Detection of cardiac arrhythmia using deep CNN and optimized SVM

Mohebbanaaz, Mohebbanaaz; Sai, Y. Padma; Kumari, L. V. Rajani

doi:10.11591/ijeecs.v24.i1.pp217-225

Cited by 10 publications

(5 citation statements)

References 22 publications

(22 reference statements)

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“…As we advocated the good performance produced by applying our LSTM model on MIT-BIH dataset into classifying 17 types of arrhythmia, we ought to give an overview of how much our method fares compared to other methods as highlighted in Table 3. Granted that we applied the same preprocessing stage, and that other methods adopted optimization algorithms to fine tune the parameters and the feature selection process, our method that uses a 2-stage LSTM produced good results, as compared to the 1D-CNN in [12]. Furthermore, unlike the methods proposed in [5], [6], we fall short when it comes to accuracy.…”

Section: Resultsmentioning

confidence: 95%

“…In [5], the authors combined the genetic algorithm with SVM. Methods based on the application of a onedimensional CNN were used in [12], [13] and gave results above 90% accuracy [13]. In [14], authors proposed two novel methods for multiclass ECG arrhythmias classification based on principal components analysis (PCA), fuzzy support vector machine and unbalanced clustering.…”

Section: Introductionmentioning

confidence: 99%

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A deep learning-based cardio-vascular disease diagnosis system

Hamdan

Aksa

Omari

et al. 2022

IJEECS

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Recently ehealth technologies are becoming an overwhelming aspect of public health services that provides seamless access to healthcare information. Machine learning tools associated with IoT technology play an important role in developing such health technologies. This paper proposes a decision support system-based system (DSS) to make diagnosis of cardiovascular diseases. It uses deep learning approaches that classify electrocardiogram (ECG) signals. Thus, a two-stage long-short term memory (LSTM) based neural network architecture, along with an adequate preprocessing of the ECG signals is designed as a diagnosis-aided system for cardiac arrhythmia detection based on an ECG signal analysis. This deep learning based cardio-vascular disease diagnosis system (namely ‘DLCVD’) is built to meet higher performance requirements in terms of accuracy, specificity, and sensitivity. This must also be capable of an online real-time classification. Experimental results using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database show that DLCVD led to outstanding performance

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Section: Resultsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

A deep learning-based cardio-vascular disease diagnosis system

Hamdan

Aksa

Omari

et al. 2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Table 8 displays the confusion matrix for a classification involving the "P" class, characterized by dimensions of P × P. The confusion matrices produced by our models are shown in Figures 5-7. Measures of performance like Accuracy, F1score, recall, and precision are computed [23][24][25][26][27][28][29][30]. The following is a collection of formulas for calculating performance measures.…”

Section: Equations For Performance Measuresmentioning

confidence: 99%

Transfer Learning Based EfficientNet for Knee Osteoarthritis Classification

Kumari,

Jagruti,

Chandra

et al. 2024

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Knee Osteoarthritis (KOA) is a prevalent condition that deteriorates with time and may lead to disability. Diagnosis depends on subjective symptom evaluation and radiograph analysis. The Kellgren-Lawrence (KL) grading system is widely used to assess the severity of knee osteoarthritis, with grades ranging from 0 (no osteoarthritis) to 4 (severe osteoarthritis). The detection and classification of KOA play a crucial role in medical diagnosis and treatment planning. It enables healthcare professionals to detect the condition at an early stage to take necessary precautions through medication to prevent its progression, leading to a better quality of life for those affected by it. In this work, we propose an approach for knee osteoarthritis classification using fine-tuned deep learning models. We employed the concept of transfer learning by utilizing three pre-trained EfficientNet models: EfficientNet-B5, EfficientNet-B6, and EfficientNet-B7. By customizing the layers of the model, transfer learning enables us to use prior knowledge and models to enhance the performance of new tasks with limited data. The proposed system aims to automate the KL grading process by analyzing knee X-rays and classifying them into one of the five grades using fine-tuned EfficientNet models. Each model's performance is evaluated. The experimental results show that the EfficientNet-B7 model achieved the highest accuracy of 78.53%, while EfficientNet-B5 and EfficientNet-B6 attained accuracies of 75.14% and 76.47%, respectively.

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“…Despite the availability of ECG monitoring devices, analyzing the data obtained from them remains a major concern for researchers. Previously proposed devices have been criticized for their lack of comprehensiveness and inability to keep up with the latest technological trends [8]- [10]. Some ECG monitoring devices operate on a context and server basis [11], [12], while others are equipped with specific technologies.…”

Section: Introductionmentioning

confidence: 99%

A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions

Rahaman,

Kashem

2024

Bulletin EEI

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Cardiovascular diseases engender serious public health concerns in developing nations since access to specialized medical equipment is often limited and standard treatment expenses can be prohibitive. This study proposes an efficient and relatively affordable electrocardiogram (ECG) monitoring system that reads and analyzes a person's electrocardiogram data to provide affordable and quality healthcare solutions. The device initially extracts features from electrocardiogram records by reading electrical signals in the heart. Extracted data are then analyzed by a trained deep learning model to determine precisely if the heart is in a healthy state or undergoing complexities. Experimental results showed that the fine-tuned ANN architecture outperformed the state-of-the-art architectures in this field with an accuracy of 98.95%. The data can also be sent to specialists through an MQTT server if necessary, allowing for remote diagnosis and treatment. The system is intended to be deployed in countries where rural regions lack access to specialized healthcare equipment and professionals. Additionally, the device is inexpensive and, hence can be made accessible to people with limited affordability.

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Detection of cardiac arrhythmia using deep CNN and optimized SVM

Cited by 10 publications

References 22 publications

A deep learning-based cardio-vascular disease diagnosis system

A deep learning-based cardio-vascular disease diagnosis system

Transfer Learning Based EfficientNet for Knee Osteoarthritis Classification

A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions

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