Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model

Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently occur in a human’s life. These arrhythmias may cause potentially fatal complications, which may lead to an immediate risk of life. Thus, the detection and classification of arrhythmias is a pertinent issue for cardiac diagnosis. (1) Background: To capture these sporadic events, an electrocardiogram (ECG), a register containing the heart’s electrical function, is considered the gold standard. However, since ECG carries a vast amount of information, it becomes very complex and challenging to extract the relevant information from visual analysis. As a result, designing an efficient (automated) system to analyse the enormous quantity of data possessed by ECG is critical. (2) Method: This paper proposes a hybrid deep learning-based approach to automate the detection and classification process. This paper makes two-fold contributions. First, 1D ECG signals are translated into 2D Scalogram images to automate the noise filtering and feature extraction. Then, based on experimental evidence, by combining two learning models, namely 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) network, a hybrid model called 2D-CNN-LSTM is proposed. (3) Result: To evaluate the efficacy of the proposed 2D-CNN-LSTM approach, we conducted a rigorous experimental study using the widely adopted MIT–BIH arrhythmia database. The obtained results show that the proposed approach provides ≈98.7%, 99%, and 99% accuracy for Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR), respectively. Moreover, it provides an average sensitivity of the proposed model of 98.33% and a specificity value of 98.35%, for all three arrhythmias. (4) Conclusions: For the classification of arrhythmias, a robust approach has been introduced where 2D scalogram images of ECG signals are trained over the CNN-LSTM model. The results obtained are better as compared to the other existing techniques and will greatly reduce the amount of intervention required by doctors. For future work, the proposed method can be applied over some live ECG signals and Bi-LSTM can be applied instead of LSTM.

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A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification

Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage, blindness, and other illnesses. Researchers are motivated to create a Machine Learning methodology that can predict diabetes in the future. Exploiting Machine Learning Algorithms (MLA) is essential if healthcare professionals are able to identify diseases more effectively. In order to improve the medical diagnosis of diabetes this research explored and contrasts various MLA that can identify diabetes risk early. The research includes the analysis on real datasets such as a clinical dataset gathered from a doctor in the Indian district of Bandipora in the years April 2021–Feb2022. MLA are currently important in the healthcare sector due to their prediction abilities. Researchers are using MLA to improve disease prediction and reduce cost. In this Paper author developed a methodology using Machine Learning Algorithms for Diabetes Disease Risk Prediction in North Kashmir. Six MLA have been successfully used in the experimental study such as Random Forest (RF), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boost (GB), Decision Tree (DT), and Logistic Regression (LR). RF is the most accurate classifier with the uppermost accuracy rate of 98 percent followed by MLP (90.99%), SVM (92%), GBC (97%), DT (96%), and LR (69%), respectively, with the balanced data set. Lastly, this study enables us to effectively identify the prevalence and prediction of diabetes.

https://downloads.hindawi.com/journals/cin/2022/2789760.pdf

Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora

Artificial intelligence-based approach for atrial fibrillation detection using normalised and short-duration time-frequency ECG

An improved cardiac arrhythmia classification using an RR interval-based approach

The agricultural production is affected by the climate changes i.e. humidity, rain, extremes of temperature etc. Additionally, abiotic stresses are causative element to the etiology of disease as well as pest on crops. The production of the crops can be improved by diagnosis as well as detecting the accurate disease on time or in early stage. Moreover, it is very difficult for accurately detecting and treatment based on the technique which used in disease and insect pests diagnosis. Few researchers have made efforts on predicting disease as well as pest crops using machine learning algorithms. Therefore, this paper presents disease identification in corn crops by analyzing the leaves in the very early stage. We have used PlantVillage dataset for experiments and analysis. The validity of the results has been cheeked on various performance metrics such as precision, accuracy, recall, storage space, running time of the model and AUC-RoC. The obtained results shows the proposed technique outperform in comparison with the traditional machine learning algorithms. Developed model is able to achieve the accuracy of 94%.

A Convolution Neural Network based approach to detect the disease in Corn Crop

Myocardial infarction (MI) happens when blood stops circulating to an explicit segment of the heart causing harm to the heart muscles. Vectorcardiography (VCG) is a technique of recording direction and magnitude of the signals that are produced by the heart in a 3-lead representation. In this work, we present a technique for detection of MI in the inferior portion of heart using short duration VCG signals. The raw signal was pre-processed using the median and Savitzky-Golay (SG) filter. The Stationary Wavelet Transform (SWT) was used for time-invariant decomposition of the signal followed by feature extraction. The selected features using minimum-redundancy-maximum-relevance (mRMR) based feature selection method were applied to the supervised classification methods. The efficacy of the proposed method was assessed under both class-oriented and a more real-life subject-oriented approach. An accuracy of 99.14 and 89.37% were achieved respectively. Results of the proposed technique are better than existing state-of-art methods and used VCG segment is shorter. Thus, a shorter segment and a high accuracy can be helpful in the automation of timely and reliable detection of MI. The satisfactory performance achieved in the subject-oriented approach shows reliability and applicability of the proposed technique.

Short duration Vectorcardiogram based inferior myocardial infarction detection: class and subject-oriented approach.

In this paper, we propose a novel design analysis for a Junctionless Double Gate Vertical MOSFET (JLVMOS) with metal gate electrode and HfO2, for which the simulations have been performed using TCAD (ATLAS), The simulated results exhibits significant improvements in comparison to conventional JLVMOS device with a polysilicon gate electrode and ITRS values for different node technology . In place of polysilicon gate and SiO2 we have used metal gate and hafnium dioxide (HfO2) respectively and observed that metal gate electrode with HfO2 in JLVMOS shows drain current is 1.77 mA, for a gate voltage of 1V and an average subthreshold swing, DIBL (Drain Induced Barrier Lowering) effect and leakage current are 61.01mV per decade, 40.4mV/V and 1.4nA/µm respectively. This significant improvement in drain current can be exploited for various low power highperformance circuit applications.

Effects of Metal Gate Electrode and HfO2 in Junction less Vertical Double Gate MOSFET

In recent years, the use of digital music is increased compare to earlier period. Based on the selected features of music or songs, the music recommendation service provider will provide or predict the suitable songs to users based on their song preferences. In this research, we build a music recommendation model that helps user to recommend music automatically based on similarity of the available music. Here in this work, initially we extract the features of the music with the help of our proposed neural network model and this neural model also used to predict the rank of music as music recommendation. The various evaluation parameters like precision, recall and F1 measure are used to evaluate our proposed system. The experimental results shows that our recommendation model performed better than the baseline approach.

Vijay Kumar Bohat

Papers

An improved cardiac arrhythmia classification using an RR interval-based approach

A Convolution Neural Network based approach to detect the disease in Corn Crop

Short duration Vectorcardiogram based inferior myocardial infarction detection: class and subject-oriented approach.

Effects of Metal Gate Electrode and HfO2 in Junction less Vertical Double Gate MOSFET

Neural Network Model for Recommending Music Based on Music Genres