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Biomedical Sensors and Data Processing in Human Monitoring for E-health

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (1 February 2022) | Viewed by 14493

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Prof. Dr. Dan Istrate

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Le laboratoire BioMécanique et BioIngénierie UMR 7338, Université de Technologie de Compiègne, 60200 Compiègne, France
Interests: biomedical signal processing; connected objects; e-health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sofiane Boudaoud

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CNRS, Biomechanics and Bioengineering BMBI UMR 7338, Centre de Recherche Royallieu, Université de Technologie de Compiègne, Alliance Sorbonne Université, CEDEX CS 60 319, 60 203 Compiègne, France
Interests: biomedical signal processing and modeling; electrophysiological instrumentation; E-health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jérôme Boudy

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Telecom SudParis - Institut Polytechnique de Paris, 9 rue Charles Fourier, 91011 EVRY CEDEX, France
Interests: biomedical signal processing; automatic data processing; e-health; speech processing for MMI and analysis

Special Issue Information

Dear Colleagues,

Biomedical sensor technology advances allow the monitoring of several physiological signals, often in wireless and non-invasive conditions, in order to better monitor and treat diseases and more especially chronical ones. The main medicine challenges are therapy personalization for the patient, predictive diagnosis and the ambulatory monitoring using non-invasive, reliable, and zero effort technology. These new devices must have innovative sensors and embedded systems with specific signal processing related to wireless information and cloud decision systems. Key challenges also involve advanced data analysis related to data fusion and artificial intelligence (AI) for health.

In this Special Issue, researchers are invited to submit contributions describing new sensors, new adapted signal processing, wireless information transmission and artificial intelligence (AI) approaches. All innovative studies related to innovative E-Health instrumental chain are also welcome (e.g., data recording and standardization, experimental protocols).

Potential topics include, but are not limited, to the following:

Situation-awareness sensors for home monitoring
Smart sensors (e.g., textiles)
Sensor fusion algorithms
Signal processing and AI algorithms
Applications: elderly home monitoring, disease or post-surgery monitoring, physiological state detection (e.g., sleep, stress), telemedicine

Dr. Dan Istrate
Dr. Sofiane Boudaoud
Prof. Dr. Jérôme Boudy
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

biomedical sensors
signal processing
features
decision system
pattern recognition
zero effort technology

Published Papers (4 papers)

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Research

15 pages, 1218 KiB

Open AccessArticle

Preprocessing Methods for Ambulatory HRV Analysis Based on HRV Distribution, Variability and Characteristics (DVC)

by Mouna Benchekroun, Baptiste Chevallier, Dan Istrate, Vincent Zalc and Dominique Lenne

Sensors 2022, 22(5), 1984; https://doi.org/10.3390/s22051984 - 03 Mar 2022

Cited by 8 | Viewed by 2444

Abstract

Thanks to wearable devices joint with AI algorithms, it is possible to record and analyse physiological parameters such as heart rate variability (HRV) in ambulatory environments. The main downside to such setups is the bad quality of recorded data due to movement, noises, and data losses. These errors may considerably alter HRV analysis and should therefore be addressed beforehand, especially if used for medical diagnosis. One widely used method to handle such problems is interpolation, but this approach does not preserve the time dependence of the signal. In this study, we propose a new method for HRV processing including filtering and iterative data imputation using a Gaussian distribution. The particularity of the method is that many physiological aspects are taken into consideration, such as HRV distribution, RR variability, and normal boundaries, as well as time series characteristics. We study the effect of this method on classification using a random forest classifier (RF) and compare it to other data imputation methods including linear, shape-preserving piecewise cubic Hermite (pchip), and spline interpolation in a case study on stress. Features from reconstructed HRV signals of 67 healthy subjects using all four methods were analysed and separately classified by a random forest algorithm to detect stress against relaxation. The proposed method reached a stable F1 score of 61% even with a high percentage of missing data, whereas other interpolation methods reached approximately 54% F1 score for a low percentage of missing data, and the performance drops to about 44% when the percentage is increased. This suggests that our method gives better results for stress classification, especially on signals with a high percentage of missing data. Full article

(This article belongs to the Special Issue Biomedical Sensors and Data Processing in Human Monitoring for E-health)

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18 pages, 1982 KiB

Open AccessArticle

Capacitively-Coupled ECG and Respiration for Sleep–Wake Prediction and Risk Detection in Sleep Apnea Patients

by Dorien Huysmans, Ivan Castro, Pascal Borzée, Aakash Patel, Tom Torfs, Bertien Buyse, Dries Testelmans, Sabine Van Huffel and Carolina Varon

Sensors 2021, 21(19), 6409; https://doi.org/10.3390/s21196409 - 25 Sep 2021

Cited by 3 | Viewed by 2132

Abstract

Obstructive sleep apnea (OSA) patients would strongly benefit from comfortable home diagnosis, during which detection of wakefulness is essential. Therefore, capacitively-coupled electrocardiogram (ccECG) and bioimpedance (ccBioZ) sensors were used to record the sleep of suspected OSA patients, in parallel with polysomnography (PSG). The three objectives were quality assessment of the unobtrusive signals during sleep, prediction of sleep–wake using ccECG and ccBioZ, and detection of high-risk OSA patients. First, signal quality indicators (SQIs) determined the data coverage of ccECG and ccBioZ. Then, a multimodal convolutional neural network (CNN) for sleep–wake prediction was tested on these preprocessed ccECG and ccBioZ data. Finally, two indices derived from this prediction detected patients at risk. The data included 187 PSG recordings of suspected OSA patients, 36 (dataset “Test”) of which were recorded simultaneously with PSG, ccECG, and ccBioZ. As a result, two improvements were made compared to prior studies. First, the ccBioZ signal coverage increased significantly due to adaptation of the acquisition system. Secondly, the utility of the sleep–wake classifier increased as it became a unimodal network only requiring respiratory input. This was achieved by using data augmentation during training. Sleep–wake prediction on “Test” using PSG respiration resulted in a Cohen’s kappa (

κ

) of 0.39 and using ccBioZ in

κ

= 0.23. The OSA risk model identified severe OSA patients with a

κ

of 0.61 for PSG respiration and

κ

of 0.39 using ccBioZ (accuracy of 80.6% and 69.4%, respectively). This study is one of the first to perform sleep–wake staging on capacitively-coupled respiratory signals in suspected OSA patients and to detect high risk OSA patients based on ccBioZ. The technology and the proposed framework could be applied in multi-night follow-up of OSA patients. Full article

(This article belongs to the Special Issue Biomedical Sensors and Data Processing in Human Monitoring for E-health)

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12 pages, 494 KiB

Open AccessArticle

On the Unification of Common Actigraphic Data Scoring Algorithms

by Piotr Biegański, Anna Stróż, Marian Dovgialo, Anna Duszyk-Bogorodzka and Piotr Durka

Sensors 2021, 21(18), 6313; https://doi.org/10.3390/s21186313 - 21 Sep 2021

Viewed by 2105

Abstract

Actigraphy is a well-known, inexpensive method to investigate human movement patterns. Sleep and circadian rhythm studies are among the most popular applications of actigraphy. In this study, we investigate seven common sleep-wake scoring algorithms designed for actigraphic data, namely Cole-Kripke algorithm, two versions of Sadeh algorithm, Sazonov algorithm, Webster algorithm, UCSD algorithm and Scripps Clinic algorithm. We propose a unified mathematical framework describing five of them. One of the observed novelties is that five of these algorithms are in fact equivalent to low-pass FIR filters with very similar characteristics. We also provide explanations about the role of some factors defining these algorithms, as none were given by their Authors who followed empirical procedures. Proposed framework provides a robust mathematical description of discussed algorithms, which for the first time allows one to fully understand their operation and basics. Full article

(This article belongs to the Special Issue Biomedical Sensors and Data Processing in Human Monitoring for E-health)

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18 pages, 43704 KiB

Open AccessArticle

Non-Contact Monitoring of Human Vital Signs Using FMCW Millimeter Wave Radar in the 120 GHz Band

by Wenjie Lv, Wangdong He, Xipeng Lin and Jungang Miao

Sensors 2021, 21(8), 2732; https://doi.org/10.3390/s21082732 - 13 Apr 2021

Cited by 34 | Viewed by 6713

Abstract

A non-contact heartbeat/respiratory rate monitoring system was designed using narrow beam millimeter wave radar. Equipped with a special low sidelobe and small-sized antenna lens at the front end of the receiving and transmitting antennas in the 120 GHz band of frequency-modulated continuous-wave (FMCW) system, this sensor system realizes the narrow beam control of radar, reduces the interference caused by the reflection of other objects in the measurement background, improves the signal-to-clutter ratio (SCR) of the intermediate frequency signal (IF), and reduces the complexity of the subsequent signal processing. In order to solve the problem that the accuracy of heart rate is easy to be interfered with by respiratory harmonics, an adaptive notch filter was applied to filter respiratory harmonics. Meanwhile, the heart rate obtained by fast Fourier transform (FFT) was modified by using the ratio of adjacent elements, which helped to improve the accuracy of heart rate detection. The experimental results show that when the monitoring system is 1 m away from the human body, the probability of respiratory rate detection error within ±2 times for eight volunteers can reach 90.48%, and the detection accuracy of the heart rate can reach 90.54%. Finally, short-term heart rate measurement was realized by means of improved empirical mode decomposition and fast independent component analysis algorithm. Full article

(This article belongs to the Special Issue Biomedical Sensors and Data Processing in Human Monitoring for E-health)

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