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Research on Biomedical Signal Processing

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 31015

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Special Issue Editors

Dr. Maurizio Varanini

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Guest Editor

Institute of Clinical Physiology, National Research Council of Italy, via Moruzzi 1, 56124 Pisa, Italy
Interests: biomedical signal processing

Dr. Alessandro Tonacci

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Guest Editor

Clinical Physiology Institute, National Research Council of Italy (IFC-CNR), Via Moruzzi, 1, 56124 Pisa, Italy
Interests: sensors; electronic nose; wearable systems; health; e-health; telemedicine; neuroscience
Special Issues, Collections and Topics in MDPI journals

Dr. Lucia Billeci

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Guest Editor

Clinical Physiology Institute, National Research Council of Italy (IFC-CNR), Via Moruzzi, 1, 56124 Pisa, Italy
Interests: sensors; wearable systems; signal processing; artificial intelligence; health; neuroscience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past forty years, technology has evolved impressively and led to unimaginable advances in computing power and memory capacity, with significant reductions in size and cost. Despite this technological progress, however, biomedical signal processing (theory, methods, and their applications) has only made small steps, while in other fields, such as speech recognition and synthesis, signal processing improvement has been remarkable with an extraordinary spread of applications. We believe that biomedical signal processing applications have not advanced mainly for two reasons: first, due to the paucity of economic investment in this field, and second, and most important, biomedical signals are the manifestation of many complex, interconnected biological processes, and hence, their apparent simplicity hides a great complexity. Furthermore, they are characterized by both high subject-to-subject dependence and a great variability over time within a subject. In the field of cardiovascular signal processing, for example, to date, automatic analysis of long-term ECG recordings has not managed to overcome the limitations of old instrumentation, and thus, healthcare professionals need to take a long time to correct the errors present in the output report. Under these conditions, the development of models for automatic signal processing requires huge amounts of data which, however, collide both with the tendency of healthcare and research facilities not to share data relating to their patients and with the tendency of manufacturing companies to produce data in proprietary formats.

Today, new signal databases become available every year, with computing power and storage capacity that have reached incredible levels. We believe that the right conditions now exist to produce significant achievements in biomedical signal processing.

This Special Issue therefore aims to collect the most interesting novel biomedical signal processing methods and applications in the healthcare and clinical field, i.e., diagnostics, personalized medicine, patient monitoring, and disease prevention and prediction.

Dr. Maurizio Varanini
Dr. Alessandro Tonacci
Dr. Lucia Billeci
Guest Editors

Manuscript Submission Information

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Keywords

biomedical signal (ECG, EEG, etc.) processing
statistical signal processing
artificial intelligence
machine learning
non-linear systems
pattern recognition
wearable sensors
patient monitoring
automatic diagnosis
risk assessment
disease prevention and prediction

Published Papers (13 papers)

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Editorial

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3 pages, 183 KiB

Open AccessEditorial

Special Issue: “Research on Biomedical Signal Processing”

by Maurizio Varanini, Alessandro Tonacci and Lucia Billeci

Appl. Sci. 2023, 13(13), 7347; https://doi.org/10.3390/app13137347 - 21 Jun 2023

Viewed by 915

Abstract

Over recent years, the number of signals of a different type that can be acquired from the human body has increased extraordinarily [...] Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

Research

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21 pages, 3585 KiB

Open AccessArticle

Multimodal Classification of Anxiety Based on Physiological Signals

by Mariana Vaz, Teresa Summavielle, Raquel Sebastião and Rita P. Ribeiro

Appl. Sci. 2023, 13(11), 6368; https://doi.org/10.3390/app13116368 - 23 May 2023

Cited by 3 | Viewed by 1490

Abstract

Multiple studies show an association between anxiety disorders and dysregulation in the Autonomic Nervous System (ANS). Thus, understanding how informative the physiological signals are would contribute to effectively detecting anxiety. This study targets the classification of anxiety as an imbalanced binary classification problem using physiological signals collected from a sample of healthy subjects under a neutral condition. For this purpose, the Electrocardiogram (ECG), Electrodermal Activity (EDA), and Electromyogram (EMG) signals from the WESAD publicly available dataset were used. The neutral condition was collected for around 20 min on 15 participants, and anxiety scores were assessed through the shortened 6-item STAI. To achieve the described goal, the subsequent steps were followed: signal pre-processing; feature extraction, analysis, and selection; and classification of anxiety. The findings of this study allowed us to classify anxiety with discriminatory class features based on physiological signals. Moreover, feature selection revealed that ECG features play a relevant role in anxiety classification. Supervised feature selection and data balancing techniques, especially Borderline SMOTE 2, increased the performance of most classifiers. In particular, the combination of feature selection and Borderline SMOTE 2 achieved the best ROC-AUC with the Random Forest classifier. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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14 pages, 2703 KiB

Open AccessArticle

The Influence Assessment of Artifact Subspace Reconstruction on the EEG Signal Characteristics

by Małgorzata Plechawska-Wójcik, Paweł Augustynowicz, Monika Kaczorowska, Emilia Zabielska-Mendyk and Dariusz Zapała

Appl. Sci. 2023, 13(3), 1605; https://doi.org/10.3390/app13031605 - 27 Jan 2023

Cited by 2 | Viewed by 1904

Abstract

EEG signals may be affected by physiological and non-physiological artifacts hindering the analysis of brain activity. Blind source separation methods such as independent component analysis (ICA) are effective ways of improving signal quality by removing components representing non-brain activity. However, most ICA-based artifact removal strategies have limitations, such as individual differences in visual assessment of components. These limitations might be reduced by introducing automatic selection methods for ICA components. On the other hand, new fully automatic artifact removal methods are developed. One of such method is artifact subspace reconstruction (ASR). ASR is a component-based approach, which can be used automatically and with small calculation requirements. The ASR was originally designed to be run not instead of, but in addition to ICA. We compared two automatic signal quality correction approaches: the approach based only on ICA method and the approach where ASR was applied additionally to ICA and run before the ICA. The case study was based on the analysis of data collected from 10 subjects performing four popular experimental paradigms, including resting-state, visual stimulation and oddball task. Statistical analysis of the signal-to-noise ratio showed a significant difference, but not between ICA and ASR followed by ICA. The results show that both methods provided a signal of similar quality, but they were characterised by different usabilities. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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20 pages, 4971 KiB

Open AccessArticle

Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers’ Levels for Training Support

by Simona Aresta, Ilaria Bortone, Francesco Bottiglione, Tommaso Di Noia, Eugenio Di Sciascio, Domenico Lofù, Mariapia Musci, Fedelucio Narducci, Andrea Pazienza, Rodolfo Sardone and Paolo Sorino

Appl. Sci. 2022, 12(23), 12350; https://doi.org/10.3390/app122312350 - 02 Dec 2022

Cited by 5 | Viewed by 1775

Abstract

Nowadays, modern technology is widespread in sports; therefore, finding an excellent approach to extracting knowledge from data is necessary. Machine Learning (ML) algorithms can be beneficial in biomechanical data management because they can handle a large amount of data. A fencing lunge represents an exciting scenario since it necessitates neuromuscular coordination, strength, and proper execution to succeed in a competition. However, to investigate and analyze a sports movement, it is necessary to understand its nature and goal and to identify the factors that affect its performance. The present work aims to define the best model to screen élite and novice fencers to develop further a tool to support athletes’ and trainers’ activity. We conducted a cross-sectional study in a fencing club to collect anthropometric and biomechanical data from élite and novice fencers. Wearable sensors were used to collect biomechanical data, including a wireless inertial system and four surface electromyographic (sEMG) probes. Four different ML algorithms were trained for each dataset, and the most accurate was further trained with hyperparameter tuning. The best Machine Learning algorithm was Multilayer Perceptron (MLP), which had

96.0

% accuracy and 90% precision, recall, and F1-score when predicting class novice (0); and 93% precision, recall, and F1-score when predicting class élite (1). Interestingly, the MLP model has a slightly higher capacity to recognize élite fencers than novices; this is important to determine which training planning and execution are the best to achieve good performances. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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16 pages, 2862 KiB

Open AccessArticle

Continuous Estimation of Finger and Wrist Joint Angles Using a Muscle Synergy Based Musculoskeletal Model

by Zixun He, Zixuan Qin and Yasuharu Koike

Appl. Sci. 2022, 12(8), 3772; https://doi.org/10.3390/app12083772 - 08 Apr 2022

Cited by 8 | Viewed by 2605

Abstract

Recently, many muscle synergy-based human motion prediction models and algorithms have been proposed. In this study, the muscle synergies extracted from electromyography (EMG) data were used to construct a musculoskeletal model (MSM) to predict the joint angles of the wrist, thumb, index finger, and middle finger. EMG signals were analyzed using independent component analysis to reduce signal noise and task-irrelevant artifacts. The weights of each independent component (IC) were converted into a heat map related to the motion pattern and compared with human anatomy to find a different number of ICs matching the motion pattern. Based on the properties of the MSM, non-negative matrix factorization was used to extract muscle synergies from selected ICs that represent the extensor and flexor muscle groups. The effects of these choices on the prediction accuracy was also evaluated. The performance of the model was evaluated using the correlation coefficient (CC) and normalized root-mean-square error (NRMSE). The proposed method has a higher prediction accuracy than those of traditional methods, with an average CC of 92.0% and an average NRMSE of 10.7%. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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10 pages, 1294 KiB

Open AccessCommunication

Comparison of Different Approaches Estimating Skeletal Muscle Oxygen Consumption Using Continuous-Wave Near-Infrared Spectroscopy at a Submaximal Contraction Level—A Comparative Study

by Florian Kurt Paternoster and Wolfgang Seiberl

Appl. Sci. 2022, 12(5), 2272; https://doi.org/10.3390/app12052272 - 22 Feb 2022

Cited by 3 | Viewed by 1640

Abstract

Continuous-wave near-infrared spectroscopy (CW-NIRS) is a method used to non-invasively estimate skeletal muscle oxygen consumption (mVO₂). Three different signals are provided by CW-NIRS devices: (1) oxygenated hemoglobin (O₂Hb); (2) deoxygenated hemoglobin (HHb); and (3) tissue saturation index (TSI). Typically, the signal’s slope is interpreted with respect to high or low mVO₂ during a muscle action. What signal (or combination of signals) is used for slope interpretation differs according to what approach is used, and there are several published in literature. It is unclear if resulting mVO₂ estimates can be used interchangeably. Hence, this work aimed to compare five commonly used approaches on the same set of CW-NIRS data regarding their agreement in estimated mVO₂. A controlled, lab-based study setting was used for this experiment. Data are based on isometric dorsiflexion contractions of 15 subjects at 30% of voluntary maximum torque, at two different ankle angles. CW-NIRS was placed on the m. tibialis anterior and blood flow was occluded. The approaches for mVO₂ estimation included calculations based on (1) TSI, (2) the difference between O₂Hb and HHb (Hbdiff), (3) the mean of slopes from O₂Hb and HHb (Hbmean), (4) the HHb signal, and (5) the O₂Hb signal. Linear regression modelling was used to calculate respective slopes (r² > 0.99). Repeated measures ANOVA identified significant differences between the approaches (p < 0.001, ω² = 0.258). Post-hoc tests revealed that only TSI vs. Hbmean and Hbdiff vs. HHb gave comparable results (p > 0.271). In addition, Bland–Altman plots showed good accuracy (mean bias ~2%) but low precision (±20%) between the comparisons. Thus, the different approaches to estimate mVO₂ cannot be used interchangeably. The results from different studies using different approaches should be compared with caution. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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25 pages, 7317 KiB

Open AccessArticle

Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications

by Augusto Bonilauri, Francesca Sangiuliano Intra, Giuseppe Baselli and Francesca Baglio

Appl. Sci. 2022, 12(1), 316; https://doi.org/10.3390/app12010316 - 29 Dec 2021

Cited by 10 | Viewed by 3044

Abstract

Functional Near-Infrared Spectroscopy (fNIRS) captures activations and inhibitions of cortical areas and implements a viable approach to neuromonitoring in clinical research. Compared to more advanced methods, continuous wave fNIRS (CW-fNIRS) is currently used in clinics for its simplicity in mapping the whole sub-cranial cortex. Conversely, it often lacks hardware reduction of confounding factors, stressing the importance of a correct signal processing. The proposed pipeline includes movement artifact reduction (MAR), bandpass filtering (BPF), and principal component analysis (PCA). Eight MAR algorithms were compared among 23 young adult volunteers under motor-grasping task. Single-subject examples are shown followed by the percentage in energy reduction (ERD%) statistics by single steps and cumulative values. The block average of the hemodynamic response function was compared with generalized linear model fitting. Maps of significant activation/inhibition were illustrated. The mean ERD% of pre-processed signals concerning the initial raw signal energy reached 4%. A tested multichannel MAR variant showed overcorrection on 4-fold more expansive windows. All of the MAR algorithms found similar activations in the contralateral motor area. In conclusion, single channel MAR algorithms are suggested followed by BPF and PCA. The importance of whole cortex mapping for fNIRS integration in clinical applications was also confirmed by our results. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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15 pages, 3595 KiB

Open AccessArticle

Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring

by Hany Ferdinando, Eveliina Seppälä and Teemu Myllylä

Appl. Sci. 2021, 11(24), 12072; https://doi.org/10.3390/app112412072 - 17 Dec 2021

Cited by 6 | Viewed by 2811

Abstract

Measuring cardiac activity from the chest using an accelerometer is commonly referred to as seismocardiography. Unfortunately, it cannot provide clinically valid data because it is easily corrupted by motion artefacts. This paper proposes two methods to improve peak detection from noisy seismocardiography data. They rely on discrete wavelet transform analysis using either biorthogonal 3.9 or reverse biorthogonal 3.9. The first method involves slicing chest vibrations for each cardiac activity, and then detecting the peak location, whereas the other method aims at detecting the peak directly from chest vibrations without segmentation. Performance evaluations were conducted on signals recorded from small children and adults based on missing and additional peaks. Both algorithms showed a low error rate (15.4% and 2.1% for children/infants and adults, respectively) for signals obtained in resting state. The average error for sitting and breathing tasks (adults only) was 14.4%. In summary, the first algorithm proved more promising for further exploration. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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17 pages, 4175 KiB

Open AccessArticle

Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy

by Iván De La Pava Panche, Viviana Gómez-Orozco, Andrés Álvarez-Meza, David Cárdenas-Peña and Álvaro Orozco-Gutiérrez

Appl. Sci. 2021, 11(21), 9803; https://doi.org/10.3390/app11219803 - 20 Oct 2021

Cited by 2 | Viewed by 1714

Abstract

Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillating at low frequencies, to local, rapidly oscillating neural assemblies. A promising approach to estimating such interactions is the use of transfer entropy (TE), a non-linear, information-theory-based effective connectivity measure. The conventional method involves feeding instantaneous phase and amplitude time series, extracted at the target frequencies, to a TE estimator. In this work, we propose that the problem of directed phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels. We implement our proposal using a kernel-based TE estimator, defined in terms of Renyi’s

α

entropy, which has successfully been used to compute single-trial phase TE. We tested our approach on the synthetic data generated through a simulation model capable of producing a time series with directed phase-amplitude interactions at two given frequencies, and on EEG data from a cognitive task designed to activate working memory, a memory system whose underpinning mechanisms are thought to include phase–amplitude couplings. Our proposal detected statistically significant interactions between the simulated signals at the desired frequencies for the synthetic data, identifying the correct direction of the interaction. It also displayed higher robustness to noise than the alternative methods. The results attained for the working memory data showed that the proposed approach codes connectivity patterns based on directed phase–amplitude interactions, that allow for the different cognitive load levels of the working memory task to be differentiated. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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16 pages, 2918 KiB

Open AccessArticle

QRS Detection Based on Medical Knowledge and Cascades of Moving Average Filters

by Lorenzo Bachi, Lucia Billeci and Maurizio Varanini

Appl. Sci. 2021, 11(15), 6995; https://doi.org/10.3390/app11156995 - 29 Jul 2021

Cited by 9 | Viewed by 2130

Abstract

Heartbeat detection is the first step in automatic analysis of the electrocardiogram (ECG). For mobile and wearable devices, the detection process should be both accurate and computationally efficient. In this paper, we present a QRS detection algorithm based on moving average filters, which affords a simple yet robust signal processing technique. The decision logic considers the rhythmic and morphological features of the QRS complex. QRS enhancing is performed with channel-specific moving average cascades selected from a pool of derivative systems we designed. We measured the effectiveness of our algorithm on well-known benchmark databases, reporting F1 scores, sensitivity on abnormal beats and processing time. We also evaluated the performances of other available detectors for a direct comparison with the same criteria. The algorithm we propose achieved satisfying performances on par with or higher than the other QRS detectors. Despite the performances we report are not the highest that have been published so far, our approach to QRS detection enhances computational efficiency while maintaining high accuracy. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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18 pages, 6962 KiB

Open AccessArticle

Recognition of Stress Activation by Unobtrusive Multi Sensing Setup

by Veronica Chiara Zuccalà, Riccardo Favilla and Giuseppe Coppini

Appl. Sci. 2021, 11(14), 6381; https://doi.org/10.3390/app11146381 - 10 Jul 2021

Cited by 1 | Viewed by 1835

Abstract

It is recognized that stress conditions play an important role in the definition of individual wellness and represent a major risk factor for most non-communicable diseases. Most studies focus on the evaluation of response to maximal stress conditions while a few of them reports results about the detection/monitoring of response to mild stimulations. In this study, we investigate the capability of some physiological signs and indicators (including Heart Rate, Heart Rate Variability, Respiratory Rate, Galvanic Skin Response) to recognize stress in response to moderate cognitive activation in daily life settings. To achieve this goal, we built up an unobtrusive platform to collect signals from healthy volunteers (10 subjects) undergoing cognitive activation via Stroop Color Word Test. We integrated our dataset with data from the Stress Recognition in the Automobile Drivers dataset. Following data harmonization, signal recordings in both datasets were split into five-minute blocks and a set of 12 features was extracted from each block. A feature selection was implemented by two complementary approaches: Sequential Forward Feature Selection (SFFS) and Auto-Encoder (AE) neural networks. Finally, we explored the use of Self-Organizing Map (SOM) to provide a flexible representation of an individual status. From the initial feature set we have determined, by SFFS analysis, that 2 of them (median Respiratory Rate and number peaks in Galvanic Skin Response signals) can discriminate activation statuses from resting ones. In addition, AE experiments also support that two features can suffice for recognition. Finally, we showed that SOM can provide a comprehensive but compact description of activation statuses allowing a fine prototypical representation of individual status. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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Review

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37 pages, 2272 KiB

Open AccessReview

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

by Subramanyam Shashi Kumar and Prakash Ramachandran

Appl. Sci. 2022, 12(16), 8368; https://doi.org/10.3390/app12168368 - 21 Aug 2022

Cited by 8 | Viewed by 3691

Abstract

Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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15 pages, 1248 KiB

Open AccessReview

Application of Machine Learning in the Field of Intraoperative Neurophysiological Monitoring: A Narrative Review

by Dougho Park and Injung Kim

Appl. Sci. 2022, 12(15), 7943; https://doi.org/10.3390/app12157943 - 08 Aug 2022

Cited by 8 | Viewed by 3511

Abstract

Intraoperative neurophysiological monitoring (IONM) is being applied to a wide range of surgical fields as a diagnostic tool to protect patients from neural injuries that may occur during surgery. However, several contributing factors complicate the interpretation of IONM, and it is labor- and training-intensive. Meanwhile, machine learning (ML)-based medical research has been growing rapidly, and many studies on the clinical application of ML algorithms have been published in recent years. Despite this, the application of ML to IONM remains limited. Major challenges in applying ML to IONM include the presence of non-surgical contributing factors, ambiguity in the definition of false-positive cases, and their inter-rater variability. Nevertheless, we believe that the application of ML enables objective and reliable IONM, while overcoming the aforementioned problems that experts may encounter. Large-scale, standardized studies and technical considerations are required to overcome certain obstacles to the use of ML in IONM in the future. Full article

(This article belongs to the Special Issue Research on Biomedical Signal Processing)

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