A Low Cost Hardware and Software Platform for Biomedical Signal Acquisition and Treatment

Abstract Background This study aimed to analyze and assess the scientific outputs of biomedical signal processing by using bibliometric analysis.Methods Data were obtained from the WoSCC of Thomson Reuters, on January 21, 2019. VOSviewer (Leiden University, Van Eck and Waltman, Netherlands) and carrot 2 (Poznan University of Technology, Dawid Weiss, Poland) were used to analyze the knowledge maps and clusters of countries, research area and hot topics.Results A total of 335 articles on biomedical signal processing were identified. The number of publications increased only mildly during from 2009 (n=14) to 2018 (n=62). The majority of articles were published in the USA, and the leading institute was University of California System. Van Huffel S was the top authors on the topic, and the research area of “Engineering” generated the most publications. Cluter analysis (keywords and terms) indicated that “algorithm” and “extracted features” was the most hot topics on biomedical signal processing.Conclusion Overall, Through analysis of biosignal single processing related research in the past 10 years, the results found that the close international cooperation in this field, and the future research trends may be the signal acquisition methods and signal processing algorithms. They can provide reference for researchers in related field to choose research directions and find cooperative resources.

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The prototype device for non-invasive diagnosis of arteriovenous fistula condition using machine learning methods

Scientific Reports ◽

10.1038/s41598-020-72336-5 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Marcin Grochowina ◽

Lucyna Leniowska ◽

Agnieszka Gala-Błądzińska

Keyword(s):

Machine Learning ◽

Arteriovenous Fistula ◽

Low Cost ◽

Principal Component ◽

Special Kind ◽

Classification Model ◽

Supervised Machine Learning ◽

Signal Acquisition ◽

Non Invasive ◽

Prototype Device

Abstract Pattern recognition and automatic decision support methods provide significant advantages in the area of health protection. The aim of this work is to develop a low-cost tool for monitoring arteriovenous fistula (AVF) with the use of phono-angiography method. This article presents a developed and diagnostic device that implements classification algorithms to identify 38 patients with end stage renal disease, chronically hemodialysed using an AVF, at risk of vascular access stenosis. We report on the design, fabrication, and preliminary testing of a prototype device for non-invasive diagnosis which is very important for hemodialysed patients. The system includes three sub-modules: AVF signal acquisition, information processing and classification and a unit for presenting results. This is a non-invasive and inexpensive procedure for evaluating the sound pattern of bruit produced by AVF. With a special kind of head which has a greater sensitivity than conventional stethoscope, a sound signal from fistula was recorded. The proces of signal acquisition was performed by a dedicated software, written specifically for the purpose of our study. From the obtained phono-angiogram, 23 features were isolated for vectors used in a decision-making algorithm, including 6 features based on the waveform of time domain, and 17 features based on the frequency spectrum. Final definition of the feature vector composition was obtained by using several selection methods: the feature-class correlation, forward search, Principal Component Analysis and Joined-Pairs method. The supervised machine learning technique was then applied to develop the best classification model.

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Compressed sensing for electrocardiogram acquisition in wireless body sensor network: A comparative analysis

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719864884 ◽

2019 ◽

Vol 15 (7) ◽

pp. 155014771986488 ◽

Cited By ~ 3

Author(s):

Junxin Chen ◽

Jiazhu Xing ◽

Leo Yu Zhang ◽

Lin Qi

Keyword(s):

Compressed Sensing ◽

Sensor Network ◽

Low Cost ◽

Reconstruction Algorithm ◽

Signal Acquisition ◽

Reconstruction Algorithms ◽

Practical Applications ◽

Body Sensor Network ◽

The Past ◽

Electrocardiogram Signals

In the past decades, compressed sensing emerges as a promising technique for signal acquisition in low-cost sensor networks. For prolonging the monitoring duration of biosignals, compressed sensing is also exploited for simultaneous sampling and compression of electrocardiogram signals in the wireless body sensor network. This article presents a comprehensive analysis of compressed sensing for electrocardiogram acquisition. The performances of involved important factors, such as wavelet basis, overcomplete dictionaries, and the reconstruction algorithms, are comparatively illustrated, with the purpose to give data reference for practical applications. Drawn from a bulk of comparative experiments, the potential of compressed sensing in electrocardiogram acquisition is evaluated in different compression levels, while preferred sparsifying basis and reconstruction algorithm are also suggested. Relative perspectives and discussions are also given.

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