@misc{SoltaniZarrinWenger, author = {Soltani Zarrin, Pouya and Wenger, Christian}, title = {Implementation of Siamese-Based Few-Shot Learning Algorithms for the Distinction of COPD and Asthma Subjects}, series = {Artificial Neural Networks and Machine Learning - ICANN 2020 : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part I}, journal = {Artificial Neural Networks and Machine Learning - ICANN 2020 : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part I}, publisher = {Springer}, address = {Cham}, doi = {10.1007/978-3-030-61609-0_34}, pages = {431 -- 440}, abstract = {This paper investigates the practicality of applying braininspired Few-Shot Learning (FSL) algorithms for addressing shortcomings of Machine Learning (ML) methods in medicine with limited data availability. As a proof of concept, the application of ML for the detection of Chronic Obstructive Pulmonary Disease (COPD) patients was investigated. The complexities associated with the distinction of COPD and asthma patients and the lack of sufficient training data for asthma subjects impair the performance of conventional ML models for the recognition of COPD. Therefore, the objective of this study was to implement FSL methods for the distinction of COPD and asthma subjects with a few available data points. The proposed FSL models in this work were capable of recognizing asthma and COPD patients with 100\% accuracy, demonstrating the feasibility of the approach for applications such as medicine with insufficient data availability.}, language = {en} } @misc{MartinLangendoerferZarrinetal., author = {Martin, Cristian and Langend{\"o}rfer, Peter and Zarrin, Pouya Soltani and D{\´i}az, Manuel and Rubio, Bartolom{\´e}}, title = {Kafka-ML: Connecting the Data Stream with ML/AI Frameworks}, series = {Future Generation Computer Systems}, volume = {126}, journal = {Future Generation Computer Systems}, issn = {0167-739X}, doi = {10.1016/j.future.2021.07.037}, pages = {15 -- 33}, language = {en} } @misc{SoltaniZarrinRockendorfWenger, author = {Soltani Zarrin, Pouya and Rockendorf, Niels and Wenger, Christian}, title = {In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools}, series = {IEEE Access}, volume = {Vol. 8}, journal = {IEEE Access}, issn = {2169-3536}, doi = {10.1109/ACCESS.2020.3023971}, pages = {168053 -- 168060}, abstract = {Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that reflect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fluids like saliva is a promising approach for staging the disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. Nonetheless, this approach is only feasible by concurrent consideration of patients' demographic and medical parameters. Therefore, Machine Learning (ML) tools are necessary for the comprehensive recognition of COPD in a PoC setting. As a result, the objective of this work was to implement ML tools on the data acquired from characterizing saliva samples of COPD patients and healthy controls for classification purposes. First, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, ML tools were applied on the acquired data for classification. The XGBoost gradient boosting algorithm provided a high classification accuracy of 91.25\%, making it a promising model for COPD recognition. Integration of this model on a neuromorphic chip, in the future, will enable the real-time detection of COPD in PoC, with low energy consumption and high patient privacy.}, language = {en} } @misc{SoltaniZarrinZahariMahadevaiahetal., author = {Soltani Zarrin, Pouya and Zahari, Finn and Mahadevaiah, Mamathamba Kalishettyhalli and Perez, Eduardo and Kohlstedt, Hermann and Wenger, Christian}, title = {Neuromorphic on‑chip recognition of saliva samples of COPD and healthy controls using memristive devices}, series = {Scientific Reports}, volume = {10}, journal = {Scientific Reports}, issn = {2045-2322}, doi = {10.1038/s41598-020-76823-7}, abstract = {Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. The results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89\% and 86\%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD.}, language = {en} }