Machine Learning and Biomedical Sensors

Abstract submission deadline

31 October 2023

Manuscript submission deadline

31 December 2023

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Topic Information

Dear Colleagues,

The increase in the information collected by an ever-growing number of biomedical sensors connected to the internet, together with the availability of a network of services recording any biological, physical, clinical and other kinds of data, has made the soil fertile for the significant use of machine learning (ML). It has become possible to use the various biomedical devices in the most diverse environments (at the depths of the seas, high altitudes, in space) in hospitals as well at home or outdoors, opening up new scenarios that were not previously conceivable. In this context, machine learning has shown great potential and might provide solutions to many issues concerning life on our planet. This Topic aims to present the most recent and innovative solutions leveraging the interplay between biomedical sensors and machine learning. Advanced and modern data-driven methods and learning approaches are sought to correlate and understand heterogeneous data in providing accurate classifications and predictions. The perspective opened by pervasive and edge computing should be properly transferred to the biomedical domain by devising novel activity monitoring and physiological computing paradigms. From the point of view of innovative sensing technologies, new transducers coupled with embedded computing for obtaining smart and possibly miniaturized or minimally invasive devices should be investigated. In such a panorama, the role of machine learning and artificial intelligence, more generally, should be adequately understood together with the issues related to their perception. In addition, user acceptance and privacy issues are important aspects to be assessed in real experimentation, which is necessary for clinical validation of the proposed technological solutions. The Topic, through its participating journals, is therefore seeking contributions that explore multifaced aspects of the convergence between biomedical sensors and machine learning: from fundamental elements related to computing over sensor networks and federated learning to innovative sensing principles and technologies for smart devices, from clinical experimentation and validation in healthcare scenarios to general application in ambient assisted living, contextually with the concurrent assessment of privacy and user acceptance factors.

• Human physiology & physiological computing
• Multimedia data analysis
• Digital signal and image processing
• Computer vision in biomedical sensing
• New materials and approaches for smart biomedical sensors
• Artificial intelligence over networks of biomedical sensors
• Protocols and middleware for smart biomedical sensors
• Sensors for Active and Healthy Ageing
• Internet of Biomedical Things (IoBT)
• Pilot studies and clinical validation
• User experience and acceptance of Artificial Intelligence in biomedical sensors
• Privacy and security issues
• Big Data
• Teleassistance & telemedicine
• Signals analysis & statistics methods

Dr. Massimo Martinelli
Dr. Davide Moroni
Prof. Dr. Aleš Procházka
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Bioengineering bioengineering	5.046	6.3	2014	14.8 Days	2000 CHF	Submit
Healthcare healthcare	3.160	2.0	2013	19.1 Days	2000 CHF	Submit
Journal of Clinical Medicine jcm	4.964	4.4	2012	18 Days	2600 CHF	Submit
Journal of Sensor and Actuator Networks jsan	-	6.9	2012	18.4 Days	1600 CHF	Submit
Sensors sensors	3.847	6.4	2001	15 Days	2400 CHF	Submit
Applied Sciences applsci	2.838	3.7	2011	14.9 Days	2300 CHF	Submit
Biosensors biosensors	5.743	5.6	2011	13.7 Days	2200 CHF	Submit

Published Papers (3 papers)

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All Bioengineering Healthcare JCM JSAN Sensors Applied Sciences Biosensors

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Open AccessCommunication

MindReader: Unsupervised Classification of Electroencephalographic Data

by

Salvador Daniel Rivas-Carrillo

,

Evgeny E. Akkuratov

,

Hector Valdez Ruvalcaba

,

Angel Vargas-Sanchez

,

Jan Komorowski

,

Daniel San-Juan

and

Manfred G. Grabherr

Sensors 2023, 23(6), 2971; https://doi.org/10.3390/s23062971 - 09 Mar 2023

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Abstract

Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes [...] Read more.

Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes interpretation time-consuming, resource-hungry, and overall an expensive process. Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data and optimizing the allocation of human resources towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader’s predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use. Full article

(This article belongs to the Topic Machine Learning and Biomedical Sensors)

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Open AccessArticle

Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images

by

Yanli Zhao

,

Chong Fu

,

Wenchao Zhang

,

Chen Ye

,

Zhixiao Wang

and

Hong-feng Ma

Bioengineering 2023, 10(1), 47; https://doi.org/10.3390/bioengineering10010047 - 30 Dec 2022

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Abstract

Cervical cancer is one of the most common cancers that threaten women’s lives, and its early screening is of great significance for the prevention and treatment of cervical diseases. Pathologically, the accurate segmentation of cervical cells plays a crucial role in the diagnosis [...] Read more.

Cervical cancer is one of the most common cancers that threaten women’s lives, and its early screening is of great significance for the prevention and treatment of cervical diseases. Pathologically, the accurate segmentation of cervical cells plays a crucial role in the diagnosis of cervical cancer. However, the frequent presence of adherent or overlapping cervical cells in Pap smear images makes separating them individually a difficult task. Currently, there are few studies on the segmentation of adherent cervical cells, and the existing methods commonly suffer from low segmentation accuracy and complex design processes. To address the above problems, we propose a novel star-convex polygon-based convolutional neural network with an encoder-decoder structure, called SPCNet. The model accomplishes the segmentation of adherent cells relying on three steps: automatic feature extraction, star-convex polygon detection, and non-maximal suppression (NMS). Concretely, a new residual-based attentional embedding (RAE) block is suggested for image feature extraction. It fuses the deep features from the attention-based convolutional layers with the shallow features from the original image through the residual connection, enhancing the network’s ability to extract the abundant image features. And then, a polygon-based adaptive NMS (PA-NMS) algorithm is adopted to screen the generated polygon proposals and further achieve the accurate detection of adherent cells, thus allowing the network to completely segment the cell instances in Pap smear images. Finally, the effectiveness of our method is evaluated on three independent datasets. Extensive experimental results demonstrate that the method obtains superior segmentation performance compared to other well-established algorithms. Full article

(This article belongs to the Topic Machine Learning and Biomedical Sensors)

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Open AccessArticle

Feasibility of Brachial Occlusion Technique for Beat-to-Beat Pulse Wave Analysis

by

Lukas Matera

,

Pavol Sajgalik

,

Vratislav Fabian

,

Yegor Mikhailov

,

David Zemanek

and

Bruce D. Johnson

Sensors 2022, 22(19), 7285; https://doi.org/10.3390/s22197285 - 26 Sep 2022

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Abstract

Czech physiologist Penaz tried to overcome limitations of invasive pulse-contour methods (PCM) in clinical applications by a non-invasive method (finger mounted BP cuff) for continuous arterial waveform detection and beat-to-beat analysis. This discovery resulted in significant interest in human physiology and non-invasive examination [...] Read more.

Czech physiologist Penaz tried to overcome limitations of invasive pulse-contour methods (PCM) in clinical applications by a non-invasive method (finger mounted BP cuff) for continuous arterial waveform detection and beat-to-beat analysis. This discovery resulted in significant interest in human physiology and non-invasive examination of hemodynamic parameters, however has limitations because of the distal BP recording using a volume-clamp method. Thus, we propose a validation of beat-to-beat signal analysis acquired by novel a brachial occlusion-cuff (suprasystolic) principle and signal obtained from Finapres during a forced expiratory effort against an obstructed airway (Valsalva maneuver). Twelve healthy adult subjects [2 females, age = (27.2 ± 5.1) years] were in the upright siting position, breathe through the mouthpiece (simultaneously acquisition by brachial blood pressure monitor and Finapres) and at a defined time were asked to generate positive mouth pressure for 20 s (Valsalva). For the purpose of signal analysis, we proposed parameter a “Occlusion Cuff Index” (OCCI). The assumption about similarities between measured signals (suprasystolic brachial pulse waves amplitudes and Finapres’s MAP) were proved by averaged Pearson’s correlation coefficient ($\stackrel{-}{r}$ = 0.60, p < 0.001). The averaged Pearson’s correlation coefficient for the comparative analysis of OCCI between methods was $\stackrel{-}{r}$ = 0.88, p < 0.001. The average percent change of OCCI during maneuver: 8% increase, 19% decrease and percent change of max/min ratio is 35%. The investigation of brachial pulse waves measured by novel brachial blood pressure monitor shows positive correlation with Finapres and the parameter OCCI shows promise as an index, which could describe changes during beat-to-beat cardiac cycles. Full article

(This article belongs to the Topic Machine Learning and Biomedical Sensors)

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