A Survey on Machine Learning Approaches to ECG Processing

Hoffmann, Javier; Mahmood, Safdar; Fogou, Priscile Suawa; George, Nevin; Raha, Solaiman; Safi, Sabur; Schmailzl, Kurt J. G.; Brandalero, Marcelo; Hübner, Michael

doi:10.23919/spa50552.2020.9241283

Cited by 14 publications

(10 citation statements)

References 45 publications

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“…For example, data size reduction is commonly used in ECG real-time monitoring [9] to reduce the communication overhead required to transfer data between both ends. In addition, changing operational strategies using Machine Learning (ML) and neural network classifiers is another approach to reduce power consumption and support real-time ECG monitoring [10]. Nonetheless, the power consumption evaluation in the low-power ECG monitoring systems that utilize DL models is still underexplored…”

Section: Overview and Motivationmentioning

confidence: 99%

Optimizing Real-Time ECG Data Transmission in Constrained Environments

Ouda

Badr

Rashwan

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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ECG monitoring systems have a significant role in detecting cardiovascular diseases and reducing the rate of sudden cardiac deaths through early warnings for heart attacks. One of the critical factors in supporting real-time ECG tracking is to guarantee monitoring system availability. This thesis focuses on battery life extension for a 12 Lead ECG patch utilizing compression and classification approaches. the opportunity to explore students' activities at Queen's while applying work-life balance.I would also like to thank all TRL and IoT Lab colleagues, partners of success, for their great support and encouragement towards making a successful and enjoyable academic experience. v

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Section: Overview and Motivationmentioning

confidence: 99%

Optimizing Real-Time ECG Data Transmission in Constrained Environments

Ouda

Badr

Rashwan

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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“…The machine learning based approaches based on multivariate statistical pattern recognition have a widespread utilization in biomedical signal processing. These methods utilize correlation analysis, regression techniques and template matching to identify abnormal patterns or a particular class of signals [222], [223]. However, as these statistical methods move towards greater accuracy, the computational cost for the system also increases.…”

Section: B Classifiersmentioning

confidence: 99%

Origins of ECG and Evolution of Automated DSP Techniques: A Review

Arora¹,

Mishra

2021

IEEE Access

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Over the years researchers have studied the evolution of Electrocardiogram (ECG) and the complex classification of cardiovascular diseases. This review focuses on the evolution of the ECG and covers the most recent signal processing schemes with milestones over the last 150 years in a systematic manner. Development phases of ECG, ECG leads, portable ECG monitors, Signal Processing schemes and Complex Transformations are discussed. This paper summarizes the development of ECG features detection for cardiac anomalies and the history of the development of ECG monitors, beginning from String Galvanometer. It also discusses the automated detections on ECG beginning from 1960 to the most recent signal processing techniques. Additionally, this paper provides recommendations.

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“…The machine learning-based approaches based on multivariate statistical pattern recognition has a widespread utilization in biomedical signal processing. These methods utilize correlation analysis, regression techniques and template matching to identify abnormal patterns or a particular class of signals [188,189]. However, as these statistical methods move towards greater accuracy, it also has a higher system complexity.…”

Section: Classifiersmentioning

confidence: 99%