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Human Signal Processing Based on Wearable Non-invasive Device

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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 (25 May 2024) | Viewed by 26631

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Prof. Dr. Wing-Kuen Ling

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School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: biomedical signal denoising; machine learning with applications in biomedical signal classification and regression; nonlinear dynamics with applications in EEG and ECG modeling
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Dr. Steve Ling

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Department of Electrical and Data Engineering, University of Technology Sydney, Sydney 00099F, Australia
Interests: neural networks; medical imaging; BCI applications and non-invasive bioinstrumentation
Special Issues, Collections and Topics in MDPI journals

Dr. Ngai Fong Bonnie Law

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Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: genomics; wavelets; image processing

Special Issue Information

Dear Colleagues,

There are many different types of human signals, such as the photoplethysmograms, electrocardiograms, electromyograms, electroencephalograms and electrooculograms. These human signals play an important role in the diagnosis of disease. However, the workloads of medical personnel for interpreting these signals are collosal. In order to address this issue, automatic human signal processing is required. To process these human signals, signal denoising, feature extraction and classification or regression are usually required. To perform denoising, time frequency analysis approaches such as wavelet transform approaches, empirical mode decomposition approaches and singular spectrum analysis approaches are employed. To perform feature extraction, statistical approaches are employed. To perform classification or regression, neural networks or tree-based systems are employed. This Special Issue mainly focuses on proposing new methods for carrying out human signal processing and exploring new applications using human signal processing techniques.

Prof. Dr. Wing-Kuen Ling
Dr. Steve Ling
Dr. Ngai Fong Bonnie Law
Guest Editors

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Keywords

photoplethysmograms
electrocardiograms
electromyograms
electroencephalograms
electrooculograms
denoising
feature extraction
classification
regression

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Published Papers (10 papers)

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Research

19 pages, 5186 KiB

Open AccessArticle

Smartphone-Derived Seismocardiography: Robust Approach for Accurate Cardiac Energy Assessment in Patients with Various Cardiovascular Conditions

by Amin Hossein, Elza Abdessater, Paniz Balali, Elliot Cosneau, Damien Gorlier, Jérémy Rabineau, Alexandre Almorad, Vitalie Faoro and Philippe van de Borne

Sensors 2024, 24(7), 2139; https://doi.org/10.3390/s24072139 - 27 Mar 2024

Cited by 2 | Viewed by 2963

Abstract

Seismocardiography (SCG), a method for measuring heart-induced chest vibrations, is gaining attention as a non-invasive, accessible, and cost-effective approach for cardiac pathologies, diagnosis, and monitoring. This study explores the integration of SCG acquired through smartphone technology by assessing the accuracy of metrics derived from smartphone recordings and their consistency when performed by patients. Therefore, we assessed smartphone-derived SCG’s reliability in computing median kinetic energy parameters per record in 220 patients with various cardiovascular conditions. The study involved three key procedures: (1) simultaneous measurements of a validated hardware device and a commercial smartphone; (2) consecutive smartphone recordings performed by both clinicians and patients; (3) patients’ self-conducted home recordings over three months. Our findings indicate a moderate-to-high reliability of smartphone-acquired SCG metrics compared to those obtained from a validated device, with intraclass correlation (ICC) > 0.77. The reliability of patient-acquired SCG metrics was high (ICC > 0.83). Within the cohort, 138 patients had smartphones that met the compatibility criteria for the study, with an observed at-home compliance rate of 41.4%. This research validates the potential of smartphone-derived SCG acquisition in providing repeatable SCG metrics in telemedicine, thus laying a foundation for future studies to enhance the precision of at-home cardiac data acquisition. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-invasive Device)

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37 pages, 1204 KiB

Open AccessArticle

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

by Panagiotis Kapetanidis, Fotios Kalioras, Constantinos Tsakonas, Pantelis Tzamalis, George Kontogiannis, Theodora Karamanidou, Thanos G. Stavropoulos and Sotiris Nikoletseas

Sensors 2024, 24(4), 1173; https://doi.org/10.3390/s24041173 - 10 Feb 2024

Cited by 5 | Viewed by 4417

Abstract

Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases’ symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-invasive Device)

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18 pages, 8682 KiB

Open AccessArticle

Low-Power Long-Term Ambulatory Electrocardiography Monitor of Three Leads with Beat-to-Beat Heart Rate Measurement in Real Time

by Frank Martínez-Suárez, José Alberto García-Limón, Jorge Enrique Baños-Bautista, Carlos Alvarado-Serrano and Oscar Casas

Sensors 2023, 23(19), 8303; https://doi.org/10.3390/s23198303 - 8 Oct 2023

Cited by 7 | Viewed by 2046

Abstract

A low-power long-term ambulatory ECG monitor was developed for the acquisition, storage and processing of three simultaneous leads DI, aVF and V2 with a beat-to-beat heart rate measurement in real time. It provides long-term continuous ECG recordings until 84 h. The monitor uses a QRS complex detection algorithm based on the continuous wavelet transform with splines, which automatically selects the scale for the analysis of ECG records with different sampling frequencies. It includes a lead-off detection to continuously monitor the electrode connections and a real-time system of visual and acoustic alarms to alert users of abnormal conditions in its operation. The monitor presented is based in an ADS1294 analogue front end with four channels, 24-bit analog-to-digital converters and programmable gain amplifiers, a low-power dual-core ESP32 microcontroller, a microSD memory for data storage in a range of 4 GB to 32 GB and a 1.4 in thin-film transistor liquid crystal display (LCD) variant with a resolution of 128 × 128 pixels. It has programmable sampling rates of 250, 500 and 1000 Hz; a bandwidth of 0 Hz to 50% of the selected sampling rate; a CMRR of −105 dB; an input margin of ±2.4 V; a resolution of 286 nV; and a current consumption of 50 mA for an average battery life of 84 h. The ambulatory ECG monitor was evaluated with the commercial data-acquisition system BIOPAC MP36 and its module for ECG LABEL SS2LB, simultaneously comparing the morphologies of two ECG records and obtaining a correlation of 91.78%. For the QRS detection in real time, the implemented algorithm had an error less than 5%. The developed ambulatory ECG monitor can be used for the analysis of the dynamics of the heart rate variability in long-term ECG records and for the development of one’s own databases of ECG recordings of normal subjects and patients with cardiovascular and noncardiovascular diseases. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-invasive Device)

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20 pages, 4653 KiB

Open AccessArticle

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching

by Jessica Centracchio, Salvatore Parlato, Daniele Esposito, Paolo Bifulco and Emilio Andreozzi

Sensors 2023, 23(10), 4684; https://doi.org/10.3390/s23104684 - 12 May 2023

Cited by 17 | Viewed by 3226

Abstract

Cardiac monitoring can be performed by means of an accelerometer attached to a subject’s chest, which produces the Seismocardiography (SCG) signal. Detection of SCG heartbeats is commonly carried out by taking advantage of a simultaneous electrocardiogram (ECG). SCG-based long-term monitoring would certainly be less obtrusive and easier to implement without an ECG. Few studies have addressed this issue using a variety of complex approaches. This study proposes a novel approach to ECG-free heartbeat detection in SCG signals via template matching, based on normalized cross-correlation as heartbeats similarity measure. The algorithm was tested on the SCG signals acquired from 77 patients with valvular heart diseases, available from a public database. The performance of the proposed approach was assessed in terms of sensitivity and positive predictive value (PPV) of the heartbeat detection and accuracy of inter-beat intervals measurement. Sensitivity and PPV of 96% and 97%, respectively, were obtained by considering templates that included both systolic and diastolic complexes. Regression, correlation, and Bland–Altman analyses carried out on inter-beat intervals reported slope and intercept of 0.997 and 2.8 ms (R² > 0.999), as well as non-significant bias and limits of agreement of ±7.8 ms. The results are comparable or superior to those achieved by far more complex algorithms, also based on artificial intelligence. The low computational burden of the proposed approach makes it suitable for direct implementation in wearable devices. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-invasive Device)

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14 pages, 4558 KiB

Open AccessArticle

Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care

by Dorothy Bai, Mu-Chieh Ho, Bhekumuzi M. Mathunjwa and Yeh-Liang Hsu

Sensors 2023, 23(3), 1736; https://doi.org/10.3390/s23031736 - 3 Feb 2023

Cited by 7 | Viewed by 2434

Abstract

Bed is often the personal care unit in hospitals, nursing homes, and individuals’ homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users’ bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents’ on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients’ sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers’ main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users’ personalized sleep-related data. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-invasive Device)

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11 pages, 693 KiB

Open AccessArticle

Differences in Tridimensional Shoulder Kinematics between Asymptomatic Subjects and Subjects Suffering from Rotator Cuff Tears by Means of Inertial Sensors: A Cross-Sectional Study

by Cristina Roldán-Jiménez, Miguel Cuadros-Romero, Paul Bennett and Antonio I. Cuesta-Vargas

Sensors 2023, 23(2), 1012; https://doi.org/10.3390/s23021012 - 16 Jan 2023

Cited by 1 | Viewed by 1961

Abstract

Background: The aim of this study was to analyze differences in three-dimensional shoulder kinematics between asymptomatic subjects and patients who were diagnosed with rotator cuff tears. Methods: This cross-sectional study recruited 13 symptomatic subjects and 14 asymptomatic subjects. Data were obtained from three inertial sensors placed on the humerus, scapula and sternum. Kinematic data from the glenohumeral, scapulothoracic and thoracohumeral joints were also calculated. The participants performed shoulder abductions and flexions. The principal angles of movements and resultant vectors in each axis were studied. Results: The glenohumeral joint showed differences in abduction (p = 0.001) and flexion (p = 0.000), while differences in the scapulothoracic joint were only significant during flexion (p = 0.001). The asymptomatic group showed higher velocity values in all sensors for both movements, with the differences being significant (p < 0.007). Acceleration differences were found in the scapula during abduction (p = 0.001) and flexion (p = 0.014), as well as in the sternum only during shoulder abduction (p = 0.022). Conclusion: The results showed kinematic differences between the patients and asymptomatic subjects in terms of the mobility, velocity and acceleration variables, with lower values for the patients. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-invasive Device)

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30 pages, 6245 KiB

Open AccessArticle

Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms

by Weirong Wu, Bingo Wing-Kuen Ling, Ruilin Li, Zhengjia Lin, Qing Liu, Jizhen Shao and Charlotte Yuk-Fan Ho

Sensors 2023, 23(2), 761; https://doi.org/10.3390/s23020761 - 9 Jan 2023

Cited by 2 | Viewed by 1646

Abstract

Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed via a classification approach. First, the single-channel electroencephalograms (EEGs) are acquired from various participants when they perform various activities. Then, fast Fourier transform (FFT) is applied to the acquired EEGs, and the high-frequency components are discarded for performing denoising. Next, empirical mode decomposition (EMD) is applied to remove the underlying trend of the signals. In order to extract more features, singular spectrum analysis (SSA) is employed to increase the total number of the components. Finally, some typical models such as the random forest-based classifier, the support vector machine (SVM)-based classifier, and the back-propagation (BP) neural network-based classifier are used for performing the classifications. Here, the percentages of the classification accuracies are employed as the attention scores. The computer numerical simulation results show that our proposed method yields a higher classification performance compared to the traditional methods without performing the EMD and SSA. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-invasive Device)

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18 pages, 3459 KiB

Open AccessArticle

Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals

by Dezhao Li, Yangtao Ruan, Fufu Zheng, Yan Su and Qiang Lin

Sensors 2022, 22(24), 9914; https://doi.org/10.3390/s22249914 - 16 Dec 2022

Cited by 6 | Viewed by 2266

Abstract

Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with single-channel EEG signals for practical applications. The original sleep recordings were collected from the Sleep-EDF database. The wavelet threshold denoising (WTD) method and wavelet packet transformation (WPT) method were applied as signal preprocessing to extract six kinds of characteristic waves. With a comprehensive feature system including time, frequency, and nonlinear dynamics, we obtained the sleep stage classification results with different Support Vector Machine (SVM) models. We proposed a novel classification method based on cascaded SVM models with various features extracted from denoised EEG signals. To enhance the accuracy and generalization performance of this method, nonlinear dynamics features were taken into consideration. With nonlinear dynamics features included, the average classification accuracy was up to 88.11% using this method. In addition, with cascaded SVM models, the classification accuracy of the non-rapid eye movement sleep stage 1 (N1) was enhanced from 41.5% to 55.65% compared with the single SVM model, and the overall classification time for each epoch was less than 1.7 s. Moreover, we demonstrated that it was possible to apply this method for long-term sleep stage monitor applications. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-invasive Device)

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17 pages, 3403 KiB

Open AccessArticle

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

by Jessica Centracchio, Daniele Esposito, Gaetano D. Gargiulo and Emilio Andreozzi

Sensors 2022, 22(23), 9339; https://doi.org/10.3390/s22239339 - 30 Nov 2022

Cited by 9 | Viewed by 2037

Abstract

The cardiac function is influenced by respiration. In particular, various parameters such as cardiac time intervals and the stroke volume are modulated by respiratory activity. It has long been recognized that cardio-respiratory interactions modify the morphology of cardio-mechanical signals, e.g., phonocardiogram, seismocardiogram (SCG), and ballistocardiogram. Forcecardiography (FCG) records the weak forces induced on the chest wall by the mechanical activity of the heart and lungs and relies on specific force sensors that are capable of monitoring respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest. This study addressed the changes in FCG heartbeat morphology caused by respiration. Two respiratory-modulated parameters were considered, namely the left ventricular ejection time (LVET) and a morphological similarity index (MSi) between heartbeats. The time trends of these parameters were extracted from FCG signals and further analyzed to evaluate their consistency within the respiratory cycle in order to assess their relationship with the breathing activity. The respiratory acts were localized in the time trends of the LVET and MSi and compared with a reference respiratory signal by computing the sensitivity and positive predictive value (PPV). In addition, the agreement between the inter-breath intervals estimated from the LVET and MSi and those estimated from the reference respiratory signal was assessed via linear regression and Bland–Altman analyses. The results of this study clearly showed a tight relationship between the respiratory activity and the considered respiratory-modulated parameters. Both the LVET and MSi exhibited cyclic time trends that remarkably matched the reference respiratory signal. In addition, they achieved a very high sensitivity and PPV (LVET: 94.7% and 95.7%, respectively; MSi: 99.3% and 95.3%, respectively). The linear regression analysis reported almost unit slopes for both the LVET (R² = 0.86) and MSi (R² = 0.97); the Bland–Altman analysis reported a non-significant bias for both the LVET and MSi as well as limits of agreement of ±1.68 s and ±0.771 s, respectively. In summary, the results obtained were substantially in line with previous findings on SCG signals, adding to the evidence that FCG and SCG signals share a similar information content. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-invasive Device)

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17 pages, 338 KiB

Open AccessArticle

Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals

by Xilin Li, Frank H. F. Leung, Steven Su and Sai Ho Ling

Sensors 2022, 22(15), 5560; https://doi.org/10.3390/s22155560 - 25 Jul 2022

Cited by 7 | Viewed by 2341

Abstract

Introduction: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. Methods: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO

_{2}

), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. Results: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60 s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-invasive Device)

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