Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random-during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.

Heart rate variability: a review

http://refbase.cvc.uab.es/files/BSF2015.pdf

WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians

/pdf/analysis-of-observed-chaotic-data-3spe9uweeb.pdf

Analysis of observed chaotic data

ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform

Automated diagnosis of epileptic EEG using entropies

Computer-assisted arrhythmia recognition is critical for the management of cardiac disorders. Various techniques have been utilized to classify arrhythmias. Generally, these techniques classify two or three arrhythmias or have significantly large processing times. A simpler autoregressive modeling (AR) technique is proposed to classify normal sinus rhythm (NSR) and various cardiac arrhythmias including atrial premature contraction (APC), premature ventricular contraction (PVC), superventricular tachycardia (SVT), ventricular tachycardia (VT) and ventricular fibrillation (VF). AR Modeling was performed on ECG data from normal sinus rhythm as well as various arrhythmias. The AR coefficients were computed using Burg's algorithm. The AR coefficients were classified using a generalized linear model (GLM) based algorithm in various stages. AR modeling results showed that an order of four was sufficient for modeling the ECG signals. The accuracy of detecting NSR, APC, PVC, SVT, VT and VF were 93.2% to 100% using the GLM based classification algorithm. The results show that AR modeling is useful for the classification of cardiac arrhythmias, with reasonably high accuracies. Further validation of the proposed technique will yield acceptable results for clinical implementation.

/pdf/cardiac-arrhythmia-classification-using-autoregressive-5ddibjao4z.pdf

Cardiac arrhythmia classification using autoregressive modeling

A novel method for segmentation of heart sounds (HSs) into single cardiac cycle (S"1-Systole-S"2-Diastole) using homomorphic filtering and K-means clustering is presented. Feature vectors were formed after segmentation by using Daubechies-2 wavelet detail coefficients at the second decomposition level. These feature vectors were then used as input to the neural networks. Grow and Learn (GAL) and Multilayer perceptron-Backpropagation (MLP-BP) neural networks were used for classification of three different HSs (Normal, Systolic murmur and Diastolic murmur). It was observed that the classification performance of GAL was similar to MLP-BP. However, the training and testing times of GAL were lower as compared to MLP-BP. The proposed framework could be a potential solution for automatic analysis of HSs that may be implemented in real time for classification of HSs.

Neural network classification of homomorphic segmented heart sounds

ECG signal conditioning by morphological filtering

Background
Detection of characteristic waves, such as QRS complex, P wave and T wave, is one of the essential tasks in the cardiovascular arrhythmia recognition from Electrocardiogram (ECG).

/pdf/characteristic-wave-detection-in-ecg-signal-using-1mc2nv9hiz.pdf

Characteristic wave detection in ECG signal using morphological transform

The heart rate is a non-stationary signal, and its variation can contain indicators of current disease or warnings about impending cardiac diseases. The indicators can be present at all times or can occur at random, during certain intervals of the day. However, to study and pinpoint abnormalities in large quantities of data collected over several hours is strenuous and time consuming. Hence, heart rate variation measurement (instantaneous heart rate against time) has become a popular, non-invasive tool for assessing the autonomic nervous system. Computer-based analytical tools for the in-depth study and classification of data over day-long intervals can be very useful in diagnostics. The paper deals with the classification of cardiac rhythms using an artificial neural network and fuzzy relationships. The results indicate a high level of efficacy of the tools used, with an accuracy level of 80-85%.

Shankar M. Krishnan

Papers

Cardiac arrhythmia classification using autoregressive modeling

Neural network classification of homomorphic segmented heart sounds

ECG signal conditioning by morphological filtering

Characteristic wave detection in ECG signal using morphological transform

Classification of cardiac abnormalities using heart rate signals.