scholarly journals A Robust and Real-Time Capable Envelope-Based Algorithm for Heart Sound Classification: Validation under Different Physiological Conditions

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 972 ◽  
Author(s):  
Angelika Thalmayer ◽  
Samuel Zeising ◽  
Georg Fischer ◽  
Jens Kirchner
Keyword(s):  
Real Time ◽  
Hilbert Transform ◽  
Computational Effort ◽  
Heart Sounds ◽  
Envelope Curve ◽  
Physiological Conditions ◽  
Sound Classification ◽  
The Hilbert Transform ◽  
Short Time

This paper proposes a robust and real-time capable algorithm for classification of the first and second heart sounds. The classification algorithm is based on the evaluation of the envelope curve of the phonocardiogram. For the evaluation, in contrast to other studies, measurements on 12 probands were conducted in different physiological conditions. Moreover, for each measurement the auscultation point, posture and physical stress were varied. The proposed envelope-based algorithm is tested with two different methods for envelope curve extraction: the Hilbert transform and the short-time Fourier transform. The performance of the classification of the first heart sounds is evaluated by using a reference electrocardiogram. Overall, by using the Hilbert transform, the algorithm has a better performance regarding the F1-score and computational effort. The proposed algorithm achieves for the S1 classification an F1-score up to 95.7% and in average 90.5%. The algorithm is robust against the age, BMI, posture, heart rate and auscultation point (except measurements on the back) of the subjects.

Model of multicomponent narrow-band periodical non-stationary random signal

2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Hilbert Transform ◽  
Spectral Properties ◽  
Narrow Band ◽  
Analytic Signal ◽  
Random Signal ◽  
Hilbert Transformation ◽  
The Hilbert Transform

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.

scholarly journals A Peak Detection Algorithm for Localization and Classification of Heart Sounds in PCG Signals using K-means Clustering

2021 ◽  
Author(s):  
Muhammad Zubair
Keyword(s):  
Detection Algorithm ◽  
Heart Sounds ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Classifier ◽  
Mel Frequency Cepstral Coefficients ◽  
Sound Classification ◽  
Clustering Model ◽  
Peak Detection Algorithm

Traditionally, the heart sound classification process is performed by first finding the elementary heart sounds of the phonocardiogram (PCG) signal. After detecting sounds S1 and S2, the features like envelograms, Mel frequency cepstral coefficients (MFCC), kurtosis, etc., of these sounds are extracted. These features are used for the classification of normal and abnormal heart sounds, which leads to an increase in computational complexity. In this paper, we have proposed a fully automated algorithm to localize heart sounds using K-means clustering. The K-means clustering model can differentiate between the primitive heart sounds like S1, S2, S3, S4 and the rest of the insignificant sounds like murmurs without requiring the excessive pre-processing of data. The peaks detected from the noisy data are validated by implementing five classification models with 30 fold cross-validation. These models have been implemented on a publicly available PhysioNet/Cinc challenge 2016 database. Lastly, to classify between normal and abnormal heart sounds, the localized labelled peaks from all the datasets were fed as an input to the various classifiers such as support vector machine (SVM), K-nearest neighbours (KNN), logistic regression, stochastic gradient descent (SGD) and multi-layer perceptron (MLP). To validate the superiority of the proposed work, we have compared our reported metrics with the latest state-of-the-art works. Simulation results show that the highest classification accuracy of 94.75% is achieved by the SVM classifier among all other classifiers.

scholarly journals A Peak Detection Algorithm for Localization and Classification of Heart Sounds in PCG Signals using K-means Clustering

2021 ◽  
Author(s):  
Muhammad Zubair
Keyword(s):  
Detection Algorithm ◽  
Heart Sounds ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Classifier ◽  
Mel Frequency Cepstral Coefficients ◽  
Sound Classification ◽  
Clustering Model ◽  
Peak Detection Algorithm

Traditionally, the heart sound classification process is performed by first finding the elementary heart sounds of the phonocardiogram (PCG) signal. After detecting sounds S1 and S2, the features like envelograms, Mel frequency cepstral coefficients (MFCC), kurtosis, etc., of these sounds are extracted. These features are used for the classification of normal and abnormal heart sounds, which leads to an increase in computational complexity. In this paper, we have proposed a fully automated algorithm to localize heart sounds using K-means clustering. The K-means clustering model can differentiate between the primitive heart sounds like S1, S2, S3, S4 and the rest of the insignificant sounds like murmurs without requiring the excessive pre-processing of data. The peaks detected from the noisy data are validated by implementing five classification models with 30 fold cross-validation. These models have been implemented on a publicly available PhysioNet/Cinc challenge 2016 database. Lastly, to classify between normal and abnormal heart sounds, the localized labelled peaks from all the datasets were fed as an input to the various classifiers such as support vector machine (SVM), K-nearest neighbours (KNN), logistic regression, stochastic gradient descent (SGD) and multi-layer perceptron (MLP). To validate the superiority of the proposed work, we have compared our reported metrics with the latest state-of-the-art works. Simulation results show that the highest classification accuracy of 94.75% is achieved by the SVM classifier among all other classifiers.

scholarly journals Real-time estimation of phase and amplitude with application to neural data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael Rosenblum ◽  
Arkady Pikovsky ◽  
Andrea A. Kühn ◽  
Johannes L. Busch
Keyword(s):  
Real Time ◽  
Hilbert Transform ◽  
Brain Activity ◽  
Synthetic Data ◽  
Time Estimation ◽  
Beta Band ◽  
Data Set ◽  
Real Time Estimation ◽  
The Hilbert Transform ◽  
Physical Phenomena

AbstractComputation of the instantaneous phase and amplitude via the Hilbert Transform is a powerful tool of data analysis. This approach finds many applications in various science and engineering branches but is not proper for causal estimation because it requires knowledge of the signal’s past and future. However, several problems require real-time estimation of phase and amplitude; an illustrative example is phase-locked or amplitude-dependent stimulation in neuroscience. In this paper, we discuss and compare three causal algorithms that do not rely on the Hilbert Transform but exploit well-known physical phenomena, the synchronization and the resonance. After testing the algorithms on a synthetic data set, we illustrate their performance computing phase and amplitude for the accelerometer tremor measurements and a Parkinsonian patient’s beta-band brain activity.

Crack characterisation by detection and classification of nonlinear behaviour using the Hilbert transform

2012 ◽  
Vol 226 (11) ◽  
pp. 2610-2626
Author(s):  
Anees U Rehman ◽  
Keith Worden ◽  
Jem A Rongong
Keyword(s):  
Frequency Response ◽  
Hilbert Transform ◽  
Fatigue Cracks ◽  
Nonlinear Behaviour ◽  
Response Functions ◽  
Frequency Response Functions ◽  
Dependent Frequency ◽  
Resonance Frequencies ◽  
The Hilbert Transform

The presence of a crack in a structure causes a local variation in the stiffness that alters the dynamics of the entire system. This article introduces an approach for crack characterisation by detection and classification of the nonlinearities arising from a crack operating in flexural and torsion modes of vibration. Nonlinearity detection is accomplished by obtaining amplitude dependent frequency response functions, whereas classification is achieved by processing those frequency response functions through the Hilbert transform. For the purpose of illustrating this process, a dog-bone-type specimen is tested. Fatigue cracks of various depths are generated and propagated in the specimen by vibration at resonance. For varying crack depths, a range of excitation levels are used to obtain amplitude dependent frequency response functions from which resonance frequencies and damping levels are extracted. While utilising the Hilbert transform for nonlinearity classification, Haoui correction terms are incorporated for accommodating the issues associated with truncated data, either baseband or zoomed. Corrections terms for residual modes outside the frequency range of interest are neglected.

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

2017 ◽  
Vol 7 (10) ◽  
pp. 52
Author(s):  
LAKSHMI PRANEETHA
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Bloom Filter ◽  
Scientific Applications ◽  
Pruning Algorithm ◽  
Density Data ◽  
Data Points ◽  
Short Time ◽  
Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

A Bayesian View on the Hilbert Transform and the Kramers-Kronig Transform of Electrochemical Impedance Data: Probabilistic Estimates and Quality Scores

2020 ◽  
Author(s):  
Jiapeng Liu ◽  
Ting Hei Wan ◽  
Francesco Ciucci
Keyword(s):  
Power Generation ◽  
Electrochemical Impedance Spectroscopy ◽  
Hilbert Transform ◽  
Electrochemical Impedance ◽  
The Self ◽  
Impedance Data ◽  
The Hilbert Transform ◽  
Validation Tests ◽  
Self Consistency ◽  
Mathematical Relations

<p>Electrochemical impedance spectroscopy (EIS) is one of the most widely used experimental tools in electrochemistry and has applications ranging from energy storage and power generation to medicine. Considering the broad applicability of the EIS technique, it is critical to validate the EIS data against the Hilbert transform (HT) or, equivalently, the Kramers–Kronig relations. These mathematical relations allow one to assess the self-consistency of obtained spectra. However, the use of validation tests is still uncommon. In the present article, we aim at bridging this gap by reformulating the HT under a Bayesian framework. In particular, we developed the Bayesian Hilbert transform (BHT) method that interprets the HT probabilistic. Leveraging the BHT, we proposed several scores that provide quick metrics for the evaluation of the EIS data quality.<br></p>

scholarly journals A Radial Basis Function Finite Difference Scheme for the Benjamin–Ono Equation

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 65
Author(s):  
Benjamin Akers ◽  
Tony Liu ◽  
Jonah Reeger
Keyword(s):  
Radial Basis Function ◽  
Hilbert Transform ◽  
Basis Function ◽  
Differential Operators ◽  
Convergence Rates ◽  
Traveling Wave Solutions ◽  
Algebraic Decay ◽  
Pseudo Differential Operators ◽  
Radial Basis ◽  
The Hilbert Transform

A radial basis function-finite differencing (RBF-FD) scheme was applied to the initial value problem of the Benjamin–Ono equation. The Benjamin–Ono equation has traveling wave solutions with algebraic decay and a nonlocal pseudo-differential operator, the Hilbert transform. When posed on R, the former makes Fourier collocation a poor discretization choice; the latter is challenging for any local method. We develop an RBF-FD approximation of the Hilbert transform, and discuss the challenges of implementing this and other pseudo-differential operators on unstructured grids. Numerical examples, simulation costs, convergence rates, and generalizations of this method are all discussed.

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

Sign in / Sign up

Export Citation Format

Share Document