Localization of myocardial infarction with multi-lead ECG based on DenseNet

Xiong, Peng; Xue, Ye; Zhang, Jieshuo; Liu, Ming; Du, Haiman; Zhang, Hong; Hou, Zeng-Guang; Wang, Hongrui; Liu, Xiuling

doi:10.1016/j.cmpb.2021.106024

Cited by 43 publications

(16 citation statements)

References 31 publications

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“…As can be seen from Table 5 , works [ 40 , 44 , 45 , 51 , 52 , 55 – 57 , 60 , 61 ] and [ 62 ] were used deep learning to achieve high classification ability on the used dataset. Using deep learning techniques Fu et al [ 56 ] attained 99.93% classification accuracy using the attention mechanism.…”

Section: Discussionmentioning

confidence: 99%

Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals

Barua

Aydemir

Doğan

et al. 2022

Int. J. Mach. Learn. & Cyber.

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Myocardial infarction (MI) is detected using electrocardiography (ECG) signals. Machine learning (ML) models have been used for automated MI detection on ECG signals. Deep learning models generally yield high classification performance but are computationally intensive. We have developed a novel multilevel hybrid feature extraction-based classification model with low time complexity for MI classification. The study dataset comprising 12-lead ECGs belonging to one healthy and 10 MI classes were downloaded from a public ECG signal databank. The model architecture comprised multilevel hybrid feature extraction, iterative feature selection, classification, and iterative majority voting (IMV). In the hybrid handcrafted feature (HHF) generation phase, both textural and statistical feature extraction functions were used to extract features from ECG beats but only at a low level. A new pooling-based multilevel decomposition model was presented to enable them to create features at a high level. This model used average and maximum pooling to create decomposed signals. Using these pooling functions, an unbalanced tree was obtained. Therefore, this model was named multilevel unbalanced pooling tree transformation (MUPTT). On the feature extraction side, two extractors (functions) were used to generate both statistical and textural features. To generate statistical features, 20 commonly used moments were used. A new, improved symmetric binary pattern function was proposed to generate textural features. Both feature extractors were applied to the original MI signal and the decomposed signals generated by the MUPTT. The most valuable features from among the extracted feature vectors were selected using iterative neighborhood component analysis (INCA). In the classification phase, a one-dimensional nearest neighbor classifier with ten-fold cross-validation was used to obtain lead-wise results. The computed lead-wise results derived from all 12 leads of the same beat were input to the IMV algorithm to generate ten voted results. The most representative was chosen using a greedy technique to calculate the overall classification performance of the model. The HHF-MUPTT-based ECG beat classification model attained excellent performance, with the best lead-wise accuracy of 99.85% observed in Lead III and 99.94% classification accuracy using the IMV algorithm. The results confirmed the high MI classification ability of the presented computationally lightweight HHF-MUPTT-based model.

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Section: Discussionmentioning

confidence: 99%

Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals

Barua

Aydemir

Doğan

et al. 2022

Int. J. Mach. Learn. & Cyber.

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“…Great improvements based on DL method use were achieved in the detection of acute myocardial infarction and stable ischemic heart disease, with the detection accuracy in the range of 83–99.9% for different model configurations (CNN, ResNet, CNN–biLSTM) ( Hinai et al, 2021 ). Myocardial infarction localization can be recognized with an accuracy of 90.20%, 99.67%, and 99.87% for biLSTM, 1D-CNN, and DenseNet, respectively ( Tripathy et al, 2019 ; Xiong et al, 2021 ). The ResNet model is a CNN with residual blocks, which allows the signals to pass through several layers in the network resulting in improved training capability of the model with no information loss.…”

Section: Ecg Analysismentioning

confidence: 99%

“…On the contrary, the overall performance of the network improves. All the above DL approaches outperform existing ML techniques based on morphological features combined with k-NN or SVM in terms of accuracy as well as time required for analysis of new patients’ data ( Tripathy et al, 2019 ; Xiong et al, 2021 ).…”

Section: Ecg Analysismentioning

confidence: 99%

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

et al. 2022

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Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

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“…DenseNet uses layer-overlay communication, connecting each layer to the next next, feed-forward-based layer to resolve ResNet's problem, which specifically preserves information by adding identity changes that add complexity. It uses dense blocks to input features of all previous layers in all subsequent layers [70][71], as described in Fig. 9.…”

Section: Densenetmentioning

confidence: 99%

Skin Lesions Classification Using Deep Learning Techniques: Review

Kareem

Abdulazeez

Zeebaree

2021

AJRCoS

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Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.

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Localization of myocardial infarction with multi-lead ECG based on DenseNet

Cited by 43 publications

References 31 publications

Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals

Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Skin Lesions Classification Using Deep Learning Techniques: Review

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