Insomnia is a sleep disorder in which the subject encounters problems in sleeping. The aim of this study is to identify insomnia events from normal or effected person using time frequency analysis of PSD approach applied on EEG signals using channel ROC-LOC. In this research article, attributes and waveform of EEG signals of Human being are examined. The aim of this study is to draw the result in the form of signal spectral analysis of the changes in the domain of different stages of sleep. The analysis and calculation is performed in all stages of sleep of PSD of each EEG segment. Results indicate the possibility of recognizing insomnia events based on delta, theta, alpha and beta segments of EEG signals.

This sleep disorder is reflected as the changes in the electrical activities and chemical activities in the brain that can be observed by capturing the brain signals and the images. In this research, Short Time-frequency analysis of Power Spectrum Density (STFAPSD) approach applied on Electroencephalogram (EEG) Signals for prediction of RBD sleep disorder. Collection of Electroencephalogram (EEG) of normal subjects & different type of sleep disordered subjects & application of signal processing on EEG data for development the algorithm for detection of sleep disorder and implementation in MATLAB.

This paper explores the application of digital signal processing (DSP) techniques in the examination of electroencephalogram (EEG) data. DSP encompasses a collection of mathematical algorithms designed to employ signals, such as EEG recordings, and finds application in diverse domains, including sleep medicine, neuroscience, and biomedical engineering. Employing DSP methods for EEG data analysis enables the extraction of pertinent insights from EEG signals, the identification of event-related patterns, and the enhancement of diagnostic and therapeutic practices across various disciplines. This article provides an overview of prevalent DSP methodologies employed in EEG signal processing, encompassing filtering, power spectral analysis, wavelet analysis, independent component analysis, and artifact removal.

Electroencephalogram (EEG) is a recording of the electrical movement of the brain from the scalp. For sleep disorder analysis the EEG test is done while the subject is sleeping. In this paper discuss about recording of brain signal (EEG) and how these signal play major role to finding in different brain diseases. EEG data can be different when subjects are asleep or when exhausted or when some sort of action takes place. When the patient is awake standard EEG test can be taken, but it may not demonstrate any unusual electrical action. During the sleep, brainwave patterns alter and may show more unusual electrical action.

State estimation of electrocardiography (ECG) signals plays a crucial role in effective cardiac monitoring systems and assists in the early detection of abnormalities. However, ECG signals are often contaminated by interfering noise such as baseline wander, power-line interference, and motion artifacts, which significantly hinder precise state estimation. To address these challenges, this paper proposes a particle filter (PF)-based method for ECG signal estimation, which effectively preserves clinically relevant waveform characteristics under nonlinear and non-stationary noise conditions. The technique is implemented in MATLAB using a synthetic ECG signal and is evaluated using fast Fourier transform, spectrogram analysis, power spectral density, autocorrelation, and wavelet transform. A comparative analysis reveals that the proposed PF-based method significantly outperforms baseline denoising techniques. Quantitatively, the process achieves a mean square error (MSE) of 0.001382, representing a 48.5% improvement over the variant without the attention mechanism (MSE =0.002682). Additionally, the signal-to-noise ratio is enhanced by 6.3 dB, reaching 15.87 dB, the highest among all tested methods. The percentage root mean square difference is also reduced to 4.62%, indicating low signal distortion. Furthermore, the accuracy of R-peak identification reaches 98.5%, ensuring a better extraction of cardiac features. The proposed method demonstrates a runtime of 0.75 s for a 10-s ECG segment, confirming its suitability for real-time biomedical applications. The simulation results validate that the PF-based state estimation framework is not only more precise and robust to noise but also computationally efficient, making it ideal for ECG signal processing in wearable health monitoring and real-time cardiac diagnostics.