Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications

During the last many years, the air quality of the capital city of India, Delhi had been hazardous. A large number of people have been diagnosed with Asthma and other breathing-related problems. The basic reason behind this has been the high concentration of life-threatening PM2.5 particles dissolved in its atmosphere. A good model, to forecast the concentration level of these dissolved particles, may help to prepare the residents with better prevention and safety strategies in order to save them from many health-related diseases. This work aims to forecast the PM2.5 concentration levels in various regions of Delhi on an hourly basis, by applying time series analysis and regression, based on various atmospheric and surface factors such as wind speed, atmospheric temperature, pressure, etc. The data for the analysis is obtained from various weather monitoring sites, set-up in the city, by the Indian Meteorological Department (IMD). A regression model is proposed, which uses Extra-Trees regression and AdaBoost, for further boosting. Experimentation for comparative study with the recent works is done and results indicate the efficacy of the proposed model.

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“…AdaBoost was combined with SVM for time-series signal classification in epileptic seizure diagnosis by Hadeethi et al. [ 35 ].…”

Section: Related Workmentioning

confidence: 99%

A machine learning-based model to estimate PM2.5 concentration levels in Delhi's atmosphere

Kumar

Mishra

Singh

2020

Heliyon

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“…However, for CD‐E cases, the proposed model has best performance compared with other methods where the state‐of‐the‐art methods yield ranged by 98 and 99.33% accuracy. For AB‐CD‐E case, Al‐Hadeethi et al [7 ] and Wu et al [10 ] with 99% accuracy stay behind Jiang et al [4 ]. The proposed model yields best performance in AB‐CD‐E case.…”

Section: Resultsmentioning

confidence: 99%

“…Evaluated performance was 99.17% with the support vector machine (SVM) classifier. Al‐Hadeethi et al [7 ] presented the adaptive least squares SVM (AdaBoost LS‐SVM) classifier to detect the seizure. Also, the covariance matrix is used for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Analysis of EEG signal for seizure detection based on WPT

Arı

2020

Electron. lett.

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Electroencephalogram (EEG) is a diagnostic method that provides information about the functioning of the brain. EEG can be used to diagnose the abnormally functioning part of the brain by monitoring electrical activities. An automated system has been proposed to create a computer‐based expert opinion needed in the detection of epilepsy and to capture a more objective view. To this end, the approximation and detail coefficients of EEG signals are calculated by using the wavelet packet transform (WPT). The coefficients were subjected to feature extraction using dispersion entropy and line length methods. The extracted feature vector has been applied as input to the support vector machine (SVM) and k‐nearest neighbour (KNN) classifiers. The proposed method was tested using the public EEG seizure dataset created by the University of Bonn. In this study, the dataset was evaluated in two different ways as binary cases and multiclass cases. Evaluated classification accuracy was 100% for binary classification with SVM. For multiclass classification evaluated accuracy was 99.85% with KNN. The proposed method was compared with other methods in the literature using the same dataset. The comparison results provide the superiority of the proposed method.

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“…We also used machine learning models to perform comparison between the results if machine learning models and the proposed DS-MLP. Five machine learning models such as logistic regression (LR) [36], [37], random forest (RF) [38], [39], decision tree (DT) [40], support vector machine (SVM) [41], K nearest neighbour (KNN) [42] and Gaussian Naive Bayes (GNB) [43] algorithms are used for this purpose. We use RF with 300 n_estimators which means that 300 decision trees are constructed by RF on each example to give the predictions and then perform majority voting between 300 predictions to make the final prediction.…”

Section: B Comparison With Other Machine Learning Modelsmentioning

confidence: 99%

Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

et al. 2020

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The recent development of machines exhibiting intelligent characteristics involves numerous techniques including computer hardware and software architecture development. Many different hardware devices, wearable sensors, machine learning, and deep learning model implementations are being applied in human activity recognition (HAR) applications in recent times. However, to develop high accuracy classification systems for activity recognition using low-cost hardware technology is of significant importance. To achieve this goal this study uses sensor data from two low-cost sensors, gyroscope and accelerometer along with the implementation of an Artificial Neural Network (ANN) based deep learning model for HAR. In particular, Deep Stacked Multilayered Perceptron (DS-MLP) has been proposed. In the implementation of DS-MLP, an ANN model has been used as a meta-learner while five MLP models have been used as base-learners. In this study, these base-learners and meta-learner have been combined using a stack ensemble technique. The performance evaluations have been done first on the applicability of individual base-models followed by the application of DS-MLP, the results prove the high accuracy of 97.3% and 99.4% for heterogeneous datasets used for testing. The performance of the proposed DS-MLP models has been compared to some existing machine learning classifiers and several state-of-the-art activity recognition systems. The comparative result analysis also proves that the proposed system performed better than these classification approaches in terms of important performance metrics such as accuracy, precision, recall, F-score, Cohen's Kappa, and Mathew correlation coefficient.

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Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications

Cited by 67 publications

References 49 publications

A machine learning-based model to estimate PM2.5 concentration levels in Delhi's atmosphere

A machine learning-based model to estimate PM2.5 concentration levels in Delhi's atmosphere

Analysis of EEG signal for seizure detection based on WPT

Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

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