Biomedical Signal Processing and Data Analytics in Healthcare Systems

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 28597

Special Issue Editor


E-Mail Website
Guest Editor
College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK
Interests: conventional and probabilistic control; data analytics; signal processing; probabilistic inference

Special Issue Information

Dear Colleagues,

Internet of Thing (IOT) devices, noninvasive measurement methods, and wearable sensors have resulted in an abundant amount of data in biomedical and healthcare systems. The proper use and analysis of these data can assist clinicians and medical doctors in making real-time decisions and saving lives by sending early warning signals, predicting, monitoring, and diagnosing patients’ conditions. On the other hand, these measurement approaches are of course subject to significant noise effects due to sensor imperfections and imperfect sensor-patient contacts and subject to a high level of uncertainty. Thus, there is a significant signal processing challenge in extracting physiological state parameters reliably for all patient types. The aim of this Special Issue is to bring together original research and review articles researching the signal and pattern processing potential of extracting physiological parameters using traditional and modern advances in principled signal processing. Topics of interest include signal processing methods, machine learning, and intelligent methods to analyze biomedical signals, pattern processing, new measuring trends and analysis, and decision making.

Dr. Randa Herzallah
Guest Editor

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. Information is an international peer-reviewed open access monthly 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 1600 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

  • Analysis of biomedical signals and images
  • Real-time decision making, prognosis, and diagnosis
  • Statistical analysis, machine learning, and deep learning for biomedical signals
  • Signal processing of data collected using noninvasive sensors
  • Frequency and time-domain analysis of biomedical signals
  • New classification, filtering, and visualization methods
  • New advancements and applications of advanced and intelligent processing of biosignals for various applications
  • New measuring trends and analysis
  • Current and future trends in analyzing the most popular biosignals such as EEG, ECG, and EMG
  • Uncertainty characterization and incorporation for more robust clinical decisions
  • Differences between adult, infant, and neonate biomedical signals and image analysis

Published Papers (8 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

21 pages, 2158 KiB  
Article
Adaptive Savitzky–Golay Filters for Analysis of Copy Number Variation Peaks from Whole-Exome Sequencing Data
by Peter Juma Ochieng, Zoltán Maróti, József Dombi, Miklós Krész, József Békési and Tibor Kalmár
Information 2023, 14(2), 128; https://doi.org/10.3390/info14020128 - 16 Feb 2023
Cited by 2 | Viewed by 2231
Abstract
Copy number variation (CNV) is a form of structural variation in the human genome that provides medical insight into complex human diseases; while whole-genome sequencing is becoming more affordable, whole-exome sequencing (WES) remains an important tool in clinical diagnostics. Because of its discontinuous [...] Read more.
Copy number variation (CNV) is a form of structural variation in the human genome that provides medical insight into complex human diseases; while whole-genome sequencing is becoming more affordable, whole-exome sequencing (WES) remains an important tool in clinical diagnostics. Because of its discontinuous nature and unique characteristics of sparse target-enrichment-based WES data, the analysis and detection of CNV peaks remain difficult tasks. The Savitzky–Golay (SG) smoothing is well known as a fast and efficient smoothing method. However, no study has documented the use of this technique for CNV peak detection. It is well known that the effectiveness of the classical SG filter depends on the proper selection of the window length and polynomial degree, which should correspond with the scale of the peak because, in the case of peaks with a high rate of change, the effectiveness of the filter could be restricted. Based on the Savitzky–Golay algorithm, this paper introduces a novel adaptive method to smooth irregular peak distributions. The proposed method ensures high-precision noise reduction by dynamically modifying the results of the prior smoothing to automatically adjust parameters. Our method offers an additional feature extraction technique based on density and Euclidean distance. In comparison to classical Savitzky–Golay filtering and other peer filtering methods, the performance evaluation demonstrates that adaptive Savitzky–Golay filtering performs better. According to experimental results, our method effectively detects CNV peaks across all genomic segments for both short and long tags, with minimal peak height fidelity values (i.e., low estimation bias). As a result, we clearly demonstrate how well the adaptive Savitzky–Golay filtering method works and how its use in the detection of CNV peaks can complement the existing techniques used in CNV peak analysis. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
Show Figures

Figure 1

22 pages, 1687 KiB  
Article
Improving Performance and Quantifying Uncertainty of Body-Rocking Detection Using Bayesian Neural Networks
by Rafael Luiz da Silva, Boxuan Zhong, Yuhan Chen and Edgar Lobaton
Information 2022, 13(7), 338; https://doi.org/10.3390/info13070338 - 12 Jul 2022
Viewed by 1587
Abstract
Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are [...] Read more.
Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, similarities of body rocking to other non-related repetitive activities may cause false detections which prevent continuous engagement, leading to alarm fatigue. We present a pipeline using Bayesian Neural Networks with uncertainty quantification for jointly reducing false positives and providing accurate detection. We show that increasing model capacity does not consistently yield higher performance by itself, while pairing it with the Bayesian approach does yield significant improvements. Disparities in uncertainty quantification are better quantified by calibrating them using deep neural networks. We show that the calibrated probabilities are effective quality indicators of reliable predictions. Altogether, we show that our approach provides additional insights on the role of Bayesian techniques in deep learning as well as aids in accurate body-rocking detection, improving our prior work on this subject. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
Show Figures

Figure 1

16 pages, 4125 KiB  
Article
A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals
by Sitao Zhang, Kainan Ma, Yibo Yin, Binbin Ren and Ming Liu
Information 2022, 13(4), 186; https://doi.org/10.3390/info13040186 - 6 Apr 2022
Cited by 3 | Viewed by 2029
Abstract
As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data [...] Read more.
As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
Show Figures

Figure 1

16 pages, 2839 KiB  
Article
Time- and Frequency-Domain Analysis of Stroke Volume Variability Using Indoor Cycling to Evaluate Physical Load of Body
by Yu-Han Lai, Wei-Chen Lai, Po-Hsun Huang and Tzu-Chien Hsiao
Information 2022, 13(3), 148; https://doi.org/10.3390/info13030148 - 11 Mar 2022
Cited by 2 | Viewed by 2592
Abstract
A potential myocardial injury can be induced by intensive sporting activities, which may be due to ventricular tachycardia or fibrillation when individuals continue to exercise during the maximum physical loading period (the aerobic capability plateau, ACP). Herein, we conducted an incremental exercise test [...] Read more.
A potential myocardial injury can be induced by intensive sporting activities, which may be due to ventricular tachycardia or fibrillation when individuals continue to exercise during the maximum physical loading period (the aerobic capability plateau, ACP). Herein, we conducted an incremental exercise test with the RR-interval and SV-series measurements as the input and output of the circulatory system. Through time and frequency analyses, we aimed to identify the indicators for distinguishing the normal stage (S1), last stage before ACP (S2), and ACP stage (S3) during different incremental physical loads. The cross-correlation results of the RR interval and SV series showed that the maximum coefficient of S2 was significantly greater (p < 0.05) than that of S1 (median 0.91 to 0.87), and also significantly lower (p < 0.05) than that of S3 (median 0.87 to 0.60). The corresponding spectrum shows that the decreasing correlation coefficient of SVV and Heart rate variability can be used to assess whether the body has reached the ACP. These findings can be used as a guide for exercise healthcare. Pausing or reducing the exercise load before entering the ACP could effectively reduce the risk of myocardial injury. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
Show Figures

Figure 1

11 pages, 2621 KiB  
Article
Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering
by Ziqi Peng, Xian Zhang, Jing Cao and Bei Liu
Information 2022, 13(3), 140; https://doi.org/10.3390/info13030140 - 7 Mar 2022
Cited by 1 | Viewed by 1846
Abstract
Recognition of biological tissue denaturation is a vital work in high-intensity focused ultrasound (HIFU) therapy. Multiscale permutation entropy (MPE) is a nonlinear signal processing method for feature extraction, widely applied to the recognition of biological tissue denaturation. However, the typical MPE cannot derive [...] Read more.
Recognition of biological tissue denaturation is a vital work in high-intensity focused ultrasound (HIFU) therapy. Multiscale permutation entropy (MPE) is a nonlinear signal processing method for feature extraction, widely applied to the recognition of biological tissue denaturation. However, the typical MPE cannot derive a stable entropy due to intensity information loss during the coarse-graining process. For this problem, an improved multiscale permutation entropy (IMPE) is proposed in this work. IMPE is obtained through refining and reconstructing MPE. Compared with MPE, the IMPE overcomes the deficiency of amplitude information loss due to the coarse-graining process when computing signal complexity. Through the simulation of calculating MPE and IMPE from white Gaussian noise, it is found that the entropy derived by IMPE is more stable than that derived by MPE. The processing method based on IMPE feature extraction is applied to the experimental ultrasonic scattered echo signals in HIFU treatment. Support vector machine and Gustafson–Kessel fuzzy clustering based on MPE and IMPE feature extraction are also used for biological tissue denaturation classification and recognition. The results calculated from the different combination algorithms show that the recognition of biological tissue denaturation based on IMPE-GK clustering is more reliable with the accuracy of 95.5%. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
Show Figures

Figure 1

32 pages, 12426 KiB  
Article
Low-Resolution Infrared Array Sensor for Counting and Localizing People Indoors: When Low End Technology Meets Cutting Edge Deep Learning Techniques
by Mondher Bouazizi, Chen Ye and Tomoaki Ohtsuki
Information 2022, 13(3), 132; https://doi.org/10.3390/info13030132 - 4 Mar 2022
Cited by 11 | Viewed by 3589
Abstract
In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 × 24 pixels IR array sensors and relies on [...] Read more.
In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 × 24 pixels IR array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end IR array sensors with even lower resolution (16 × 12 and 8 × 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 × 12. For frames of size 8 × 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
Show Figures

Figure 1

18 pages, 6956 KiB  
Article
Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal
by Ernia Susana, Kalamullah Ramli, Hendri Murfi and Nursama Heru Apriantoro
Information 2022, 13(2), 59; https://doi.org/10.3390/info13020059 - 24 Jan 2022
Cited by 20 | Viewed by 10470
Abstract
Monitoring systems for the early detection of diabetes are essential to avoid potential expensive medical costs. Currently, only invasive monitoring methods are commercially available. These methods have significant disadvantages as patients experience discomfort while obtaining blood samples. A non-invasive method of blood glucose [...] Read more.
Monitoring systems for the early detection of diabetes are essential to avoid potential expensive medical costs. Currently, only invasive monitoring methods are commercially available. These methods have significant disadvantages as patients experience discomfort while obtaining blood samples. A non-invasive method of blood glucose level (BGL) monitoring that is painless and low-cost would address the limitations of invasive techniques. Photoplethysmography (PPG) collects a signal from a finger sensor using a photodiode, and a nearby infrared LED light. The combination of the PPG electronic circuit with artificial intelligence makes it possible to implement the classification of BGL. However, one major constraint of deep learning is the long training phase. We try to overcome this limitation and offer a concept for classifying type 2 diabetes (T2D) using a machine learning algorithm based on PPG. We gathered 400 raw datasets of BGL measured with PPG and divided these points into two classification levels, according to the National Institute for Clinical Excellence, namely, “normal” and “diabetes”. Based on the results for testing between the models, the ensemble bagged trees algorithm achieved the best results with an accuracy of 98%. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
Show Figures

Figure 1

20 pages, 1130 KiB  
Article
Advanced Fusion and Empirical Mode Decomposition-Based Filtering Methods for Breathing Rate Estimation from Seismocardiogram Signals
by Christina Kozia and Randa Herzallah
Information 2021, 12(9), 368; https://doi.org/10.3390/info12090368 - 11 Sep 2021
Cited by 5 | Viewed by 2498
Abstract
Breathing Rate (BR), an important deterioration indicator, has been widely neglected in hospitals due to the requirement of invasive procedures and the need for skilled nurses to be measured. On the other hand, biomedical signals such as Seismocardiography (SCG), which measures heart vibrations [...] Read more.
Breathing Rate (BR), an important deterioration indicator, has been widely neglected in hospitals due to the requirement of invasive procedures and the need for skilled nurses to be measured. On the other hand, biomedical signals such as Seismocardiography (SCG), which measures heart vibrations transmitted to the chest-wall, can be used as a non-invasive technique to estimate the BR. This makes SCG signals a highly appealing way for estimating the BR. As such, this work proposes three novel methods for extracting the BR from SCG signals. The first method is based on extracting respiration-dependent features such as the fundamental heart sound components, S1 and S2 from the SCG signal. The second novel method investigates for the first time the use of data driven methods such as the Empirical Mode Decomposition (EMD) method to identify the respiratory component from an SCG signal. Finally, the third advanced method is based on fusing frequency information from the respiration signals that result from the aforementioned proposed methods and other standard methods. The developed methods in this paper are then evaluated on adult recordings from the combined measurement of ECG, the Breathing and Seismocardiograms database. Both fusion and EMD filter-based methods outperformed the individual methods, giving a mean absolute error of 1.5 breaths per minute, using a one-minute window of data. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
Show Figures

Figure 1

Back to TopTop