Optimization and implementation of the wavelet based algorithms for embedded biomedical signal processing

Stojanović, Radovan; Knežević, Saša; Karadaglic, Dejan; Devedzic, Goran

doi:10.2298/csis120517013s

Cited by 14 publications

(6 citation statements)

References 20 publications

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“…In accordance with the aforementioned issue, the 7-layer technique is carried out in parallel units, with programming written at high rates employing processor units. Many advantages can be seen for biomedical applications with such high rates, where justification can be processed before pre-processing phases ( 12 – 14 ). These advantageous systems are created using wearable devices where auto-learning models are activated.…”

Section: Survey Of Conventional Modelsmentioning

confidence: 99%

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Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach

Manoharan¹,

Shitharth

Yafoz

et al. 2022

Front. Public Health

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The production, testing, and processing of signals without any interpretation is a crucial task with time scale periods in today's biological applications. As a result, the proposed work attempts to use a deep learning model to handle difficulties that arise during the processing stage of biomedical information. Deep Conviction Systems (DCS) are employed at the integration step for this procedure, which uses classification processes with a large number of characteristics. In addition, a novel system model for analyzing the behavior of biomedical signals has been developed, complete with an output tracking mechanism that delivers transceiver results in a low-power implementation approach. Because low-power transceivers are integrated, the cost of implementation for designated output units will be decreased. To prove the effectiveness of DCS feasibility, convergence and robustness characteristics are observed by incorporating an interface system that is processed with a deep learning toolbox. They compared test results using DCS to prove that all experimental scenarios prove to be much more effective for about 79 percent for variations with time periods.

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Section: Survey Of Conventional Modelsmentioning

confidence: 99%

“…All the different approaches ( 1 – 14 ) that are discussed using different methods for processing biomedical signals which are not accurate in the field of medical diagnosis. The biomedical signals cannot be passed without the presence of proper communication units at both the transmitter and receiver sides.…”

Section: Survey Of Conventional Modelsmentioning

confidence: 99%

Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach

Manoharan¹,

Shitharth

Yafoz

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…This is not desirable when talking about signal analysis because low frequencies require a slow evolution of the signal, while high ones are found in sudden transitions in the signal, whose "capture" is favored by a good temporal resolution. This represents a "supervised learning" method, in which the basic functions are chosen a priori; detailed information may be found in [47][48][49][50]. This way of sharing the time-frequency plane can be obtained by translating and scaling on the time axis a unique function called the mother wave Ψ(t) : ψ s,τ = 1 √ s ψ t−τ s , where the scale (s) variables and those of positioning on the time axis (τ) are continuous variables.…”

Section: Wavelet Transforms (Wt) Ecg Processingmentioning

confidence: 99%

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Minea

Dumitrescu

Costea

2021

Sensors

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Background: The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution. Methods: This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram—EEG, electrocardiogram—ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine. Results: The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance. Conclusion: The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient’s life, with the support of the e-Call system.

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“…The goal of this paper is to design an algorithm for the screening of the denoising process, based on the DWT and applied to rotor bearing monitoring. The DWT is more used than the CWT because it gives enough information and a significant reduction in computational time [ 5 ]. Applying the DWT on a signal, two coefficient vectors are obtained, with two different frequency ranges, which describe the signal: the coefficient vector with a range in the higher frequencies is called the detail coefficient

, while the one corresponding to lower frequencies is called the approximation coefficient

.…”

Section: Introductionmentioning

confidence: 99%

Screening of Discrete Wavelet Transform Parameters for the Denoising of Rolling Bearing Signals in Presence of Localised Defects

Brusa

Delprete

Gargiuli

et al. 2022

Sensors

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Maintenance scheduling is a fundamental element in industry, where excessive downtime can lead to considerable economic losses. Active monitoring systems of various components are ever more used, and rolling bearings can be identified as one of the primary causes of failure on production lines. Vibration signals extracted from bearings are affected by noise, which can make their nature unclear and the extraction and classification of features difficult. In recent years, the use of the discrete wavelet transform for denoising has been increasing, but studies in the literature that optimise all the parameters used in this process are lacking. In the current article, the authors present an algorithm to optimise the parameters required for denoising based on the discrete wavelet transform and thresholding. One-hundred sixty different configurations of the mother wavelet, threshold evaluation method, and threshold function are compared on the Case Western Reserve University database to obtain the best combination for bearing damage identification with an iterative method and are evaluated with tradeoff and kurtosis. The analysis results show that the best combination of parameters for denoising is dmey, rigrSURE, and the hard threshold. The signals were then distributed in a 2D plane for classification through an algorithm based on principal component analysis, which uses a preselection of features extracted in the time domain.

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Optimization and implementation of the wavelet based algorithms for embedded biomedical signal processing

Cited by 14 publications

References 20 publications

Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach

Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Screening of Discrete Wavelet Transform Parameters for the Denoising of Rolling Bearing Signals in Presence of Localised Defects

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