Review
Chemistry, Analytical
Roberto De Fazio, Veronica Mattei, Bassam Al-Naami, Massimo De Vittorio, Paolo Visconti
Summary: Sleep is crucial for human health, and wearable systems play an important role in detecting sleep quality and disorders. This paper presents the current state-of-the-art wearable systems and software tools, discussing the functions of sleep, the impact of COVID-19 on sleep, and future trends in sleep monitoring systems.
Article
Clinical Neurology
Bernice M. Wulterkens, Pedro Fonseca, Lieke W. A. Hermans, Marco Ross, Andreas Cerny, Peter Anderer, Xi Long, Johannes P. van Dijk, Nele Vandenbussche, Sigrid Pillen, Merel M. van Gilst, Sebastiaan Overeem
Summary: This study developed and validated an algorithm for sleep staging based on wrist-worn PPG and accelerometry. The algorithm showed substantial agreement and accuracy in various age groups and sleep disorders, demonstrating the feasibility of automatic wearable sleep staging in clinical populations.
NATURE AND SCIENCE OF SLEEP
(2021)
Article
Clinical Neurology
Po-Kang Liu, Nettie Ting, Hung-Chih Chiu, Yu-Cheng Lin, Yu-Ting Liu, Bo -Wen Ku, Pei-Lin Lee
Summary: This study aimed to establish and validate an automatic sleep staging system (ASSS) utilizing photoplethysmography and acceleration data collected from a wrist-worn wearable device. Results showed that ASSS was reliable for participants with sleep efficiency >= 80% and had a smaller bias than Actiwatch among those with SE < 80%, suggesting it may be a promising alternative to Actiwatch.
JOURNAL OF CLINICAL SLEEP MEDICINE
(2023)
Article
Engineering, Biomedical
Li Peng, Yanzhen Ren, Zhiheng Luan, Xiong Chen, Xiuping Yang, Weiping Tu
Summary: In this paper, SleepViTransformer, a new scheme for automatic sleep staging, is proposed to improve performance. The scheme transfers cross-modal knowledge pre-trained on image and audio datasets and uses PSG augmentation based on signal characteristics to achieve state-of-the-art performance on publicly available datasets. It adopts the patch encoding technique from Vision Transformer (ViT) on the sleep PSG spectrogram, showing its potential for PSG signal analysis.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Hongfei Wang, Jingjing Wang, Da Chen, Song Ge, Yijian Liu, Zhengjie Wang, Xiaojun Zhang, Qiuquan Guo, Jun Yang
Summary: In this study, a paper-assisted strategy was proposed to fabricate a tattoo ECG electrode based on water transfer printing technology. The fabricated electrode demonstrated high signal to noise ratio, robustness, mechanical stability, and strong immunity to motion artifact, enabling convenient and long-term ECG monitoring during various activities. A soft wireless ECG system was developed by integrating the tattoo electrode, commercialized IC chips, and low-energy Bluetooth module, showing promising application for wearable health monitoring system.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Multidisciplinary
Gloria Cosoli, Susanna Spinsante, Francesco Scardulla, Leonardo D'Acquisto, Lorenzo Scalise
Summary: Wireless ElectroCardioGram (ECG) systems are widely used in various fields such as tele-monitoring, sport applications, support for aging people at home, fetal ECG, and wearable devices. Important aspects to consider when developing a wireless ECG system include electrodes, analog front-end, data acquisition systems, wireless transmission technology, and power consumption. Technological advancements have led to miniaturized and comfortable devices, but further improvements can still be made in multiple aspects.
Article
Chemistry, Analytical
Sigrid Hackl-Wimmer, Marina Tanja Waltraud Eglmaier, Lars Eichen, Karoline Rettenbacher, Daniel Macher, Catherine Walter-Laager, Helmut Karl Lackner, Ilona Papousek, Manuela Paechter
Summary: This study utilized wearable sensor technology to develop algorithms for monitoring toddlers' sleep quality. The research found that touchscreen media use can impact toddlers' physiological sleep quality, especially affecting their nighttime recovery ability.
Review
Chemistry, Analytical
Jiaju Yin, Jiandong Xu, Tian-Ling Ren
Summary: This paper first introduces the basic knowledge and significance of sleep monitoring. Then, it describes the research progress of bioelectrical, biomechanical, and biochemical signals used for sleep monitoring based on the types of physiological signals monitored. Since monitoring sleep quality based on only one signal is not ideal, this paper also reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.
Article
Engineering, Multidisciplinary
Alessandra Galli, Giada Giorgi, Claudio Narduzzi
Summary: Wearable cardiac monitors play a positive role in the early detection of cardiovascular pathologies, but wireless transmission of ECG trace data faces challenges due to the large amount of data. This study proposes a signal analysis approach based on a Gaussian dictionary to compress ECG traces, achieving effective compression for wireless data transmission and accurate reconstruction of ECG traces.
Review
Clinical Neurology
Kiran Maski, Emmanuel Mignot, Giuseppe Plazzi, Yves Dauvilliers
Summary: This review summarizes the current knowledge about disrupted nighttime sleep and sleep instability in narcolepsy. Disrupted nighttime sleep is a key symptom of narcolepsy that has received less attention compared to excessive daytime sleepiness and cataplexy. The intrinsic sleep instability of narcolepsy leads to frequent spontaneous wakings and sleep stage transitions, contributing to disrupted nighttime sleep. Multimodal treatment, including behavioral therapies, counseling on sleep hygiene, and medication, can effectively manage disrupted nighttime sleep, with strong evidence showing improvement with sodium oxybate.
JOURNAL OF CLINICAL SLEEP MEDICINE
(2022)
Article
Instruments & Instrumentation
Marco Chu, Hani E. Naguib
Summary: This study assessed the performance of various conductive composite polymers in collecting electrical signals from the heart, and found that adding 5% carbon nanotubes significantly increased the elastic modulus and conductivity of the composites. SBS-CNT composites at 5% and 10% showed the best performance in detecting ECG waves from the heart.
SMART MATERIALS AND STRUCTURES
(2021)
Article
Engineering, Biomedical
Q. Pan, D. Brulin, E. Campo
Summary: Sleep is crucial for human health, but bad sleep and sleep disorders are increasingly prevalent. Polysomnography (PSG) is the current gold standard for sleep monitoring, but its invasiveness and limited accessibility make it impractical for home use. This paper presents a wireless body network for long-term home sleep monitoring, with a focus on hardware and algorithms for sleep stage classification. The results show that the proposed method is effective for detecting awake and deep sleep, and has good agreement with PSG for detecting periodic leg movements during sleep.
Article
Clinical Neurology
Mathias Baumert, Simon Hartmann, Huy Phan
Summary: Using the deep neural network XSleepNet2, we trained and tested four separate sleep stage classifiers on polysomnograms from children, adults, and older adults. The underrepresentation of certain age groups, especially children, significantly affects the performance of automated sleep staging systems and limits their clinical use.
Article
Engineering, Biomedical
Ning Shen, Tian Luo, Chen Chen, Yanjiong Zhang, Hangyu Zhu, Yuanfeng Zhou, Yi Wang, Wei Chen
Summary: This study proposes an automatic narcolepsy detection method for multiple sleep latency test (MSLT) by combining automatic sleep staging and sleep transition dynamics. The method can accurately distinguish patients with narcolepsy based on MSLT.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Clinical Neurology
Line Pickering, Katharina M. Main, Astrid Sehested, Rene Mathiasen, Ulla Feldt-Rasmussen, Marianne Klose, Suresh Kotagal, Poul J. Jennum
Summary: This study found that children with brain tumours involving the sleep-wake regulatory areas were sleepier/fatigued, had more emotional and mental health problems, and had poorer quality of life compared to those with tumours that did not involve these areas.
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)