Electrocardiogram (ECG) Classification Based On Dynamic Beats Segmentation

El-Saadawy, Hadeer; Tantawi, Manal M.; Shedeed, Howida A.; Tolba, Mohamed F.

doi:10.1145/2908446.2908452

Cited by 13 publications

(17 citation statements)

References 15 publications

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“…The Wavelet Transform was also used in [7,60,67,95,96]. Alternatively, Wavelet Transform, combined with other techniques, was also applied in [7,66,97].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Most of the research work reviewed in this study focuses on ECG signal monitoring that generally requires touch-based sensing of the patient's skin. Patient skin irritation caused by touch-based sensory devices were highlighted by a number of researchers [97,125]. A convenient and reliable touch-free alternative is radar cardiography.…”

Section: Ecg Futuristic Monitoring Systemsmentioning

confidence: 99%

“…Another design challenge is related to the patient's comfort with sticky electrodes. Concerns around the adverse effect of electrodes on the human body were addressed in [97]. Moreover, Karaoguz et al [125] highlighted in their study that 7.3% of participants had minor skin irritations related to the sticky gel electrodes.…”

Section: Challenges Related To Electrode/sensor Type and Designmentioning

confidence: 99%

See 2 more Smart Citations

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

Serhani

Kassabi

Ismail

et al. 2020

Sensors

230

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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.

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“…The Wavelet Transform was also used in [7,60,67,95,96]. Alternatively, Wavelet Transform, combined with other techniques, was also applied in [7,66,97].…”

Section: Feature Extractionmentioning

confidence: 99%

Section: Ecg Futuristic Monitoring Systemsmentioning

confidence: 99%

Section: Challenges Related To Electrode/sensor Type and Designmentioning

confidence: 99%

See 1 more Smart Citation

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

Serhani

Kassabi

Ismail

et al. 2020

Sensors

230

View full text Add to dashboard Cite

show abstract

“…A perfect time resolution is the main advantage of DWT [57]. It provides good frequency resolution at low frequency and good resolution at high frequency [58]. The DWT can reveal the local characteristics of the input signal because of this great time and frequency localization ability [59].…”

Section: Dwtmentioning

confidence: 99%

A Review Study for Electrocardiogram Signal Classification

Abdulla

Al-Ani

2020

UHD J SCI TECH

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An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.

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“…Traditionally, the study of arrhythmia diagnosis has mainly focused on the noise filtering of electrocardiogram (ECG) signals [2][3][4], signal segmentation [5][6][7], and manual feature extraction [8][9][10][11]. Osowski et al [9] proposed a machine learning method that uses higher-order statistics (HOS) and Hermite functions to extract features, and a support vector machine (SVM) to classify heart diseases.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology

Zhen-yu

Chen

et al. 2020

Electronics

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Electrocardiogram (ECG) signal evaluation is routinely used in clinics as a significant diagnostic method for detecting arrhythmia. However, it is very labor intensive to externally evaluate ECG signals, due to their small amplitude. Using automated detection and classification methods in the clinic can assist doctors in making accurate and expeditious diagnoses of diseases. In this study, we developed a classification method for arrhythmia based on the combination of a convolutional neural network and long short-term memory, which was then used to diagnose eight ECG signals, including a normal sinus rhythm. The ECG data of the experiment were derived from the MIT-BIH arrhythmia database. The experimental method mainly consisted of two parts. The input data of the model were two-dimensional grayscale images converted from one-dimensional signals, and detection and classification of the input data was carried out using the combined model. The advantage of this method is that it does not require performing feature extraction or noise filtering on the ECG signal. The experimental results showed that the implemented method demonstrated high classification performance in terms of accuracy, specificity, and sensitivity equal to 99.01%, 99.57%, and 97.67%, respectively. Our proposed model can assist doctors in accurately detecting arrhythmia during routine ECG screening.

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Electrocardiogram (ECG) Classification Based On Dynamic Beats Segmentation

Cited by 13 publications

References 15 publications

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

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

A Review Study for Electrocardiogram Signal Classification

An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology

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