Automated Identification of Myocardial Infarction Using Harmonic Phase Distribution Pattern of ECG Data

Sadhukhan, Deboleena; Pal, Saurabh; Mitra, Madhuchhanda

doi:10.1109/tim.2018.2816458

Cited by 94 publications

(32 citation statements)

References 24 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%

“…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%

“…In this paper, we only worked on one lead ECG. • Confining classification to either one rhythm or two rhythms as in [15,17].…”

Section: Introductionmentioning

confidence: 99%

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A Multitier Deep Learning Model for Arrhythmia Detection

Hammad

Iliyasu

Subaşı

et al. 2021

IEEE Trans. Instrum. Meas.

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%

“…In this paper, we only worked on one lead ECG. • Confining classification to either one rhythm or two rhythms as in [15,17].…”

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.

172

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“…Electrocardiographic (ECG) can be employed to recognize MI [2], which serves as the most popular diagnostic tool for its convenience, non-invasiveness and low cost. ECG records the electrical signals generated by the heart muscle fibers during the alternate contraction and relaxation of the heart chambers [3]. A normal ECG is characterized by the cardiac cycle sequence, and each cycle mainly contains P, QRS, and T waves.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals

Nie

et al. 2020

Sensors

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The electrocardiogram (ECG) is a non-invasive, inexpensive, and effective tool for myocardial infarction (MI) diagnosis. Conventional detection algorithms require solid domain expertise and rely heavily on handcrafted features. Although previous works have studied deep learning methods for extracting features, these methods still neglect the relationships between different leads and the temporal characteristics of ECG signals. To handle the issues, a novel multi-lead attention (MLA) mechanism integrated with convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) framework (MLA-CNN-BiGRU) is therefore proposed to detect and locate MI via 12-lead ECG records. Specifically, the MLA mechanism automatically measures and assigns the weights to different leads according to their contribution. The two-dimensional CNN module exploits the interrelated characteristics between leads and extracts discriminative spatial features. Moreover, the BiGRU module extracts essential temporal features inside each lead. The spatial and temporal features from these two modules are fused together as global features for classification. In experiments, MI location and detection were performed under both intra-patient scheme and inter-patient scheme to test the robustness of the proposed framework. Experimental results indicate that our intelligent framework achieved satisfactory performance and demonstrated vital clinical significance.

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