Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet.

PURPOSE: The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achieve optimal classification performance due to a paucity of raw data. To leverage the data characteristics and enhance the classification accuracy, we propose and evaluate a novel dual-link deep neural network model, 'DoubleLinkSleepCLNet'. METHODS: The DoubleLinkSleepCLNet model performs feature extraction and efficient classification on both the raw EEG and the EEG processed with the Hilbert transform. It leverages the frequency domain and time domain feature modules, resulting in superior performance compared to other models. RESULTS: The DoubleLinkSleepCLNet model, using the 2 Raw/2 Hilbert data modes, achieved the highest classification performance with an accuracy of 88.47%. The average accuracy of the EEG was improved by approximately 4.08% after the application of the Hilbert transform. Additionally, Convolutional Neural Network (CNN) demonstrated superior performance in processing phase information, whereas Long Short-Term Memory (LSTM) excelled in handling time series data. CONCLUSION: The application of the Hilbert transform to EEG data, followed by processing it with a convolutional neural network, enhances the accuracy of the model. These findings introduce novel concepts for accelerating sleep stage prediction research, suggesting potential applications of these methods to other EEG analyses.

Automatic recognition of schizophrenia from brain-network features using graph convolutional neural network.

Schizophrenia is a severe mental illness that imposes considerable economic burden on families and society. However, its clinical diagnosis primarily relies on scales and doctors' clinical experience and lacks an objective and accurate diagnostic approach. In recent years, graph convolutional neural networks (GCN) have been used to assist in psychiatric diagnosis owing to their ability to learn spatial-association information. Therefore, this study proposes a schizophrenia automatic recognition model based on graph convolutional neural network. Herein, the resting-state electroencephalography (EEG) data of 103 first-episode schizophrenia patients and 92 normal controls (NCs) were obtained. The automatic recognition model was trained with a nodal feature matrix that comprised the time and frequency-domain features of the EEG signals and local features of the brain network. The most significant regions that contributed to the model classification were identified, and the correlation between the node topological features of each significant region and clinical evaluation metrics was explored. Experiments were conducted to evaluate the performance of the model using 10-fold cross-validation. The best performance in the theta frequency band with a 6 s epoch length and phase-locked value. The recognition accuracy was 90.01%. The most significant region for identifying with first-episode schizophrenia patients and NCs was located in the parietal lobe. The results of this study verify the applicability of the proposed novel method for the identification and diagnosis of schizophrenia.

Dried Blood Spheroids for Dry-State Room Temperature Stabilization of Microliter Blood Samples.

It is well-known that 2D dried blood spots on paper offer a facile sample collection, storage, and transportation of blood. However, large volume requirements, possible analyte instability, and difficult sample recovery plague this method, lowering confidence in analyte quantification. For the first time, we demonstrate a new approach using 3D dried blood spheroids for stabilization of small volume blood samples, mitigating these effects without cold storage. Blood spheroids form on hydrophobic paper, preventing interaction between the sample and paper substrate, eliminating all chromatographic effects. Stability of the enzyme alanine transaminase and labile organic compounds such as cocaine and diazepam were also shown to increase in the spheroid by providing a critical radius of insulation. On-surface analysis of the dried blood spheroids using paper spray mass spectrometry resulted in sub-ng/mL limits of detection for all illicit drugs tested, representing 1 order of magnitude improvement compared with analysis from 2D dried blood spots.

2D wax-printed paper substrates with extended solvent supply capabilities allow enhanced ion signal in paper spray ionization.

Paper-based microfluidic channels were created from solid wax printing, and the resultant 2D wax-printed paper substrates were used for paper spray (PS) mass spectrometry (MS) analysis of small organic compounds. Controlling fluid flow at the tip of the wax-printed paper triangles enabled the use of lower spray voltages (0.5-1 kV) and extended signal lifetime (10 minutes) in PS-MS. High sensitivity (sub ng mL(-1) levels) and quantitation precision (<10% RSD) have been achieved in the analysis of illicit drugs in 4 µL of raw urine (fresh and dry), as well as corrosion inhibitors and pesticides in water samples. The reported study encourages the future development of disposable 3D microfluidic paper-based analytical devices, which function with simple operation but capable of on-chip analyte detection by MS; such a device can replace the traditional complex laboratory procedures for MS analysis to enable on-site in situ sampling with portable mass spectrometers.