Detection of abnormal heart conditions based on characteristics of ECG signals

Hammad, Mohamed; Maher, Asmaa; Wang, Kuanquan; Jiang, Feng; Amrani, Moussa

doi:10.1016/j.measurement.2018.05.033

Cited by 157 publications

(73 citation statements)

References 30 publications

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“…Recently, focus on ECG rhythm (ECGr) classification has similarly been on the increase. ECGr classification can be grouped into areas that focus on finding effective extraction methods, [13,14] improving classification outcomes, [15][16][17][18][19] and utilization of deep learning methods to enhance the performance of classification [20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…They obtained an accuracy of 99.7% for arrhythmia classification. In [17], Hammad et al reported the use of a classifier based on characteristics of ECG signals to detect abnormal heart conditions. As reported therein, their strategy yielded an average classification accuracy of 99%.…”

Section: Introductionmentioning

confidence: 99%

“…However, all these ML methods [15][16][17][18][19] reported good performances, when faced with a big data of ECG records their results might be less effective. This is attributed to the inherent shortcoming attributed to training ML models on limited or small data sets.…”

Section: Introductionmentioning

confidence: 99%

“…• The focus of the majority of these papers on designing a method for preprocessing of ECG signals, feature extraction, feature selection and classification, as in [15][16][17][18][19]. In this paper, only DNN model optimized by genetic algorithm (GA) is proposed.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

Self Cite

172

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Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECGbased CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

Self Cite

172

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“…Contrary to HR, HRV increases during resting periods and decreases during stress. Apart from this, ECG feature extraction is used in current research to detect abnormal heart conditions [20] and for the development of human authentication systems [21].…”

Section: Introductionmentioning

confidence: 99%

On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals

et al. 2020

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We present performance results concerning the validation for anxiety level detection based on trained mathematical models using supervised machine learning techniques. The model training is based on biosignals acquired in a randomized controlled trial. Wearable sensors were used to collect electrocardiogram, electrodermal activity, and respiration from spiderfearful individuals. We designed and applied ten approaches for data labeling considering individual biosignals as well as subjective ratings. Performance results revealed a selection of trained models adapted for two-level (low and high) and three-level (low, medium and high) classification of anxiety using a minimal set of six features. We obtained a remarkable accuracy of 89.8% for the two-level classification and of 74.4% for the three-level classification using a short time window length of ten seconds when applying the approach that uses subjective ratings for data labeling. Bagged Trees proved to be the most suitable classifier type among the classification models studied. The trained models will have a practical impact on the feasibility study of an augmented reality exposure therapy based on a therapeutic game for the treatment of arachnophobia.

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Flexible Wearable Sensors for Cardiovascular Health Monitoring

Chen

Fan

et al. 2021

Adv Healthcare Materials

262

148

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Cardiovascular diseases account for the highest mortality globally, but recent advances in wearable technologies may potentially change how these illnesses are diagnosed and managed. In particular, continuous monitoring of cardiovascular vital signs for early intervention is highly desired. To this end, flexible wearable sensors that can be comfortably worn over long durations are gaining significant attention. In this review, advanced flexible wearable sensors for monitoring cardiovascular vital signals are outlined and discussed. Specifically, the functional materials, configurations, mechanisms, and recent advances of these flexible sensors for heart rate, blood pressure, blood oxygen saturation, and blood glucose monitoring are highlighted. Different mechanisms in bioelectric, mechano‐electric, optoelectric, and ultrasonic wearable sensors are presented to monitor cardiovascular vital signs from different body locations. Present challenges, possible strategies, and future directions of these wearable sensors are also discussed. With rapid development, these flexible wearable sensors will potentially be applicable for both medical diagnosis and daily healthcare use in tackling cardiovascular diseases.

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Detection of abnormal heart conditions based on characteristics of ECG signals

Cited by 157 publications

References 30 publications

A Multitier Deep Learning Model for Arrhythmia Detection

A Multitier Deep Learning Model for Arrhythmia Detection

On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals

Flexible Wearable Sensors for Cardiovascular Health Monitoring

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