Optimal Design of Three-Band Orthogonal Wavelet Filter Bank with Stopband Energy for Identification of Epileptic Seizure EEG Signals

2018 ◽  
pp. 197-207 ◽  
Author(s):  
Dinesh Bhati ◽  
Ram Bilas Pachori ◽  
Vikram M. Gadre
Keyword(s):  
Optimal Design ◽  
Epileptic Seizure ◽  
Filter Bank ◽  
Wavelet Filter ◽  
Eeg Signals ◽  
Orthogonal Wavelet ◽  
Wavelet Filter Bank

scholarly journals Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank

2019 ◽  
Vol 16 (21) ◽  
pp. 4068 ◽  
Author(s):  
Jaypal Singh Rajput ◽  
Manish Sharma ◽  
U. Rajendra Acharya
Keyword(s):  
Blood Pressure ◽  
High Risk ◽  
Filter Bank ◽  
Early Stage ◽  
Fractional Dimension ◽  
Wavelet Filter ◽  
Orthogonal Wavelet ◽  
Ecg Signals ◽  
Wavelet Filter Bank ◽  
Hypertension Diagnosis

Hypertension (HT) is an extreme increment in blood pressure that can prompt a stroke, kidney disease, and heart attack. HT does not show any symptoms at the early stage, but can lead to various cardiovascular diseases. Hence, it is essential to identify it at the beginning stages. It is tedious to analyze electrocardiogram (ECG) signals visually due to their low amplitude and small bandwidth. Hence, to avoid possible human errors in the diagnosis of HT patients, an automated ECG-based system is developed. This paper proposes the computerized segregation of low-risk hypertension (LRHT) and high-risk hypertension (HRHT) using ECG signals with an optimal orthogonal wavelet filter bank (OWFB) system. The HRHT class is comprised of patients with myocardial infarction, stroke, and syncope ECG signals. The ECG-data are acquired from physionet’s smart health for accessing risk via ECG event (SHAREE) database, which contains recordings of a total 139 subjects. First, ECG signals are segmented into epochs of 5 min. The segmented epochs are then decomposed into six wavelet sub-bands (WSBs) using OWFB. We extract the signal fractional dimension (SFD) and log-energy (LOGE) features from all six WSBs. Using Student’s t-test ranking, we choose the high ranked WSBs of LOGE and SFD features. We develop a novel hypertension diagnosis index (HDI) using two features (SFD and LOGE) to discriminate LRHT and HRHT classes using a single numeric value. The performance of our developed system is found to be encouraging, and we believe that it can be employed in intensive care units to monitor the abrupt rise in blood pressure while screening the ECG signals, provided this is tested with an extensive independent database.

Three Channel Wavelet Filter Banks With Minimal Time Frequency Spread for Classification of Seizure-Free and Seizure EEG Signals

2020 ◽  
pp. 220-236
Author(s):  
Dinesh Bhati ◽  
Akruti Raikwar ◽  
Ram Bilas Pachori ◽  
Vikram M. Gadre
Keyword(s):  
Classification Accuracy ◽  
Filter Bank ◽  
Optimality Criteria ◽  
Wavelet Filter ◽  
Eeg Signals ◽  
Time Frequency ◽  
Minimal Time ◽  
Frequency Spread ◽  
Band Filter ◽  
Wavelet Filter Bank

The authors compute the classification accuracy of minimal time-frequency spread wavelet filter bank with three channels in discriminating seizure-free and seizure electroencephalogram (EEG) signals. Wavelet filter bank with three channels generates two wavelet functions and one scaling function at the first level of wavelet decomposition. A time-frequency localized filter bank can be generated by minimizing the time spread and frequency spread of any one or all the functions simultaneously. The minimal time-frequency spread wavelet filter bank with three channels of regularity order, one designed with several different time-frequency optimality criteria and length six, are chosen, and the effect of each optimality criterion on the discrimination of seizure-free and seizure EEG signals is computed. The classification accuracy for five different optimality criteria are computed. Time-frequency localized three-band filter bank of length six classifies, the seizure-free and seizure EEG signals of Bonn University EEG database, with 98.25% of accuracy.

Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank

2020 ◽  
Vol 123 ◽  
pp. 103924 ◽  
Author(s):  
Jaypal Singh Rajput ◽  
Manish Sharma ◽  
Ru San Tan ◽  
U. Rajendra Acharya
Keyword(s):  
Filter Bank ◽  
Automated Detection ◽  
Wavelet Filter ◽  
Orthogonal Wavelet ◽  
Ecg Signals ◽  
Wavelet Filter Bank
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