[Electrocardiogram signal classification algorithm of nested long short-term memory network based on focal loss function].

Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples.

[Research on high-efficiency electrocardiogram automatic classification based on autoregressive moving average model fitting].

The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting.

Resting-State MEG Source Space Network Metrics Associated with the Duration of Temporal Lobe Epilepsy.

To evaluate the relationship between the network metrics of 68 brain regions and duration of temporal lobe epilepsy (TLE). Magnetoencephalography (MEG) data from 53 patients with TLE (28 left TLE, 25 right TLE) were recorded between seizures at resting state and analyzed in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), lower alpha (8-10 Hz), upper alpha (10-13 Hz), beta (13-30 Hz), and lower gamma (30-48 Hz).

Novel Hybrid p- and n-Type Organic Thermoelectric Materials Based on Mussel-Inspired Polydopamine.

Mussel-inspired polydopamine (PDA) can serve as building blocks and interfaces in designing functional materials. Here, the use of PDA as an interlayer between polyaniline (PANi) and multidimensional carbon materials, such as graphene quantum dots (GQD), multiwalled carbon nanotubes (MWCNT), and graphene nanosheets (GNS), to improve the thermoelectric performance of p-type polymer-based materials has been reported. The introduction of PDA promotes the carrier mobility of GQD/PDA/PANi, CNT/PDA/PANi, and GNS/PDA/PANi ternary composites due to the superior adhesive property of PDA.

Data on flexibility and thermal stability of Polypyrrole-based ternary nanocomposite films.

Carbon materials, such as multi-walled carbon nanotube (MWCNT), single-walled carbon nanotubes (SWCNT), and graphene sheets (GNS), filling into polymer substrates can effectively improve performance of composite materials [1], [2]. The data presented here in this article illustrates the different impacts of GNS and MWCNT on the mechanical properties of polypyrrole (PPy)-based composites systems. PPy/GNS and PPy/MWCNT binary composites were added into poly(3,4-ethylenedioxythiophene): poly (styrene sulfonate) (PEDOT: PSS) matrix.

A Look Into the Power of fNIRS Signals by Using the Welch Power Spectral Estimate for Deception Detection.

Neuroimaging technologies have improved our understanding of deception and also exhibit their potential in revealing the origins of its neural mechanism. In this study, a quantitative power analysis method that uses the Welch power spectrum estimation of functional near-infrared spectroscopy (fNIRS) signals was proposed to examine the brain activation difference between the spontaneous deceptive behavior and controlled behavior. The power value produced by the model was applied to quantify the activity energy of brain regions, which can serve as a neuromarker for deception detection.

Influence of EEG References on N170 Component in Human Facial Recognition.

The choice of the reference electrode scheme is an important step in event-related potential (ERP) analysis. In order to explore the optimal electroencephalogram reference electrode scheme for the ERP signal related to facial recognition, we investigated the influence of average reference (AR), mean mastoid reference (MM), and Reference Electrode Standardization Technique (REST) on the N170 component via statistical analysis, statistical parametric scalp mappings (SPSM) and source analysis. The statistical results showed that the choice of reference electrode scheme has little effect on N170 latency ( > 0.