Design of Time–Frequency Localized Filter Banks: Transforming Non-convex Problem into Convex Via Semidefinite Relaxation Technique

Sharma, Manish; Bhati, Dinesh; Pillai, Swapna K; Pachori, Ram Bilas; Gadre, Vikram M.

doi:10.1007/s00034-015-0228-9

Cited by 37 publications

(10 citation statements)

References 31 publications

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“…It has been addressed in [19][20][21][22][23] that the optimal FBs designed to achieve different balance between the time and frequency localizations are very effective in image compression, image segmentation and feature extraction algorithms. Moreover, the timefrequency localized optimization criteria has been used in the design of optimal HPFB.…”

Section: Review Of Related Filter Banksmentioning

confidence: 99%

“…Also, a new design of halfband filters is proposed with narrow transition bands having their points of flatness at the middle of the pass and stop bands [24]. The design of these halfband filters (HBF) uses optimization criteria such as regularity, maximally flat conditions, energy of passband and stopband or frequency selectivity.…”

Section: Review Of Related Filter Banksmentioning

confidence: 99%

“…However, this proposed scheme is complicated and the optimization of second filter depends upon the optimization of first filter. Also, the optimal FBs are designed using SDP technique with an objective of minimizing either time spread or frequency spread subject to the fixed frequency spread or time spread [23].…”

Section: Review Of Related Filter Banksmentioning

confidence: 99%

See 2 more Smart Citations

A new approach to design triplet halfband filter banks based on balanced-uncertainty optimization

Gawande¹,

Rahulkar

Holambe³

2016

Digital Signal Processing

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Section: Review Of Related Filter Banksmentioning

confidence: 99%

Section: Review Of Related Filter Banksmentioning

confidence: 99%

See 1 more Smart Citation

A new approach to design triplet halfband filter banks based on balanced-uncertainty optimization

Gawande¹,

Rahulkar

Holambe³

2016

Digital Signal Processing

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“…In the proposed study, we aim to get a low-pass filter that has the least stopband ripple energy for the underlying two-band orthogonal wavelet filter bank and to judge the efficacy of the optimum filter in SAR detection. The construction of filter bank has been transformed to a semidefinite programming (SDP) optimization problem [48]. The solution to this SDP will provide us with the desired filter bank.…”

Section: Wavelet Filter Banks For Ecg Analysismentioning

confidence: 99%

Detection of shockable ventricular arrhythmia using optimal orthogonal wavelet filters

Sharma

Tan

Acharya

2019

Neural Comput & Applic

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Sudden cardiac death (SCD) is caused by lethal arrhythmia. Ventricular fibrillation (VF) and ventricular tachycardia (VT) are amenable to defibrillation or electrical shock therapy (''shockable'' arrhythmia) that can abolish the VF/VT and restore normal electrical and mechanical heart function. The challenge is to differentiate between shockable and non-shockable arrhythmia during the emergency response to SCD. When it comes to saving the life, accurate electrocardiogram (ECG) diagnosis and fast delivery of appropriate treatment is imperative. Automated systems to differentiate shockable from nonshockable arrhythmia have been developed to overcome the difficulty, and possible errors due to the manual inspection. In the present work, we have devised an efficient, effective and robust automated system to detect shockable and nonshockable arrhythmia using an optimal wavelet-based features extracted from ECG epochs of 2 s durations. We employed optimal two-channel frequency selective orthogonal wavelet filter bank to diagnose shockable ventricular arrhythmia. The optimization was carried out by minimizing the stop band ripple energy of the wavelet filter. The optimal orthogonal wavelet filter has been designed using a semi-definite programming (SDP) formulation without the use of any parameterization. The SDP solution gave us the desired optimal orthogonal wavelet filter bank with minimum stop band energy and the desired degree of regularity for the given length of filter. Fuzzy entropy and Renyi entropy features were extracted from the 2-s ECG epochs. These extracted features were then fed into the classifiers for discrimination of shockable arrhythmia rhythms and non-shockable arrhythmia rhythms. The best results were obtained from support vector machine. Accuracy of 97.8%, sensitivity of 93.42%, and specificity of 98.35% were obtained using a tenfold cross validation scheme. The developed automated system is accurate and robust; therefore, it can be integrated in automated external defibrillators that can be deployed for hospitals as well as out-of-hospital emergency resuscitation of SCD.

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“…In our study, we used the SL optimal OWFB. Using a semi-definite program (SDP) technique [31,32], we optimized the filter coefficients, and the interior point algorithm provided the optimized solutions [33,34,35]. Hence, we tested the optimized OWFBs for analyzing ECG signals in order to separate LRHT from HRHT patients.…”

Section: Introductionmentioning

confidence: 99%

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

Rajput

Sharma

Acharya

2019

IJERPH

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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.

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Design of Time–Frequency Localized Filter Banks: Transforming Non-convex Problem into Convex Via Semidefinite Relaxation Technique

Cited by 37 publications

References 31 publications

A new approach to design triplet halfband filter banks based on balanced-uncertainty optimization

A new approach to design triplet halfband filter banks based on balanced-uncertainty optimization

Detection of shockable ventricular arrhythmia using optimal orthogonal wavelet filters

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

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