Automatic Detection of Premature Ventricular Contraction Based on Photoplethysmography Using Chaotic Features and High Order Statistics

Yousefi, Mohammad Reza; Khezri, Mahdi; Bagheri, Razieh; Jafari, Reza

doi:10.1109/memea.2018.8438697

Cited by 12 publications

(16 citation statements)

References 7 publications

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“…As far as the manuscript' authors are aware, no other article has present studies on the dynamics of PPG with a convolutional neural network that tries to estimate it. Other papers address the study of PPG dynamics from different points of view [6,7,15,16].…”

Section: B Dynamic Behavior Classification With a Cnn Architecturementioning

confidence: 99%

“…This loss tries to combine the advantages of the L 1 -norm and L 2norm, being robust to outliers at the same time that encourages the correct points to learn. The proposed log-cosh loss evaluates the differences between the predicted points P i and the ground truth ones Q i , as we can see in (7).…”

Section: )mentioning

confidence: 99%

“…By learning these known dynamics, even in the presence of noise, together with their predictive capability, the neural networks in this work have made it possible to examine the dynamic composition embedded in the time evolution of any PPG signal at different timescales. Some attempts to use neural networks as classification algorithms to detect pathologies in a PPG signal database can be found in [7].…”

Section: Introductionmentioning

confidence: 99%

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Is the PPG Signal Chaotic?

et al. 2020

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PhotoPlethysmoGraphic (PPG) signal is an easily accessible biological signal that gives valuable diagnostic information. The novelty is the study procedure of the dynamic of the PPG signals, in our case of young and healthy individuals, with Deep Neural Network, which allows finding the dynamic behavior at different timescales. On a small timescale, the dynamic behavior of the PPG signal is predominantly quasi-periodic. On a large timescale, a more complex dynamic diversity emerges, but never a chaotic behavior as earlier studies had reported. The procedure that determines the dynamics of the PPG signal consists of contrasting the dynamics of a PPG signal with well-known dynamics-named reference signals in this study-, mostly present in physical systems, such as periodic, quasi-periodic, aperiodic, chaotic or random dynamics. For this purpose, this paper provides two methods of analysis based on Deep Neural Network (DNN) architectures. The former uses a Convolutional Neural Network (CNN) architecture model. Upon training with reference signals, the CNN model identifies the dynamics present in the PPG signal at different timescales, assigning, according to a classification process, an occurrence probability to each of them. The latter uses a Recurrent Neural Network (RNN) based on a Long Short-Term Memory (LSTM) architecture. With each of the signals, whether reference signals or PPG signals, the RNN model infers an evolution function (nonlinear regression model) based on training data, and considers its predictive capability over a relatively short time horizon. INDEX TERMS Biological signal, DNN architectures, PPG signal dynamic, timescales.

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Section: B Dynamic Behavior Classification With a Cnn Architecturementioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Is the PPG Signal Chaotic?

et al. 2020

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“…Previous works in the aspects of analysis of PPG [ 11 ], classification of PPG [ 12 ], artifact reduction of PPG [ 13 ], cardiac arrhythmia classification of PPG [ 14 ], heart rate monitoring from PPG [ 15 ], and development of PPG sensors [ 16 ] have been reported in the literature. The automatic Region of Interest (ROI) for remote PPG was performed by Gallego and Haan [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders

Prabhakar

Rajaguru²,

Kim

2020

Diagnostics

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The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine–Radial Basis Function (SVM–RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.

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“…For automatic detection of Pre-ventricular Contraction (PVC), a PPG method was used by Solosenko et al [15]. With the help of chaotic features and Higher Order Statistics (HOS), the automatic detection of PVC based on PPG was done by Yousefi et al [16]. Cardiac arrhythmia classification was done for PPG signals by Polania et al [17].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Prabhakar

Rajaguru²,

Lee

2019

IEEE Access

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One of the most useful methodologies which provide a specific waveform showing the pulsating peripheral blood flow in a non-invasive manner is Photoplethysmography (PPG). The design, application and implementation of a PPG system are quite inexpensive and have a very easy maintenance. Without having direct contact with the surface of the skin, PPG can easily take the measurements. Therefore, PPG has a good medical competency and due to its widespread availability, it has a lot of advantages. A PPG signal can sometimes be substituted or complemented by an Electrocardiography (ECG) signal as it can provide Heart Rate Variability (HRV) analysis. In this work, an in-depth analysis of classification of Cardiovascular Disease (CVD) is done with the help of Capnobase dataset. Initially, metaheuristic optimization algorithms are utilized as dimensionality reduction techniques and then the dimensionally reduced values are classified with the help of different classifiers for the classification of CVD. The results show that for the PPG normal cases, a high classification accuracy of 99.48% is obtained when Chi square Probability Density Function (PDF) optimized values are classified with Artificial Neural Networks (ANN) and a second highest classification accuracy of 98.96% is obtained when Chicken swarm optimized values are classified with Naïve Bayesian Classifier (NBC). Similarly when the PPG abnormal cases or PPG with CVD cases are concerned, a high classification accuracy of 99.48% is obtained when Chi square PDF optimized values are classified with Logistic Regression and a second highest classification accuracy of 98.96% is obtained when Chi square CDF optimized values are classified with Gaussian Support Vector Machine (SVM) and when Chicken swarm optimized values are classified with NBC.

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Automatic Detection of Premature Ventricular Contraction Based on Photoplethysmography Using Chaotic Features and High Order Statistics

Cited by 12 publications

References 7 publications

Is the PPG Signal Chaotic?

Is the PPG Signal Chaotic?

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

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