Alerting system for sport activity based on ECG signals using proportional integral derivative

Octaviani, Vika; Kurniawan, Arief; Suprapto, Yoyon K.; Zaini, Ahmad

doi:10.1109/eecsi.2017.8239104

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…The signal is dismantled into P, Q, R, S and T waves globally as seen in Figure 1. The time interval between R of two successive ECG waves reveals the heart rate [6][7][8]. The highest amplitude is obtained from QRS complex which gives more information on the health condition of the heart [9].…”

Section: Research Methods 21 Characterization Of Ecgmentioning

confidence: 99%

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On the classification of arrhythmia using supplementary features from Tetrolet transforms

Jayagopi

Pushpa

2019

IJECE

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<span>Heart diseases had been molded as potential threats to human lives, especially to elderly people in recent days due to the dynamically varying food habits among the people. However, these diseases could be easily caught by proper analysis of Electrocardiogram (ECG) signals acquired from individuals. This paper proposes a better method to detect and classify the arrhythmia using 15 features which include 4 R-R interval features, 3 statistical and 6 chaotic features estimated from ECG signals. Additionally, Entropy and Energy features had been gained after converting one dimensional ECG signals to two dimensional data and applied Tetrolet transforms on that. Total numbers of 15 features had been utilized to classify the heart beats from the benchmark MIT-Arrhythmia database using Support Vector Machines (SVM). The classification performance was analyzed under various kernel functions and different Tetrolet decomposition levels. It is found that Radial Basis Function (RBF) kernel could perform better than linear and polynomial kernels. This research attempt yielded an accuracy of 99.35 % against the existing works. Moreover, addition of two more features had introduced a negligible overhead of time. Hence, this method is better suitable to detect and classify the Arrhythmia in both online and offline.</span>

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Section: Research Methods 21 Characterization Of Ecgmentioning

confidence: 99%

“…As an overall performance, out of 86113 test signals in total, 85554 signals are correctly classified and 559 signals are misclassified for all 16 classes of ECG signals. The accuracy and error obtained from the proposed method is evaluated from Equations(6) and(7), which is 99.35% and 0.65% respectively.…”

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confidence: 99%

On the classification of arrhythmia using supplementary features from Tetrolet transforms

Jayagopi

Pushpa

2019

IJECE

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“…Other systems support the objective of monitoring user activities; examples include sports, driving, daily activities, and elderly activities. ECG monitoring systems dedicated to sports activities represent a significant share among existing monitoring systems [37,38,183]. For example, Peritz et al proposed a method for diagnosing athletes' heart palpitations [184].…”

Section: Service-based Monitoring Systemsmentioning

confidence: 99%

“…In addition, they implement various computational settings in terms of processing frequencies, as well as monitoring schemes. They have also evolved to serve purposes and targets other than disease diagnosis and control, including daily activities [33][34][35], sports [36][37][38], and even mode-related purposes [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

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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“…Recently, the ECG has been one of the tools used for COVID-19 diagnosis [ 12 , 13 , 14 , 15 ]. As a result, the ECG monitoring system is used in hospitals [ 16 , 17 ], smart homes [ 18 , 19 ], remote contexts [ 20 ], or sports [ 21 , 22 ]. In reality, there are many available techniques, such as wearable and portable devices, which make embedding ECG systems into them convenient and appropriate for remote patient monitoring.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction

Pham

Tai

et al. 2023

Sensors

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An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.

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Alerting system for sport activity based on ECG signals using proportional integral derivative

Cited by 5 publications

References 7 publications

On the classification of arrhythmia using supplementary features from Tetrolet transforms

On the classification of arrhythmia using supplementary features from Tetrolet transforms

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

Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction

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