Analysis of human PPG, ECG and EEG signals by eigenvector methods

Journal of Medical Systems

In this study, Fast Fourier transform (FFT) and autoregressive (AR) methods were selected for processing the photoplethysmogram (PPG), electrocardiogram (ECG), electroencephalogram (EEG) signals recorded in order to examine the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) upon the human electrophysiological signal behavior. The parameters in the autoregressive (AR) method were found by using the least squares method. The power spectra of the PPG, ECG, and EEG signals were obtained by using these spectral analysis techniques. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in extraction of the features representing the PPG, ECG, and EEG signals. Some conclusions were drawn concerning the efficiency of the FFT and least squares AR methods as feature extraction methods used for representing the signals under study.

Digital Signal Processing, 2008

This paper presents the experimental pilot study to investigate the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) in response to photoplethysmographic (PPG), electrocardiographic (ECG), electroencephalographic (EEG) activity. The assessment of wavelet transform (WT) as a feature extraction method was used in representing the electrophysiological signals. Considering that classification is often more accurate when the pattern is simplified through representation by important features, the feature extraction and selection play an important role in classifying systems such as neural networks. The PPG, ECG, EEG signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and the statistical features were calculated to depict their distribution. Our pilot study investigation for any possible electrophysiological activity alterations due to ELF PEMF exposure, was evaluated by the efficiency of DWT as a feature extraction method in representing the signals. As a result, this feature extraction has been justified as a feasible method.

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

Recently we have studied the effects of extremely low frequency pulsed electromagnetic fields (ELF-PEMF) on the human biosignals. Electrocardiogram (ECG) and electroencephalogram (EEG) of seventeen healthy volunteers before and after the electromagnetic (EMF) exposure were recorded and analyzed. The root mean square (RMS) values of the recoded data were considered as comparison criteria. EEG results were shown that there were small variations in the brain electrical activity before and after exposure. The ECG power level was increased up to 1% for most of the subjects. In this paper, we further investigate the effects of the ELF-PEMF on the ECG signal using the hyperbolic T-distributions (HTD). This distribution was shown to be suitable for efficient amplitude and instantaneous frequency (IF) estimation of monoand multicomponent FM signals. In this work, we introduce this distribution to the analysis of ECG signals. The simulation results show that the HTD have a good performance in...

IFMBE Proceedings, 2014

Many researches have been done in the last years upon the various effects of electromagnetic fields (EMF) on biological signals. There are several studies involving subjects who perform various tasks while being exposed to EMF, concluding that some aspects of cognitive function and some distinctive parts of brain physiology may be affected by the exposure to EMF. This paper aims to study the influence of electromagnetic field produced by a medical equipment on the EEG signals processed in real time.

Journal of Medical Systems, 2010

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be

Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process. Spectral features are extracted from EEG signal using Multi Wavelet Transform (MWT). Epileptic and Normal cases are classified using k-Nearest Neighbors (k-NN) classifier. Independent Component Analysis (ICA) and Discrete Wavelet Transform (DWT) are used to extract features from ECG signals.

2014

Biomedical signals are described as the collection of electrical signal acquired from any organ that represents a physical variable of interest. This signal is normally a function of time .It is described in terms of its amplitude, frequency, and phase. The analysis of these signals is important both for research and for medical diagnosis and treatment. If the signals are not properly diagnosed and analyzed it will lead to wrong diagnosis and can be fatal to life. Biomedical signals such as ECG, EMG, and EEG are extremely important in the diagnosis of patients. These signals have noise as well as artifacts which have to be removed for proper treatment of a patient. Different methodologies are used to remove noise and artifacts .This paper describes the different filtering techniques like IIR, FIR and Wavelet.

2012

In the article there have been presented methods of measuring and analysis biological signals, which may be used as signals control mechanical system. Among others, ther have been decribed the usage of EEG (electroencephalographic signal). Like in the case of other signals, the analysis of bio-medical signals most often resolves itself to the frequency analysis of their content with the help of Fourier transformation, and their processing the most often has a form of frequency filtering; in other words, removing from a signal its components with defined frequencies, for example, interferences. The researches have two parts. In the first part date was generated in Lab View program, and next the analysis was done (it was an example of EEG signal). In the next part the EEG signal was measured using 32 channels apertures and next real signal was analyzed using Lab View.

2013

The paper presents a methodology of research on the magnetic field influence on the human body, based on pattern recognition algorithms. A group of 15 volunteers has been exposed to a 50Hz magnetic field of strength 60 A/m. There were recorded 30 ECG signals, 2 for each experiment participant. The first signal was recorded before an exposure to a magnetic field, and the second signal immediately after exposure to the field. The first signal was recorded before an exposure to a magnetic field, and the second signal immediately after exposure, creating two classes of signals. In the paper we discuss supervised classification methods. The purpose of these methods is to detect whether the exposure to a magnetic field occurred, basing on the automatic analysis of the ECG signal.