State identification of Parkinson's disease based on transfer learning.

Background: The local field potential (LFP) signals are a vital signal for studying the mechanisms of deep brain stimulation (DBS) and constructing adaptive DBS containing information related to the motor symptoms of Parkinson's disease (PD).

Objective: A Parkinson's disease state identification algorithm based on the feature extraction strategy of transfer learning was proposed.

Methods: The algorithm uses continuous wavelet transform (CWT) to convert one-dimensional LFP signals into two-dimensional gray-scalogram images and color images respectively, and designs a Bayesian optimized random forest (RF) classifier to replace the three fully connected layers for the classification task in the VGG16 model, to realize automatic identification of the pathological state of PD patients.

[Research on fault diagnosis of patient monitor based on text mining].

The conventional fault diagnosis of patient monitors heavily relies on manual experience, resulting in low diagnostic efficiency and ineffective utilization of fault maintenance text data. To address these issues, this paper proposes an intelligent fault diagnosis method for patient monitors based on multi-feature text representation, improved bidirectional gate recurrent unit (BiGRU) and attention mechanism. Firstly, the fault text data was preprocessed, and the word vectors containing multiple linguistic features was generated by linguistically-motivated bidirectional encoder representation from Transformer.

[Research on Parkinson's disease recognition algorithm based on sample enhancement].

Parkinson's disease patients have early vocal cord damage, and their voiceprint characteristics differ significantly from those of healthy individuals, which can be used to identify Parkinson's disease. However, the samples of the voiceprint dataset of Parkinson's disease patients are insufficient, so this paper proposes a double self-attention deep convolutional generative adversarial network model for sample enhancement to generate high-resolution spectrograms, based on which deep learning is used to recognize Parkinson's disease. This model improves the texture clarity of samples by increasing network depth and combining gradient penalty and spectral normalization techniques, and a family of pure convolutional neural networks (ConvNeXt) classification network based on Transfer learning is constructed to extract voiceprint features and classify them, which improves the accuracy of Parkinson's disease recognition.

Micro SleepNet: efficient deep learning model for mobile terminal real-time sleep staging.

The real-time sleep staging algorithm that can perform inference on mobile devices without burden is a prerequisite for closed-loop sleep modulation. However, current deep learning sleep staging models have poor real-time efficiency and redundant parameters. We propose a lightweight and high-performance sleep staging model named Micro SleepNet, which takes a 30-s electroencephalography (EEG) epoch as input, without relying on contextual signals.

A screening method based on analytic hierarchy process for closed-loop DBS strategies of Parkinson's disease.

Background: Closed-loop deep brain stimulation (DBS) is a research hotspot in the treatment of Parkinson's disease. However, a variety of stimulation strategies will increase the selection time and cost in animal experiments and clinical studies. Moreover, the stimulation effect is little difference between similar strategies, so the selection process will be redundant.

An improved YOLO Nano model for dorsal hand vein detection system.

To date, venipuncture, the most necessary and fundamental medical means, still remains a challenging task for medical stuff due to significant individual differences in vein condition. Thanks to mature development in near-infrared (NIR) imaging technology, a series of venepuncture auxiliary equipment has been devised and put into use. Yet, previous researches concentrated more on vein pattern segmentation, failing to materialize the identification of veins suitable to puncture in an embedded system.

A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging.

Background: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection.

Objective: This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory.

Synchrosqueezing with short-time fourier transform method for trinary frequency shift keying encoded SSVEP.

Background: The frequencies that can evoke strong steady state visual evoked potentials (SSVEP) are limited, which leads to brain-computer interface (BCI) instruction limitation in the current SSVEP-BCI. To solve this problem, the visual stimulus signal modulated by trinary frequency shift keying was introduced.

Objective: The main purpose of this paper is to find a more reliable recognition algorithm for SSVEP-BCI based on trinary frequency shift keying modulated stimuli.

Synthesis of Surfactant-Assisted C/Fe-FeVO₄ Nanostructure: Characterization and Photocatalytic Degradation of Ciprofloxacin.

The novel C/Fe-FeVO₄ composite photocatalyst were synthesized by using a two-step hydrothermal synthesis method. Through a detailed exploration on the chemical and phisical properties by some spectroscopic and analytical techniques, the as-prepared C/Fe-FeVO₄ exhibted a nanosheet and meso porosity structure. Accordingly, we further utilized this C/Fe-FeVO₄ composite as a photocatalist for degradating the notorious ciprofloxacin (CIP) under simulated solar light (SSL) irradiation.

Comparative analysis of different characteristics of automatic sleep stages.

Background And Objective: With the acceleration of social rhythm and the increase of pressure, there are various sleep problems among people. Sleep staging is an important basis for the diagnosis of sleep disorders and other related diseases. The process of automatic staging of sleep is mainly divided into three core steps: data preprocessing, feature extraction, and classification.

Ensemble learning algorithm based on multi-parameters for sleep staging.

The aim of this study is to propose a high-accuracy and high-efficiency sleep staging algorithm using single-channel electroencephalograms (EEGs). The process consists four parts: signal preprocessing, feature extraction, feature selection, and classification algorithms. In the preconditioning of EEG, wavelet function and IIR filter are used for noise reduction.

A feature extraction method for adaptive DBS using an improved EMD.

Objective: Local field potential (LFP) of a patient with Parkinson's disease often shows abnormal oscillation phenomenon. Extracting and studying this phenomenon and designing adaptive deep brain stimulation (DBS) control library have great significance in the treatment of disease.

Materials And Methods: This paper has designed a feature extraction method based on modified empirical mode decomposition (EMD) which extracts the abnormal oscillation signal in the time domain to increase the overall performance.

A SSVEP Stimuli Encoding Method Using Trinary Frequency-Shift Keying Encoded SSVEP (TFSK-SSVEP).

SSVEP is a kind of BCI technology with advantage of high information transfer rate. However, due to its nature, frequencies could be used as stimuli are scarce. To solve such problem, a stimuli encoding method which encodes SSVEP signal using Frequency Shift-Keying (FSK) method is developed.

Combining canonical correlation analysis and infinite reference for frequency recognition of steady-state visual evoked potential recordings: a comparison with periodogram method.

Steady-state visual evoked potentials (SSVEP) are the visual system responses to a repetitive visual stimulus flickering with the constant frequency and of great importance in the study of brain activity using scalp electroencephalography (EEG) recordings. However, the reference influence for the investigation of SSVEP is generally not considered in previous work. In this study a new approach that combined the canonical correlation analysis with infinite reference (ICCA) was proposed to enhance the accuracy of frequency recognition of SSVEP recordings.

[Review of the active locomotion system for capsule endoscope].

This review summarized the progress of researches on the active locomotion system for capsule endoscope, analyzed the moving and controlling principles in different locomotion systems, and compared their merits and shortcomings. Owing to the complexity of human intestines and the limits to the size and consumption of locomotion system from the capsule endoscope, there is not yet one kind of active locomotion system currently used in clinical practice. The locomotive system driven by an outer rotational magnetic field could improve the commercial endoscope capsule, while its magnetic field controlling moving is complex.

[Development of a video image system for wireless capsule endoscopes based on DSP].

A video image recorder to record video picture for wireless capsule endoscopes was designed. TMS320C6211 DSP of Texas Instruments Inc. is the core processor of this system.

[An image acquisition & processing system of the wireless endoscope based on DSP].

This paper covers an image acquisition & processing system of the capsule-style endoscope. Images sent by the endoscope are compressed and encoded with the digital signal processor (DSP) saving data in HD into PC for analyzing and processing in the image browser workstation.