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Signal Processing in Biomedical Sensor Systems

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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 (28 February 2023) | Viewed by 7906

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Special Issue Editors

Dr. Ganesh Naik

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Guest Editor

College of Science and Engineering, Flinders University, Adelaide, Australia
Interests: blind source separation; independent component analysis; biomedical signal processing; human computer interaction; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gaetano D. Gargiulo

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Guest Editor

School of Engineering, Design and Built Environment, Western Sydney University, Milperra, NSW 2214, Australia
Interests: biomedical signal processing; wearable and electrode-less physiological monitoring; brain–computer interface; biomedical engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Bastien Lechat

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Guest Editor

College of Science and Engineering, Flinders University, Adelaide, Australia
Interests: sleep; machine learning; EEG; OSA; signal processing; respiratory system; snoring; electroencephalography; sleep disorders and sleep medicine; NREM sleep

Special Issue Information

Dear Colleagues,

The recent advances in modern signal processing techniques in medicine have improved the accuracy and reliability of medical diagnoses. Today, biomedical signal analysis is becoming one of the most important methods of visualization and interpretation in biology and medicine.

Biomedical signal processing involves the analysis of physiological measurements to provide useful information upon which clinicians can make decisions. Biomedical signal analysis is of importance not only to the physiologists conducting research and the clinicians treating patients but also to the biomedical engineers who are required to process and interpret the physiological signals by designing systems and algorithms for their manipulations. Working with traditional bio-measurement tools, the signals can be computed by software to provide physicians with real-time data and greater insights to aid in clinical assessments. These challenges motivate further effort on the study of biomedical signal analysis, and this Special Issue intends to report the new results of these efforts.

The goal of this Special Issue is to present a complete range of proven and new methods which play a leading role in the improvement of biomedical signal analysis and interpretation. This Special Issue is intended for biomedical, computer science and electronics engineering researchers and graduate students who wish to explore novel research ideas and receive some training in novel biomedical research areas, especially in terms of ECG, EEG and EMG signal applications.

Dr. Ganesh Naik
Dr. Gaetano D. Gargiulo
Dr. Bastien Lechat
Guest Editors

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Published Papers (4 papers)

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Research

12 pages, 442 KiB

Open AccessArticle

Best Parameters of Heart Rate Variability for Assessing Autonomic Responses to Brief Rectal Distention in Patients with Irritable Bowel Syndrome

by M. Khawar Ali, Shiyuan Gong, Borko Nojkov, Colin Burnett and Jiande D. Z. Chen

Sensors 2023, 23(19), 8128; https://doi.org/10.3390/s23198128 - 28 Sep 2023

Viewed by 881

Abstract

Heart rate variability (HRV) has been used to measure autonomic nervous system (ANS) activity noninvasively. The purpose of this study was to identify the most suitable HRV parameters for ANS activity in response to brief rectal distension (RD) in patients with Irritable Bowel Syndrome (IBS). IBS patients participated in a five-session study. During each visit, an ECG was recorded for 15 min for baseline values and during rectal distension. For rectal distension, a balloon was inflated in the rectum and the pressure was increased in steps of 5 mmHg for 30 s; each distension was followed by a 30 s rest period when the balloon was fully deflated (0 mmHg) until either the maximum tolerance of each patient was reached or up to 60 mmHg. The time-domain, frequency-domain and nonlinear HRV parameters were calculated to assess the ANS activity. The values of each HRV parameter were compared between baseline and RD for each of the five visits as well as for all five visits combined. The sensitivity and robustness/reproducibility of each HRV parameter were also assessed. The parameters included the Sympathetic Index (SI); Root Mean Square of Successive Differences (RMSSD); High-Frequency Power (HF); Low-Frequency Power (LF); Normalized HF Power (HFn); Normalized LF Power (LFn); LF/HF; Respiratory Sinus Arrhythmia (RSA); the Poincare Plot’s SD1, SD2 and their ratio; and the pNN50, SDSD, SDNN and SDNN Index. Data from 17 patients were analyzed and compared between baseline and FD and among five sessions. The SI was found to be the most sensitive and robust HRV parameter in detecting the ANS response to RD. Out of nine parasympathetic parameters, only the SDNN and SDNN Index were sensitive enough to detect the parasympathetic modulation to RD during the first visit. The frequency-domain parameters did not show any change in response to RD. It was also observed that the repetitive RD in IBS patients resulted in a decreased autonomic response due to habituation because the amount of change in the HRV parameters was the highest during the first visit but diminished during subsequent visits. In conclusion, the SI and SDNN/SDNN Index are most sensitive at assessing the autonomic response to rectal distention. The autonomic response to rectal distention diminishes in repetitive sessions, demonstrating the necessity of randomization for repetitive tests. Full article

(This article belongs to the Special Issue Signal Processing in Biomedical Sensor Systems)

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18 pages, 1787 KiB

Open AccessArticle

Deep Reinforcement Learning for Articulatory Synthesis in a Vowel-to-Vowel Imitation Task

by Denis Shitov, Elena Pirogova, Tadeusz A. Wysocki and Margaret Lech

Sensors 2023, 23(7), 3437; https://doi.org/10.3390/s23073437 - 24 Mar 2023

Viewed by 1054

Abstract

Articulatory synthesis is one of the approaches used for modeling human speech production. In this study, we propose a model-based algorithm for learning the policy to control the vocal tract of the articulatory synthesizer in a vowel-to-vowel imitation task. Our method does not require external training data, since the policy is learned through interactions with the vocal tract model. To improve the sample efficiency of the learning, we trained the model of speech production dynamics simultaneously with the policy. The policy was trained in a supervised way using predictions of the model of speech production dynamics. To stabilize the training, early stopping was incorporated into the algorithm. Additionally, we extracted acoustic features using an acoustic word embedding (AWE) model. This model was trained to discriminate between different words and to enable compact encoding of acoustics while preserving contextual information of the input. Our preliminary experiments showed that introducing this AWE model was crucial to guide the policy toward a near-optimal solution. The acoustic embeddings, obtained using the proposed approach, were revealed to be useful when applied as inputs to the policy and the model of speech production dynamics. Full article

(This article belongs to the Special Issue Signal Processing in Biomedical Sensor Systems)

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18 pages, 4488 KiB

Open AccessEditor’s ChoiceArticle

Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries

by Shadi Ghiasi, Tingting Zhu, Ping Lu, Jannis Hagenah, Phan Nguyen Quoc Khanh, Nguyen Van Hao, Vital Consortium, Louise Thwaites and David A. Clifton

Sensors 2022, 22(10), 3866; https://doi.org/10.3390/s22103866 - 19 May 2022

Cited by 12 | Viewed by 3210

Abstract

Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis. Full article

(This article belongs to the Special Issue Signal Processing in Biomedical Sensor Systems)

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12 pages, 3216 KiB

Open AccessArticle

Scorepochs: A Computer-Aided Scoring Tool for Resting-State M/EEG Epochs

by Matteo Fraschini, Simone Maurizio La Cava, Giuseppe Rodriguez, Andrea Vitale and Matteo Demuru

Sensors 2022, 22(8), 2853; https://doi.org/10.3390/s22082853 - 8 Apr 2022

Cited by 1 | Viewed by 1783

Abstract

M/EEG resting-state analysis often requires the definition of the epoch length and the criteria in order to select which epochs to include in the subsequent steps. However, the effects of epoch selection remain scarcely investigated and the procedure used to (visually) inspect, label, and remove bad epochs is often not documented, thereby hindering the reproducibility of the reported results. In this study, we present Scorepochs, a simple and freely available tool for the automatic scoring of resting-state M/EEG epochs that aims to provide an objective method to aid M/EEG experts during the epoch selection procedure. We tested our approach on a freely available EEG dataset containing recordings from 109 subjects using the BCI2000 64 channel system. Full article

(This article belongs to the Special Issue Signal Processing in Biomedical Sensor Systems)

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