FM-FCN: A Neural Network with Filtering Modules for Accurate Vital Signs Extraction.

Neural networks excel at capturing local spatial patterns through convolutional modules, but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals. In this work, we propose a novel network named filtering module fully convolutional network (FM-FCN), which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise. First, instead of using a fully connected layer, we use an FCN to preserve the time-dimensional correlation information of physiological signals, enabling multiple cycles of signals in the network and providing a basis for signal processing. Second, we introduce the FM as a network module that adapts to eliminate unwanted interference, leveraging the structure of the filter. This approach builds a bridge between deep learning and signal processing methodologies. Finally, we evaluate the performance of FM-FCN using remote photoplethysmography. Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse (BVP) signal and heart rate (HR) accuracy. It substantially improves the quality of BVP waveform reconstruction, with a decrease of 20.23% in mean absolute error (MAE) and an increase of 79.95% in signal-to-noise ratio (SNR). Regarding HR estimation accuracy, FM-FCN achieves a decrease of 35.85% in MAE, 29.65% in error standard deviation, and 32.88% decrease in 95% limits of agreement width, meeting clinical standards for HR accuracy requirements. The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction. The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.

Identifying the personal characteristics of decent work perception for nursing students in China using latent profile analysis.

BACKGROUND: Given the importance of perceptions of decent work for nursing students' future career choices, we attempted to determine potential classifications and characteristics of nursing students' perceptions of decent work so that targeted interventions could be developed. METHODS: A convenience sample of 1004 s- to fourth-year nursing students completed the General Information Questionnaire, Self-Regulatory Fatigue Scale, Occupational Identity Questionnaire, and Decent Work Perceptions Scale in a cross-sectional survey in Heilongjiang Province, China, resulting in 630 valid questionnaires with a valid return rate of 62.75%. Nursing students' perceptions of decent work were defined using descriptive and regression analysis. RESULTS: Latent profile analysis (LPA) identified three subgroups: low perceived decent work group, medium perceived decent work group, and high perceived decent work group, accounting for 4.76%, 69.37%, and 25.87% of the sample, respectively. The results of unordered multiclass logistic regression show that nursing students with relatively low levels of perceived decent work are more likely to have a low professional identity, a lack of respect for nursing seniors, an involuntary choice of nursing major, and a low family income. CONCLUSION: Different types of nursing students have different perceptions of decent work, and these universities and related departments can use different educational guidance strategies.

Prevalence of mobile phone addiction among medical students: a systematic review.

The incidence and factors related to mobile phone addiction among Chinese medical students were analyzed through meta-analysis. Chinese literature databases (such as China Knowledge Network and VIP Information Resource System) and English literature databases (such as PubMed and Web of Science) were searched for cross-sectional studies on the incidence and factors related to mobile phone addiction, and the required data were extracted. Meta-analysis was performed using a random effects model with RevMan 5.3 statistical software, and publication bias was tested with Stata 12.0. A total of 20 studies were included, including 36,365 study subjects. Among them, there were 10,597 cases of mobile phone addiction with an incidence of 29.14%. The results of the meta-analysis showed that the combined OR values (95% CI) of the factors were: gender 1.070 (1.030-1.120), residence 1.118 (1.090-1.146), school type 1.280 (1.241-1.321), mobile phone use time 1.098 (1.068-1.129), sleep quality 1.280 (1.288-1.334), self-perception of learning 0.737 (0.710-0.767), and family relationship 0.821 (0.791-0.852). The study showed that being a male student from cities and towns, being at a vocational college, excessive use of mobile phones, and poor sleep quality were the risk factors for mobile phone addiction among medical students in China. Positive self-perception of learning and family relationships were protective factors, and more related factors are still controversial and need to be further explored and confirmed.

[Heart rate extraction algorithm based on adaptive heart rate search model].

Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: -0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.