A statistical designing approach to MATLAB based functions for the ECG signal preprocessing

Rahman, Md. Asadur; Milu, Md. Mahmudul Haque; Anjum, Anika; Siddik, Abu Bakar; Sifat, Md. Mohidul Hasan; Chowdhury, Mohibur Rahman; Khanam, Farzana; Ahmad, Mohiuddin

doi:10.1007/s42044-019-00035-0

Cited by 16 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 13 Following that, 1-D ECG was pre-processed using a simple MATLAB function by Ref. 34 to eliminate baseline wander caused by breathing, electrically charged electrodes, or muscle noise. The signal was then divided into four segments corresponding to 15 seconds each.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system

et al. 2022

View full text Add to dashboard Cite

Background: The electrocardiogram (ECG) is a physiological signal used to diagnose and monitor cardiovascular disease, usually using 2- D ECG. Numerous studies have proven that ECG can be used to detect human emotions using 1-D ECG; however, ECG is typically captured as 2-D images rather than as 1-D data. There is still no consensus on the effect of the ECG input format on the accuracy of the emotion recognition system (ERS). The ERS using 2-D ECG is still inadequately studied. Therefore, this study compared ERS performance using 1-D and 2-D ECG data to investigate the effect of the ECG input format on the ERS. Methods: This study employed the DREAMER dataset, which contains 23 ECG recordings obtained during audio-visual emotional elicitation. Numerical data was converted to ECG images for the comparison. Numerous approaches were used to obtain ECG features. The Augsburg BioSignal Toolbox (AUBT) and the Toolbox for Emotional feature extraction from Physiological signals (TEAP) extracted features from numerical data. Meanwhile, features were extracted from image data using Oriented FAST and rotated BRIEF (ORB), Scale Invariant Feature Transform (SIFT), KAZE, Accelerated-KAZE (AKAZE), Binary Robust Invariant Scalable Keypoints (BRISK), and Histogram of Oriented Gradients (HOG). Dimension reduction was accomplished using linear discriminant analysis (LDA), and valence and arousal were classified using the Support Vector Machine (SVM). Results: The experimental results show 1-D ECG-based ERS achieved 65.06% of accuracy and 75.63% of F1 score for valence, and 57.83% of accuracy and 44.44% of F1-score for arousal. For 2-D ECG-based ERS, the highest accuracy and F1-score for valence were 62.35% and 49.57%; whereas, the arousal was 59.64% and 59.71%. Conclusions: The results indicate that both inputs work comparably well in classifying emotions, which demonstrates the potential of 1-D and 2-D as input modalities for the ERS.

show abstract

Section: Methodsmentioning

confidence: 99%

“…It should be removed before further analysis. A simple technique with Matlab code is described in Rahman et al (2019) 1 to remove baseline wander. I am expecting a result comparing the emotion recognition rate before and after the baseline wander removal from the ECG signal.…”

mentioning

confidence: 99%

Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Filtering: ECG are susceptible to different noise, such as powerline interference, muscle artifacts, and electrode motion artifacts. To address these issues, filtering techniques like bandpass and notch filters are employed to effectively remove these noise components (Rahman et al, 2019).…”

Section: Preprocessing Of Ecg Signalsmentioning

confidence: 99%

Atrial Fibrillation Detection Through ML Approach: A Comparitive Study

Rosy Madaan

2023

tjjpt

View full text Add to dashboard Cite

Atrial Fibrillation (AF) is a common “cardiac arrhythmia” with significant health implications. Traditional AF detection methods have limitations in continuous monitoring and data analysis. The emergence of machine learning (ML) offers promising solutions for accurate and timely AF detection. This study aims to explore and evaluate various ML techniques for AF detection, considering data quality, clinical validation, and algorithm performance. A diverse dataset of ECG signals and patient information is collected and pre-processed for training and testing ML models. The study implements supervised and unsupervised learning algorithms, deep learning (DL) architectures, and ensemble methods to compare their effectiveness in AF detection. Results demonstrate the potential of ML-based AF detection to revolutionize diagnosis and management, leading to improved patient care and healthcare outcomes in cardiology. The results of our comparative study demonstrate that all ML approaches achieved impressive results in detecting AF from ECG signals. “The logistic regression classifier achieved an accuracy of 92.48% and sensitivity of 91.89%. The Naïve Bayes classifier achieved an accuracy of 90.26% and sensitivity of 89.27%. The SVM classifier achieved an accuracy of 93.87% and sensitivity of 92.43%. The Decision tree achieved an accuracy of 93.87% and sensitivity of 90.63%. Finally, the Random Forest model attained an accuracy of 95.8% and sensitivity of 92.88%”.

show abstract

“…The following are the main observations that have been made from the literature on ECG signal preprocessing ( (Uwaechia & Ramli, 2021); (Tychkov et al, 2019); (Rahman et al, 2019) b) Filtering from FIR ranges at a very high frequency, which makes it more time consuming and requires large storage.…”

Section: Figure 1: Ecg Signalmentioning

confidence: 99%

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

Poonam Chhabra,

Rosy Madaan

2023

J. Adv. Zool.

View full text Add to dashboard Cite

An electrocardiogram (ECG) is a continuous electrical signal from the heart that is recorded to understand the activity and condition of the heart. A recorded ECG signal always follows a defined pattern for a normal heart condition. Variation in the normal ECG pattern can be seen in cases of numerous cardiac abnormalities. A recorded ECG is also affected by a number of noises and distortions, resulting in a low SNR. A variation in ECG pattern can lead to incorrect study and improper diagnosis of heart condition. Thus, to perform an efficient analysis, it is necessary to preprocess the ECG waveform. ECG preprocessing requires noise removal and analysis of necessary features needed to study cardiac activity. In this paper, ECG preprocessing is evaluated by using two noise removal techniques, i.e., finite and infinite impulse response. After this, the R-peaks are detected using discrete wavelet transform (DWT), maximal optimal DWT, principal component analysis and independent component analysis. A wavelet transform technique is further proposed using Savitzky-Golay filtering and DWT. The results obtained from the proposed methodology represent the best results compared to those of other methods explicated in this paper.

show abstract

A statistical designing approach to MATLAB based functions for the ECG signal preprocessing

Cited by 16 publications

References 16 publications

Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system

Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system

Atrial Fibrillation Detection Through ML Approach: A Comparitive Study

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

Contact Info

Product

Resources

About