R peak detection in electrocardiogram signal based on a combination between empirical mode decomposition and Hilbert transform

Mabrouki, Rebeh; Khaddoumi, Balkine; Sayadi, Mounir

doi:10.1109/atsip.2014.6834603

Cited by 16 publications

(5 citation statements)

References 3 publications

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“…Additionally, a combined algorithm based on empirical mode decomposition and Hilbert transform for detecting R-peaks in ECG signals emerged in 2009. However, this algorithm is relatively complex and involves a substantial number of R-peak detection blocks [4]. Halil et al employed various machine learning techniques for classifying P, Q, R, S, and T waves in ECG signals, integrating the BP (Back Propagation) algorithm with the MLP classifier, as well as the KA (Kernel-Adatron) algorithm with SVM classifier [5].…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Exercise Health Analysis Algorithm Based on GRU and Convolutional Neural Network

Ji,

Zhu

2024

IEEE Access

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Sudden cardiac death (SCD) is one of the main causes of death among athletes during exercise and physical activity. By analyzing the results of ECG classification, doctors can promptly detect the presence of cardiovascular diseases and implement appropriate treatment measures, thereby reducing disease progression and the occurrence of complications. Previous studies typically us ed machine learning methods that often required manual feature selection, which could be subjective, time-consuming, and limited in scope. Additionally, the relationships between features may be overlooked, leading to a decrease in model performance. Therefore, our proposed approach automatically learns and selects relevant features, avoiding the issues associated with manual feature selection. In this paper, we introduce the Grunet deep learning network model, which utilizes convolutional neural networks to automatically extract features from ECG signals, thereby enhancing feature utilization and representation capabilities. Given the time-sensitive nature of ECG signals, we introduce gated recurrent units (GRU) to better capture these features. The gating mechanism of GRU helps manage information flow and facilitates capturing long-term dependencies. In our experiments conducted on the MIT-BIH Arrhythmia Database and the European ST-T Database, our proposed method achieved an overall classification accuracy of 99.47% on the European ST-T Database, with a precision of 98.76% and a recall of 97.92% on the MIT-BIH Arrhythmia Database. The classification accuracy for classes N and Q reached 100%. The experimental results demonstrate that our method outperforms existing techniques, significantly reducing the cost of manual intervention and improving the accuracy of heartbeat classification .

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Section: Introductionmentioning

confidence: 99%

ECG Classification Exercise Health Analysis Algorithm Based on GRU and Convolutional Neural Network

Ji,

Zhu

2024

IEEE Access

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“…The diagnose of CVDs usually starts with heartbeat detection on the electrocardiogram (ECG). R-peak detection from single-lead ECGs has been extensively studied [2,3,4,5,6,7,8,9,10,11,12,13]. Although it may be considered an easy task under ideal conditions (proper sensor location on the patient’s body, good contact of the electrodes with the skin, bedridden and completely still patient, and absence of electrical noise sources, among others), these conditions are usually not maintained throughout the entire recording in real applications [14].…”

Section: Introductionmentioning

confidence: 99%

Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review

Tejedor

García

Márquez

et al. 2019

Sensors

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This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out.

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“…The intrinsic patterns of these signals can be extracted using MEMD to separate the underlying cardiac data from artifacts [10]. Furthermore, following recent research by [11,12,13], the MEMD approach was used to extract respiratory effort form the face-lead recording.…”

Section: Introductionmentioning

confidence: 99%

Vital signs from inside a helmet: A multichannel face-lead study

Rosenberg

Chanwimalueang

Looney

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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It is essential to measure physiological parameters such as heart rate variability and respiratory rate of drivers to evaluate their performance. The results from this measurement can be used to assess the state of body and mind, for instance concentration and stress. However, current systems only work in controlled environments, or sensors obstruct and interfere with operations of the driver. In this study, a face-lead ECG is placed inside a helmet to enhance comfort and convenience in racing scenarios. Multiple electrodes were attached to facial locations, which exhibit good contact with a helmet, and bipolar configurations were examined between the left and right side of the subject's face. Standard and data-driven filtering algorithms were employed to improve the extraction of R peaks from the ECG data. The so-extracted R peaks were subsequently used to estimate heart activity and respiration effort, and the results were compared with standard recording protocols. It is shown that ECG recordings obtained from locations on the lower jaw match closely with conventional recording paradigms (limb-lead ECG), highlighting the potential of vital sign monitoring from within a racing helmet.Index Terms-Electrocardiogram ECG, vital signs, racing helmet, respiratory rate, MEMD.

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R peak detection in electrocardiogram signal based on a combination between empirical mode decomposition and Hilbert transform

Cited by 16 publications

References 3 publications

ECG Classification Exercise Health Analysis Algorithm Based on GRU and Convolutional Neural Network

ECG Classification Exercise Health Analysis Algorithm Based on GRU and Convolutional Neural Network

Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review

Vital signs from inside a helmet: A multichannel face-lead study

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