Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition

Chandrasekar, Aditya; Shekar, Dhanush D; Hiremath, Abhishek C.; Chemmangat, Krishnan

doi:10.1016/j.bspc.2021.103469

Cited by 14 publications

(9 citation statements)

References 31 publications

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“…As summarized in Table.9, most of the works that report an overall F1 score higher than ours [21,44,78,79] performed classification for a limited 2 to 12 heart pathologies, highest F1 being 92.63% achieved by [38] for 11 classes. The current study achieves best F1 score considering 15 class heartbeat recognition.…”

Section: Discussionmentioning

confidence: 60%

“…The feature vectors were used to train a NN, SVM and KNN for classification. In [38] a Gaussian assisted signal smoothing was proposed to increase the peak signal-to-noise ratio followed by a two-stage multiclass CNN. A quadratic SVM was further used to classify signals to respective sub-classes.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the cardiac beat classification algorithms proposed in literature (see Section-IV) use computationally intense feature extraction step after the beat segmentation (the beat segmentation criteria may be different than the one used by us i.e., some authors use 5, 6 or 10-second signal classifying rhythm rather than exact beat labels as provided by MIT-BIH data) such as frequency transforms [22,29,50,70,74,77,78,79], higher-order statistics [70,78,79,80], CNN [36,38,39,40,43,44,51], and others. Feature extraction has to be implemented on every section of the incoming timeseries ECG signal being continuously acquired by wearable device (Holter in this case).…”

Section: Introductionmentioning

confidence: 99%

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An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

et al. 2022

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Potentially lethal heart abnormalities can be detected/spotted with recent evolution in continuous, long-term cardiac health monitoring using wearable sensors. However, the huge data accumulated presents a challenge in terms of storage, knowledge extraction and computing time. Moreover, manual examination of long-term ECG recordings presents various problems like huge time and work demand, inter-observer variations and difficulty classifying complex non-linear single-lead ECG signal. To address these problems, we propose an automatic heartbeat classification system that uses the optimized minimum number of features using ECG time-series amplitude directly as input, without feature extraction and provides a primary classification and diagnosis for 1 normal and 14 types of arrhythmic heartbeats. Multi-objective particle swarm optimization (MOPSO) is used to achieve the best feature fitness. A novel fitness function is designed to be the sum of macro F1 loss and normalized dimension, with the optimization objective calculated as the minimum of the fitness function. Multi-layer perceptron (MLP), k-nearest neighbor, support vector machine, random forest and extra decision tree classifiers are trained using the selected features. For the targeted 15-class classification problem, MOPSO-optimized features with MLP consistently performed best with significantly reduced number of features. The proposed method proves to be an efficient and effective arrhythmia identification system for continuous, long-term cardiac health monitoring using single-lead ECG signal.

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Section: Discussionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

et al. 2022

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“…In this algorithm, the Pan-Tompkins algorithm was used to detect the QRS complex of the heartbeat (12). This algorithm was used because it is considered to be the gold standard for detecting R peaks (13). First, however, the signal had to be filtered.…”

Section: Algorithm II : Heart Rate Checkmentioning

confidence: 99%

ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality

Krämer

Menon

Elgendi

2022

Front. Digit. Health

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Electrocardiography (ECG) is the method most often used to diagnose cardiovascular diseases. To obtain a high-quality recording, the person conducting an ECG must be a trained expert. When these experts are not available, this important diagnostic tool cannot be used, consequently impacting the quality of healthcare. To avoid this problem, it must be possible for untrained healthcare professionals to record diagnostically useful ECGs so they can send the recordings to experts for diagnosis. The ECGAssess Python-based toolbox developed in this study provides feedback regarding whether ECG signals are of adequate quality. Each lead of the 12-lead recordings was classified as acceptable or unacceptable. This feedback allows people to identify and correct errors in the use of the ECG device. The toolbox classifies the signals according to stationary, heart rate, and signal-to-noise ratio. If the limits of these three criteria are exceeded, this is indicated to the user. To develop and optimize the toolbox, two annotators reviewed a data set of 1,200 ECG leads to assess their quality, and each lead was classified as acceptable or unacceptable. The evaluation of the toolbox was done with a new data set of 4,200 leads, which were annotated the same way. This evaluation shows that the ECGAssess toolbox correctly classified over 94% of the 4,200 ECG leads as either acceptable or unacceptable in comparison to the annotations.

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“…Several studies [ 18 , 21 , 22 , 115 , 116 ], which used capacitive sensors for long-term monitoring, have achieved good signal-to-noise ratios (SNRs). Capacitive sensors also have great potential for disease identification using machine learning algorithms adopting strategies such as feature engineering [ 117 , 118 ] and deep learning [ 119 , 120 , 121 , 122 , 123 ].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Stretchable and Wearable Capacitive Electrophysiological Sensors for Long-Term Health Monitoring

et al. 2022

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Over the past several years, wearable electrophysiological sensors with stretchability have received significant research attention because of their capability to continuously monitor electrophysiological signals from the human body with minimal body motion artifacts, long-term tracking, and comfort for real-time health monitoring. Among the four different sensors, i.e., piezoresistive, piezoelectric, iontronic, and capacitive, capacitive sensors are the most advantageous owing to their reusability, high durability, device sterilization ability, and minimum leakage currents between the electrode and the body to reduce the health risk arising from any short circuit. This review focuses on the development of wearable, flexible capacitive sensors for monitoring electrophysiological conditions, including the electrode materials and configuration, the sensing mechanisms, and the fabrication strategies. In addition, several design strategies of flexible/stretchable electrodes, body-to-electrode signal transduction, and measurements have been critically evaluated. We have also highlighted the gaps and opportunities needed for enhancing the suitability and practical applicability of wearable capacitive sensors. Finally, the potential applications, research challenges, and future research directions on stretchable and wearable capacitive sensors are outlined in this review.

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Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition

Cited by 14 publications

References 31 publications

An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality

Recent Advances in Stretchable and Wearable Capacitive Electrophysiological Sensors for Long-Term Health Monitoring

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