Heartbeat classification with low computational cost using Hjorth parameters

Leite, João; Moreno, Robson Luiz

doi:10.1049/iet-spr.2017.0296

Cited by 23 publications

(14 citation statements)

References 31 publications

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“…Table 6 lists all the extracted features along with their statistical measures of mean and standard deviation (STD) for method I (DWT) and method II (EMD). We extracted time domain [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ], spectral [ 46 , 47 ], fractal and chaos [ 48 , 49 ], chroma [ 50 , 51 ], cepstral [ 52 ], and texture features [ 53 ] and analyzed them statistically.…”

Section: Methodsmentioning

confidence: 99%

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Khan

Aziz

Akram

et al. 2021

Sensors

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Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

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Section: Methodsmentioning

confidence: 99%

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Khan

Aziz

Akram

et al. 2021

Sensors

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“…Activity is the measure of the squared standard deviation of the amplitude and is referred to as mean power or variance. Mobility is given by the standard deviation of the slope with reference to the standard deviation of the amplitude while complexity is expressed as the number of standard slopes actually generated during the average time required for the generation of one standard amplitude [29–31]. Hjorth parameters are used in this study to analyse the statistical properties of BLIMFs in non‐fatigue and fatigue conditions.…”

Section: Methodsmentioning

confidence: 99%

Variational mode decomposition based differentiation of fatigue conditions in muscles using surface electromyography signals

Krishnamani

Karthick

Swaminathan

2020

IET signal process.

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Surface electromyography (sEMG) signals are stochastic, multicomponent and non‐stationary, and therefore their interpretation is challenging. In this study, an attempt has been made to develop an automated muscle fatigue detection system using variational mode decomposition (VMD) features of sEMG signals and random forest classifier. The sEMG signals are acquired from 103 healthy volunteers during isometric (45 subjects) and dynamic (58 subjects) muscle fatiguing contractions and preprocessed. The band‐limited intrinsic mode functions (BLIMFs) are extracted from non‐fatigue and fatigue segments of the signals using the VMD algorithm. Hjorth features, such as activity, mobility and complexity are extracted from each BLIMF and are given to the random forest classifier. The performance of these features is evaluated using leave‐one‐subject‐out cross‐validation. The results show that the complexity feature performs better than others and it has resulted in an accuracy of 83% in dynamic contractions and 80% in isometric contractions. The performance is increased by about 8% in a dynamic condition when the most significant complexity features (p < 0.001) are used and by about 12% for isometric when the authors use all significant features. Therefore, the proposed approach could be used to detect fatigue conditions in various neuromuscular activities and real‐time monitoring in the workplace.

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“…The following statistical measures are given by Eqs. (16)(17)(18)(19)(20)(21) are used for performance analysis of the proposed O-WCNN model.…”

Section: Performance Metricsmentioning

confidence: 99%

“…The conventional classifiers’ performance is supplemented using feature extraction and feature selection techniques. The popular feature extraction and selection techniques include empirical mode decomposition (EMD) [ 15 ], wavelet decomposition [ 12 ], Hjorth parameters [ 16 ], discrete cosine transform (DCT) [ 9 ], higher-order statistics (HOS) [ 17 ], principal component analysis (PCA) [ 9 , 12 ], ant colony optimization (ACO) [ 18 ], particle swarm optimization (PSO) [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification

Jangra

Dhull

Singh

et al. 2021

Complex Intell. Syst.

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The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.

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Heartbeat classification with low computational cost using Hjorth parameters

Cited by 23 publications

References 31 publications

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Variational mode decomposition based differentiation of fatigue conditions in muscles using surface electromyography signals

O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification

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