Article
Computer Science, Information Systems
Masud Shah Jahan, Marjan Mansourvar, Sadasivan Puthusserypady, Uffe Kock Wiil, Abdolrahman Peimankar
Summary: This study proposes the use of machine learning algorithms to diagnose atrial fibrillation and compares several common algorithms. The results show that some algorithms perform well in short-term ECG segments, especially ensemble methods.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2022)
Article
Engineering, Electrical & Electronic
Caiyun Ma, Shoushui Wei, Tongshuai Chen, Jingquan Zhong, Zhenhua Liu, Chengyu Liu
Summary: This study proposed three methods for atrial fibrillation (AF) diagnosis, with the third method achieving the highest detection accuracy across different databases and experimental conditions, demonstrating high accuracy and reliable recognition for AF events.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Biochemistry & Molecular Biology
Gonzalo Ricardo Rios-Munoz, Francisco Fernandez-Aviles, Angel Arenal
Summary: This paper proposes a solution using convolutional recurrent neural networks to automatically identify rotational activity drivers in endocardial electrograms. The proposed method outperforms other methods when using bipolar EGMs as input signals. There is no significant impact of signal length and sampling frequency on the algorithm performance. This architecture opens up new possibilities for ablation strategies and driver detection methods in understanding and treating atrial fibrillation.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Mohamed Abdelazez, Sreeraman Rajan, Adrian D. C. Chan
Summary: Atrial fibrillation (AF) is a serious cardiovascular condition with potential complications such as stroke, heart attack, and death. Compressive sensing techniques can help reduce the requirements of continuous monitoring. The study proposed an AF detector using a deterministic compressively sensed ECG and achieved good performance in detecting AF.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Biophysics
Ying Wang, Yongjian Li, Meng Chen, Rui Huo, Lei Liu, Yesong Liang, Shoushui Wei
Summary: This study proposes an automatic detector combining deep learning and semi-supervised learning to assist cardiologists in identifying atrial fibrillation in a large amount of ECG data. The proposed method shows very promising performance on different datasets and has good applicability in clinical practice.
JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY
(2023)
Article
Chemistry, Analytical
Mingu Kang, Siho Shin, Gengjia Zhang, Jaehyo Jung, Youn Tae Kim
Summary: The study presented a method for classifying ECG data into different emotional states based on stress levels, improving accuracy by calculating specific ECG features, with an average accuracy of 97.6%. The proposed model increased accuracy by 8.7% compared to previous algorithms, offering a tool for effectively managing mental state by quantifying stress signals experienced by individuals.
Article
Chemistry, Analytical
Yaru Yue, Chengdong Chen, Pengkun Liu, Ying Xing, Xiaoguang Zhou
Summary: This study proposed a method using the FSWT-GKSVM model for automatic detection of atrial fibrillation. By conducting time-frequency analysis and machine learning classification on ECG signals, it achieved stable and accurate detection performance.
Review
Physiology
Andrew S. Tseng, Peter A. Noseworthy
Summary: Machine learning techniques have garnered significant interest in predicting and screening atrial fibrillation due to their ability to utilize clinical data for accurate predictions and screenings, showcasing the potential of artificial intelligence in cardiovascular medicine.
FRONTIERS IN PHYSIOLOGY
(2021)
Article
Cardiac & Cardiovascular Systems
Jiacheng He, Sen Liu, Cuiwei Yang, Yong Wei
Summary: Atrial fibrillation (AF) is associated with heart failure and stroke. Early management can reduce stroke rates and mortality. Current guidelines screen high-risk individuals based solely on age, but this study aims to explore other potential predictors of AF risk.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2023)
Article
Cardiac & Cardiovascular Systems
Keng Tat Koh, Wan Chung Law, Win Moe Zaw, Diana Hui Ping Foo, Chen Ting Tan, Anderson Steven, Desmond Samuel, Tem Lom Fam, Ching Hua Chai, Zhai Sing Wong, Sivaraj Xaviar, Chandan Deepak Bhavnani, Jason Seng Hong Tan, Yen Yee Oon, Asri Said, Alan Yean Yip Fong, Tiong Kiam Ong
Summary: The study aimed to compare the diagnostic yield of 30-day smartphone ECG recording with 24-hour Holter monitoring in detecting AF after ischaemic stroke. The results showed that smartphone ECG recording significantly improved AF detection and increased the proportion of patients on oral anticoagulation therapy.
Article
Health Care Sciences & Services
Ju-Seung Kwun, Jang Hoon Lee, Bo Eun Park, Jong Sung Park, Hyeon Jeong Kim, Sun-Hwa Kim, Ki-Hyun Jeon, Hyoung-won Cho, Si-Hyuck Kang, Wonjae Lee, Tae-Jin Youn, In-Ho Chae, Chang-Hwan Yoon
Summary: This study found that the use of a patch-type device (AT-Patch) increased the detection rate of new-onset atrial fibrillation in high-risk patients. Further research is needed to investigate the value of early detection of atrial fibrillation and its potential in reducing adverse clinical outcomes.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Cardiac & Cardiovascular Systems
Srikanth Perike, Francisco J. Gonzalez-Gonzalez, Issam Abu-Taha, Frederick W. Damen, Laurin M. Hanft, Ken S. Lizama, Anahita Aboonabi, Andrielle E. Capote, Yuriana Aguilar-Sanchez, Benjamin Levin, Zhenbo Han, Arvind Sridhar, Jacob Grand, Jody Martin, Joseph G. Akar, Chad M. Warren, R. John Solaro, Sang-Ging Ong, Dawood Darbar, Kerry S. Mcdonald, Craig J. Goergen, Beata M. Wolska, Dobromir Dobrev, Xander H. T. Wehrens, Mark D. McCauley
Summary: The study revealed that patients with AF have increased expression of PPP1R12C protein, leading to decreased atrial contractility. Overexpression of PPP1R12C in mice increases PP1c targeting to MLC2a, causing MLC2a dephosphorylation, and subsequently increasing AF inducibility.
CIRCULATION RESEARCH
(2023)
Review
Biochemistry & Molecular Biology
Joanna Zygadlo, Grzegorz Procyk, Pawel Balsam, Piotr Lodzinski, Marcin Grabowski, Aleksandra Gasecka
Summary: Atrial fibrillation (AF) is the most common cardiac arrhythmia, and autoimmunity is believed to play a crucial role in its development. Autoantibodies are implicated in regulating heart rhythm and associated with AF. Identifying the autoantibody profile of different AF patient groups is essential for developing effective treatments.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Medicine, General & Internal
P. Krisai, P. Hammerle, S. Blum, P. Meyre, S. Aeschbacher, P. Melchiorre-Mayer, O. Baretella, N. Rodondi, D. Conen, S. Osswald, M. Kuhne, C. S. Zuern
Summary: The presence of atrial fibrillation (AF) on a single surface ECG is significantly associated with increased risk of mortality and hospitalizations for congestive heart failure in AF patients, suggesting that these patients are a high-risk group that may benefit from intensified treatment.
JOURNAL OF INTERNAL MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Devender Kumar, Sadasivan Puthusserypady, Helena Dominguez, Kamal Sharma, Jakob E. Bardram
Summary: This study aims to investigate the contextual and temporal distribution of false positives (FPs) in an advanced deep learning (DL)-based algorithm for atrial fibrillation (AF) detection when applied to a free-living ambulatory electrocardiogram (ECG) dataset. The study trains and evaluates a DL model on public arrhythmia datasets and applies it to a contextualized ECG dataset collected under free-living conditions. The results show that a significant portion of segments marked as AF by the model are FPs, with correlations to specific contextual events. These findings have implications for the use and design of DL models for AF detection and the role of context information in reducing FPs.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Neurosciences
Lucia Billeci, Ettore Caterino, Alessandro Tonacci, Maria Luisa Gava
Review
Chemistry, Analytical
Chiara Baldini, Lucia Billeci, Francesco Sansone, Raffaele Conte, Claudio Domenici, Alessandro Tonacci
Article
Psychiatry
Elisa Santocchi, Letizia Guiducci, Margherita Prosperi, Sara Calderoni, Melania Gaggini, Fabio Apicella, Raffaella Tancredi, Lucia Billeci, Paola Mastromarino, Enzo Grossi, Amalia Gastaldelli, Maria Aurora Morales, Filippo Muratori
FRONTIERS IN PSYCHIATRY
(2020)
Review
Engineering, Chemical
Lucia Billeci, Asia Badolato, Lorenzo Bachi, Alessandro Tonacci
Article
Chemistry, Analytical
Alessandro Tonacci, Lucia Billeci, Irene Di Mambro, Roberto Marangoni, Chiara Sanmartin, Francesca Venturi
Summary: Wearable sensors are commonly used to assess physiological signals without obtrusiveness. Olfactory training showed different autonomic responses, with increased familiarity leading to higher relaxation tendencies. This may have potential applications in personalized treatments for neuropsychiatric and eating disorders.
Article
Chemistry, Multidisciplinary
Lorenzo Bachi, Lucia Billeci, Maurizio Varanini
Summary: This paper presents a QRS detection algorithm based on moving average filters, which makes decisions based on the features of the QRS complex and achieves satisfactory performance on benchmark databases. Despite not reaching the highest published performance levels, the proposed QRS detection method enhances computational efficiency while maintaining high accuracy.
APPLIED SCIENCES-BASEL
(2021)
Article
Neurosciences
Lucia Billeci, Ugo Faraguna, Enrica L. Santarcangelo, Paola d'Ascanio, Maurizio Varanini, Laura Sebastiani
Summary: Individuals with different levels of hypnotizability display varying interoceptive sensitivity and awareness, which can be observed during sleep through heartbeatevoked cortical potential amplitude. The study found that hypnotizability influences the correlation between interoceptive sensitivity and HEP amplitude during sleep.
Article
Medicine, General & Internal
Giuseppe Murdaca, Simone Caprioli, Alessandro Tonacci, Lucia Billeci, Monica Greco, Simone Negrini, Giuseppe Cittadini, Patrizia Zentilin, Elvira Ventura Spagnolo, Sebastiano Gangemi
Summary: Machine Learning algorithms can be used to predict early lung involvement in Systemic sclerosis (SSc) patients and provide useful exams for diagnostic purposes. The random forest algorithm performed well in this study, however, other classifiers are also valuable when shorter response time is needed.
Article
Chemistry, Analytical
Alessandro Tonacci, Alessandro Scafile, Lucia Billeci, Francesco Sansone
Summary: The technological developments have enabled innovative approaches for disease diagnosis. Researchers have been studying the microbiota composition in biological fluids to develop less invasive and more affordable tools such as electronic nose and electronic tongue which are gaining importance in the field.
Review
Chemistry, Analytical
Margherita Modesti, Alessandro Tonacci, Francesco Sansone, Lucia Billeci, Andrea Bellincontro, Gloria Cacopardo, Chiara Sanmartin, Isabella Taglieri, Francesca Venturi
Summary: This review investigates the recent applications of traditional and novel methods, such as electronic senses, sensory analysis, and wearables, in food quality assessment. The synergy between traditional and innovative approaches is deemed to be the best way to achieve a balance between accuracy and feasibility.
Article
Engineering, Chemical
Giuseppe Murdaca, Sara Banchero, Marco Casciaro, Alessandro Tonacci, Lucia Billeci, Alessio Nencioni, Giovanni Pioggia, Sara Genovese, Fiammetta Monacelli, Sebastiano Gangemi
Summary: Vascular dementia is a cognitive impairment associated with age and vascular etiology, typically occurring when brain vessels suffer micro-accidents leading to insufficient oxygen and nutrient supply to the brain. Machine learning methods can be utilized to identify predictive biomarkers for cognitive worsening early on, helping save time and money, and reducing the burden on patients.
Article
Nutrition & Dietetics
Olivia Curzio, Lucia Billeci, Vittorio Belmonti, Sara Colantonio, Lorenzo Cotrozzi, Carlotta Francesca De Pasquale, Maria Aurora Morales, Cristina Nali, Maria Antonietta Pascali, Francesca Venturi, Alessandro Tonacci, Nicola Zannoni, Sandra Maestro
Summary: Studies have found that adding horticultural therapy to conventional clinical treatment can have a positive effect on reducing stress levels in patients with anorexia nervosa restricting type (AN-R). This pilot study aimed to evaluate the impact of horticultural therapy on AN-R patients compared to those receiving only conventional treatment. The results showed that horticultural therapy improved stress levels and affective problems in the AN-R group.
Article
Chemistry, Analytical
Lucia Billeci, Chiara Sanmartin, Alessandro Tonacci, Isabella Taglieri, Lorenzo Bachi, Giuseppe Ferroni, Gian Paolo Braceschi, Luigi Odello, Francesca Venturi
Summary: In recent decades, traditional descriptive analysis performed by skilled sensory panels has been deemed too complex and time-consuming for the industry needs, leading to the exploration of more efficient methods of sensory training. This study investigated the effectiveness of a short, intensive sensory training period using wearable sensors to monitor physiological signals related to emotions in a group of wine tasters. The results showed that even a two-day training period could effectively modulate autonomic nervous system activity and familiarize tasters with odorous compounds.
Editorial Material
Chemistry, Multidisciplinary
Maurizio Varanini, Alessandro Tonacci, Lucia Billeci
APPLIED SCIENCES-BASEL
(2023)
Article
Geriatrics & Gerontology
Marco Laurino, Gaspare Alfi, Lucia Billeci, Ilaria Bortone, Emma Buzzigoli, Antonella Cecchi, Silvia Del Ry, Amalia Gastaldelli, Elisa Lai, Maria Aurora Morales, Cristina Pagni, Claudio Passino, Silvia Severino, Alessandro Tonacci, Angelo Gemignani, Maria Giovanna Trivella
Summary: Aging not only leads to a reduction in psychophysical and sensory capacities, but also increases the risk of neurodegenerative disorders, including dementia. To promote healthy aging, it is crucial to prevent and correct factors that may decrease these capacities. The use of technology and telemedicine can support health professionals in providing better care and support for aging individuals in their home environment.
AGING CLINICAL AND EXPERIMENTAL RESEARCH
(2021)