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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 September 2020) | Viewed by 73635

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Special Issue Editor

Dr. Florent Baty

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Guest Editor

Kantonsspital St Gallen, St Gallen, Switzerland
Interests: medical statistics; bioinformatics; multivariate statistics; algorithms and analysis of high-dimensional longitudinal biomedical signals

Special Issue Information

Dear Colleagues,

Detecting the electrical activity of the heart using an electrocardiogram (ECG) is a simple, non-invasive measurement providing key indicators of the heart condition of individuals, including patients suffering from a variety of diseases.

In recent years, emphasis has been placed on the miniaturization of ECG devices in order to cover a broader range of disorders, in a long-term setting. In addition to cardiovascular system diseases, ECG provides crucial treating information regarding diseases as diverse as Alzheimer, epilepsy, renal failure or obstructive sleep apnea. Continuous real-time monitoring of ECG is beneficial to patient follow-up. It can also provide important information on the performance of healthy individuals during monitoring of sporting activities. For these multiple purposes, a new range of portable (smartphones, watches, etc.) and wearable (belt, shirts, etc.) devices are currently being developed by an ever-growing number of research institutes and biotech companies.

The validation and assessment of the clinical value of these newly developed ECG monitoring systems is still debated and currently under the investigation of a many researchers working in this very active field of research. Quantifying the health of a heart through ECG read-outs for the sake of diagnosis is methodologically challenging. In this context, heart rate variability analysis which quantifies the variations in the time interval between heartbeats as measured by ECG has proven to provide important indicators of disease diagnosis.

This Special Issue on “ECG Monitoring System” aims to provide an overview on the current developments of the ECG monitoring system. The topics of particular interest include (but are not limited to) novel ECG acquisition systems, algorithm development, visualization, medical applications, lifestyle products, etc.

Dr. Florent Baty
Guest Editor

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Keywords

ECG
portable/wearable monitoring system
heart rate variability
long-term assessment

Published Papers (14 papers)

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Editorial

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2 pages, 143 KiB

Open AccessEditorial

Special Issue: ECG Monitoring System

by Florent Baty

Sensors 2021, 21(2), 651; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020651 - 19 Jan 2021

Cited by 1 | Viewed by 1678

Abstract

This editorial of the Special Issue “ECG Monitoring System” provides a short overview of the 13 contributed articles published in this issue [...] Full article

(This article belongs to the Special Issue ECG Monitoring System)

Research

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21 pages, 7566 KiB

Open AccessArticle

An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis

by Tae Wuk Bae, Sang Hag Lee and Kee Koo Kwon

Sensors 2020, 20(21), 6144; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216144 - 29 Oct 2020

Cited by 16 | Viewed by 3523

Abstract

With the advancement of the Internet of Medical Things technology, many vital sign-sensing devices are being developed. Among the diverse healthcare devices, portable electrocardiogram (ECG) measuring devices are being developed most actively with the recent development of sensor technology. These ECG measuring devices use different sampling rates according to the hardware conditions, which is the first variable to consider in the development of ECG analysis technology. Herein, we propose an R-point detection method using an adaptive median filter based on the sampling rate and analyze major arrhythmias using the signal characteristics. First, the sliding window and median filter size are determined according to the set sampling rate, and a wider median filter is applied to the QRS section with high variance within the sliding window. Then, the R point is detected by subtracting the filtered signal from the original signal. Methods for detecting major arrhythmias using the detected R point are proposed. Different types of ECG signals were used for a simulation, including ECG signals from the MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, signals generated by a simulator, and actual measured signals with different sampling rates. The experimental results indicated the effectiveness of the proposed R-point detection method and arrhythmia analysis technique. Full article

(This article belongs to the Special Issue ECG Monitoring System)

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21 pages, 1820 KiB

Open AccessArticle

How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG

by Helmut Karl Lackner, Marina Tanja Waltraud Eglmaier, Sigrid Hackl-Wimmer, Manuela Paechter, Christian Rominger, Lars Eichen, Karoline Rettenbacher, Catherine Walter-Laager and Ilona Papousek

Sensors 2020, 20(20), 5959; https://0-doi-org.brum.beds.ac.uk/10.3390/s20205959 - 21 Oct 2020

Cited by 12 | Viewed by 3323

Abstract

Recent developments in noninvasive electrocardiogram (ECG) monitoring with small, wearable sensors open the opportunity to record high-quality ECG over many hours in an easy and non-burdening way. However, while their recording has been tremendously simplified, the interpretation of heart rate variability (HRV) data is a more delicate matter. The aim of this paper is to supply detailed methodological discussion and new data material in order to provide a helpful notice of HRV monitoring issues depending on recording conditions and study populations. Special consideration is given to the monitoring over long periods, across periods with different levels of activity, and in adults versus children. Specifically, the paper aims at making users aware of neglected methodological limitations and at providing substantiated recommendations for the selection of appropriate HRV variables and their interpretation. To this end, 30-h HRV data of 48 healthy adults (18–40 years) and 47 healthy toddlers (16–37 months) were analyzed in detail. Time-domain, frequency-domain, and nonlinear HRV variables were calculated after strict signal preprocessing, using six different high-frequency band definitions including frequency bands dynamically adjusted for the individual respiration rate. The major conclusion of the in-depth analyses is that for most applications that implicate long-term monitoring across varying circumstances and activity levels in healthy individuals, the time-domain variables are adequate to gain an impression of an individual’s HRV and, thus, the dynamic adaptation of an organism’s behavior in response to the ever-changing demands of daily life. The sound selection and interpretation of frequency-domain variables requires considerably more consideration of physiological and mathematical principles. For those who prefer using frequency-domain variables, the paper provides detailed guidance and recommendations for the definition of appropriate frequency bands in compliance with their specific recording conditions and study populations. Full article

(This article belongs to the Special Issue ECG Monitoring System)

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13 pages, 602 KiB

Open AccessArticle

Validity of Smartphone Heart Rate Variability Pre- and Post-Resistance Exercise

by Clifton J. Holmes, Michael V. Fedewa, Lee J. Winchester, Hayley V. MacDonald, Stefanie A. Wind and Michael R. Esco

Sensors 2020, 20(20), 5738; https://0-doi-org.brum.beds.ac.uk/10.3390/s20205738 - 09 Oct 2020

Cited by 8 | Viewed by 2608

Abstract

The aim was to examine the validity of heart rate variability (HRV) measurements from photoplethysmography (PPG) via a smartphone application pre- and post-resistance exercise (RE) and to examine the intraday and interday reliability of the smartphone PPG method. Thirty-one adults underwent two simultaneous ultrashort-term electrocardiograph (ECG) and PPG measurements followed by 1-repetition maximum testing for back squats, bench presses, and bent-over rows. The participants then performed RE, where simultaneous ultrashort-term ECG and PPG measurements were taken: two pre- and one post-exercise. The natural logarithm of the root mean square of successive normal-to-normal (R-R) differences (LnRMSSD) values were compared with paired-sample t-tests, Pearson product correlations, Cohen’s d effect sizes (ESs), and Bland–Altman analysis. Intra-class correlations (ICC) were determined between PPG LnRMSSDs. Significant, small–moderate differences were found for all measurements between ECG and PPG: Base_Pre1 (ES = 0.42), Base_Pre2 (0.30), RE_Pre1 (0.26), RE_Pre2 (0.36), and RE_Post (1.14). The correlations ranged from moderate to very large: Base_Pre1 (r = 0.59), Base_Pre2 (r = 0.63), RE_Pre1 (r = 0.63), RE_Pre2 (r = 0.76), and RE_Post (r = 0.41)—all p < 0.05. The agreement for all the measurements was “moderate” (0.10–0.16). The PPG LnRMSSD exhibited “nearly-perfect” intraday reliability (ICC = 0.91) and “very large” interday reliability (0.88). The smartphone PPG was comparable to the ECG for measuring HRV at rest, but with larger error after resistance exercise. Full article

(This article belongs to the Special Issue ECG Monitoring System)

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15 pages, 4932 KiB

Open AccessArticle

Fabrication of Parylene-Coated Microneedle Array Electrode for Wearable ECG Device

by Afraiz Tariq Satti, Jinsoo Park, Jangwoong Park, Hansang Kim and Sungbo Cho

Sensors 2020, 20(18), 5183; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185183 - 11 Sep 2020

Cited by 24 | Viewed by 5068

Abstract

Microneedle array electrodes (MNE) showed immense potential for the sensitive monitoring of the bioelectric signals by penetrating the stratum corneum with high electrical impedance. In this paper, we introduce a rigid parylene coated microneedle electrode array and portable electrocardiography (ECG) circuit for monitoring of ECG reducing the motion artifacts. The developed MNE showed stability and durability for dynamic and long-term ECG monitoring in comparison to the typical silver-silver chloride (Ag/AgCl) wet electrodes. The microneedles showed no mechanical failure under the compression force up-to 16 N, but successful penetration of skin tissue with a low insertion force of 5 N. The electrical characteristics of the fabricated MNE were characterized by impedance spectroscopy with equivalent circuit model. The designed wearable wireless ECG monitoring device with MNE proved feasibility of the ECG recording which reduces the noise of movement artifacts during dynamic behaviors. Full article

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17 pages, 4538 KiB

Open AccessArticle

Electrode Humidification Design for Artifact Reduction in Capacitive ECG Measurements

by Yue Tang, Ronghui Chang, Limin Zhang, Feng Yan, Haowen Ma and Xiaofeng Bu

Sensors 2020, 20(12), 3449; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123449 - 18 Jun 2020

Cited by 13 | Viewed by 3112

Abstract

For wearable capacitive electrocardiogram (ECG) acquisition, capacitive electrodes may cause severe motion artifacts due to the relatively large friction between the electrodes and the dielectrics. In some studies, water can effectively suppress motion artifacts, but these studies lack a complete analysis of how water can suppress motion artifacts. In this paper, the effect of water on charge decay of textile electrode is studied systematically, and an electrode controllable humidification design using ultrasonic atomization is proposed to suppress motion artifacts. Compared with the existing electrode humidification designs, the proposed electrode humidification design can be controlled by a program to suppress motion artifacts at different ambient humidity, and can be highly integrated for wearable application. Firstly, the charge decay mode of the textile electrode is given and it is found that the process of free water evaporation at an appropriate free water content can be the dominant way of triboelectric charge dissipation. Secondly, theoretical analysis and experiment verification both illustrate that water contained in electrodes can accelerate the decay of triboelectric charge through the free water evaporation path. Finally, a capacitive electrode controllable humidification design is proposed by applying integrated ultrasonic atomization to generate atomized drops and spray them onto textile electrodes to accelerate the decay of triboelectric charge and suppress motion artifacts. The performance of the proposed design is verified by the experiment results, which shows that the proposed design can effectively suppress motion artifacts and maintain the stability of signal quality at both low and high ambient humidity. The signal-to-noise ratio of the proposed design is 33.32 dB higher than that of the non-humidified design at 25% relative humidity and is 22.67 dB higher than that of non-humidified electrodes at 65% relative humidity. Full article

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21 pages, 7528 KiB

Open AccessArticle

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram

by Michael Klum, Mike Urban, Timo Tigges, Alexandru-Gabriel Pielmus, Aarne Feldheiser, Theresa Schmitt and Reinhold Orglmeister

Sensors 2020, 20(7), 2033; https://0-doi-org.brum.beds.ac.uk/10.3390/s20072033 - 04 Apr 2020

Cited by 50 | Viewed by 8958

Abstract

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HD^TM and Osypka ICON-Core^TM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation. Full article

(This article belongs to the Special Issue ECG Monitoring System)

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12 pages, 1010 KiB

Open AccessArticle

Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

by Dongqi Wang, Qinghua Meng, Dongming Chen, Hupo Zhang and Lisheng Xu

Sensors 2020, 20(6), 1579; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061579 - 12 Mar 2020

Cited by 18 | Viewed by 3531

Abstract

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition. Full article

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12 pages, 363 KiB

Open AccessArticle

Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device

by Florent Baty, Maximilian Boesch, Sandra Widmer, Simon Annaheim, Piero Fontana, Martin Camenzind, René M. Rossi, Otto D. Schoch and Martin H. Brutsche

Sensors 2020, 20(1), 286; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010286 - 04 Jan 2020

Cited by 31 | Viewed by 4065

Abstract

Sleep apnea (SA) is a prevalent disorder diagnosed by polysomnography (PSG) based on the number of apnea–hypopnea events per hour of sleep (apnea–hypopnea index, AHI). PSG is expensive and technically complex; therefore, its use is rather limited to the initial diagnostic phase and simpler devices are required for long-term follow-up. The validity of single-parameter wearable devices for the assessment of sleep apnea severity is still debated. In this context, a wearable electrocardiogram (ECG) acquisition system (ECG belt) was developed and its suitability for the classification of sleep apnea severity was investigated using heart rate variability analysis with or without data pre-filtering. Several classification algorithms were compared and support vector machine was preferred due to its simplicity and overall performance. Whole-night ECG signals from 241 patients with a suspicion of sleep apnea were recorded using both the ECG belt and patched ECG during PSG recordings. 65% of patients had an obstructive sleep apnea and the median AHI was 21 [IQR: 7–40]

h^{- 1}

. The classification accuracy obtained from the ECG belt (accuracy: 72%, sensitivity: 70%, specificity: 74%) was comparable to the patched ECG (accuracy: 74%, sensitivity: 88%, specificity: 61%). The highest classification accuracy was obtained for the discrimination between individuals with no or mild SA vs. moderate to severe SA. In conclusion, the ECG belt provided signals comparable to patched ECG and could be used for the assessment of sleep apnea severity, especially during follow-up. Full article

(This article belongs to the Special Issue ECG Monitoring System)

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14 pages, 4453 KiB

Open AccessArticle

A Low-Power High-Data-Transmission Multi-Lead ECG Acquisition Sensor System

by Liang-Hung Wang, Wei Zhang, Ming-Hui Guan, Su-Ya Jiang, Ming-Hui Fan, Patricia Angela R. Abu, Chiung-An Chen and Shih-Lun Chen

Sensors 2019, 19(22), 4996; https://0-doi-org.brum.beds.ac.uk/10.3390/s19224996 - 16 Nov 2019

Cited by 28 | Viewed by 4273

Abstract

This study presents a low-power multi-lead wearable electrocardiogram (ECG) signal sensor system design that can simultaneously acquire the electrocardiograms from three leads, I, II, and V1. The sensor system includes two parts, an ECG test clothing with five electrode patches and an acquisition device. Compared with the traditional 12-lead wired ECG detection instrument, which limits patient mobility and needs medical staff assistance to acquire the ECG signal, the proposed vest-type ECG acquisition system is very comfortable and easy to use by patients themselves anytime and anywhere, especially for the elderly. The proposed study incorporates three methods to reduce the power consumption of the system by optimizing the micro control unit (MCU) working mode, adjusting the radio frequency (RF) parameters, and compressing the transmitted data. In addition, Huffman lossless coding is used to compress the transmitted data in order to increase the sampling rate of the acquisition system. It makes the whole system operate continuously for a long period of time and acquire abundant ECG information, which is helpful for clinical diagnosis. Finally, a series of tests were performed on the designed wearable ECG device. The results have demonstrated that the multi-lead wearable ECG device can collect, process, and transmit ECG data through Bluetooth technology. The ECG waveforms collected by the device are clear, complete, and can be displayed in real-time on a mobile phone. The sampling rate of the proposed wearable sensor system is 250 Hz per lead, which is dependent on the lossless compression scheme. The device achieves a compression ratio of 2.31. By implementing a low power design on the device, the resulting overall operational current of the device is reduced by 37.6% to 9.87 mA under a supply voltage of 2.1 V. The proposed vest-type multi-lead ECG acquisition device can be easily employed by medical staff for clinical diagnosis and is a suitable wearable device in monitoring and nursing the off-ward patients. Full article

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Review

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27 pages, 465 KiB

Open AccessReview

Is Continuous Heart Rate Monitoring of Livestock a Dream or Is It Realistic? A Review

by Luwei Nie, Daniel Berckmans, Chaoyuan Wang and Baoming Li

Sensors 2020, 20(8), 2291; https://0-doi-org.brum.beds.ac.uk/10.3390/s20082291 - 17 Apr 2020

Cited by 21 | Viewed by 4814

Abstract

For all homoeothermic living organisms, heart rate (HR) is a core variable to control the metabolic energy production in the body, which is crucial to realize essential bodily functions. Consequently, HR monitoring is becoming increasingly important in research of farm animals, not only for production efficiency, but also for animal welfare. Real-time HR monitoring for humans has become feasible though there are still shortcomings for continuously accurate measuring. This paper is an effort to estimate whether it is realistic to get a continuous HR sensor for livestock that can be used for long term monitoring. The review provides the reported techniques to monitor HR of living organisms by emphasizing their principles, advantages, and drawbacks. Various properties and capabilities of these techniques are compared to check the potential to transfer the mostly adequate sensor technology of humans to livestock in term of application. Based upon this review, we conclude that the photoplethysmographic (PPG) technique seems feasible for implementation in livestock. Therefore, we present the contributions to overcome challenges to evolve to better solutions. Our study indicates that it is realistic today to develop a PPG sensor able to be integrated into an ear tag for mid-sized and larger farm animals for continuously and accurately monitoring their HRs. Full article

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40 pages, 3329 KiB

Open AccessReview

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

by Mohamed Adel Serhani, Hadeel T. El Kassabi, Heba Ismail and Alramzana Nujum Navaz

Sensors 2020, 20(6), 1796; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061796 - 24 Mar 2020

Cited by 156 | Viewed by 22798

Abstract

Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems. Full article

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Other

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8 pages, 1136 KiB

Open AccessLetter

ECG QT-I nterval Measurement Using Wavelet Transformation

by Takao Ohmuta, Kazuyuki Mitsui and Nitaro Shibata

Sensors 2020, 20(16), 4578; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164578 - 15 Aug 2020

Cited by 4 | Viewed by 1896

Abstract

Wavelet transformation, with its markedly high time resolution, is an optimal technique for the analysis of non-stationary waveform signals, such as physiological signals. Therefore, wavelet transformation is widely applied to electrocardiographic (ECG) signal processing. However, an appropriate application method for automated QT-interval measurement has yet to be established. In this study, we developed an ECG recognition technique using wavelet transformation and assessed its efficacy and functionality. The results revealed that the difference between the values obtained using our algorithm and the visually measured QT interval was as low as 4.8 ms. Our technique achieves precise automated QT-interval measurement, as well as Te recognition, that is difficult to accomplish even by visual examination under the electromyography noise environment. Full article

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12 pages, 1654 KiB

Open AccessLetter

Multi-Beat Averaging Reveals U Waves Are Ubiquitous and Standing Tall at Elevated Heart Rates Following Exercise

by Marwa S. Al-Karadi and Philip Langley

Sensors 2020, 20(14), 4029; https://0-doi-org.brum.beds.ac.uk/10.3390/s20144029 - 20 Jul 2020

Cited by 2 | Viewed by 2684

Abstract

The reporting of U wave abnormalities is clinically important, but the measurement of this small electrocardiographic (ECG) feature is extremely difficult, especially in challenging recording conditions, such as stress exercise, due to contaminating noise. Furthermore, it is widely stated that ECG U waves are rarely observable at heart rates greater than 90 bpm. The aims of the study were (i) to assess the ability of multi-beat averaging to reveal the presence of U waves in ECGs contaminated by noise following exercise and (ii) to quantify the effect of exercise on U wave amplitude. The multi-beat averaging algorithm was applied to recover U waves in 20 healthy subjects in pre- and post-exercise recordings. Average beats were generated from 30 beat epochs. The prevalence of U waves and their amplitudes were measured in pre- and post-exercise recordings and changes in amplitude due to exercise were quantified. U waves were present in all subjects in pre-exercise recordings. Following exercise, U waves could not be seen in standard ECG but were observable in all 20 subjects by multi-beat averaging and despite significantly increased mean (±SD) heart rate (63 ± 8 bpm vs. 100 ± 9 bpm, p < 0.0001). Furthermore, U waves were observable in all subjects with heart rates greater than 90 bpm. U waves significantly increased in amplitude following exercise (38 ± 15 μV vs. 80 ± 48 μV, p = 0.0005). Multi-beat averaging is effective at recovering U waves contaminated by noise due to exercise. U waves were measurable in all subjects, dispelling the myth that U waves are rarely seen at elevated heart rates. U waves exhibit increased amplitudes at elevated heart rates following exercise. Full article

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