Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

Khan, Ali Haider; Hussain, Muzammil; Malik, Misbah

doi:10.1155/2021/5512243

Cited by 63 publications

(27 citation statements)

References 21 publications

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“…The authors used artificial neural networks to discriminate between normal and abnormal ECG patterns reaching an accuracy of 99%. The authors of [ 57 ] used ECG trace images to classify four types of cardiac diseases. They employed the MobileNet v2-deep learning technique for the classification and reached an accuracy of 98%.…”

Section: Related Workmentioning

confidence: 99%

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

Attallah

2022

Computers in Biology and Medicine

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The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several limitations, thus new techniques need to be investigated to improve the diagnosis and overcome these limitations. Taking into consideration the great benefits of electrocardiogram (ECG) application, this paper proposes a new pipeline called ECG-BiCoNet to investigate the potential of using ECG data for diagnosing COVID-19. ECG-BiCoNet employs five deep learning models of distinct structural design. ECG-BiCoNet extracts two levels of features from two different layers of each deep learning technique. Features mined from higher layers are fused using discrete wavelet transform and then integrated with lower-layers features. Afterward, a feature selection approach is utilized. Finally, an ensemble classification system is built to merge predictions of three machine learning classifiers. ECG-BiCoNet accomplishes two classification categories, binary and multiclass. The results of ECG-BiCoNet present a promising COVID-19 performance with an accuracy of 98.8% and 91.73% for binary and multiclass classification categories. These results verify that ECG data may be used to diagnose COVID-19 which can help clinicians in the automatic diagnosis and overcome limitations of manual diagnosis.

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Section: Related Workmentioning

confidence: 99%

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

Attallah

2022

Computers in Biology and Medicine

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“…For the last two decades, machine and deep learning techniques have made a large contribution in handling the information extraction problem from various application areas including medical image analysis and retrieval [19][20][21][22][23], biometrics recognition [24][25][26], disease diagnosis [27,28], agriculture, etc. The following literature study shows the related work on the agricultural sector using machine learning techniques.…”

Section: Literature Surveymentioning

confidence: 99%

Semantic Information Extraction from Multi-Corpora Using Deep Learning

Kumar¹,

Sastry²,

Marriboyina³

et al. 2022

Computers, Materials &Amp; Continua

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Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learningbased techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity in input corpora, and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer and multilayer perceptron to find agricultural-based named entity recognition, events, and relations between them. The proposed algorithm has been trained and tested on four input corpora i.e., agriculture, weather, soil, and pest & fertilizers. The experimental results have been compared with existing techniques and it was observed that the proposed algorithm outperforms Weighted-SOM, LSTM+RAO, PLR-DBN, KNN, and Naïve Bayes on standard parameters like accuracy, sensitivity, and specificity.

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“…A crisp input is translated into various identity membership functions based on its value. FIS performance is based on membership function; these membership functions can be evaluated as a collection of inputs [11,12]. More specifically, severe pneumonia leads to the coronavirus and coronavirus disease leads to death or kidney failure of a patient.…”

Section: Introductionmentioning

confidence: 99%

Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

Alam¹,

Shaukat²,

Khelifi³

et al. 2022

Computers, Materials &Amp; Continua

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Disease diagnosis is a challenging task due to a large number of associated factors. Uncertainty in the diagnosis process arises from inaccuracy in patient attributes, missing data, and limitation in the medical expert's ability to define cause and effect relationships when there are multiple interrelated variables. This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things (IoT) empowered by the fuzzy inference system (FIS) to diagnose various diseases. The Fuzzy System is one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties, and fuzzy logic is the best way to handle uncertainties. Our proposed system differentiates new cases provided symptoms of the disease. Generally, it becomes a time-sensitive task to discriminate symptomatic diseases. The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently. Different coefficients have been employed to predict and compute the identified disease's severity for each sign of disease. This study aims to differentiate and diagnose COVID-19, Typhoid, Malaria, and Pneumonia. This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms. MATLAB tool is utilised for the implementation of FIS. Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms. The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases. This study may assist doctors, patients, medical practitioners, and other healthcare professionals in early diagnosis and better treat diseases.

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Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

Cited by 63 publications

References 21 publications

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

Semantic Information Extraction from Multi-Corpora Using Deep Learning

Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

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