including Einthoven and Wilson-like leads by a smartwatch is feasible with good ECG signal quality. Thus, this simulated six-lead smartwatch ECG may be useable for the detection of cardiac diseases necessitating more than one ECG lead like myocardial ischemia or more complex cardia arrhythmias. Full article

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

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23 pages, 4958 KiB

Open AccessArticle

Enabling Heart Self-Monitoring for All and for AAL—Portable Device within a Complete Telemedicine System

by Andrés-Lorenzo Bleda, Francisco-Manuel Melgarejo-Meseguer, Francisco-Javier Gimeno-Blanes, Arcadi García-Alberola, José Luis Rojo-Álvarez, Javier Corral, Ricardo Ruiz and Rafael Maestre-Ferriz

Sensors 2019, 19(18), 3969; https://0-doi-org.brum.beds.ac.uk/10.3390/s19183969 - 14 Sep 2019

Cited by 9 | Viewed by 5006

Abstract

During the last decades there has been a rapidly growing elderly population and the number of patients with chronic heart-related diseases has exploded. Many of them (such as those with congestive heart failure or some types of arrhythmias) require close medical supervision, thus imposing a big burden on healthcare costs in most western economies. Specifically, continuous or frequent Arterial Blood Pressure (ABP) and electrocardiogram (ECG) monitoring are important tools in the follow-up of many of these patients. In this work, we present a novel remote non-ambulatory and clinically validated heart self-monitoring system, which allows ABP and ECG monitoring to effectively identify clinically relevant arrhythmias. The system integrates digital transmission of the ECG and tensiometer measurements, within a patient-comfortable support, easy to recharge and with a multi-function software, all of them aiming to adapt for elderly people. The main novelty is that both physiological variables (ABP and ECG) are simultaneously measured in an ambulatory environment, which to our best knowledge is not readily available in the clinical market. Different processing techniques were implemented to analyze the heart rhythm, including pause detection, rhythm alterations and atrial fibrillation, hence allowing early detection of these diseases. Our results achieved clinical quality both for in-lab hardware testing and for ambulatory scenario validations. The proposed active assisted living (AAL) Sensor-based system is an end-to-end multidisciplinary system, fully connected to a platform and tested by the clinical team from beginning to end. Full article

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

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Open AccessLetter

Contactless Capacitive Electrocardiography Using Hybrid Flexible Printed Electrodes

by Mathieu Lessard-Tremblay, Joshua Weeks, Laura Morelli, Glenn Cowan, Ghyslain Gagnon and Ricardo J. Zednik

Sensors 2020, 20(18), 5156; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185156 - 10 Sep 2020

Cited by 6 | Viewed by 3929

Abstract

Traditional capacitive electrocardiogram (cECG) electrodes suffer from limited patient comfort, difficulty of disinfection and low signal-to-noise ratio in addition to the challenge of integrating them in wearables. A novel hybrid flexible cECG electrode was developed that offers high versatility in the integration method, is well suited for large-scale manufacturing, is easy to disinfect in clinical settings and exhibits better performance over a comparable rigid contactless electrode. The novel flexible electrode meets the frequency requirement for clinically important QRS complex detection (0.67–5 Hz) and its performance is improved over rigid contactless electrode across all measured metrics as it maintains lower cut-off frequency, higher source capacitance and higher pass-band gain when characterized over a wide spectrum of patient morphologies. The results presented in this article suggest that the novel flexible electrode could be used in a medical device for cECG acquisition and medical diagnosis. The novel design proves also to be less sensitive to motion than a reference rigid electrode. We therefore anticipate it can represent an important step towards improving the repeatability of cECG methods while requiring less post-processing. This would help making cECG a viable method for remote cardiac health monitoring. Full article

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

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