New regular M-band orthogonal wavelet filter bank design using zero-insertion method

1994 ◽  
Vol 30 (10) ◽  
pp. 753-754 ◽  
Author(s):  
S.-K. Kwon ◽  
J.-K. Kim
Keyword(s):  
Filter Bank ◽  
Wavelet Filter ◽  
Orthogonal Wavelet ◽  
Insertion Method ◽  
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.

scholarly journals Wavelet Filter Banks Using Allpass Filters

2020 ◽  
Author(s):  
Xi Zhang
Keyword(s):  
Filter Bank ◽  
Filter Banks ◽  
Digital Filtering ◽  
Wavelet Filter ◽  
Linear Phase ◽  
Computationally Efficient ◽  
Orthogonal Wavelet ◽  
Allpass Filters ◽  
Wavelet Filter Bank ◽  
Allpass Filter

Allpass filter is a computationally efficient versatile signal processing building block. The interconnection of allpass filters has found numerous applications in digital filtering and wavelets. In this chapter, we discuss several classes of wavelet filter banks by using allpass filters. Firstly, we describe two classes of orthogonal wavelet filter banks composed of two real allpass filters or a complex allpass filter, and then consider design of orthogonal filter banks without or with symmetry, respectively. Next, we present two classes of filter banks by using allpass filters in lifting scheme. One class is causal stable biorthogonal wavelet filter bank and another class is orthogonal wavelet filter bank, all with approximately linear phase response. We also give several design examples to demonstrate the effectiveness of the proposed method.

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