Intelligence and security in big 5G-oriented IoNT: An overview

Machine learning for wearable IoT‐based applications: A survey

Objectives Timely and accurate R-peak detection is very important for analyzing electrocardiogram (ECG) signal in critical conditions. The main obstacle in observing the correct relation between underlying physiology and features is that there is no specific method to select the features that are needed for diagnosis of a particular heart disease. Therefore, choice of an advanced feature extraction technique is a major concern especially due to non-linear nature of the ECG signal. Material and methods In this study, physioNet (standard) and real-time ECG records have been used. During recording, ECG signal is affected by various noises/interferences which create further challenges in ECG signal analysis. Hence, it requires an effective pre-processing, advanced feature extraction and detection techniques. In this paper, independent principal component analysis (IPCA) is used for pre-processing, since it possesses good characteristic of both principal component analysis (PCA) and independent component analysis (ICA). Due to non-linear nature of ECG signals, chaos analysis is applied in feature extraction stage for different ECG databases. The monitoring and wide description of chaotic patterns of heartbeats are prime concerns for cardiologists. Chaos analysis has been used by estimating different attractors against various time delay dimensions. Correct R-peak detection is useful in diagnosing cardiac diseases and performance of the proposed methodology has been evaluated in terms of sensitivity (Se), positive predictivity (PP), and detection error rate (DER) for both PhysioNet (PN DB) and real-time (RT DB) databases. Results-case-I: Without pre-processing In this case, R-peaks have been detected using chaos analysis+PCA. The proposed method yields Se of 99.91%, PP of 99.93%, and DER of 0.163% for PN DB and Se of 99.77%, PP of 99.83%, and DER of 0.387% for RT DB. Case-II: With pre-processing In this case, R-peaks have been detected using IPCA+chaos analysis+PCA. The proposed method yields Se of 99.95%, PP of 99.96%, and DER of 0.093% for PN DB and Se of 99.96%, PP of 99.97%, and DER of 0.055% for RT DB. Conclusion The proposed technique outperforms the other existing works on various selected evaluation parameters even without pre-processing. Hence, the proposed technique has successfully demonstrated its ability to discriminate different types of heartbeats in most of the critical situations. Therefore, there are strong merits in using chaos analysis as a feature extraction method to reduce the incidence of false diagnosis.

R-Peak Detection Using Chaos Analysis in Standard and Real Time ECG Databases

This paper presents a novel electrocardiogram (ECG) denoising approach based on variational mode decomposition (VMD) This work also incorporates the efficacy of the non-local means (NLM) estimation and the discrete wavelet transform (DWT) filtering technique Current ECG denoising methods fail to remove noise from the entire frequency range of the ECG signal To achieve the effective ECG denoising goal, the noisy ECG signal is decomposed into narrow-band variational mode functions (VMFs) using VMD method The idea is to filter out noise from these narrow-band VMFs To achieve that, the center frequency information associated with each VMFs is used to exclusively divide them into lower- and higher-frequency signal groups The higher frequency VMFs were filtered out using DWT-thresholding technique The lower frequency VMFs are denoised through NLM estimation technique The non-recursive nature of VMD enables the parallel processing of NLM estimation and DWT filtering The traditional DWT-based approaches need large decomposition levels to filter low frequency noises and at the same time NLM technique suffers from the rare-patch effect in high-frequency region On the contrary, in the proposed framework both NLM and DWT approaches complement each other to overcome their individual ill-effects The signal reconstruction is performed using the denoised high frequency and low frequency VMFs The simulation performed on the MIT-BIH Arrhythmia database shows that the proposed method outperforms the existing state-of-the-art ECG denoising techniques

Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering

The morbidity of cardiovascular disease increasingly rises, which makes great impact upon people’s health and life. Electrocardiogram (ECG) beat classification is of great significance to clinical ...

Heartbeat classification algorithm based on one-dimensional convolution neural network

ECG(Electrocardiogram) signals get corrupted due to different noises and artifacts such as power line interference, baseline wander, motion artifacts, contact noise that hide lot of required information. This information is required for detecting various cardiac diseases. Hence before processing ECG signal denoising plays important role to obtain significant features of ECG signal. For denoising, Independent Component Analysis (ICA) is applied on contaminated ECG signal. ICA is a blind source separation technique used to find the independent source signal from non-gaussian noisy signal. The ICA technique deals with maximization of non-gaussian source signal using higher order parameters such as kurtosis and Negentropy. The process of maximization is based on the central limit theorem. ICA can be used for ECG denoising only because ECG signal has super Gaussian shape. This technique is capable to remove noise and artifacts even though they have same frequency as original signal. The FastICA with different granularity functions like tanh, power 3 isolates noise and original signal using kurtosis values of separated signal. Analysis of FastICA shows tanh granularity has faster convergence as compared to power 3 granularity and SNR ratio is of 7db.

Noise analysis of ECG signal using fast ICA

The Electrocardiogram (ECG) is most widely used techniques to detect cardiac diseases. In this paper we propose ECG signal analysis and classification method using wavelet energy histogram method and support vector machine (SVM). The classification of cardiac arrhythmia in the ECG signal consists of three stages including ECG signal preprocessing, feature extraction and heartbeats classification. The discrete wavelet transform is used as preprocessing tool for signal denoising and feature extraction such as R point location, QRS complex detection. Morphological features extracted from the QRS complex are employed as input to the classifier. Binary SVM is used as a classifier to classify the input ECG beat into four classes i.e. Normal, Left bundle branch block, Right bundle branch block and Premature ventricular contraction. MIT-BIH arrhythmia database is used for performance analysis. The proposed classifier performs well with an average sensitivity of 100%, specificity of 99.66%, positive prediction of 99%, false prediction of 0.0033, and average classification rate of 99.75%.

QRS complex detection and arrhythmia classification using SVM

Heart disease is canopy term for any disorder that distress health of an individual. There are heart diseases like Ischemic heart disease, arrhythmia etc. These diseases can be classified using Electrocardiography(ECG) signal. ECG is preferred to the display condition of the patient and for the diagnosis and treatment of various types of cardiac diseases. The variations in P-wave, QRS complex and T-wave parameters in ECG are used to identify the type of illness of the human heart. some heart diseases are not served though other may prove fatal. Thus, there is need of classification of different heart diseases. To classify various heart diseases, it is intended to implement CNN algorithm in the proposed work.

Heart diseases classification using convolutional neural network

Biometric technologies are the automated procedures for validating and identifying the originality of an individual. The biometric systems for automatic authentication are fingerprint, iris, retina, voice, hand geometry recognition. Nowadays, the retina recognition has received an importance, as the unique characteristics of the retina. In the acquisition method, the retinal images might experience some deformations like a change in background intensity, rotation and scale etc. These troubles may disturb the feature extraction process, as a result of the identification process. The Optical Disc Ring (ODR) is used as a preprocessing method. Subsequently, the Speeded Up Robust Feature (SURF) is employed. It is fast as well as vigorous. Oriented FAST and Rotated and BRIEF (ORB) is also used as a feature extraction process. ORB is efficient, unaffected by noise and rotation invariant. The true acceptance rate for SURF and ORB is 98.148 percent.

Retina based biometric identification using SURF and ORB feature descriptors

Cardiac event detection is one of the essential steps in cardiac signal processing, analysis and disease diagnosis. Complete morphology of cardiac waves (P–QRS–T) extracted from the location of R-peak is helpful for feature extraction of many applications related to cardiac diseases classification. Therefore cardiac event detection is a prerequisite for reliable cardiac disease diagnosis, and hence it should be robust and time-efficient so that it can be used for real-time signal processing. This work proposes a novel method for R-peak detection using curvelet transform (CT). It demonstrates the use of curvelet energy with an adaptive threshold to estimate the boundaries around R-peak. The exact R-peak locations are then detected from the input signal with the predefined estimated boundaries. The proposed method is evaluated and analysed with all 48 records from the MIT-BIH arrhythmia database. The experimental analysis result yields an average sensitivity of 99.62%, average positive productivity of 99.74% and average detection error rate of 0.6%. The results obtained have higher than or comparable indices to those in literature. Thus, the proposed system yields high accuracy, low complexity and high processing speed.

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Alka Barhatte

Papers

Noise analysis of ECG signal using fast ICA

QRS complex detection and arrhythmia classification using SVM

Heart diseases classification using convolutional neural network

Retina based biometric identification using SURF and ORB feature descriptors

Cardiac events detection using curvelet transform