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112 articles for “signal extraction”
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Signal Extraction and Frequency Measurement in a Vortex Flow meter: A Review
Abstract: Normal 0 false false false EN-US X-NONE X-NONE Vortex flowmeter are very popular in process industries due to their advantages. Vortex flow meter measures the frequency of the vortex signal which is linearly proportional to the flowrate. However vortex flow meter is not performing well in low flow range. Research is being carried out to reduce errors in vortex flow meter and improve its performance. Since vortex flow meter is …
Published in Journal of Instrumentation Technology & Innovations · Vol. 1, Issue 1, 2011 · pp. 8–12 Read article
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Signal Feature Extraction and Machine Learning Techniques for Human Activity Recognition
Abstract: Human Activity Recognition (HAR) has emerged as a critical field of study with diverse applications in healthcare, fitness tracking, smart homes, and human-computer interaction. The aim of this research is to create an efficient HAR system through advanced techniques characterized by signal feature extraction and machine learning algorithms. The MEMS sensors are used appropriately during data mining to extract time-domain, frequency-domain, and statistical features, which are subsequently passed to the …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 1, 2025 · pp. 24–41 Read article
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Study of ECG Signal Features Extraction using Wavelet Transform: A Review
Abstract: QRS-complex detection is used for analysis of ECG signal. ECG gives the information about electrical activity of the heart Since an ECG is a non-stationary signal and consists of various types of noise such as baseline wander, power line interference and muscle noise. To eliminate such types of noise wavelet transform is used. Mother wavelet is widely used to extract information from these types of signal by denoising. This paper …
Published in Current Trends in Signal Processing · Vol. 4, Issue 2, 2014 · pp. 1–6 Read article
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Stationary Wavelet and Recursive Least Square Filtering Based Fetal ECG Data Extraction from Composite Abdominal Signal
Abstract: This paper introduces a fast methodology for fetal ECG extraction based on stationary wavelet and noise canceler adaptive filter with the recursive least square filter. Firstly, signals are preprocessed by moving averaging filter for removing baseline wander. The stationary wavelet and the recursive least square filter is applied on preprocessed signal that effectively separates the maternal ECG and extracts fetal ECG (fECG) from the abdominal ECG. Finally, fetal ECG(fECG) is …
Published in Current Trends in Signal Processing · Vol. 7, Issue 2, 2017 · pp. 46–58 Read article
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Pattern Extraction and Classification of ECG Signals using Neuro-Fuzzy Techniques
Abstract: Electrocardiography is a technique which provides the interpretation of the electrical activity of the heart over a period of time. The condition of cardiac health is given by Electrocardiogram (ECG) and heart rate. This paper represents the techniques, which extracts features from the ECG signals and classify them. ECG signal were taken from preselected data segment of the MIT‐BIH Arrhythmia Database. Pattern extraction provides the PQRST-wave values, heart rate and …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 1, Issue 3, 2014 · pp. 1–7 Read article
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Non-invasive Extraction of Fetal Electrocardiogram and Maternal Electrocardiogram from Mixed Source Signal
Abstract: This paper is to bring in self-regulating methodology to obtain Fetal Electrocardiogram (FECG) and Maternal Electrocardiogram (MECG) from the mixed source signal. The Abdominal Electrocardiogram (AECG) is treated as an input signal to achieve the above-mentioned goal. FECG and MECG can be differentiated according to the power of a signal. FECG are weak signals as compared to that of MECG. But, FECG signals are engrossed in various other interference signals …
Published in Current Trends in Signal Processing · Vol. 6, Issue 3, 2016 · pp. 16–22 Read article
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Intelligent Polymer-Integrated Wearable Platforms for Sustainable IoT and Predictive Health Monitoring for Migraine Detection
Abstract: Migraine is a neurological disorder, and its effect on the global workforce is resultantly significant. However, the fact of the matter is the absence of notable technological breakthroughs and the fact that the technology presently available is reactive, meaning it tackles the symptoms of the attack after the attack has occurred. The requirement for this paper is, therefore, the provision of an innovative approach, and this paper will describe the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 946–960 Read article
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ECG De-noising Techniques and Optimal Feature Selection Using Principle Component Analysis
Abstract: AbstractECG (Electrocardiography) is used to record and determine the condition of the heart. This paper provides an overview of various ECG de-noising techniques that are used to eliminate different type of noises; therefore, noise reduction procedure to be performed to eliminate different type of noises such as baseline wander, dc offset and high frequency interference, and then the pre-processed signal is used to extract features from the ECG signal. This …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 5, Issue 1, 2018 · pp. 14–20 Read article
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Investigation of Surface Electromyography Signal for an Upper Limb Amputee
Abstract: Surface electromyography (SEMG) is a method to measure the muscle activity of the skin. Sensors placed on the skin detect the electrical current or signal generated by active muscles. Lot of the research has focuses on the detection of signal from upper limb amputee with activity of triceps and biceps muscles. Our main aim of this study is to process the acquired signal from the different muscles of the shoulder …
Published in Current Trends in Signal Processing · Vol. 7, Issue 1, 2017 · pp. 19–28 Read article
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Alzheimer disorders diagnosis system design using machine learning for EEG signal
Abstract: The diagnosis of Alzheimer's disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered …
Published in Journal of Control & Instrumentation Read article
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Analysis of impact of Meditation on Cognitive Workload using EEG Signals
Abstract: AbstractMental activities can be indicated by the Cognitive workload which are useful in applications like Biomedical, Human Machine Interaction and Task analysis. The mental effort applied on the Working memory at a certain given time is commonly known as Cognitive load. The EEG Signals of Cognitive Workload can be studied and classified. The features such as Entropy, Energy, Power, etc. can be extracted from the EEG signals and processed using …
Published in Current Trends in Signal Processing · Vol. 10, Issue 1, 2020 · pp. 29–39 Read article
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Harnessing the Bioactive Potential of Calotropis gigantea and Calotropis procera
Abstract: The medicinal plants of the Apocynaceae family Calotropis gigantea and Calotropis procera have gained more and more interest as source of bioactive molecules for biomedical applications like cancer therapy. They have various pharmacological activities, which are believed to be linked to various phytochemical constituents such as cardenolides, cardiac glycosides, flavonoids, triterpenoids, steroids, phenolic compounds, alkaloids, and proteins in their latex. The recent molecular and cellular studies show that extracts and …
Published in International Journal of Molecular Biotechnological Research · Vol. 4, Issue 2, 2026 Read article
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Intelligent ECG Signal Noise Removal with Moving Median Filter Using Neural Network
Abstract: In this paper, the electrocardiogram (ECG) signal is susceptible to noise and artifact and it is essential to remove noise using neural network. The noise in order to support first 3600 of noisy heart signal is collected from MIT-BIH data base. In this paper, the use of average median filter and artificial neural network is analyzed. The available filters for power-line interference need a reference channel or regard that the …
Published in Current Trends in Signal Processing · Vol. 3, Issue 2, 2013 · pp. 19–25 Read article
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Identifying and Implementing a Machine Learning Model Suitable for Processing Visually Evoked Potential
Abstract: A Brain-Computer Interface (BCI) is a system that translates brain activity patterns into computer commands, bypassing physical movement. Electroencephalography (EEG) is commonly used to acquire signals in BCI research. Visual evoked potentials (VEPs) are brain responses in the visual cortex to visual stimuli. Recent studies show that exposing individuals to flickering at a consistent frequency generates EEG signals synchronized with the stimulation. Efficient extraction of VEP signals begins with preprocessing …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 1–8 Read article
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Enhancing Smart Grid Resilience Through AI-Based Fault Classification
Abstract: Traditional power grids can be developed into smart grids, and they are comprised of the latest information and communication technologies (ICTs), which are based on establishing the relationship between the conventional electricity systems along with the usage of smart meters and distributed generation. This dynamic improves energy efficiency and the integration of renewables. Well, the dynamic and reversible power injection from Distributed Energy Resources (DERs) creates substantial operational problems. These …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 10–15 Read article
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A New Technique of Simultaneous Measurement of ECG and PCG Signals
Abstract: Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 In this work, we measure the bioelectrical signals ECG (electrocardiogram) and PCG (phonocardiogram) simultaneously. At present individual signal measuring equipment are available such as ECG machine and stethoscope (digital) etc. The diagnosis purpose both the signal have significant meaning individually, the PCG signal measured through stethoscope by an expert medical person. But the technique for simultaneous display and measurement of ECG …
Published in Journal of Instrumentation Technology & Innovations · Vol. 1, Issue 1, 2011 · pp. 1–7 Read article
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Survey of Techniques for Clustering and Classification of ECG using WEKA
Abstract: ECG analysis can be done for automatic detection of abnormality in cardiac activity. This can be helpful in generating alert for saving a precious life. Various techniques have been proposed in literature for feature detection and classification such as; fuzzy logic methods, artificial neural networks (ANN), and support vector machines (SVM), wavelet transform, Hilbert transform, etc. Recently, numerous researches and techniques have been developed for analyzing the ECG signal on …
Published in Journal of Microcontroller Engineering and Applications · Vol. 3, Issue 2, 2016 · pp. 14–19 Read article
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Alzheimer disorders diagnosis system design using machine learning for EEG signal
Abstract: The diagnosis of Alzheimer's disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered …
Published in Journal of Control & Instrumentation · Vol. 14, Issue 1, 2023 · pp. 9–22 Read article
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Multi-Parameter Biomedical Sensor-Based Mental State Classification Using EEG And Deep Learning Techniques
Abstract: With mental health concerns becoming increasingly widespread, there is a strong need for systems that can monitor conditions like stress, anxiety, and fatigue in a continuous and non- invasive manner. This research proposes a novel multi-parameter biomedical sensing framework for mental state classification by integrating electroencephalography (EEG) signals with physiological parameters, including body temperature acquired using LM35 sensors, heart rate from pulse sensors, and blood oxygen saturation (SpO₂) measurements. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 1–8 Read article
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Design of EMG based Control System for Prosthetic Arm
Abstract: Prosthesis, field of bio-mechatronics, is an artificial extension used to replace the function or appearance of a missing limb or lost body part due to injury (traumatic), to correct congenital deficiencies or to supplement defective body parts. A myoelectrically controlled prosthesis detects muscle contractions using electrodes and this electrical activity acts as a signal to activate the prosthesis. In the presented work, the EMG signal is acquired using surface electrodes …
Published in Journal of Electronic Design Technology · Vol. 4, Issue 2, 2013 · pp. 1–6 Read article