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Book ; Online: Bicentenario e intelectualidad

Urviola Montesinos, Luis H

2022

Keywords	Law ; Intelectualidad ; Centenario y bicentenario de la Independencia ; Generación ; Nacionalismo ; Cultura
Language	Spanish
Size	1 electronic resource (179-193 pages)
Publisher	Facultad de Ciencias Jurídicas y Políticas - Universidad Nacional del Altiplano de Puno
Publishing place	Peru
Document type	Book ; Online
Note	Spanish
HBZ-ID	HT030374998
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Recurrence quantification analysis of center of pressure trajectories for balance and fall-risk assessment in young and older adults.

Fernandez-Cervantes, Emiliano / Montesinos, Luis / Gonzalez-Nucamendi, Andres / Pecchia, Leandro

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

2023 Volume PP

Abstract: The prevalence and impact of balance impairments and falls in older adults have motivated several studies on the characterization of human balance. This study aimed to determine the ability of recurrence quantification analysis (RQA) measures to ... ...

Abstract	The prevalence and impact of balance impairments and falls in older adults have motivated several studies on the characterization of human balance. This study aimed to determine the ability of recurrence quantification analysis (RQA) measures to characterize balance control during quiet standing in young and older adults and to discriminate between different fall risk groups. We analyze center pressure trajectories in the medial-lateral and anterior-posterior directions from a publicly available static posturography dataset that contains tests acquired under four vision-surface testing conditions. Participants were retrospectively classified as young adults (age<60, n=85), non-fallers (age≥60, falls=0, n=56), and fallers (age≥60, falls≥1, n=18). Mixed ANOVA and post hoc analyzes were performed to test for differences between groups. For CoP fluctuations in the anterior-posterior direction, all RQA measures showed significantly higher values for young than older adults when standing on a compliant surface, indicating less predictable and stable balance control among seniors under testing conditions where sensory information is restricted or altered. However, no significant differences between non-fallers and fallers were observed. These results support the use of RQA to characterize balance control in young and old adults, but not to discriminate between different fall risk groups.
Language	English
Publishing date	2023-01-12
Publishing country	United States
Document type	Journal Article
ZDB-ID	1166307-8
ISSN	1558-0210 ; 1063-6528 ; 1534-4320
ISSN (online)	1558-0210
ISSN	1063-6528 ; 1534-4320
DOI	10.1109/TNSRE.2023.3236454
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Transdisciplinary experiential learning in biomedical engineering education for healthcare systems improvement.

Montesinos, Luis / Salinas-Navarro, David Ernesto / Santos-Diaz, Alejandro

BMC medical education

2023 Volume 23, Issue 1, Page(s) 207

Abstract: Background: The growing demand for more efficient, timely, and safer health services, together with insufficient resources, put unprecedented pressure on health systems worldwide. This challenge has motivated the application of principles and tools of ... ...

Abstract	Background: The growing demand for more efficient, timely, and safer health services, together with insufficient resources, put unprecedented pressure on health systems worldwide. This challenge has motivated the application of principles and tools of operations management and lean systems to healthcare processes to maximize value while reducing waste. Consequently, there is an increasing need for professionals with the appropriate clinical experience and skills in systems and process engineering. Given their multidisciplinary education and training, biomedical engineering professionals are likely among the most suitable to assume this role. In this context, biomedical engineering education must prepare students for a transdisciplinary professional role by including concepts, methods, and tools that commonly belong to industrial engineering. This work aims to create relevant learning experiences for biomedical engineering education to expand transdisciplinary knowledge and skills in students to improve and optimize hospital and healthcare care processes. Methods: Healthcare processes were translated into specific learning experiences using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. This model allowed us to systematically identify the context where learning experiences were expected to occur, the new concepts and skills to be developed through these experiences, the stages of the student's learning journey, the resources required to implement the learning experiences, and the assessment and evaluation methods. The learning journey was structured around Kolb's experiential learning cycle, which considers four stages: concrete experience, reflective observation, abstract conceptualization, and active experimentation. Data on the student's learning and experience were collected through formative and summative assessments and a student opinion survey. Results: The proposed learning experiences were implemented in a 16-week elective course on hospital management for last-year biomedical engineering undergraduate students. Students engaged in analyzing and redesigning healthcare operations for improvement and optimization. Namely, students observed a relevant healthcare process, identified a problem, and defined an improvement and deployment plan. These activities were carried out using tools drawn from industrial engineering, which expanded their traditional professional role. The fieldwork occurred in two large hospitals and a university medical service in Mexico. A transdisciplinary teaching team designed and implemented these learning experiences. Conclusions: This teaching-learning experience benefited students and faculty concerning public participation, transdisciplinarity, and situated learning. However, the time devoted to the proposed learning experience represented a challenge.
MeSH term(s)	Humans ; Problem-Based Learning ; Biomedical Engineering ; Delivery of Health Care ; Students ; Curriculum
Language	English
Publishing date	2023-04-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2044473-4
ISSN	1472-6920 ; 1472-6920
ISSN (online)	1472-6920
ISSN	1472-6920
DOI	10.1186/s12909-023-04171-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A machine learning approach for hypertension detection based on photoplethysmography and clinical data.

Martinez-Ríos, Erick / Montesinos, Luis / Alfaro-Ponce, Mariel

Computers in biology and medicine

2022 Volume 145, Page(s) 105479

Abstract: High blood pressure early screening remains a challenge due to the lack of symptoms associated with it. Accordingly, noninvasive methods based on photoplethysmography (PPG) or clinical data analysis and the training of machine learning techniques for ... ...

Abstract	High blood pressure early screening remains a challenge due to the lack of symptoms associated with it. Accordingly, noninvasive methods based on photoplethysmography (PPG) or clinical data analysis and the training of machine learning techniques for hypertension detection have been proposed in the literature. Nevertheless, several challenges arise when analyzing PPG signals, such as the need for high-quality signals for morphological feature extraction from PPG related to high blood pressure. On the other hand, another popular approach is to use deep learning techniques to avoid the feature extraction process. Nonetheless, this method requires high computational power and behaves as a black-box approach, which impedes application in a medical context. In addition, considering only the socio-demographic and clinical data of the subject does not allow constant monitoring. This work proposes to use the wavelet scattering transform as a feature extraction technique to obtain features from PPG data and combine it with clinical data to detect early hypertension stages by applying Early and Late Fusion. This analysis showed that the PPG features derived from the wavelet scattering transform combined with a support vector machine can classify normotension and prehypertension with an accuracy of 71.42% and an F1-score of 76%. However, classifying normotension and prehypertension by considering both the features extracted from PPG signals through wavelet scattering transform and clinical variables such as age, body mass index, and heart rate by either Late Fusion or Early Fusion did not provide better performance than considering each data type separately in terms of accuracy and F1-score.
MeSH term(s)	Blood Pressure ; Humans ; Hypertension/diagnosis ; Machine Learning ; Photoplethysmography/methods ; Prehypertension
Language	English
Publishing date	2022-04-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2022.105479
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Article ; Online: Editorial: New challenges and trends in rehabilitation devices based on AI and optimization.

Ponce, Pedro / Alfaro-Ponce, Mariel / López-Caudana, Edgar Omar / McDaniel, Troy / Montesinos, Luis / López-Gutiérrez, Jesús Ricardo / Lugo-González, Esther

Frontiers in robotics and AI

2023 Volume 10, Page(s) 1248973

Language	English
Publishing date	2023-09-08
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2781824-X
ISSN	2296-9144 ; 2296-9144
ISSN (online)	2296-9144
ISSN	2296-9144
DOI	10.3389/frobt.2023.1248973
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Article: Machine learning and deep learning predictive models for type 2 diabetes: a systematic review.

Fregoso-Aparicio, Luis / Noguez, Julieta / Montesinos, Luis / García-García, José A

Diabetology & metabolic syndrome

2021 Volume 13, Issue 1, Page(s) 148

Abstract: Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and ... ...

Abstract	Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model's efficiency. Models trained on tidy datasets achieved almost perfect models.
Language	English
Publishing date	2021-12-20
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2518786-7
ISSN	1758-5996
ISSN	1758-5996
DOI	10.1186/s13098-021-00767-9
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Article ; Online: On the use of approximate entropy and sample entropy with centre of pressure time-series.

Montesinos, Luis / Castaldo, Rossana / Pecchia, Leandro

Journal of neuroengineering and rehabilitation

2018 Volume 15, Issue 1, Page(s) 116

Abstract: Background: Approximate entropy (ApEn) and sample entropy (SampEn) have been previously used to quantify the regularity in centre of pressure (COP) time-series in different experimental groups and/or conditions. ApEn and SampEn are very sensitive to ... ...

Abstract	Background: Approximate entropy (ApEn) and sample entropy (SampEn) have been previously used to quantify the regularity in centre of pressure (COP) time-series in different experimental groups and/or conditions. ApEn and SampEn are very sensitive to their input parameters: m (subseries length), r (tolerance) and N (data length). Yet, the effects of changing those parameters have been scarcely investigated in the analysis of COP time-series. This study aimed to investigate the effects of changing parameters m, r and N on ApEn and SampEn values in COP time-series, as well as the ability of these entropy measures to discriminate between groups. Methods: A public dataset of COP time-series was used. ApEn and SampEn were calculated for m = {2, 3, 4, 5}, r = {0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5} and N = {600, 1200} (30 and 60 s, respectively). Subjects were stratified in young adults (age < 60, n = 85), and older adults (age ≥ 60) with (n = 18) and without (n = 56) falls in the last year. The effects of changing parameters m, r and N on ApEn and SampEn were investigated with a three-way ANOVA. The ability of ApEn and SampEn to discriminate between groups was investigated with a mixed ANOVA (within-subject factors: m, r and N; between-subject factor: group). Specific combinations of m, r and N producing significant differences between groups were identified using the Tukey's honest significant difference procedure. Results: A significant three-way interaction between m, r and N confirmed the sensitivity of ApEn and SampEn to the input parameters. SampEn showed a higher consistency and ability to discriminate between groups than ApEn. Significant differences between groups were mostly observed in longer (N = 1200) COP time-series in the anterior-posterior direction. Those differences were observed for specific combinations of m and r, highlighting the importance of an adequate selection of input parameters. Conclusions: Future studies should favour SampEn over ApEn and longer time-series (≥ 60 s) over shorter ones (e.g. 30 s). The use of parameter combinations such as SampEn (m = {4, 5}, r = {0.25, 0.3, 0.35}) is recommended.
MeSH term(s)	Adult ; Aged ; Computer Simulation ; Entropy ; Female ; Humans ; Male ; Middle Aged ; Models, Biological ; Postural Balance/physiology ; Pressure ; Young Adult
Language	English
Publishing date	2018-12-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1743-0003
ISSN (online)	1743-0003
DOI	10.1186/s12984-018-0465-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Wearable Inertial Sensors for Fall Risk Assessment and Prediction in Older Adults: A Systematic Review and Meta-Analysis.

Montesinos, Luis / Castaldo, Rossana / Pecchia, Leandro

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

2018 Volume 26, Issue 3, Page(s) 573–582

Abstract: Wearable inertial sensors have been widely investigated for fall risk assessment and prediction in older adults. However, heterogeneity in published studies in terms of sensor location, task assessed and features extracted is high, making challenging ... ...

Abstract	Wearable inertial sensors have been widely investigated for fall risk assessment and prediction in older adults. However, heterogeneity in published studies in terms of sensor location, task assessed and features extracted is high, making challenging evidence-based design of new studies and/or real-life applications. We conducted a systematic review and meta-analysis to appraise the best available evidence in the field. Namely, we applied established statistical methods for the analysis of categorical data to identify optimal combinations of sensor locations, tasks, and feature categories. We also conducted a meta-analysis on sensor-based features to identify a set of significant features and their pivot values. The results demonstrated that with a walking test, the most effective feature to assess the risk of falling was the velocity with the sensor placed on the shins. Conversely, during quite standing, linear acceleration measured at the lower back was the most effective combination of feature-placement. Similarly, during the sit-to-stand and/or the stand-to-sit tests, linear acceleration measured at the lower back seems to be the most effective feature-placement combination. The meta-analysis demonstrated that four features resulted significantly higher in fallers: the root-mean-square acceleration in the mediolateral direction during quiet standing with eyes closed [Mean Difference (MD): 0.01 g; 95% Confidence Interval (CI95%): 0.006 to 0.014]; the number of steps (MD: 1.638 steps; CI95%: 0.384 to 2.892) and total time (MD: 2.274 seconds; CI95%: 0.531 to 4.017) to complete the timed up and go test; and the step time (MD: 0.053; CI95%: 0.012 to 0.095; p = 0.01) during walking.
MeSH term(s)	Accidental Falls/prevention & control ; Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Postural Balance ; Predictive Value of Tests ; Risk Assessment ; Wearable Electronic Devices
Language	English
Publishing date	2018-03-08
Publishing country	United States
Document type	Journal Article ; Meta-Analysis ; Research Support, Non-U.S. Gov't ; Systematic Review
ZDB-ID	1166307-8
ISSN	1558-0210 ; 1063-6528 ; 1534-4320
ISSN (online)	1558-0210
ISSN	1063-6528 ; 1534-4320
DOI	10.1109/TNSRE.2017.2771383
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Article: Are ultra-short heart rate variability features good surrogates of short-term ones? State-of-the-art review and recommendations.

Pecchia, Leandro / Castaldo, Rossana / Montesinos, Luis / Melillo, Paolo

Healthcare technology letters

2018 Volume 5, Issue 3, Page(s) 94–100

Abstract: Ultra-short heart rate variability (HRV) analysis refers to the study of HRV features in excerpts of length <5 min. Ultra-short HRV is widely growing in many healthcare applications for monitoring individual's health and well-being status, especially in ... ...

Abstract	Ultra-short heart rate variability (HRV) analysis refers to the study of HRV features in excerpts of length <5 min. Ultra-short HRV is widely growing in many healthcare applications for monitoring individual's health and well-being status, especially in combination with wearable sensors, mobile phones, and smart-watches. Long-term (nominally 24 h) and short-term (nominally 5 min) HRV features have been widely investigated, physiologically justified and clear guidelines for analysing HRV in 5 min or 24 h are available. Conversely, the reliability of ultra-short HRV features remains unclear and many investigations have adopted ultra-short HRV analysis without questioning its validity. This is partially due to the lack of accepted algorithms guiding investigators to systematically assess ultra-short HRV reliability. This Letter critically reviewed the existing literature, aiming to identify the most suitable algorithms, and harmonise them to suggest a standard protocol that scholars may use as a reference in future studies. The results of the literature review were surprising, because, among the 29 reviewed papers, only one paper used a rigorous method, whereas the others employed methods that were partially or completely unreliable due to the incorrect use of statistical tests. This Letter provides recommendations on how to assess ultra-short HRV features reliably and proposes an inclusive algorithm that summarises the state-of-the-art knowledge in this area.
Language	English
Publishing date	2018-03-14
Publishing country	England
Document type	Journal Article
ZDB-ID	2782924-8
ISSN	2053-3713
ISSN	2053-3713
DOI	10.1049/htl.2017.0090
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Article ; Online: Day-to-day variations in sleep quality affect standing balance in healthy adults.

Montesinos, Luis / Castaldo, Rossana / Cappuccio, Francesco P / Pecchia, Leandro

Scientific reports

2018 Volume 8, Issue 1, Page(s) 17504

Abstract: Acute sleep deprivation is known to affect human balance and posture control. However, the effects of variations in sleep quality and pattern over consecutive days have received less attention. This study investigated the associations between day-to-day ... ...

Abstract	Acute sleep deprivation is known to affect human balance and posture control. However, the effects of variations in sleep quality and pattern over consecutive days have received less attention. This study investigated the associations between day-to-day variations in sleep quality and standing balance in healthy subjects. Twenty volunteers (12 females and 8 males; age: 28.8 ± 5.7 years, body mass index: 23.4 ± 3.4 kg/m
MeSH term(s)	Actigraphy ; Adult ; Female ; Healthy Volunteers ; Humans ; Male ; Postural Balance/physiology ; Sleep ; Sleep Deprivation ; Young Adult
Language	English
Publishing date	2018-11-30
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-018-36053-4
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