Polysomnography

2017 ◽  
Author(s):  
Nic Butkov
Keyword(s):  
Signal Processing ◽  
Digital Data ◽  
Electrode Placement ◽  
Electrical Safety ◽  
Preventative Measures ◽  
Recording Process ◽  
Patient Preparation ◽  
Sleep Recording ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitoring. This chapter also includes record samples of the various types of recording artifacts commonly found in sleep studies, with a detailed description of their causes, preventative measures, and recommended corrective actions.

Nonlinear Approach to Brain Signal Modeling

2011 ◽  
pp. 2834-2839
Author(s):  
Tugce Balli ◽  
Ramaswamy Palaniappan
Keyword(s):  
Signal Processing ◽  
Electrical Activity ◽  
Nonlinear Modeling ◽  
Biological Mechanisms ◽  
Linear Modeling ◽  
Memory Impairments ◽  
Biological Signal ◽  
Biological Signals ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Biological signal is a common term used for time series measurements that are obtained from biological mechanisms and basically represent some form of energy produced by the biological mechanisms. Examples of such signals are electroencephalogram (EEG), which is the electrical activity of brain recorded by electrodes placed on the scalp; electrocardiogram (ECG), which is electrical activity of heart recorded from chest, and electromyogram (EMG), which is recorded from skin as electrical activity generated by skeletal muscles (Akay, 2000). Nowadays, biological signals such as EEG and ECG are analysed extensively for diagnosing conditions like cardiac arrhythmias in the case of ECG and epilepsy, memory impairments, and sleep disorders in case of EEG. Apart from clinical diagnostic purposes, in recent years there have been many developments for utilising EEG for brain computer interface (BCI) designs (Vaughan & Wolpaw, 2006). The field of signal processing provides many methods for analysis of biological signals. One of the most important steps in biological signal processing is the extraction of features from the signals. The assessment of such information can give further insights to the functioning of the biological system. The selection of proper methods and algorithms for feature extraction (i.e., linear/nonlinear methods) are current challenges in the design and application of real time biological signal analysis systems. Traditionally, linear methods are used for the analysis of biological signals (mostly in analysis of EEG). Although the conventional linear analysis methods simplify the implementation, they can only give an approximation to the underlying properties of the signal when the signal is in fact nonlinear. Because of this, there has been an increasing interest for utilising nonlinear analysis techniques in order to obtain a better characterisation of the biological signals. This chapter will lay the backgrounds to linear and nonlinear modeling of EEG signals, and propose a novel nonlinear model based on exponential autoregressive (EAR) process, which proves to be superior to conventional linear modeling techniques.

scholarly journals BioSig: The Free and Open Source Software Library for Biomedical Signal Processing

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Carmen Vidaurre ◽  
Tilmann H. Sander ◽  
Alois Schlögl
Keyword(s):  
Signal Processing ◽  
Open Source ◽  
Open Source Software ◽  
Biomedical Signal Processing ◽  
Software Library ◽  
Biomedical Signal ◽  
Computer Interfaces ◽  
Relevant Element ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi
Keyword(s):  
Time Series ◽  
Biomedical Science ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Olfactory Stimuli ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

scholarly journals Распознавание движений руки по сигналам электроэнцефалограммы на основе флуктуационного анализа

2019 ◽  
Vol 45 (4) ◽  
pp. 8 ◽  
Author(s):  
А.Н. Павлов ◽  
А.Е. Руннова ◽  
В.А. Максименко ◽  
О.Н. Павлова ◽  
Д.С. Гришина ◽  
...  
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Mental Representation ◽  
Digital Signal ◽  
Fluctuation Analysis ◽  
Human Arm ◽  
Electroencephalogram Eeg

AbstractWe consider the task of recognition of fragments of multichannel electroencephalogram (EEG) records corresponding to motions of the human arm and to mental representation of these motions. It is shown that the problem of recognition can be solved by processing short EEG segments by the method of fluctuation analysis. The obtained results suggest that fluctuation analysis can be used as an algorithm of the digital signal processing in development of the neurointerface software.

scholarly journals Fourier analysis: the simplification of a complex waveform into simple component sine waves of different amplitudes and frequencies

2020 ◽  
pp. S102-S103
Author(s):  
AI Mamoojee
Keyword(s):  
Fourier Analysis ◽  
Fundamental Frequency ◽  
Simple Wave ◽  
Sine Wave ◽  
Frequency Wave ◽  
Invasive Blood Pressure ◽  
Time Frequency ◽  
Simple Component ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Fourier analysis is the simplification of a complex waveform into simple component sine waves of different amplitudes and frequencies. A discussion on Fourier analysis necessitates reiteration of the physics of waves. A wave is a series of repeating disturbances that propagate in space and time. Frequency: the number of oscillations, or cycles per second. It is measured in Hertz and denoted as 1/time or s-1. Fundamental frequency: the lowest frequency wave in a series. It is also known as the first harmonic. Every other wave in the series is an exact multiple of the fundamental frequency. Harmonic: whole number multiples of the fundamental frequency. Amplitude: the maximum disturbance or displacement from zero caused by the wave. This is the height of the wave. Period: time to complete one oscillation. Wavelength: physical length of one complete cycle. This can be between two crests or two troughs. The higher the frequency, the shorter the wavelength. Velocity: frequency x wavelength. Phase: displacement of one wave compared to another, described as 0°–360°. A sine wave is a simple wave. It can be depicted as the path of a point travelling round a circle at a constant speed, defined by the equation ‘y = sinx’. Combining sine waves of different frequency, amplitude and phase can yield any waveform, and, conversely, any wave can be simplified into its component sine waves. Fourier analysis is a mathematical method of analysing a complex periodic waveform to find its constituent frequencies (as sine waves). Complex waveforms can be analysed, with very simple results. Usually, few sine and cosine waves combine to create reasonably accurate representations of most waves. Fourier analysis finds its anaesthetic applications in invasive blood pressure, electrocardiogram (ECG) and electroencephalogram (EEG) signals, which are all periodic waveforms. It enables monitors to display accurate representations of these biological waveforms. Fourier analysis was developed by Joseph Fourier, a mathematician who analysed and altered periodic waveforms. It is done by computer programmes that plot the results of the analysis as a spectrum of frequencies with amplitude on the y-axis and frequency on the x-axis.

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination

2021 ◽  
Author(s):  
Md Samiul Haque Sunny ◽  
Shifat Hossain ◽  
Nashrah Afroze ◽  
Md. Kamrul Hasan ◽  
Eklas Hossain ◽  
...  
Keyword(s):  
Nonlinear Behavior ◽  
Cognitive Status ◽  
Brain Diseases ◽  
Eeg Signal ◽  
Hilbert Huang Transform ◽  
Neuro Fuzzy ◽  
Research Goal ◽  
Adaptive Noise ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Abstract Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.

Epileptic Seizure-Induced Hypoxemia in Infants with Apparent Life-Threatening Events

PEDIATRICS ◽  
1994 ◽  
Vol 94 (2) ◽  
pp. 148-156
Author(s):  
John Hewertson ◽  
Martin P. Samuels ◽  
David P. Southall ◽  
Christian F. Poets ◽  
Stewart G. Boyd ◽  
...  
Keyword(s):  
Median Duration ◽  
Epileptic Seizure ◽  
Sinus Tachycardia ◽  
Partial Seizures ◽  
Severe Hypoxemia ◽  
Life Threatening ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg ◽  
Normal Standard ◽  
Median Interval

Objective. To describe the physiologic changes that occur during epileptic seizure (ES)-induced apparent life-threatening events (ALTE) and to provide an explanation for the mechanism whereby the hypoxemia characterizing these events occurred. Patients and design. Six infants were retrospectively selected from a group of 17 because they had ALTE documented on physiologic recordings where the first change in signals was in the electroencephalogram (EEG). The 17 infants had clinical features suggestive of partial seizures (but normal standard EEGs) and were from a sample of 172 infants with recurrent ALTE. All 17 infants underwent continuous recordings of breathing, electrocardiogram (ECG), oxygenation, and EEG, but only in 6 was an ES-induced ALTE recorded and the physiologic changes described. Results. Twenty-three ALTE were documented in six infants. Events commenced with an abnormality in the EEG, followed by a decrease in SaO2 after a median interval of 27 seconds (range 2 to 147). Despite resuscitation, the median duration of severe hypoxemia (SaO2 ≤60%) was 40 seconds (range 8 to 74). In 18 events (five infants) there was a median of four apneic pauses (range 1 to 9) preceding the decrease in SaO2 by a median duration of 24 seconds (range 3 to 48). The longest apneic pause per event lasted a median of 19 seconds (range 8 to 47). Breathing movements continued in five events (four infants), and expiratory airflow in one. Sinus tachycardia was found in 19 of the 23 events (six infants), but there were no cardiac arrhythmias. Conclusions. ES in infants can manifest as ALTE and be accompanied by potentially life-threatening episodes of severe hypoxemia and apnea, despite a normal EEG between events.

Bernoulli's Chaotic Map-Based 2D ECG Image Steganography

2019 ◽  
pp. 208-241 ◽  
Author(s):  
Anukul Pandey ◽  
Barjinder Singh Saini ◽  
Butta Singh ◽  
Neetu Sood
Keyword(s):  
Signal Processing ◽  
Chaotic Map ◽  
Image Steganography ◽  
Cover Image ◽  
Ecg Signal ◽  
Mean Square ◽  
Secret Key ◽  
Mean Square Difference ◽  
Electrocardiogram Ecg ◽  
Ecg Steganography

Signal processing technology comprehends fundamental theory and implementations for processing data. The processed data is stored in different formats. The mechanism of electrocardiogram (ECG) steganography hides the secret information in the spatial or transformed domain. Patient information is embedded into the ECG signal without sacrificing the significant ECG signal quality. The chapter contributes to ECG steganography by investigating the Bernoulli's chaotic map for 2D ECG image steganography. The methodology adopted is 1) convert ECG signal into the 2D cover image, 2) the cover image is loaded to steganography encoder, and 3) secret key is shared with the steganography decoder. The proposed ECG steganography technique stores 1.5KB data inside ECG signal of 60 seconds at 360 samples/s, with percentage root mean square difference of less than 1%. This advanced 2D ECG steganography finds applications in real-world use which includes telemedicine or telecardiology.

scholarly journals EEG Based Brain Computer Interfacing for Hand Assistant System Using Wavelet Transform

2020 ◽  
Vol 32 ◽  
pp. 03035
Author(s):  
Dayanand Dhongade ◽  
Mukesh Patil
Keyword(s):  
Wavelet Transform ◽  
Analog Signal ◽  
Digital Data ◽  
Robotic Hand ◽  
Eeg Signals ◽  
Arduino Uno ◽  
Brain Computer Interfacing ◽  
Low Amplitude ◽  
Near Future ◽  
Electroencephalogram Eeg

Robots have been of great use to mankind for several years. In situation where human body fails to operate as per the need robot’s functions in those situations quite efficiently. Electroencephalogram (EEG) controlled hand assistant makes use of EEG signals and Brain Computer Interface (BCI). EEG signals are obtained from the brain using Emotiv Insight headset, after which processing and features extraction of the signals is performed and then conditioning of signals is done as it is a low amplitude signal with additive noise. Signals processing is done on the analog signal by using wavelet transform. Wavelet transform will help to extract information from the analog signal. Then the signals are assigned with the signatures to perform the dedicated task Filtered signal is given to analog pins of Arduino Uno. With the help of inbuilt ADC available on Arduino Uno, Digital Data is also made available on the digital pins. Then through MATLAB access Arduino board. In near future if it gets similar kind of input it will understand exactly what operation to perform. Further the Robotic hand assistant can be operated as we want.

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