@article{ZDRAVEVSKI2020106164,

title = {From Big Data to business analytics: The case study of churn prediction},

author = {Eftim Zdravevski and Petre Lameski and Cas Apanowicz and Dominik Ślȩzak},

url = {https://www.sciencedirect.com/science/article/pii/S1568494620301046?dgcid=author},

doi = {https://doi.org/10.1016/j.asoc.2020.106164},

issn = {1568-4946},

year  = {2020},

date = {2020-01-01},

journal = {Applied Soft Computing},

volume = {90},

pages = {106164},

abstract = {The success of companies hugely depends on how well they can analyze the available data and extract meaningful knowledge. The Extract-Transform-Load (ETL) process is instrumental in accomplishing these goals, but requires significant effort, especially for Big Data. Previous works have failed to formalize, integrate, and evaluate the ETL process for Big Data problems in a scalable and cost-effective way. In this paper, we propose a cloud-based ETL framework for data fusion and aggregation from a variety of sources. Next, we define three scenarios regarding data aggregation during ETL: (i) ETL with no aggregation; (ii) aggregation based on predefined columns or time intervals; and (iii) aggregation within single user sessions spanning over arbitrary time intervals. The third scenario is very valuable in the context of feature engineering, making it possible to define features as “the time since the last occurrence of event X”. The scalability was evaluated on Amazon AWS Hadoop clusters by processing user logs collected with Kinesis streams with datasets ranging from 30 GB to 2.6 TB. The business value of the architecture was demonstrated with applications in churn prediction, service-outage prediction, fraud detection, and more generally — decision support and recommendation systems. In the churn prediction case, we showed that over 98% of churners could be detected, while identifying the individual reason. This allowed support and sales teams to perform targeted retention measures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ferreira2020identification,

title = {Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review},

author = {José M Ferreira and Ivan Miguel Pires and Gonçalo Marques and Nuno M Garcia and Eftim Zdravevski and Petre Lameski and Francisco Flórez-Revuelta and Susanna Spinsante},

year  = {2020},

date = {2020-01-01},

journal = {Electronics},

volume = {9},

number = {1},

pages = {192},

publisher = {Multidisciplinary Digital Publishing Institute},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ferreira2020activities,

title = {Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study},

author = {José M Ferreira and Ivan Miguel Pires and Gonçalo Marques and Nuno M Garcia and Eftim Zdravevski and Petre Lameski and Francisco Flórez-Revuelta and Susanna Spinsante and Lina Xu},

year  = {2020},

date = {2020-01-01},

journal = {Electronics},

volume = {9},

number = {1},

pages = {180},

publisher = {Multidisciplinary Digital Publishing Institute},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jpm10010012,

title = {Promotion of Healthy Nutrition and Physical Activity Lifestyles for Teenagers: A Systematic Literature Review of The Current Methodologies},

author = {María Vanessa Villasana and Ivan Miguel Pires and Juliana Sá and Nuno M Garcia and Eftim Zdravevski and Ivan Chorbev and Petre Lameski and Francisco Flórez-Revuelta},

url = {https://www.mdpi.com/2075-4426/10/1/12},

doi = {10.3390/jpm10010012},

issn = {2075-4426},

year  = {2020},

date = {2020-01-01},

journal = {Journal of Personalized Medicine},

volume = {10},

number = {1},

abstract = {Amid obesity problems in the young population and apparent trends of spending a significant amount of time in a stationary position, promoting healthy nutrition and physical activities to teenagers is becoming increasingly important. It can rely on different methodologies, including a paper diary and mobile applications. However, the widespread use of mobile applications by teenagers suggests that they could be a more suitable tool for this purpose. This paper reviews the methodologies for promoting physical activities to healthy teenagers explored in different studies, excluding the analysis of different diseases. We found only nine studies working with teenagers and mobile applications to promote active lifestyles, including the focus on nutrition and physical activity. Studies report using different techniques to captivate the teenagers, including questionnaires and gamification techniques. We identified the common features used in different studies, which are: paper diary, diet diary, exercise diary, notifications, diet plan, physical activity registration, gamification, smoking cessation, pictures, game, and SMS, among others.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info11030161,

title = {Circular Economy for Clothes Using Web and Mobile Technologies—A Systematic Review and a Taxonomy Proposal},

author = {Rui Faria and Inês Lopes and Ivan Miguel Pires and Gonçalo Marques and Solange Fernandes and Nuno M Garcia and José Lucas and Aleksandar Jevremović and Eftim Zdravevski and Vladimir Trajkovik},

url = {https://www.mdpi.com/2078-2489/11/3/161},

doi = {10.3390/info11030161},

issn = {2078-2489},

year  = {2020},

date = {2020-01-01},

journal = {Information},

volume = {11},

number = {3},

abstract = {Nowadays, it is possible to buy clothing using online platforms, either by accessing online brand stores, general online stores or circular economy platforms. This paper presents a study on mobile applications that support online commerce for clothing, focusing on the review of the mobile applications with features that are characteristic of the circular economy paradigm. Findings include the fact that almost all the mobile applications analysed have pictures illustrative of the clothes and accessories that are available for trading as well as their brief description. Furthermore, this paper presents a study of various scientific articles about the circular economy of clothes and how it can be beneficial to the future of the environment. It is a junction with a Web platform for its growth and its disclosure. The paper builds conclusions upon the assumption that the circular economy is a growing business that is part of a sustainable development where the main goal is to reduce the environmental impact. The paper proposes the analysis of an innovative taxonomy of mobile applications about the circular economy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{electronics9030509,

title = {Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer},

author = {Ivan Miguel Pires and Gonçalo Marques and Nuno M Garcia and Francisco Flórez-Revuelta and Maria Canavarro Teixeira and Eftim Zdravevski and Susanna Spinsante and Miguel Coimbra},

url = {https://www.mdpi.com/2079-9292/9/3/509},

doi = {10.3390/electronics9030509},

issn = {2079-9292},

year  = {2020},

date = {2020-01-01},

journal = {Electronics},

volume = {9},

number = {3},

abstract = {The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{electronics9030528,

title = {Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review},

author = {Vasco Ponciano and Ivan Miguel Pires and Fernando Reinaldo Ribeiro and Gonçalo Marques and Nuno M Garcia and Nuno Pombo and Susanna Spinsante and Eftim Zdravevski},

url = {https://www.mdpi.com/2079-9292/9/3/528},

doi = {10.3390/electronics9030528},

issn = {2079-9292},

year  = {2020},

date = {2020-01-01},

journal = {Electronics},

volume = {9},

number = {3},

abstract = {The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CORIZZO2020113378,

title = {Scalable Auto-Encoders for Gravitational Waves Detection from Time Series Data},

author = {Roberto Corizzo and Michelangelo Ceci and Eftim Zdravevski and Nathalie Japkowicz},

url = {https://urldefense.proofpoint.com/v2/url?u=https-3A__authors.elsevier.com_a_1arLH3PiGTFIQY&d=DwMFaQ&c=U0G0XJAMhEk_X0GAGzCL7Q&r=WE84e9sB6Uj2n-XoDopdX_NkwOv5ZP0Le-ZnM4RCMrA&m=TlVflvdAHHskEMFgvnOh-HCFxlLrAvAf7Rr3TpLNC_c&s=-ZKJpc_us0Xz_uHV-cVLDQJ3irzDcKagiBgA2OwTzRU&e=},

doi = {https://doi.org/10.1016/j.eswa.2020.113378},

issn = {0957-4174},

year  = {2020},

date = {2020-01-01},

journal = {Expert Systems with Applications},

pages = {113378},

abstract = {Gravitational waves represent a new opportunity to study and interpret phenomena from the universe. In order to efficiently detect and analyze them, advanced and automatic signal processing and machine learning techniques could help to support standard tools and techniques. Another challenge relates to the large volume of data collected by the detectors on a daily basis, which creates a gap between the amount of data generated and effectively analyzed. In this paper, we propose two approaches involving deep auto-encoder models to analyze time series collected from Gravitational Waves detectors and provide a classification label (noise or real signal). The purpose is to discard noisy time series accurately and identify time series that potentially contain a real phenomenon. Experiments carried out on three datasets show that the proposed approaches implemented using the Apache Spark framework, represent a valuable machine learning tool for astrophysical analysis, offering competitive accuracy and scalability performances with respect to state-of-the-art methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Maresova2020ict,

title = {Health–Related ICT Solutions of Smart Environments for Elderly–Systematic Review},

author = {P Maresova and O Krejcar and S Barakovic and J Barakovic Husic and P Lameski and E Zdravevski and I Chorbev and V Trajkovik},

doi = {10.1109/ACCESS.2020.2981315},

issn = {2169-3536},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Access},

volume = {8},

pages = {54574-54600},

abstract = {By improving the quality of life and extending the length of life, Western society is becoming an increasingly ageing population with a higher proportion of seniors. From another point of view, there is a critical shortage of care staff, both in hospitals and for in-home care. Thanks to new technology trends such as Smart Homes and Smart Furniture, there is an opportunity for increased support for seniors by utilizing new technologies. This paper presents the current trends and possibilities in applying smart information and communications technology (ICT) solutions for in-home care concerning diseases in old age. The paper consists of a systematic review according to the PRISMA methodology of the available literature in Web of Science, IEEE Xplore, PubMed, Springer, and the Espacenet patent database. Publications report the usage of some types of artificial intelligence and their implementation and non-intrusive sensing technologies. The patents review identified solutions with a focus on monitoring the state of older adults and mobility improvement. Existing ICT smart solutions must address the following issues: (1) ease-of-use; (2) invisibility and disuse that isolate older adults; (3) privacy and security; (4) affordability of technology in terms of cost; and (5) supporting elderly individuals to stay in their homes or move in different environments independently. There is a significant gap between a large number of scientific publications and commercial solutions. The existing products reflect the specifics of the diseases in a rather wider context instead of the fulfilment of exact needs. It is often stated that such devices can be used across diseases, but the direct connection and benefits for the disease is still rather weak. The challenge remains to tap the existing potential of a large number of innovative ideas on the market and improve the quality of life.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{turukalo2019jmir,

title = {Literature on Wearable Technology for Connected Health: Scoping Review of Research Trends, Advances, and Barriers},

author = {Tatjana Loncar-Turukalo and Eftim Zdravevski and José Machado Silva and Ioanna Chouvarda and Vladimir Trajkovik},

url = {http://www.ncbi.nlm.nih.gov/pubmed/31489843},

doi = {10.2196/14017},

issn = {1438-8871},

year  = {2019},

date = {2019-09-05},

journal = {J Med Internet Res},

volume = {21},

number = {9},

pages = {e14017},

abstract = {Background: Wearable sensing and information and communication technologies are key enablers driving the transformation of health care delivery toward a new model of connected health (CH) care. The advances in wearable technologies in the last decade are evidenced in a plethora of original articles, patent documentation, and focused systematic reviews. Although technological innovations continuously respond to emerging challenges and technology availability further supports the evolution of CH solutions, the widespread adoption of wearables remains hindered. Objective: This study aimed to scope the scientific literature in the field of pervasive wearable health monitoring in the time interval from January 2010 to February 2019 with respect to four important pillars: technology, safety and security, prescriptive insight, and user-related concerns. The purpose of this study was multifold: identification of (1) trends and milestones that have driven research in wearable technology in the last decade, (2) concerns and barriers from technology and user perspective, and (3) trends in the research literature addressing these issues. Methods: This study followed the scoping review methodology to identify and process the available literature. As the scope surpasses the possibilities of manual search, we relied on the natural language processing tool kit to ensure an efficient and exhaustive search of the literature corpus in three large digital libraries: Institute of Electrical and Electronics Engineers, PubMed, and Springer. The search was based on the keywords and properties to be found in articles using the search engines of the digital libraries. Results: The annual number of publications in all segments of research on wearable technology shows an increasing trend from 2010 to February 2019. The technology-related topics dominated in the number of contributions, followed by research on information delivery, safety, and security, whereas user-related concerns were the topic least addressed. The literature corpus evidences milestones in sensor technology (miniaturization and placement), communication architectures and fifth generation (5G) cellular network technology, data analytics, and evolution of cloud and edge computing architectures. The research lag in battery technology makes energy efficiency a relevant consideration in the design of both sensors and network architectures with computational offloading. The most addressed user-related concerns were (technology) acceptance and privacy, whereas research gaps indicate that more efforts should be invested into formalizing clear use cases with timely and valuable feedback and prescriptive recommendations. Conclusions: This study confirms that applications of wearable technology in the CH domain are becoming mature and established as a scientific domain. The current research should bring progress to sustainable delivery of valuable recommendations, enforcement of privacy by design, energy-efficient pervasive sensing, seamless monitoring, and low-latency 5G communications. To complement technology achievements, future work involving all stakeholders providing research evidence on improved care pathways and cost-effectiveness of the CH model is needed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hoang2019bigdata,

title = {Report on the “Advanced Big Data Training School for Life Sciences”, Barcelona 3th-7th September 2018},

author = {Yen Hoang and Juliane Pfeil and Maja Zagorščak and Axel Thieffry and Eftim Zdravevski and Živa Ramšak and Petre Lameski and Sabrina Schulze and Eleni Papakonstantinou and Louis Papageorgiou and Tarry Singh and Ariel Duarte-López and Marta Pérez-Casany},

url = {https://journal.embnet.org/index.php/embnetjournal/article/view/917},

doi = {10.14806/ej.24.0.917},

issn = {2226-6089},

year  = {2019},

date = {2019-01-01},

journal = {EMBnet.journal},

volume = {24},

number = {0},

pages = {917},

abstract = {The “Advanced Big Data Training School for Life Sciences” took place during September 3-7, 2018, organized by the Data Management Group (DAMA-UPC) at the Technical University of Catalonia (UPC) in Barcelona, Spain. It is the follow-up training school of the first “Big Data Training School for Life Sciences”, held in Uppsala, Sweden, in September 2017, which was defined and structured at the “Think Tank Hackathon”, held in Ljubljana, Slovenia, in February 2018. The aim of this training school was to get participants acquainted with emerging Big Data processing techniques in the field of Computational Biology and Bioinformatics.This article explains in detail the development of the training school, the covered contents and the interaction of the participants within and out of the training event by the student, organizer and lecturer perspective.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{maresova2018technological,

title = {Technological Solutions for Older People with Alzheimer's Disease},

author = {Petra Maresova and Signe Tomsone and Petre Lameski and Joana Madureira and Ana Mendes and Eftim Zdravevski and Ivan Chorbev and Vladimir Trajkovik and Moriah Ellen and Kasper Rodile},

year  = {2018},

date = {2018-01-01},

journal = {Current Alzheimer Research},

volume = {15},

number = {10},

pages = {975--983},

publisher = {Bentham Science Publishers},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pires2018android,

title = {Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions},

author = {Ivan Miguel Pires and Maria Canavarro Teixeira and Nuno Pombo and Nuno M Garcia and Francisco Flórez-Revuelta and Susanna Spinsante and Rossitza Goleva and Eftim Zdravevski and others},

year  = {2018},

date = {2018-01-01},

publisher = {Bentham Open},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pfeil2018bigdata,

title = {Report on the “Big Data Training School for Life Sciences”, 18-22 September 2017, Uppsala, Sweden},

author = {Juliane Pfeil and Sabrina Schulze and Eftim Zdravevski and Yen Hoang},

url = {https://journal.embnet.org/index.php/embnetjournal/article/view/905},

doi = {10.14806/ej.23.0.905},

issn = {2226-6089},

year  = {2018},

date = {2018-01-01},

journal = {EMBnet.journal},

volume = {23},

number = {0},

pages = {905},

abstract = {In September 2017 a "Big Data Training School for Life Sciences" took place in Uppsala, Sweden, jointly organised by EMBnet and the COST Action CHARME (Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research - CA15100). The week programme was divided into hands-on sessions and lectures. In both cases, insights into dealing with big amounts of data were given. This paper describes our personal experience as students’ by providing also some suggestions that we hope can help the organisers as well as other trainers to further increase the efficiency of such intensive courses for students with diverse backgrounds.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{schulze2018bigdata,

title = {Proceedings of the “Think Tank Hackathon’’, Big Data Training School for Life Sciences Follow-up, Ljubljana 6th – 7th February 2018},

author = {Sabrina Schulze and Živa Ramšak and Yen Hoang and Eftim Zdravevski and Juliane Pfeil and Ariel Duarte-López and Uwe Baier and Maja Zagorščak},

url = {https://journal.embnet.org/index.php/embnetjournal/article/view/912},

doi = {10.14806/ej.24.0.912},

issn = {2226-6089},

year  = {2018},

date = {2018-01-01},

journal = {EMBnet.journal},

volume = {24},

number = {0},

pages = {912},

abstract = {On 6th and 7th February 2018, a Think Tank took place in Ljubljana, Slovenia. It was a follow-up of the “Big Data Training School for Life Sciences” held in Uppsala, Sweden, in September 2017. The focus was on identifying topics of interest and optimising the programme for a forthcoming “Advanced” Big Data Training School for Life Science, that we hope is again supported by the COST Action CHARME (Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research - CA15110). The Think Tank aimed to go into details of several topics that were - to a degree - covered by the former training school. Likewise, discussions embraced the recent experience of the attendees in light of the new knowledge obtained by the first edition of the training school and how it comes from the perspective of their current and upcoming work. The 2018 training school should strive for and further facilitate optimised applications of Big Data technologies in life sciences. The attendees of this hackathon entirely organised this workshop.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stojanovski2018real,

title = {Real-time sleep apnea detection with one-channel ECG based on edge computing paradigm},

author = {Aleksandar Stojanovski and Eftim Zdravevski and Saso Koceski and Vladimir Trajkovik},

year  = {2018},

date = {2018-01-01},

booktitle = {ICT Innovations 2018, Web Proceedings},

organization = {IKT-ACT},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pires2018sensors,

title = {Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review},

author = {Ivan Miguel Pires and Rui Santos and Nuno Pombo and Nuno M Garcia and Francisco Flórez-Revuelta and Susanna Spinsante and Rossitza Goleva and Eftim Zdravevski},

url = {http://www.mdpi.com/1424-8220/18/1/160},

doi = {10.3390/s18010160},

issn = {1424-8220},

year  = {2018},

date = {2018-01-01},

journal = {Sensors},

volume = {18},

number = {1, ARTICLE NUMBER = 160},

abstract = {An increase in the accuracy of identification of Activities of Daily Living (ADL) is very important for different goals of Enhanced Living Environments and for Ambient Assisted Living (AAL) tasks. This increase may be achieved through identification of the surrounding environment. Although this is usually used to identify the location, ADL recognition can be improved with the identification of the sound in that particular environment. This paper reviews audio fingerprinting techniques that can be used with the acoustic data acquired from mobile devices. A comprehensive literature search was conducted in order to identify relevant English language works aimed at the identification of the environment of ADLs using data acquired with mobile devices, published between 2002 and 2017. In total, 40 studies were analyzed and selected from 115 citations. The results highlight several audio fingerprinting techniques, including Modified discrete cosine transform (MDCT), Mel-frequency cepstrum coefficients (MFCC), Principal Component Analysis (PCA), Fast Fourier Transform (FFT), Gaussian mixture models (GMM), likelihood estimation, logarithmic moduled complex lapped transform (LMCLT), support vector machine (SVM), constant Q transform (CQT), symmetric pairwise boosting (SPB), Philips robust hash (PRH), linear discriminant analysis (LDA) and discrete cosine transform (DCT).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tina2017oasi,

title = {Can we predict obstetric anal sphincter injury?},

author = {Kristina Drusany Starič and Petra Bukovec and Katja Jakopič and Eftim Zdravevski and Vladimir Trajkovik and Adolf Lukanović},

url = {http://linkinghub.elsevier.com/retrieve/pii/S0301211516310910},

doi = {10.1016/j.ejogrb.2016.12.029},

issn = {03012115},

year  = {2017},

date = {2017-03-01},

journal = {European Journal of Obstetrics & Gynecology and Reproductive Biology},

volume = {210},

pages = {196--200},

abstract = {Objective The aim of the study was to identify primiparous pregnant women with a higher risk for obstetric anal sphincter injuries (OASIS) based on obstetric characteristics (risk factors). Study design In the retrospective case control study primiparous women were examined using endoanal ultrasonography (EUS) for OASIS identification 6–12 weeks after delivery. Obstetric characteristics for OASIS were collected from the mothers' medical records. The univariate analysis of maternal (age at delivery, maternal height, weight, BMI), infant (length, weight and head circumference) and birth (pregnancy duration, labour and delivery duration, episiotomy, vacuum extraction and oxytocin augmentation) risk factors, Pearson correlations and information gain were carried out. The cut-off values for the aforementioned risk factors divided the patients into groups with higher and lower risk of OASIS. Results The data of 84 primiparous women with OASIS, and 58 without, were analysed. Those newborns born to women in the OASIS group were heavier (P textless 0.05), with the cut-off at 3420 g (72% probability of OASIS), had a larger head circumference (P textless 0.001), cut-off at 36 cm (84% probability of OASIS), and were longer (P textless 0.05), cut-off at 50.5 cm (74% probability of OASIS). The maternal age and body mass index (BMI) were risk factors for OASIS (P textless 0.05 and P textless 0.05, respectively) with a probability of 83% in women younger than 27.5 years and a 78% probability if BMI was higher than 28 kg/m2. The incidence of OASIS was not higher in women with episiotomy or vacuum extraction, but it was higher in oxytocin augmentation (P textless 0.031). Conclusion The findings can assist in identification of pregnant women with a higher risk of OASIS who require special attention at delivery to prevent it. In high risk women EUS is indicated to identify and treat possible OASIS as early as possible in order to prevent anal incontinence.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zdravevski_Access2017,

title = {Improving Activity Recognition Accuracy in Ambient-Assisted Living Systems by Automated Feature Engineering},

author = {E Zdravevski and P Lameski and V Trajkovik and A Kulakov and I Chorbev and R Goleva and N Pombo and N Garcia},

doi = {10.1109/ACCESS.2017.2684913},

issn = {2169-3536},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Access},

volume = {5},

pages = {5262-5280},

abstract = {Ambient-assisted living (AAL) is promising to become a supplement of the current care models, providing enhanced living experience to people within context-aware homes and smart environments. Activity recognition based on sensory data in AAL systems is an important task because 1) it can be used for estimation of levels of physical activity, 2) it can lead to detecting changes of daily patterns that may indicate an emerging medical condition, or 3) it can be used for detection of accidents and emergencies. To be accepted, AAL systems must be affordable while providing reliable performance. These two factors hugely depend on optimizing the number of utilized sensors and extracting robust features from them. This paper proposes a generic feature engineering method for selecting robust features from a variety of sensors, which can be used for generating reliable classification models. From the originally recorded time series and some newly generated time series [i.e., magnitudes, first derivatives, delta series, and fast Fourier transformation (FFT)-based series], a variety of time and frequency domain features are extracted. Then, using two-phase feature selection, the number of generated features is greatly reduced. Finally, different classification models are trained and evaluated on an independent test set. The proposed method was evaluated on five publicly available data sets, and on all of them, it yielded better accuracy than when using hand-tailored features. The benefits of the proposed systematic feature engineering method are quickly discovering good feature sets for any given task than manually finding ones suitable for a particular task, selecting a small feature set that outperforms manually determined features in both execution time and accuracy, and identification of relevant sensor types and body locations automatically. Ultimately, the proposed method could reduce the cost of AAL systems by facilitating execution of algorithms on devices with limited- resources and by using as few sensors as possible.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lameski_Access2017,

title = {Suppression of Intensive Care Unit False Alarms based on the Arterial Blood Pressure Signal},

author = {P Lameski and E Zdravevski and S Koceski and A Kulakov and V Trajkovik},

doi = {10.1109/ACCESS.2017.2690380},

issn = {2169-3536},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Access},

volume = {PP},

number = {99},

pages = {1-7},

abstract = {Patient monitoring in Intensive Care Units (ICU) requires collection and processing of high volumes of data. High sensitivity of sensors leads to significant number of false alarms which cause alarm fatigue. Reduction of false alarms can lead to better reaction time of medical personnel. This study aims to develop a method for false alarm suppression and evaluate it on a publicly available dataset with manually annotated alarms. First, an automated feature engineering was performed using the signal for Arterial Blood Pressure (ABP) and a processed signal that contained the times of each heartbeat from the ABP signal. Next, Support Vector Machines, Random Forest and Extreme Random Trees classifiers were trained to create classification models. The best suppression performance was achieved for the Extreme tachycardia alarm, for which we 90.3% of the false alarms were suppressed, while only 0.54% of the true alarms were incorrectly suppressed. This study demonstrates that alarm suppression can be achieved with high accuracy using on automated feature engineering coupled with machine learning algorithms. The proposed approach can be utilized as aid to medical personnel and experts, allowing them to be more productive and to respond to alarms in a more timely manner},

note = {in press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zdravevski2017jogging,

title = {Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions},

author = {Eftim Zdravevski and Biljana Risteska Stojkoska and Marie Standl and Holger Schulz},

url = {https://doi.org/10.1371/journal.pone.0184216},

doi = {10.1371/journal.pone.0184216},

year  = {2017},

date = {2017-01-01},

journal = {PLOS ONE},

volume = {12},

number = {9},

pages = {1-28},

publisher = {Public Library of Science},

abstract = {Background Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position. Methods The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers. Results The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be achieved from either accelerometer position. Conclusions Machine learning techniques can be used for automatic activity recognition, as they provide very accurate activity recognition, significantly more accurate than when keeping a diary. Identification of jogging periods in adolescents can be performed using only one accelerometer. Performance-wise there is no significant benefit from using accelerometers on both locations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}