Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model.

BACKGROUND AND OBJECTIVE: People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by splitting the dataset into time slices or sliding windows. However, such approaches to data augmentation may cause the testing data to be contaminated by the training data. To solve this problem, this study developed a novel method for detecting ASD with EEG data. METHODS: This study quantified the functional connectivity of the subject's brain from EEG signals and defined the individual to be the unit of analysis. Publicly available EEG data were gathered from 97 and 92 subjects with ASD and typical development (TD), respectively, while they were at rest or performing a task. Time-series maps of brain functional connectivity were constructed, and the data were augmented using a deep convolutional generative adversarial network. In addition, a combined network for ASD detection, based on convolutional neural network (CNN) and long short-term memory (LSTM), was designed and implemented. RESULTS: Based on functional connectivity, the network achieved classification accuracies of 81.08% and 74.55% on resting state and task state data, respectively. In addition, we found that the functional connectivity of ASD differed from TD primarily in the short-distance functional connectivity of the parietal and occipital lobes and in the distant connections from the right temporoparietal junction region to the left posterior temporal lobe. CONCLUSIONS: This paper provides a new perspective for better utilizing EEG to understand ASD. The method proposed in our study is expected to be a reliable tool to assist in the diagnosis of ASD.

Assessing systemic vascular resistance using arteriolar pulse transit time based on multi-wavelength photoplethysmography.

Objective.Sympathetic nerve activity affects blood pressure by contracting the arteriole, which can increase systemic vascular resistance (SVR). Consequently, SVR is a key factor affecting blood pressure. However, a method for measuring SVR continuously is lacking. This paper formulated and experimentally validated a method that uses the arteriolar pulse transmit time (aPTT) to track changes in SVR.Approach.multi-wavelength photoplethysmogram (PPG), electrocardiogram (ECG), and galvanic skin response (GSR) data were simultaneously gathered using a measurement system designed by this study. Blood perfusion was monitored by laser Doppler. Least mean square (LMS) is an adaptive filtering algorithm. Our LMS-based algorithm formulated in this study was used to calculate the aPTT from the multi-wavelength PPGs. A cold stimulation experiment was conducted to verify the relationship between aPTT determined by algorithm and arteriole vasodilation. An emotinal stimulation experiment conducted, in which GSR was employed to further verify the relationship between aPTT and SVR. Twenty healthy young participants were asked to watch movie clips, which excited their sympathetic nerves. The dynamic time warping (DTW) distance is applied to evaluate between correlation of GSR and aPTT.Main results.The changes in aPTT was extracted using our LMS-based method. During the recovery period after cold stimulation, aPTT decreased with the average slope of -0.2080, while blood perfusion increased with the average slope of 0.7046. Meanwhile, 70% participants' DTW distances median between aPTT and GSR were significantly smaller than that between PTT and GSR during emotion stimulation.Significance.Our method uses aPTT, a continuous measurable parameter, to closely reflect SVR, as verified through experiments.

Real-Time Multiple Gesture Recognition: Application of a Lightweight Individualized 1D CNN Model to an Edge Computing System.

The human-machine interface (HMI) detects electrophysiological signals from the subject and controls the machine based on the signal information. However, most applications are still only in the testing stage and are generally unavailable to the public. In recent years, researchers have been devoted to making wearable HMI devices smarter and more comfortable. In this study, a wearable, intelligent eight-channel electromyography (EMG) signal-based system was designed to recognize 21 types of gestures. An analog front end (AFE) integrated chip (IC) was developed to detect the EMG signals, and an integrated EMG signal acquisition device integrating an elastic armband was fabricated. An SIAT database of 21 gestures was established by collecting EMG gesture signals from 10 volunteers. A lightweight 1D CNN model was constructed and subjected to individualized training by using the SIAT database. The maximum signal recognition accuracy was 89.96%, and the average model training time was 14 min 13 s. Given its small size, the model can be applied on lower-performance edge computing devices and is expected to be applied to smartphone terminals in the future. The source code is available at https://github.com/Siat-F9/EMG-Tools.