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Novel Wearable ECG Sensors and Signal Analysis of ECG Data
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Novel Wearable ECG Sensors and Signal Analysis of ECG Data

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

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 6815

Special Issue Editors

Dr. Yeon Sik Noh

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, College of Nursing, University of Massachusetts Amherst, Amherst, MA 01003, USA
Interests: biomedical instrumentation; biosignal sensors and electrodes; wearable devices; smart and connected health; Internet of Things; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ki H. Chon

E-Mail Website
Guest Editor
Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: biomedical instrumentation; signal processing; machine learning; smart health diagnostics; wearable devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable ECG sensors—including smartwatches, smartphones, and smart textiles as well as sensors placed on the chest and arms—have become so ubiquitous and sophisticated that they now serve as continuous and affordable health monitors. By taking advantage of a smart device’s processing power, peripheral noninvasive and cost-effective sensors, and wireless communication capabilities, recent efforts have been made to create various medical applications for self-monitoring. For example, recent advances have allowed on-demand atrial fibrillation detection, either directly or using attachable ECG sensors on a smartwatch. Given that smartwatches provide intermittent monitoring, for cases where continuous monitoring is needed, recently, there has been emphasis on more convenient and nonconventional methods to measure ECG from other parts of the body. For example, the collection of ECG signals using an armband is a recent development that allows for continuous ECG monitoring. While there has been some impressive progress to date, this Special Issue aims to publish further advances in ECG biosensors, such as flexible electronics and textile sensors, to obtain more compact and high-fidelity ECG signals. In addition, given that nonconventional ECG sensors have lower signal-to-noise ratios when compared to traditional electrodes placed on the chest, a new algorithm for removing motion artifacts that is applicable to smart wearable devices must be developed, and is also of significant interest for this Special Issue.

Prof. Dr. Yeon Sik Noh
Prof. Dr. Ki H. Chon
Guest Editors

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Keywords

  • ECG
  • wearable devices
  • smart health diagnostics
  • flexible sensors
  • motion artifact detection and correction algorithms
  • machine and deep learning
  • signal processing
  • QRS detection
  • arrhythmia detection

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Published Papers (2 papers)

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Research

18 pages, 3137 KiB  
Article
Automated Condition-Based Suppression of the CPR Artifact in ECG Data to Make a Reliable Shock Decision for AEDs during CPR
by Shirin Hajeb-Mohammadalipour, Alicia Cascella, Matt Valentine and Ki H. Chon
Sensors 2021, 21(24), 8210; https://doi.org/10.3390/s21248210 - 8 Dec 2021
Cited by 12 | Viewed by 3327
Abstract
Cardiopulmonary resuscitation (CPR) corrupts the morphology of the electrocardiogram (ECG) signal, resulting in an inaccurate automated external defibrillator (AED) rhythm analysis. Consequently, most current AEDs prohibit CPR during the rhythm analysis period, thereby decreasing the survival rate. To overcome this limitation, we designed [...] Read more.
Cardiopulmonary resuscitation (CPR) corrupts the morphology of the electrocardiogram (ECG) signal, resulting in an inaccurate automated external defibrillator (AED) rhythm analysis. Consequently, most current AEDs prohibit CPR during the rhythm analysis period, thereby decreasing the survival rate. To overcome this limitation, we designed a condition-based filtering algorithm that consists of three stop-band filters which are turned either ‘on’ or ‘off’ depending on the ECG’s spectral characteristics. Typically, removing the artifact’s higher frequency peaks in addition to the highest frequency peak eliminates most of the ECG’s morphological disturbance on the non-shockable rhythms. However, the shockable rhythms usually have dynamics in the frequency range of (3–6) Hz, which in certain cases coincide with CPR compression’s harmonic frequencies, hence, removing them may lead to destruction of the shockable signal’s dynamics. The proposed algorithm achieves CPR artifact removal without compromising the integrity of the shockable rhythm by considering three different spectral factors. The dataset from the PhysioNet archive was used to develop this condition-based approach. To quantify the performance of the approach on a separate dataset, three performance metrics were computed: the correlation coefficient, signal-to-noise ratio (SNR), and accuracy of Defibtech’s shock decision algorithm. This dataset, containing 14 s ECG segments of different types of rhythms from 458 subjects, belongs to Defibtech commercial AED’s validation set. The CPR artifact data from 52 different resuscitators were added to artifact-free ECG data to create 23,816 CPR-contaminated data segments. From this, 82% of the filtered shockable and 70% of the filtered non-shockable ECG data were highly correlated (>0.7) with the artifact-free ECG; this value was only 13 and 12% for CPR-contaminated shockable and non-shockable, respectively, without our filtering approach. The SNR improvement was 4.5 ± 2.5 dB, averaging over the entire dataset. Defibtech’s rhythm analysis algorithm was applied to the filtered data. We found a sensitivity improvement from 67.7 to 91.3% and 62.7 to 78% for VF and rapid VT, respectively, and specificity improved from 96.2 to 96.5% and 91.5 to 92.7% for normal sinus rhythm (NSR) and other non-shockables, respectively. Full article
(This article belongs to the Special Issue Novel Wearable ECG Sensors and Signal Analysis of ECG Data)
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18 pages, 1128 KiB  
Article
Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
by Jun Zhong, Dong Hai, Jiaxin Cheng, Changzhe Jiao, Shuiping Gou, Yongfeng Liu, Hong Zhou and Wenliang Zhu
Sensors 2021, 21(21), 7163; https://doi.org/10.3390/s21217163 - 28 Oct 2021
Cited by 3 | Viewed by 2240
Abstract
Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. [...] Read more.
Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices. Full article
(This article belongs to the Special Issue Novel Wearable ECG Sensors and Signal Analysis of ECG Data)
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